Ramla Dula Saleh*
| Ernan Rustiadi | Akhmad Fauzi | Hania Rahma
© 2025 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).
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Development in archipelagic regions requires a more specific approach that addresses the objective challenges faced by these areas. Therefore, mapping village typologies—particularly based on geographic similarities, spatial characteristics, and socio-economic conditions—is essential. This is especially important considering the diverse potentials and challenges faced by villages scattered across different islands or island clusters. This study was conducted in Maluku Province, an archipelagic region in Eastern Indonesia characterized by thousands of islands, including large islands, small islands, and outermost islands. The province comprises 1,233 villages, 118 sub-districts, and 11 districts/cities. The analysis employed a combination of Principal Component Analysis (PCA), K-Means Clustering, and ArcGIS 10 to produce a Village Typology Map. The results identified four principal components that formed five distinct village typologies: (1) “Coastal” Typology, comprising 298 coastal villages; (2) “Non-Coastal” Typology, comprising 137 inland villages; (3) “Outer/Border” Typology, comprising 522 outermost villages; (4) “Urban Zone” Typology, comprising 116 urban villages; and (5) “Small Islands” Typology, comprising 160 villages located on small islands. The implication of these findings is that village typologies—constructed based on geographic, spatial, and socio-economic aspects—serve as an entry point for implementing targeted and contextually relevant development strategies to accelerate development in the archipelagic regions of Maluku Province, Indonesia.
village typology, archipelago, Maluku, PCA, K-Means Clustering
Archipelagic regions are characterized by their unique, complex, and diverse nature, where each island often faces relatively distinct challenges—not only in terms of geographical conditions but also regarding connectivity, population distribution, socio-economic aspects, and the availability of natural resources [1, 2]. This diversity in characteristics contributes to disparities in access to resources, infrastructure, and economic opportunities, which in turn results in unequal distribution of development benefits [3-6].
One of Indonesia’s prominent archipelagic regions is Maluku Province, where approximately 92% of its area consists of marine territory, and only 7% is land, comprising 1,388 islands of varying sizes and natural resource potentials [7]. As an archipelagic region, Maluku faces several fundamental challenges, most notably limited connectivity and accessibility. These limitations restrict economic activities and hinder regional development. Furthermore, development policies implemented in Maluku have largely followed a “continental-centric” approach, often failing to accommodate the specific needs of island regions. As a result, many policy interventions do not adequately address the root causes and urgent development priorities [8].
One of the consequences of this policy misalignment is the persistently high poverty rate in Maluku, especially on islands with minimal infrastructure, basic services, and limited access to economic opportunities. As of 2022, statistical data indicate that Maluku ranked as the fourth poorest province in Indonesia, with a poverty rate of 16.42% [9].
To formulate more accommodative and measurable development policies for Maluku, it is essential to map the typology of its archipelagic regions based on their physical, geographical, and socio-economic characteristics. Such typological classification supports more targeted development planning, enables the formulation of affirmative policies, and facilitates more efficient resource and budget allocation in line with actual needs—thereby promoting more effective and sustainable development outcomes [10-12].
Based on these considerations, this study aims to address the research question: how can village typologies be defined based on the spatial characteristics of island regions and the socio-economic conditions in Maluku Province?
In the context of an archipelagic region, village typology analysis is crucial, given that villages scattered across various islands or island clusters face distinct challenges and possess unique potential. Villages located on large islands encounter different development issues compared to those on small islands; likewise, coastal villages have different socio-economic dynamics than those in mountainous or inland regions. This also applies to remote, outermost, and border-area villages.
2.1 Data collection and location of the study sites
The study was conducted in Maluku Province, Indonesia, encompassing 1,233 villages (including urban administrative units or kelurahan) distributed across 11 districts/ municipalities and 118 sub-districts (Figure 1). Socio-economic data were sourced from the Village Development Index (Indeks Pembangunan Desa, IPD) provided by Statistics Indonesia (Badan Pusat Statistik, BPS) and the Village Development Index (Indeks Desa Membangun, IDM) published by the Ministry of Villages, Development of Disadvantaged Regions, and Transmigration of the Republic of Indonesia. Spatial data were obtained from the Village Administrative Boundary Map provided by the Geospatial Information Agency (Badan Informasi Geospasial, BIG), the Land Cover Map issued by the Ministry of Environment and Forestry, as well as regulatory documents related to coastal areas, small islands, and outermost and border islands.
Figure 1. Map of study sites
2.2 Data analysis
There are 3 methods applied, namely: 1) Principal Component Analysis (PCA); 2) K-Means Clustering; and 3) Regional Typology Analysis which is visualized in the form of a map. This study employed 11 input variables, namely: (1) population density; (2) percentage of built-up area; (3) availability of urban facilities; (4) outermost status; (5) distance from the national border; (6) island size; (7) island category; (8) marine resource utilization; (9) direct adjacency to the sea; (10) absence of coastline; and (11) location within forest areas. These variables were selected as they are considered to represent the geospatial characteristics of island regions and the socio-economic conditions of villages in Maluku Province, and because relevant data are available.
2.2.1 PCA
PCA method was employed to classify villages based on regional characteristics and multidimensional poverty indicators, including economic aspects, socio-cultural conditions, geographical challenges, and several spatial indicators relevant to archipelagic areas. This method transforms the original dataset linearly into a new coordinate system that maximizes variance, without significantly altering the intrinsic properties of the data. Smaller, transformed datasets are generally easier to explore, visualize, and analyze.
Technically, dimensionality reduction in PCA is guided by the proportion of variance explained by the principal components. First, if the cumulative variance of the original data exceeds 70% or the eigenvalue (λ) of a component is greater than 1, then the analysis is considered sufficient up to that component. The PCA process was carried out in five main steps as follows:
Step 1. Data standardization, to produce data of the same scale. Data standardization is carried out with the following equation:
$Z_{a p} \frac{x_{a p}-x_p}{S_p}$ (1)
where,
Zap: standardized data
Xap: a data on the p variable
$\overline{x_p}$: average data of the p variable
Sp: standard deviation of the p variable
Step 2. Calculating the covariance matrix based on correlation coefficients, which are derived using the following equation:
$rz_p, z_q=\frac{n\left(\sum_{a=1}^n z_{a p} z_{a q}\right)-\left(\sum_{a=1}^n z_{a p}\right) \cdot\left(\sum_{a=1}^n z_{a q}\right)}{\sqrt{\left(\sum_{a=1}^n z_{a p}^2\right)-\left(\sum_{a=1}^n z_{a p}\right)^2} \cdot \sqrt{\left(\sum_{a=1}^n z_{a q}^2\right)-\left(\sum_{a=1}^n z_{a q}\right)^2}}$ (2)
Step 3. Calculating the eigenvalues and eigenvectors of the covariance matrix using the following equations:
Eigenvalue: $|\lambda I-R|=\overrightarrow{0}$ (3)
Eigenvector: $(R \vec{v}=\lambda \vec{v})$ (4)
Step 4. Determining the number of principal components (PCs) based on the criterion of eigenvalues greater than or equal to 1, and constructing the component loading matrix, which represents the correlations between the original variables and the component scores, using the following equation:
$r_{X_p, P C_t}=\overrightarrow{v_{a t}} \sqrt{\lambda_t}$ (5)
Step 5. Calculating the transformed dataset resulting from PCA dimensionality reduction, using the following equation:
$P C_{a t}=\overrightarrow{v_{1 a}} Z_1+\overrightarrow{v_{2 a}} Z_2+\ldots+\overrightarrow{v_{a p}} Z_p$ (6)
Step 6. Interpretation of components was conducted using the Rotated Component Matrix to identify the dominant variables in each component. The interpretation was based on the factor loadings, or the highest correlation values between variables and components.
2.2.2 K-Means Clustering
The PCA results were further analyzed using clustering techniques, which involve grouping objects into several clusters based on the similarity of their data characteristics. In general, clustering methods are divided into two types: hierarchical clustering (structure-based) and partitional clustering (non-hierarchical). Considering the relatively large dataset, this study employed a partitional clustering method, in which the data were grouped into a predefined number of clusters without a hierarchical structure. Each cluster has a central point (centroid) that minimizes the dissimilarity distance between all data points and their respective cluster centers.
The stages of the K-Means Clustering analysis are as follows:
Step 1. Determine the dataset to be clustered in the form of a matrix X with dimensions n × m (where n is the number of samples and m is the number of attributes per data point);
Step 2. Specify the number of clusters (c), fuzzification coefficient or exponent (w), maximum number of iterations (maxiter), the desired minimum error (ε), the initial objective function value (P₀ = 0), and the initial iteration (t = 1);
Step 3. Generate random numbers μᵢₖ, where i = 1, 2, 3, …, n and k = 1, 2, 3, …, c, as elements of the initial partition matrix U, which represent the degree of membership of each data point to a cluster.
Step 4. Calculate the center of the k-th cluster Vₖⱼ, where k = 1, 2, …, c and j = 1, 2, …, m.
$V_{k j}=\frac{\sum_{i=1}^n\left(\left(\mu_{i k}\right)^w \times X_{i j}\right)}{\sum_{i=1}^n\left(\mu_{i k}\right)^w}$ (7)
Step 5. Calculates the objective function on the t-iteration, Pt, which describes the total distance of the data to the center of the cluster.
$P t=\sum_{i=1}^n \sum_{k=1}^c\left(\left[\sum_{j=1}^m\left(X_{i j}-v_{k j}\right)^2\left(\mu_{i k}\right)^w\right]\right)$ (8)
where,
Pt: Objective function
Xij: element X row i, column j
Vkj: Cluster Centers
Step 6. Calculate partition matrix changes:
$\mu_{i k}=\frac{\left(\left(\sum_{j=1}^m\left(X_{i j}-V_{k j}\right)\right)^2\right)^{\frac{-1}{w-1}}}{\sum_{k=1}^c\left(\left(\sum_{j=1}^m\left(X_{i j}-V_{k j}\right)\right)^2\right)^{\frac{-1}{w-1}}}$ (9)
where,
i: 1,2,…, n
k: 1,2, ..., c
Xij: the i-th data sample for the j-th variable
Vkj: the center of the k-th cluster for the j-th variable
w: the weighting exponent (fuzzification coefficient)
Step 7. Check the stopping condition:
•If $\left|P_t-P_{t-1}\right|<\varepsilon$ or (t ＞ maxiter), then the algorithm terminates.
•If not, set t = t + 1 and repeat step 4.
Step 8. Melakukan Validate the cluster results using the Davies-Bouldin Index (DBI), calculated using the following equation:
$D B I=\frac{1}{k} \sum_{i=1}^k \max _{i \neq j}\left(R_{i, j}\right)$ (10)
where,
DBI: Davies-Bouldin Index
Ri,j: The ratio of comparison values between the i-th cluster and the j-th cluster
k: The number of clusters used
Step 9. Interpret the best clustering results based on the smallest Davies-Bouldin Index (DBI) value.
2.2.3 Regional typology analysis
The clustering results generated through the K-Means method were further processed using ArcGIS 10.4 and visualized in the form of thematic maps. This process began with importing the clustering data into ArcGIS, stored in compatible formats such as .csv, .shp, or other tabular formats. The dataset included geographic coordinates (latitude and longitude) along with cluster labels as additional attributes indicating the classification outcomes.
The next step involved integrating the clustering data with spatial maps. If the data lacked direct geographic coordinates, the integration was performed based on relevant attributes—such as administrative region IDs—using corresponding spatial data like administrative boundary shapefiles.
Once the data were merged, visualization was conducted using several techniques. Symbology settings were applied to assign different colors to each cluster, facilitating visual interpretation. Additionally, thematic maps were employed to depict regions using colors or symbols that represent their respective clusters.
The final stage encompassed the analysis and interpretation of the resulting maps. This involved identifying specific spatial patterns, recognizing distinct geographic distributions, and characterizing each cluster based on the clustering outcomes. As such, the spatial visualization provided a deeper understanding of the structural and geographic distribution of the data.
Based on the Principal Component Analysis (PCA) conducted using SPSS version 27, the extraction reduced the original 11 variables into only 4 components. This reduction is indicated by the total initial eigenvalues greater than 1, as presented in Table 1.
Table 1. Principal component extraction result
|
Component |
Initial Eigenvalues |
||
|
Total |
% of Variance |
Cumulative % |
|
|
1 |
3.726 |
33.876 |
33.876 |
|
2 |
2.593 |
23.573 |
57.450 |
|
3 |
1.963 |
17.850 |
75.299 |
|
4 |
1.506 |
13.694 |
88.993 |
|
5 |
0.425 |
3.863 |
92.857 |
|
6 |
0.344 |
3.123 |
95.980 |
|
7 |
0.301 |
2.740 |
98.720 |
|
8 |
0.064 |
0.579 |
99.299 |
|
9 |
0.046 |
0.421 |
99.720 |
|
10 |
0.031 |
0.280 |
100.000 |
|
11 |
-1.249E-16 |
-1.135E-15 |
100.000 |
Components with initial eigenvalues greater than 1 are considered to contribute significantly to the total variance of the data, whereas components with eigenvalues less than 1 are regarded as less informative and can be disregarded. In general, the larger the eigenvalue of a component, the greater the proportion of variance it explains. An eigenvalue greater than 1 indicates that the component accounts for more variance than a single original variable [13]. This conclusion is further supported by the scree plot presented in Figure 2.
It can be observed that the scree plot displays a steep decline from components 1 to 4, while components 5 and beyond show a more gradual slope after the elbow point. According to the scree plot criterion, only components 1, 2, 3, and 4 are considered to represent the data structure [14, 15]. To determine which variables load onto each of these four principal components, the Rotated Component Matrix is used, as presented in Table 2.
Figure 2. Scree plot
Table 2. Rotated component matrix
|
Rotated Component Matrixa |
||||
|
Variable |
Principal Component (PC) |
|||
|
1 |
2 |
3 |
4 |
|
|
Population density |
0.006 |
0.874 |
0.007 |
0.018 |
|
Built-Up area percentage |
-0.036 |
0.969 |
-0.044 |
0.015 |
|
Urban facilities |
0.009 |
0.941 |
0.010 |
-0.014 |
|
Outermost region |
-0.006 |
-0.011 |
0.992 |
-0.022 |
|
Distance from National Border |
-0.031 |
0.010 |
-0.989 |
-0.066 |
|
Island size |
0.217 |
0.010 |
0.021 |
0.964 |
|
Island category |
0.198 |
0.010 |
0.024 |
0.967 |
|
Marine utilization |
0.817 |
-0.006 |
0.006 |
0.227 |
|
Directly Adjacent to the Sea |
0.961 |
-0.012 |
0.008 |
0.116 |
|
Without a Coastline |
-0.961 |
0.012 |
-0.008 |
-0.116 |
|
Village Location within Forest Areas |
-0.823 |
-0.006 |
-0.012 |
-0.085 |
The Rotated Component Matrix illustrates how the original variables are projected onto the principal components after the rotation process. This rotation aims to facilitate the interpretation of components by producing a clearer and more distinct loading pattern across variables. In this matrix, each element represents a loading value ranging from -1 to 1, which indicates the extent to which a variable contributes to a given component. The greater the absolute value of the loading—whether positive or negative—the greater the contribution of that variable [16, 17].
Table 2 shows that the highest loading values for Principal Component 1 (PC-1) are found in the variables “Directly Adjacent to the Sea” (0.961), “Marine Utilization” (0.817), “Without a Coastline” (-0.961), and “Village Location within Forest Areas” (-0.823). For PC-2, the three variables with the highest loadings are “Population Density” (0.874), “Built-up Area Percentage” (0.969), and “Urban Facilities” (0.941). In PC-3, the highest loadings are observed in the variables “Outermost Region” (0.992) and “Distance from National Border” (-0.989). Lastly, for PC-4, the variables with the highest loading values are “Island Size” (0.964) and “Island Category” (0.967).
Subsequently, the naming of the principal components was conducted to assign attributes to each component. The naming was done purposively to represent each component based on the characteristics of the grouped variables. Accordingly, Component 1 was labeled “Coastal,” Component 2 as “Urban,” Component 3 as “Outermost/Border,” and Component 4 as “Small Islands.” These component names and groupings are further detailed in Table 3.
Table 3. Principal component
|
Variable |
Principal Component |
Name |
|
Marine utilization |
PC-1 |
Coastal |
|
Directly Adjacent to the Sea |
||
|
Without a Coastline |
||
|
Village Location within Forest Areas |
||
|
Population density |
PC-2 |
Urban |
|
Built-Up area percentage |
||
|
Urban facilities |
||
|
Outermost region |
PC-3 |
Outermost / Border |
|
Distance from National Border |
||
|
Island size |
PC-4 |
Small Islands |
|
Island category |
Based on the interpretation of the principal components, clustering was subsequently performed using the K-Means method with five clusters. The number of clusters was determined using the Elbow Plot, which showed a sharp decline in the Within-Cluster Sum of Squares (WSS) from the second to the fifth cluster. Beyond the fifth cluster, the decrease in WSS became marginal, indicating that adding more clusters would no longer be efficient. This is further illustrated in the Elbow Plot shown in Figure 3.
Figure 3. Elbow plot for determining the number of clusters
The results are presented through the Final Cluster Centers, as shown in Table 4.
Table 4. K-Means Clustering results from principal components
|
Final Cluster Centers |
Cluster |
Principal Component |
|||
|
Coastal |
Urban |
Outermost / Border |
Small Islands |
||
|
1 |
0.871 |
-0.319 |
-0.993 |
-1.053 |
|
|
2 |
-2.536 |
-0.259 |
-1.000 |
-0.307 |
|
|
3 |
-0.010 |
-0.320 |
1.111 |
-0.021 |
|
|
4 |
0.028 |
2.907 |
0.041 |
-0.042 |
|
|
5 |
0.518 |
-0.269 |
-0.929 |
2.296 |
|
|
ANOVA |
F |
3743 |
2413 |
18038 |
11000 |
|
Sig |
0.000 |
0.000 |
0.000 |
0.000 |
|
The interpretation of Table 4 is as follows:
Cluster 1 shows a positive final cluster center value of 0.871 for the Coastal component, while the values for the other three components are negative. This indicates that Cluster 1 primarily consists of villages located in coastal areas, but not in urban zones, outer/border areas, or small islands.
Cluster 2 exhibits negative final cluster center values across all four components. This suggests that the villages in this cluster are not coastal, not located in urban settings, not positioned in outer/border regions, and are not situated on small islands—essentially representing non-coastal and non-urban settlements with minimal spatial distinctiveness.
In Cluster 3, the highest and only positive final cluster center value is observed in the Outermost/Border component, with a score of 1.111. This clearly indicates that Cluster 3 is composed of villages situated in the outermost or border regions of the country.
Cluster 4 is characterized by a highly positive final cluster center value in the Urban component (2.907), while the values in the remaining components are negligible or negative. This reflects that Cluster 4 consists of villages located within urban zones.
Lastly, Cluster 5 shows its highest positive score in the Small Islands component (2.296), indicating that the villages grouped in this cluster are primarily located on small islands.
Furthermore, the ANOVA test results demonstrate statistically significant differences across the clusters for all four principal components, as evidenced by the significance values (p = 0.000). This confirms that the clusters are meaningfully distinct from one another in terms of their underlying spatial and typological characteristics.
In summary, the five resulting clusters represent five distinct village typologies in the Maluku Province, namely:
Coastal Typology, consisting of villages located in coastal areas;
Non-Coastal Typology, consisting of inland villages not characterized by specific spatial features;
Outermost/Border Typology, composed of villages located in the outermost or border areas of the country;
Urban Zone Typology, comprising villages situated within urban areas; and
Small Islands Typology, representing villages located on small islands.
The spatial distribution and classification of these five village typologies in Maluku Province, derived from Principal Component Analysis and K-Means Clustering, are visually illustrated in Figure 4.
Figure 4. Profile plot based on PCA and K-Means Clustering
The village typologies in this study refer to the classification of villages based on similarities in specific characteristics, particularly spatial, geographic, and socio-economic attributes. Each group or cluster consists of villages that share common features, while these features are distinct and differentiable across clusters.
Table 5 presents the number of cases in each cluster, which is an essential part of the K-Means Clustering output, indicating the number of villages assigned to each cluster. In this table, a total of 1,233 villages in Maluku Province are distributed into five distinct groups or clusters, forming village typologies based on spatial characteristics, geographic positioning, and socio-economic aspects of each village.
Table 5. Number of cases in each cluster
|
Cluster |
Number of Members |
|
|
Village |
Percent (%) |
|
|
Coastal |
298 |
24 |
|
Non-Coastal |
137 |
11 |
|
Outer/ Border |
522 |
42 |
|
Urban Zone |
116 |
10 |
|
Small Islands |
160 |
13 |
|
Valid |
1233 |
100 |
|
Missing |
0 |
0 |
The clustering process above resulted in five village typologies based on their characteristic attributes. These typologies are not solely determined by geographic proximity or administrative boundaries, but also by similarities in spatial patterns, configurations, and socio-economic aspects. Consequently, villages located far apart geographically may belong to the same cluster if they share similar characteristics. Conversely, villages that are geographically close or located within the same administrative region may fall into different clusters if their typological features differ.
Table 6 summarizes the village typologies in relation to the similarity of features or characteristics associated with each typology, along with the number of administrative regions (districts/regencies) represented within each cluster or typology.
Table 6. Aspects of typology and number of district
|
Cluster/ Typology |
Aspects |
Number of District |
|
Coastal |
Spatial pattern |
6 |
|
Non Coastal |
Spatial pattern |
6 |
|
Outer/ Border |
Geographic location |
5 |
|
Urban Zone |
Socio-economic |
11 |
|
Small Islands |
Spatial form |
4 |
It is evident that the village typologies transcend administrative and geographic boundaries. The Urban Zone typology, for instance, spans all 11 regencies/municipalities in Maluku Province. This indicates that villages classified under the Urban Zone typology are not concentrated in a single area but are distributed across the entire province. Similarly, other typologies also extend across multiple administrative regions: the Coastal typology covers 6 regencies, Non-Coastal typology spans 6 regencies, Outermost/Border typology includes 5 regencies, and the Small Islands typology is represented in 4 regencies.
Figure 5 illustrates the spatial distribution of village typologies across the archipelagic territory of Maluku Province. With the assignment of codes to each typology, code C is the Coastal, code NC is the Non-coastal, code OB is Outer/border, code U is Urban and code SI is small island.
Figure 5. Map of village typology in Maluku, Indonesia (Appendix Table 1 Part A-G)
The regional clustering that resulted in the five village typologies in this study differs from the existing “Island Cluster” concept previously adopted in Maluku Province, which classifies the region into 12 island clusters. The basis for that classification is primarily geographical, where islands located in close proximity within defined geographic boundaries are grouped into one cluster, and subsequent groups are formed based on successive geographic boundaries—resulting in a total of 12 clusters. However, this approach does not adequately account for the diversity of spatial characteristics or the socio-economic conditions within each cluster. In principle, this form of regional classification is not significantly different from administrative divisions such as regencies/municipalities or sub-districts. In fact, in several clusters, it is difficult to distinguish between the boundaries of the island cluster and those of existing administrative regions. As a result, the practical implementation of the Island Cluster concept in more targeted policy frameworks has proven difficult, particularly in addressing the specific and objective challenges faced by island communities.
In contrast, the typology model developed in this study is more scientifically robust and policy-relevant, as it incorporates the multi-dimensional nature of island regions—encompassing geographic, spatial, and socio-economic dimensions. This approach aligns with findings from prior research that emphasize the importance of combining spatial and socio-economic indicators to construct meaningful classifications of rural and island settlements [18-20]. Furthermore, by focusing the unit of analysis at the village level, the typologies produced in this study offer greater precision and practical utility, as supported by Li et al. [21], who emphasized the need for high-resolution spatial and functional categorization in rural planning.
In this study, each area was grouped based on similarities in these attributes, resulting in five typologies collectively referred to as Island Village Typologies, namely:
Coastal Typology, comprising villages located in coastal areas
Non-Coastal Typology, consisting of inland villages not adjacent to the sea
Outermost/Border Typology, including villages situated in the outermost regions or national border zones
Urban Zone Typology, consisting of villages located in urban settings
Small Islands Typology, representing villages located on small islands.
The identification of these five Island Village Typologies is expected to support policymakers in formulating more precise, equitable, and sustainable development strategies. For instance, development strategies appropriate for the Non-Coastal Typology may differ significantly from those required in the Coastal Typology or the Small Islands Typology. Likewise, policies designed for urban village development cannot be uniformly applied to villages within the Outermost/Border Typology. Each typology has its own unique characteristics, necessitating distinct policy approaches aligned with their specific potentials, needs, and development challenges.
Ultimately, it is hoped that the findings of this study will contribute to the enrichment of knowledge and understanding regarding the typologies and characteristics of island regions and their inherent diversity. Practically, this research is expected to serve as a useful reference for designing various programs, policies, and regional development strategies—particularly those targeting village development in archipelagic regions.
Villages classified under the Coastal typology require policies that emphasize strengthening coastal resilience through the protection of erosion-prone areas, the development of marine and fisheries-based economies, as well as improved market access and supporting infrastructure such as cold storage facilities and fishing ports.
While Non-Coastal villages clearly demand greater attention to improving road infrastructure, access to basic services, and the promotion of sustainable agricultural practices.
For villages categorized as Small Islands, policy focus should be directed toward enhancing inter-island connectivity, providing access to renewable energy, fostering environmentally sustainable economic development, and increasing access to essential services such as education and healthcare.
Villages within the Urban Zone typology also face their own unique challenges, including land conversion, high population density, and increased demand for services. Therefore, appropriate policies should focus on spatial planning, public service digitalization, and the development of service sectors and creative industries.
Meanwhile, the Outer/Border typology requires a strategic policy approach, as these villages represent the frontline of the nation's territory, yet often remain marginalized and underdeveloped. In addition to strengthening territorial defense and security functions, there is a pressing need for basic infrastructure development, improved connectivity, and the provision of integrated public services.
Ultimately, it is hoped that the findings of this study can contribute to a broader understanding of the types and characteristics of island regions and their diversity. Practically, the results may serve as a valuable reference for the formulation of programs, policies, and development planning strategies, particularly for village development in archipelagic regions.
This study has several limitations that should be acknowledged to clarify the scope of the analysis and to encourage caution in interpreting the findings. These limitations are outlined as follows:
Temporal Scope: This study is based on data from the year 2021. Therefore, all conclusions drawn are descriptive of that particular period and do not reflect temporal dynamics or changes in village typologies over time.
Unit of Analysis: The unit of analysis used is the village. While spatial granularity at the village level provides a strong advantage in capturing local diversity, it also presents challenges related to data gaps or incompleteness for certain indicators. As such, variable selection and observation units were carefully chosen to ensure representativeness and reliability.
Geographical Scope: The scope of this study is explicitly limited to Maluku Province. The resulting classification and typological characteristics of villages are not intended to be generalized to other provinces, especially those with significantly different geographical conditions.
Methodological Approach: This study employs exploratory statistical methods, namely PCA and K-Means Clustering. These methods were selected to identify latent typological structures based on geospatial and socio-economic variables in a data-driven manner. However, due to limited availability and completeness of ideal variables at the village level, the selection of variables was guided by considerations of data availability and consistency. This may influence the scope of indicators included in the classification model.
Nature of the Typology: Although the resulting clusters show meaningful spatial differentiation, the classification should not be regarded as final or absolute. The typology produced is indicative in nature. Therefore, further policy validation or field studies are needed to confirm the relevance and accuracy of the classifications.
By acknowledging these limitations, this study is expected to serve as an initial foundation for formulating archipelagic village typology-based policies, while leaving room for refinement through expanded datasets and more advanced analytical approaches.
Appendix Table 1. Village typology in Maluku Province- part A
|
NO. |
VILLAGE |
CODE |
NO. |
VILLAGE |
CODE |
NO. |
VILLAGE |
CODE |
NO. |
VILLAGE |
CODE |
|
1 |
Abat |
OB118 |
51 |
Aran |
SI74 |
101 |
Batuasa |
C227 |
151 |
Disuk |
OB353 |
|
2 |
Abean |
OB334 |
52 |
Arewang |
SI75 |
102 |
Batugajah |
OB227 |
152 |
Doka Barat |
OB12 |
|
3 |
Abio Ahiolo |
NC99 |
53 |
Argam |
SI76 |
103 |
Batugoyang |
OB6 |
153 |
Doka Timur |
OB13 |
|
4 |
Aboru |
SI10 |
54 |
Ariate |
C169 |
104 |
Batuley |
OB7 |
154 |
Dosimar |
OB14 |
|
5 |
Abubu |
SI11 |
55 |
Arma |
OB128 |
105 |
Batumiau |
OB228 |
155 |
Dosinamalau |
OB15 |
|
6 |
Abulate |
C215 |
56 |
Armada |
SI77 |
106 |
Bebar Timur |
OB229 |
156 |
Dreamland Hills |
NC126 |
|
7 |
Abusur |
OB220 |
57 |
Arnau |
OB223 |
107 |
Bellis |
C228 |
157 |
Dudunwahan |
OB354 |
|
8 |
Ad Ngurwul |
OB335 |
58 |
Aroa Kataloka |
SI78 |
108 |
Beltubur |
OB8 |
158 |
Dulak |
SI84 |
|
9 |
Ad Ohoiwaf |
OB336 |
59 |
Arso |
OB338 |
109 |
Bemo |
C229 |
159 |
Dullah |
OB194 |
|
10 |
Ad Wear Aur |
OB337 |
60 |
Artafela |
NC124 |
110 |
Bemo Perak |
C230 |
160 |
Dullah Laut |
OB195 |
|
11 |
Adabai |
C216 |
61 |
Aruan Gaur |
C223 |
111 |
Bemun |
OB9 |
161 |
Durjela |
OB16 |
|
12 |
Adar |
SI69 |
62 |
Arui Bab |
OB129 |
112 |
Benjina |
OB10 |
162 |
Duryar Rumoy |
SI85 |
|
13 |
Adaut |
OB119 |
63 |
Arui Das |
OB130 |
113 |
Benjuring |
OB11 |
163 |
Dwiwarna |
U66 |
|
14 |
Adodo Fordata |
OB120 |
64 |
Arwala |
OB224 |
114 |
Benteng |
U22 |
164 |
Effa |
SI86 |
|
15 |
Adodo Molu |
OB121 |
65 |
Asilulu |
C103 |
115 |
Besi |
C104 |
165 |
El Ralang |
OB355 |
|
16 |
Afang Defol |
C217 |
66 |
Atiahu |
C224 |
116 |
Biloro |
C46 |
166 |
Elaar Lamagorang |
OB356 |
|
17 |
Afang Kota |
C218 |
67 |
Atubul Da |
OB131 |
117 |
Bitorik |
C231 |
167 |
Elaar Let |
OB357 |
|
18 |
Ahanari |
OB221 |
68 |
Atubul Dol |
OB132 |
118 |
Boinfia |
C232 |
168 |
Elaar Ngursoin |
OB358 |
|
19 |
Ahusen |
U16 |
69 |
Awear |
OB133 |
119 |
Boiyauw |
SI14 |
169 |
Elara |
SI1 |
|
20 |
Ainena |
C219 |
70 |
Awear Rumngeur |
OB134 |
120 |
Bomaki |
OB136 |
170 |
Elat |
OB359 |
|
21 |
Air Besar |
C100 |
71 |
Awilinan |
C2 |
121 |
Bombay |
OB342 |
171 |
Elemata |
NC60 |
|
22 |
Air Buaya |
C1 |
72 |
Babiotang |
OB225 |
122 |
Booi |
SI15 |
172 |
Elfule |
C47 |
|
23 |
Air Nanang |
C220 |
73 |
Bala-bala |
C41 |
123 |
Buan Kataloka |
SI81 |
173 |
Eliasa |
OB137 |
|
24 |
Air Ternate |
C40 |
74 |
Balatan |
OB3 |
124 |
Buano Hatuputih |
SI57 |
174 |
Elnusa |
C237 |
|
25 |
Airkasar |
C221 |
75 |
Balbalu |
NC1 |
125 |
Buano Selatan |
SI58 |
175 |
Elo |
OB231 |
|
26 |
Akatfadedo |
SI70 |
76 |
Balpetu |
C42 |
126 |
Buano Utara |
SI59 |
176 |
Elpaputih |
C170 |
|
27 |
Aketernate |
C101 |
77 |
Banda Baru |
NC59 |
127 |
Bula |
U111 |
177 |
Elsulith |
OB232 |
|
28 |
Aki Jaya |
NC123 |
78 |
Banda Efruan |
OB339 |
128 |
Bula Air Fatolo |
C233 |
178 |
Ema |
NC56 |
|
29 |
Akoon |
SI12 |
79 |
Banda Eli |
OB340 |
129 |
Bululora |
OB230 |
179 |
Emguhen |
C48 |
|
30 |
Algadang |
OB1 |
80 |
Banda Suku Tigapuluh |
OB341 |
130 |
Bumey |
U65 |
180 |
Emplawas |
OB233 |
|
31 |
Allang |
C102 |
81 |
Banggoi |
C225 |
131 |
Buria |
NC100 |
181 |
Englas |
C238 |
|
32 |
Allang Asaude |
C168 |
82 |
Banggoi Pancorang |
C226 |
132 |
Combir Kasestoren |
SI16 |
182 |
Eray |
OB234 |
|
33 |
Alusi Batjasi |
OB122 |
83 |
Bara |
C3 |
133 |
Dada Kataloka |
SI82 |
183 |
Erersin |
OB17 |
|
34 |
Alusi Bukjalim |
OB123 |
84 |
Bardefan |
OB4 |
134 |
Daftel |
OB343 |
184 |
Erwiri |
C49 |
|
35 |
Alusi Kelaan |
OB124 |
85 |
Bas |
SI79 |
135 |
Danama |
C234 |
185 |
Etaralu |
SI87 |
|
36 |
Alusi Krawain |
OB125 |
86 |
Basada |
OB5 |
136 |
Danar Lumefar |
OB344 |
186 |
Eti |
U102 |
|
37 |
Alusi Tamrian |
OB126 |
87 |
Basalale |
NC2 |
137 |
Danar Ohoiseb |
OB345 |
187 |
Evu |
OB360 |
|
38 |
Amahai |
U63 |
88 |
Basarin |
SI80 |
138 |
Danar Ternate |
OB346 |
188 |
Faa |
OB361 |
|
39 |
Amahusu |
U17 |
89 |
Bati Kilwouw |
NC125 |
139 |
Dangarat |
OB347 |
189 |
Faan |
OB362 |
|
40 |
Amantelu |
U18 |
90 |
Batlale |
NC3 |
140 |
Dava |
NC4 |
190 |
Fakal |
NC39 |
|
41 |
Amarlaut |
SI71 |
91 |
Batu Boy |
C4 |
141 |
Dawang |
C235 |
191 |
Fako |
OB363 |
|
42 |
Amarsekaru |
SI72 |
92 |
Batu Gajah |
U19 |
142 |
Day |
SI83 |
192 |
Fangamas |
OB364 |
|
43 |
Amarwatu |
SI73 |
93 |
Batu Jungku |
C5 |
143 |
Debowae |
NC5 |
193 |
Fanwav |
OB365 |
|
44 |
Amdasa |
OB127 |
94 |
Batu Kasa |
C43 |
144 |
Debut |
OB348 |
194 |
Fatlabata |
OB18 |
|
45 |
Ameth |
NC58 |
95 |
Batu Layar |
C44 |
145 |
Dender |
SI17 |
195 |
Fatmite |
C50 |
|
46 |
Ameth |
SI13 |
96 |
Batu Meja |
U20 |
146 |
Denwet |
OB349 |
196 |
Fattolo |
C239 |
|
47 |
Ampera |
U64 |
97 |
Batu Merah |
OB226 |
147 |
Depur |
OB350 |
197 |
Fatural |
OB19 |
|
48 |
Analutur |
OB222 |
98 |
Batu Merah |
U21 |
148 |
Dian Darat |
OB351 |
198 |
Feer |
OB366 |
|
49 |
Angar |
C222 |
99 |
Batu Putih |
OB135 |
149 |
Dian Pulau |
OB352 |
199 |
Feruni |
OB20 |
|
50 |
Apara |
OB2 |
100 |
Batu Tulis |
C45 |
150 |
Dihil |
C236 |
200 |
Fiditan |
OB196 |
Appendix Table 1. Village typology in Maluku Province- part B
|
NO. |
VILLAGE |
CODE |
NO. |
VILLAGE |
CODE |
NO. |
VILLAGE |
CODE |
NO. |
VILLAGE |
CODE |
|
201 |
Finualen |
OB197 |
251 |
Hatuolo |
NC62 |
301 |
Ilpokil |
OB244 |
351 |
Kampung Baru |
C248 |
|
202 |
Fogi |
C51 |
252 |
Hatusua |
C172 |
302 |
Ilputih |
OB245 |
352 |
Kampung Baru |
U70 |
|
203 |
Foket |
OB21 |
253 |
Haya |
C109 |
303 |
Ilwaki |
U60 |
353 |
Kampung Baru |
SI2 |
|
204 |
Funa Naibaya |
C240 |
254 |
Herlau Pauni |
C110 |
304 |
Ilway |
OB246 |
354 |
Kampung Baru |
SI93 |
|
205 |
Fursuy |
OB138 |
255 |
Herley |
OB235 |
305 |
Imroing |
OB247 |
355 |
Kampung Gorom |
C249 |
|
206 |
Gah |
C241 |
256 |
Hertuti |
OB236 |
306 |
Irloy |
OB32 |
356 |
Kampung Tengah Wermaf |
SI94 |
|
207 |
Gaimar |
OB22 |
257 |
Hiay |
OB237 |
307 |
Iso |
OB385 |
357 |
Kampung Wailola |
U113 |
|
208 |
Galai Dubu |
U8 |
258 |
Hila |
OB238 |
308 |
Issu |
NC64 |
358 |
Kanara |
OB200 |
|
209 |
Galala |
U23 |
259 |
Hila |
U68 |
309 |
Itawaka |
SI23 |
359 |
Kandar |
OB142 |
|
210 |
Gale-gale |
C105 |
260 |
Hirit |
OB198 |
310 |
Jabulenga |
OB33 |
360 |
Kanikeh |
NC68 |
|
211 |
Garara |
OB367 |
261 |
Hitulama |
C111 |
311 |
Jakarta Baru |
NC127 |
361 |
Karang Jaya |
NC8 |
|
212 |
Gardakau |
OB23 |
262 |
Hitumessing |
C112 |
312 |
Jambu Air |
OB34 |
362 |
Karang Panjang |
U29 |
|
213 |
Geser |
U112 |
263 |
Hoat |
OB376 |
313 |
Jamilu |
C8 |
363 |
Karangguli |
OB48 |
|
214 |
Goda-Goda |
OB24 |
264 |
Hokmar |
OB31 |
314 |
Jelia |
OB35 |
364 |
Karatat |
OB143 |
|
215 |
Gogorea |
NC6 |
265 |
Hoko |
OB377 |
315 |
Jembatan Basah |
NC128 |
365 |
Karawai |
OB49 |
|
216 |
Goha |
SI88 |
266 |
Hoko |
OB378 |
316 |
Jerili |
NC65 |
366 |
Karay |
C250 |
|
217 |
Gomar Meti |
OB25 |
267 |
Hollat |
OB379 |
317 |
Jerol |
OB36 |
367 |
Karbubu |
OB251 |
|
218 |
Gomar Sungai |
OB26 |
268 |
Hollat Solair |
OB380 |
318 |
Jerusu |
OB248 |
368 |
Karey |
OB50 |
|
219 |
Gomo-Gomo |
OB27 |
269 |
Hollay |
OB381 |
319 |
Jerwatu |
OB37 |
369 |
Kariu |
SI26 |
|
220 |
Gomsey |
OB28 |
270 |
Hollo |
U69 |
320 |
Jikumerasa |
C9 |
370 |
Karkarit |
OB386 |
|
221 |
Gorar |
OB29 |
271 |
Honipopu |
U27 |
321 |
Jirlay |
OB38 |
371 |
Karlokin |
SI95 |
|
222 |
Grahwaen |
C52 |
272 |
Honitetu |
NC101 |
322 |
Jorang |
OB39 |
372 |
Karlomin |
SI96 |
|
223 |
Grandeng |
NC7 |
273 |
Hoor Islam |
OB382 |
323 |
Juring |
OB40 |
373 |
Karlutu Kara |
C116 |
|
224 |
Guliar |
SI89 |
274 |
Hoor Kristen |
OB383 |
324 |
Jursiang |
OB41 |
374 |
Kartutin Kartenga |
SI97 |
|
225 |
Guli-Guli |
C242 |
275 |
Horale |
C113 |
325 |
Kabalsiang |
OB42 |
375 |
Kase |
C54 |
|
226 |
Gulili |
OB30 |
276 |
Hote |
C53 |
326 |
Kabalukin |
OB43 |
376 |
Kasieh |
C177 |
|
227 |
Gunak |
C243 |
277 |
Hote |
C246 |
327 |
Kabauhari |
NC66 |
377 |
Kataloka |
U114 |
|
228 |
Gusalaut |
C244 |
278 |
Hualoy |
C173 |
328 |
Kabauw |
SI24 |
378 |
Kawa |
C178 |
|
229 |
Haar Ohoimel |
OB368 |
279 |
Huaulu |
NC63 |
329 |
Kabiarat |
OB140 |
379 |
Keffing |
SI98 |
|
230 |
Haar Ohoimur GPM |
OB369 |
280 |
Huku Kecil |
NC102 |
330 |
Kabufin |
OB44 |
380 |
Kehli |
OB252 |
|
231 |
Haar Ohoimur RK |
OB370 |
281 |
Hukuanakota |
NC103 |
331 |
Kaforing |
SI92 |
381 |
Kelaan |
OB144 |
|
232 |
Haar Ohoiwait |
OB371 |
282 |
Hukurila |
C92 |
332 |
Kahilin |
OB249 |
382 |
Kelaba |
C251 |
|
233 |
Haar Renrahantel |
OB372 |
283 |
Hulaliu |
SI20 |
333 |
Kaibobo |
C176 |
383 |
Kelang Asaude |
SI60 |
|
234 |
Haar Wassar |
OB373 |
284 |
Hulung |
C174 |
334 |
Kaibolafin |
OB45 |
384 |
Kelangan |
SI99 |
|
235 |
Halong |
U24 |
285 |
Hunisi |
C114 |
335 |
Kaiely |
C10 |
385 |
Kelanit |
OB387 |
|
236 |
Hangur |
OB374 |
286 |
Hunuth/Durian Patah |
U28 |
336 |
Kailolo |
SI25 |
386 |
Keldor |
SI100 |
|
237 |
Harangur |
OB375 |
287 |
Hutumury |
C93 |
337 |
Kaimear |
OB199 |
387 |
Kelibingan |
SI101 |
|
238 |
Haria |
SI18 |
288 |
Iblatmuntah |
OB239 |
338 |
Kairatu |
U103 |
388 |
Kellu |
SI102 |
|
239 |
Haruku |
SI19 |
289 |
Ibra |
OB384 |
339 |
Kaitetu |
C115 |
389 |
Ker Ker |
SI103 |
|
240 |
Haruru |
U67 |
290 |
Iha |
C175 |
340 |
Kaiwabar |
OB46 |
390 |
Keta |
C252 |
|
241 |
Hatalai |
NC57 |
291 |
Iha |
SI21 |
341 |
Kaiwatu |
OB250 |
391 |
Keta Rumadan |
C253 |
|
242 |
Hatawano |
C6 |
292 |
Ihamahu |
SI22 |
342 |
Kaki Air |
C11 |
392 |
Ketsoblak |
U56 |
|
243 |
Hative Besar |
U25 |
293 |
Ilath |
C7 |
343 |
Kalar-Kalar |
OB47 |
393 |
Ketty |
OB253 |
|
244 |
Hative Kecil |
U26 |
294 |
Ilbutung |
OB240 |
344 |
Kaloa |
NC67 |
394 |
Kian Darat |
C254 |
|
245 |
Hatu |
C106 |
295 |
Ilih |
OB241 |
345 |
Kamal |
U104 |
395 |
Kian Laut |
C255 |
|
246 |
Hatu |
C107 |
296 |
Ilili |
SI90 |
346 |
Kamar |
C247 |
396 |
Kilalir Kilwouw |
SI104 |
|
247 |
Hatuhenu |
NC61 |
297 |
Ilili |
SI91 |
347 |
Kamarian |
U105 |
397 |
Kilang |
C94 |
|
248 |
Hatuimeten |
C245 |
298 |
Ilmamau |
OB242 |
348 |
Kamatubun |
OB141 |
398 |
Kilbat |
C256 |
|
249 |
Hatumete |
C108 |
299 |
Ilmarang |
OB243 |
349 |
Kamlanglale |
U6 |
399 |
Kilbon Kway |
C257 |
|
250 |
Hatunuru |
C171 |
300 |
Ilngei |
OB139 |
350 |
Kampung Baru |
C12 |
400 |
Kilbutak |
SI105 |
Appendix Table 1. Village typology in Maluku Province- part C
|
NO. |
VILLAGE |
CODE |
NO. |
VILLAGE |
CODE |
NO. |
VILLAGE |
CODE |
NO. |
VILLAGE |
CODE |
|
401 |
Kilean |
SI106 |
451 |
Kurwara |
SI115 |
501 |
Leinitu |
SI29 |
551 |
Luang Barat |
OB273 |
|
402 |
Kileser |
NC129 |
452 |
Kuwaos |
C264 |
502 |
Leiting |
OB66 |
552 |
Luang Timur |
OB274 |
|
403 |
Kilfura |
SI107 |
453 |
Kwarbola |
OB62 |
503 |
Lekloor |
OB265 |
553 |
Luhu |
C183 |
|
404 |
Kilga Kilwouw |
C258 |
454 |
Kwawor Besar Ena |
SI116 |
504 |
Leksula |
C55 |
554 |
Luhuely |
OB275 |
|
405 |
Kilga Watubau |
C259 |
455 |
Kwawor Besar Witau |
SI117 |
505 |
Lektama |
C56 |
555 |
Luhutuban |
SI61 |
|
406 |
Kiliwouw |
SI108 |
456 |
Kwawor Kecil Mata Ata |
SI118 |
506 |
Leku |
C57 |
556 |
Lumahlatal |
C184 |
|
407 |
Kilkoda |
SI109 |
457 |
Kwawor Kecil Mata Wawa |
SI119 |
507 |
Lelang |
OB266 |
557 |
Lumahpelu |
C185 |
|
408 |
Kilmasa |
OB145 |
458 |
Laar |
OB390 |
508 |
Lele |
NC10 |
558 |
Lumasebu |
U10 |
|
409 |
Kilmoy |
C260 |
459 |
Labelau |
OB260 |
509 |
Lelingluan |
OB153 |
559 |
Lumoli |
NC107 |
|
410 |
Kilmury |
C261 |
460 |
Labetawi |
OB201 |
510 |
Lemanpoli |
NC11 |
560 |
Lumoy |
SI3 |
|
411 |
Kilobar |
OB146 |
461 |
Labobar |
OB148 |
511 |
Lena |
C58 |
561 |
Lurang |
OB276 |
|
412 |
Kiloon |
OB147 |
462 |
Labuan |
C118 |
512 |
Ler Ohoilim |
OB394 |
562 |
Lutur |
OB71 |
|
413 |
Kilotak |
SI110 |
463 |
Labuang |
U7 |
513 |
Lermatang |
OB154 |
563 |
Maar |
OB398 |
|
414 |
Kiltay |
SI111 |
464 |
Lafa |
C119 |
514 |
Lesane |
U75 |
564 |
Madak |
NC130 |
|
415 |
Kiltufa |
SI112 |
465 |
Laha |
C120 |
515 |
Lesluru |
NC71 |
565 |
Madwat |
OB399 |
|
416 |
Kilwair |
OB388 |
466 |
Laha |
U31 |
516 |
Letman |
OB395 |
566 |
Maekor |
OB72 |
|
417 |
Kilwaru |
SI113 |
467 |
Laha Kaba |
C121 |
517 |
Letmasa |
OB267 |
567 |
Magat |
SI124 |
|
418 |
Kilwat |
OB389 |
468 |
Lahema |
SI120 |
518 |
Letoda |
OB268 |
568 |
Mahaleta |
OB277 |
|
419 |
Klis |
OB254 |
469 |
Laimu |
C122 |
519 |
Letsiara |
OB269 |
569 |
Mahu |
SI31 |
|
420 |
Klishatu |
OB255 |
470 |
Laininir |
OB63 |
520 |
Letwaru |
U76 |
570 |
Mahuan |
OB278 |
|
421 |
Kobadangar |
OB51 |
471 |
Lairngangas |
OB391 |
521 |
Letwuan |
OB396 |
571 |
Maijuring |
OB73 |
|
422 |
Kobamar |
OB52 |
472 |
Laitutun |
OB261 |
522 |
Letwurung |
OB270 |
572 |
Makariki |
U79 |
|
423 |
Kobasel Fara |
OB53 |
473 |
Lala |
C13 |
523 |
Lewah |
OB271 |
573 |
Makatian |
OB159 |
|
424 |
Kobasel Timur |
OB54 |
474 |
Lalasa |
SI121 |
524 |
Lian Tasik |
C266 |
574 |
Makububui |
NC108 |
|
425 |
Kobi |
C117 |
475 |
Lamahang |
C14 |
525 |
Liang |
NC40 |
575 |
Malaku |
C127 |
|
426 |
Kobimukti |
U71 |
476 |
Lamdesar Barat |
OB149 |
526 |
Liang |
U77 |
576 |
Maloang |
C186 |
|
427 |
Kobisonta |
U72 |
477 |
Lamdesar Timur |
OB150 |
527 |
Liang |
U78 |
577 |
Mamala |
C128 |
|
428 |
Kobraur |
OB55 |
478 |
Langgiar Haar |
OB392 |
528 |
Liliama |
C267 |
578 |
Mamur |
SI125 |
|
429 |
Kobror |
OB56 |
479 |
Langgur |
U100 |
529 |
Liliboy |
C125 |
579 |
Maneo Rendah |
C129 |
|
430 |
Koijabi |
OB57 |
480 |
Langhalau |
OB64 |
530 |
Limumir |
U115 |
580 |
Maneoratu |
C130 |
|
431 |
Kokroman |
U73 |
481 |
Lapang Kampung Jawa |
SI122 |
531 |
Lingada |
OB155 |
581 |
Mangeswaen |
NC41 |
|
432 |
Kokwari |
OB256 |
482 |
Lapela |
C265 |
532 |
Lingat |
OB156 |
582 |
Mangga Dua |
U35 |
|
433 |
Kolaha |
OB58 |
483 |
Larat |
OB393 |
533 |
Lisabata |
C181 |
583 |
Manggis |
C268 |
|
434 |
Kolamar |
OB59 |
484 |
Larike |
C123 |
534 |
Lisabata Timur |
C126 |
584 |
Manglusi |
OB160 |
|
435 |
Kolser |
U99 |
485 |
Latalola Besar |
OB262 |
535 |
Lodar El |
U57 |
585 |
Manjau |
OB74 |
|
436 |
Kompane |
OB60 |
486 |
Latalola Kecil |
OB263 |
536 |
Lohiasapalewa |
NC105 |
586 |
Manusa |
NC109 |
|
437 |
Kota Lama |
SI9 |
487 |
Latdalam |
OB151 |
537 |
Lohiatala |
NC106 |
587 |
Manusela |
NC73 |
|
438 |
Kota Sirih |
OB522 |
488 |
Latea |
C124 |
538 |
Lokki |
C182 |
588 |
Manuweri |
OB279 |
|
439 |
Kroing |
OB257 |
489 |
Lateri |
U32 |
539 |
Loko |
SI123 |
589 |
Maraina |
NC74 |
|
440 |
Kuaimelu |
OB258 |
490 |
Latta |
U33 |
540 |
Lokwirin |
OB202 |
590 |
Marantutul |
OB161 |
|
441 |
Kubalahin |
NC9 |
491 |
Latu |
C180 |
541 |
Lola |
OB67 |
591 |
Marasahua |
NC75 |
|
442 |
Kudamati |
U30 |
492 |
Latuhalat |
U34 |
542 |
Lolotuara |
OB272 |
592 |
Marfenfen |
OB75 |
|
443 |
Kufar |
C262 |
493 |
Laturake |
NC104 |
543 |
Longgar |
OB68 |
593 |
Marfun |
OB400 |
|
444 |
Kulugowa |
SI114 |
494 |
Lau-Lau |
OB65 |
544 |
Lonthoir |
SI30 |
594 |
Mariri |
OB76 |
|
445 |
Kulur |
C179 |
495 |
Lauran |
OB152 |
545 |
Loon |
OB397 |
595 |
Maririmar |
OB77 |
|
446 |
Kulur |
SI27 |
496 |
Lautang |
SI28 |
546 |
Loping Mulyo |
NC72 |
596 |
Marlasi |
OB78 |
|
447 |
Kumelang |
C263 |
497 |
Lawawang |
OB264 |
547 |
Lorang |
OB69 |
597 |
Marsela |
OB280 |
|
448 |
Kumul |
OB61 |
498 |
Layeni |
U74 |
548 |
Lor-lor |
OB70 |
598 |
Masapun |
OB281 |
|
449 |
Kumur |
OB259 |
499 |
Leahari |
C95 |
549 |
Lorulun |
OB157 |
599 |
Masarete |
C15 |
|
450 |
Kuralele |
NC69 |
500 |
Leaway |
NC70 |
550 |
Lorwembun |
OB158 |
600 |
Masawoy |
SI4 |
Appendix Table 1. Village typology in Maluku Province- part D
|
NO. |
VILLAGE |
CODE |
NO. |
VILLAGE |
CODE |
NO. |
VILLAGE |
CODE |
NO. |
VILLAGE |
CODE |
|
601 |
Masawoy |
SI62 |
651 |
Nakupia |
NC78 |
701 |
Nuniali |
C192 |
751 |
Osong |
C273 |
|
602 |
Masihulan |
NC76 |
652 |
Nalahia |
SI33 |
702 |
Nura |
OB288 |
752 |
Otademan |
SI137 |
|
603 |
Masnana |
C59 |
653 |
Nalbessy |
C61 |
703 |
Nurkat |
OB166 |
753 |
Ouw |
SI36 |
|
604 |
Masrum |
U58 |
654 |
Nama Andan |
SI134 |
704 |
Nurnyaman |
OB289 |
754 |
Paa |
C136 |
|
605 |
Mastur |
OB401 |
655 |
Nama Lena |
SI135 |
705 |
Nuruwe |
C193 |
755 |
Pandan Kasturi |
U39 |
|
606 |
Mastur Baru |
OB402 |
656 |
Namaelo |
U81 |
706 |
Nusaniwe |
C97 |
756 |
Papakula |
OB88 |
|
607 |
Mataholat |
OB403 |
657 |
Namalean |
SI136 |
707 |
Nusaniwe |
U38 |
757 |
Paperu |
SI37 |
|
608 |
Matakus |
OB162 |
658 |
Namar |
OB412 |
708 |
Nusantara |
U83 |
758 |
Parbulu |
NC14 |
|
609 |
Matapa |
C187 |
659 |
Namara |
OB85 |
709 |
Nusarua |
NC44 |
759 |
Pasahari |
C137 |
|
610 |
Matwair |
OB404 |
660 |
Namasina |
U82 |
710 |
Nusiata |
OB290 |
760 |
Pasanea |
C138 |
|
611 |
Mepa |
C60 |
661 |
Namlea |
U1 |
711 |
Nuweletetu |
NC81 |
761 |
Pasinalo |
C194 |
|
612 |
Merdeka |
SI32 |
662 |
Namlea Ilath |
C16 |
712 |
Nuwewang |
OB291 |
762 |
Pasir Putih |
SI5 |
|
613 |
Meror |
OB79 |
663 |
Namrinat |
NC42 |
713 |
Ohilahin |
NC13 |
763 |
Passo |
U40 |
|
614 |
Mesiang |
OB80 |
664 |
Namsina |
C17 |
714 |
Ohoibadar |
OB427 |
764 |
Patahuwe |
C195 |
|
615 |
Mesidang |
OB81 |
665 |
Namtabung |
OB165 |
715 |
Ohoider Atas |
OB428 |
765 |
Patti |
OB294 |
|
616 |
Messa |
NC77 |
666 |
Namto |
NC79 |
716 |
Ohoidertawun |
OB429 |
766 |
Pela |
C18 |
|
617 |
Meyano Das |
OB163 |
667 |
Nanali |
C62 |
717 |
Ohoidertom |
OB430 |
767 |
Pelauw |
U84 |
|
618 |
Meyano Das |
OB164 |
668 |
Nania |
U36 |
718 |
Ohoidertutu |
OB431 |
768 |
Perik Basaranggi |
SI138 |
|
619 |
Mida |
SI126 |
669 |
Naumatang |
OB286 |
719 |
Ohoiel |
OB432 |
769 |
PIliana |
NC82 |
|
620 |
Miran |
SI127 |
670 |
Nayet |
C271 |
720 |
Ohoifaruan |
OB433 |
770 |
Piru |
U107 |
|
621 |
Miran Gota |
SI128 |
671 |
Neath |
NC43 |
721 |
Ohoifau |
OB434 |
771 |
Pohon Batu |
C65 |
|
622 |
Miran Keledar |
SI129 |
672 |
Negeri Lama |
U37 |
722 |
Ohoijang Watdek |
U101 |
772 |
Poka |
U41 |
|
623 |
Miran Kilian |
SI130 |
673 |
Negeri Lima |
C133 |
723 |
Ohoilean |
OB435 |
773 |
Polin |
C274 |
|
624 |
Miran Manaban |
SI131 |
674 |
Nekan |
C272 |
724 |
Ohoililir |
OB436 |
774 |
Ponom |
OB89 |
|
625 |
Miran Rumuar |
SI132 |
675 |
Neniari |
NC111 |
725 |
Ohoilim |
OB437 |
775 |
Popjetur |
OB90 |
|
626 |
Mising |
C269 |
676 |
Nerong |
OB413 |
726 |
Ohoiluk |
OB438 |
776 |
Porto |
SI38 |
|
627 |
Moain |
OB282 |
677 |
Ngabub |
OB414 |
727 |
Ohoilus |
OB439 |
777 |
Pota Besar |
OB295 |
|
628 |
Mohongsel |
OB82 |
678 |
Ngadi |
OB203 |
728 |
Ohoimajang |
OB440 |
778 |
Pota Kecil |
OB296 |
|
629 |
Moning |
OB283 |
679 |
Ngafan |
OB415 |
729 |
Ohoinangan |
OB441 |
779 |
Pulau Ay |
SI39 |
|
630 |
Morekau |
NC110 |
680 |
Ngaibor |
OB86 |
730 |
Ohoinangan Atas |
OB442 |
780 |
Pulau Hatta |
SI40 |
|
631 |
Morella |
C131 |
681 |
Ngaiguli |
OB87 |
731 |
Ohoinol |
OB443 |
781 |
Pulau Panjang |
SI139 |
|
632 |
Morokai |
U80 |
682 |
Ngan |
OB416 |
732 |
Ohoira |
OB444 |
782 |
Pulau Rhun |
SI41 |
|
633 |
Mosso |
C132 |
683 |
Ngat |
OB417 |
733 |
Ohoiraut |
OB445 |
783 |
Pupliora |
OB297 |
|
634 |
Mugusinis |
SI133 |
684 |
Ngayub |
OB418 |
734 |
Ohoiren |
OB446 |
784 |
Purpura |
OB298 |
|
635 |
Mun Essoy |
OB405 |
685 |
Ngefuit |
OB419 |
735 |
Ohoirenan |
OB447 |
785 |
Rahangiar |
OB452 |
|
636 |
Mun Kahar |
OB406 |
686 |
Ngefuit Atas |
OB420 |
736 |
Ohoitahit |
OB205 |
786 |
Rahareng |
OB453 |
|
637 |
Mun Ngurditwain |
OB407 |
687 |
Ngilngof |
OB421 |
737 |
Ohoitel |
OB206 |
787 |
Rahareng Atas |
OB454 |
|
638 |
Mun Ohoiir |
OB408 |
688 |
Ngurdu |
OB422 |
738 |
Ohoituf |
OB448 |
788 |
Raheriat |
NC15 |
|
639 |
Mun Ohoitadiun |
OB409 |
689 |
Ngurko |
OB423 |
739 |
Ohoiwait |
OB449 |
789 |
Rajawali |
SI42 |
|
640 |
Mun Werfan |
OB410 |
690 |
Ngursit |
OB424 |
740 |
Ohoiwang |
OB450 |
790 |
Rambatu |
NC113 |
|
641 |
Murai |
OB83 |
691 |
Ngurwalek |
OB425 |
741 |
Ohoiwirin |
OB451 |
791 |
Rarat |
SI140 |
|
642 |
Murnaten |
C188 |
692 |
Ngurwul |
OB426 |
742 |
Oirata Barat |
OB292 |
792 |
Rat |
OB455 |
|
643 |
Musihuwey |
C189 |
693 |
Niela |
OB204 |
743 |
Oirata Timur |
OB293 |
793 |
Rebi |
OB91 |
|
644 |
Nabaheng |
OB411 |
694 |
Nikulukan |
C190 |
744 |
Oki Baru |
C63 |
794 |
Regoha |
OB299 |
|
645 |
Nabar |
OB284 |
695 |
Niniari |
NC112 |
745 |
Oki Lama |
C64 |
795 |
Renfaan GPM |
OB456 |
|
646 |
Nafar |
OB84 |
696 |
Niwelehu |
U106 |
746 |
Olilit Raya |
OB167 |
796 |
Renfaan Islam |
OB457 |
|
647 |
Nafrua |
NC12 |
697 |
Nolloth |
SI34 |
747 |
Olong |
C134 |
797 |
Renfan |
OB458 |
|
648 |
Naiwel Ahinulin |
C270 |
698 |
Nomaha |
OB287 |
748 |
Oma |
SI35 |
798 |
Rerean |
OB459 |
|
649 |
Nakarhamto |
OB285 |
699 |
Nua Nea |
NC80 |
749 |
Ondor |
U116 |
799 |
Rewav |
OB460 |
|
650 |
Naku |
C96 |
700 |
Nukuhai |
C191 |
750 |
Oping |
C135 |
800 |
Reyamru |
OB461 |
Appendix Table 1. Village typology in Maluku Province- part E
|
NO. |
VILLAGE |
CODE |
NO. |
VILLAGE |
CODE |
NO. |
VILLAGE |
CODE |
NO. |
VILLAGE |
CODE |
|
801 |
Ridool |
U11 |
851 |
Saparua |
U86 |
901 |
Siri Sori Islam |
SI47 |
951 |
Tasinwaha |
OB101 |
|
802 |
Rijali |
U42 |
852 |
Sare |
OB464 |
902 |
Sitniohoi |
OB470 |
952 |
Tawiri |
U46 |
|
803 |
Riring |
NC114 |
853 |
Sariputih |
NC84 |
903 |
Siwa Lima |
U9 |
953 |
Tayando Langgiar |
OB212 |
|
804 |
Ritabel |
U12 |
854 |
Sathean |
OB465 |
904 |
Siwar |
SI7 |
954 |
Tayando Ohoiel |
OB213 |
|
805 |
Roho |
NC83 |
855 |
Sather |
OB466 |
905 |
Siwatlahin |
NC45 |
955 |
Tayando Yamru |
OB214 |
|
806 |
Rohomoni |
SI43 |
856 |
Saumlaki |
U13 |
906 |
Siya |
OB98 |
956 |
Tayando Yamtel |
OB215 |
|
807 |
Romdara |
OB300 |
857 |
Saumlaki Utara |
U14 |
907 |
Skikilale |
NC17 |
957 |
Tehoru |
U92 |
|
808 |
Romean |
OB168 |
858 |
Saunulu |
C145 |
908 |
Slealale |
NC46 |
958 |
Tehua |
C153 |
|
809 |
Romnus |
OB169 |
859 |
Savana Jaya |
C21 |
909 |
Soahuku |
C151 |
959 |
Teineman |
OB175 |
|
810 |
Rotnama |
OB301 |
860 |
Sawa |
C22 |
910 |
Sofyanin |
OB174 |
960 |
Tela |
OB309 |
|
811 |
Ruku Ruku |
SI141 |
861 |
Sawai |
U87 |
911 |
Sohuwe |
C201 |
961 |
Telalora |
OB310 |
|
812 |
Rukun Jaya |
NC131 |
862 |
Seakasale |
C198 |
912 |
Soin |
OB471 |
962 |
Telemar |
OB311 |
|
813 |
Ruma Durun |
SI142 |
863 |
Sehati |
C146 |
913 |
Soindat |
OB472 |
963 |
Telutih Baru |
C154 |
|
814 |
Rumaat |
OB462 |
864 |
Seilale |
C99 |
914 |
Soinrat |
OB473 |
964 |
Tenbuk |
OB478 |
|
815 |
Rumadian |
OB463 |
865 |
Seith |
C23 |
915 |
Solang |
C284 |
965 |
Tengah Tengah |
C155 |
|
816 |
Rumah Tiga |
U43 |
866 |
Seith |
C147 |
916 |
Solath |
OB308 |
966 |
Teor |
SI152 |
|
817 |
Rumahkay |
C196 |
867 |
Sekat |
C66 |
917 |
Sole |
SI63 |
967 |
Tepa |
OB312 |
|
818 |
Rumahlewang Besar |
OB302 |
868 |
Selamon |
SI45 |
918 |
Solea |
NC85 |
968 |
Terkuri |
C69 |
|
819 |
Rumahlewang Kecil |
OB303 |
869 |
Selasi |
SI6 |
919 |
Solea |
NC117 |
969 |
Tetoat |
OB479 |
|
820 |
Rumahsalut |
OB170 |
870 |
Selayar |
OB467 |
920 |
Somlain |
OB474 |
970 |
Themin |
OB176 |
|
821 |
Rumahsoal |
NC115 |
871 |
Selibata-bata |
OB94 |
921 |
Soya |
U45 |
971 |
Tiakur |
U61 |
|
822 |
Rumahsokat |
C139 |
872 |
Selilau |
OB95 |
922 |
Sukaraja |
C202 |
972 |
Tial |
C156 |
|
823 |
Rumahwey |
C140 |
873 |
Selmona |
OB96 |
923 |
Suli |
U90 |
973 |
Tiflen |
OB216 |
|
824 |
Rumanama Kotawouw Kataloka |
SI143 |
874 |
Selor |
C280 |
924 |
Sumbawa |
C285 |
974 |
Tifu |
C70 |
|
825 |
Rumberu |
NC116 |
875 |
Selwadu |
NC16 |
925 |
Sumber Agung |
NC133 |
975 |
Tifu |
NC18 |
|
826 |
Rumeon |
SI144 |
876 |
Semawi |
OB468 |
926 |
Sumeith Pasinaro |
NC118 |
976 |
Tihu |
U47 |
|
827 |
Rumfakar |
C275 |
877 |
Sepa |
C148 |
927 |
Suru |
C286 |
977 |
Tihuana |
NC87 |
|
828 |
Rumkisar |
OB304 |
878 |
Sera |
OB305 |
928 |
Taa |
C287 |
978 |
Tihulale |
U108 |
|
829 |
Rumngeur |
OB171 |
879 |
Sera |
SI146 |
929 |
Taar |
OB209 |
979 |
Tikbary |
C71 |
|
830 |
Rumoga |
NC132 |
880 |
Seriholo |
C199 |
930 |
Tabarfane |
OB99 |
980 |
Tinarin |
SI153 |
|
831 |
Rumoin |
OB207 |
881 |
Serili |
OB306 |
931 |
Tahalupu |
SI64 |
981 |
Tiouw |
SI49 |
|
832 |
Rutah |
C141 |
882 |
Sermaf |
OB208 |
932 |
Tala |
C203 |
982 |
Titawaai |
SI50 |
|
833 |
Rutong |
C98 |
883 |
Seruawan |
C200 |
933 |
Tam Ngurhir |
OB210 |
983 |
Tobo |
C290 |
|
834 |
Sabuai |
C276 |
884 |
Sesar |
C281 |
934 |
Tamangil Nuhuten |
OB475 |
984 |
Tomalehu |
C205 |
|
835 |
Sagey |
SI145 |
885 |
Sesar |
C282 |
935 |
Tamangil Nuhuyanat |
OB476 |
985 |
Tomalehu Barat |
SI65 |
|
836 |
Sahulauw |
C142 |
886 |
Seti |
C149 |
936 |
Tamedan |
OB211 |
986 |
Tomalehu Timur |
SI66 |
|
837 |
Salagor Air |
C277 |
887 |
Sewer |
OB97 |
937 |
Tamher Timur |
SI148 |
987 |
Tomliapat |
OB313 |
|
838 |
Salagor Kota |
C278 |
888 |
Siahoni |
C24 |
938 |
Tamher Warat |
SI149 |
988 |
Tomra |
OB314 |
|
839 |
Salamahu |
C143 |
889 |
Siatele |
C150 |
939 |
Tamilouw |
C152 |
989 |
Tonu Jaya |
SI67 |
|
840 |
Salarem |
OB92 |
890 |
Sifluru |
U88 |
940 |
Tana Soa |
SI150 |
990 |
Tounussa |
NC119 |
|
841 |
Salas |
C279 |
891 |
Sifnana |
U15 |
941 |
Tanah Baru |
SI151 |
991 |
Tounwawan |
OB315 |
|
842 |
Saleman |
C144 |
892 |
Sikaro Kataloka |
SI147 |
942 |
Tanah Merah |
NC86 |
992 |
Trana |
NC88 |
|
843 |
Samal |
U85 |
893 |
Sila |
SI46 |
943 |
Tanah Miring |
OB100 |
993 |
Trukat |
NC47 |
|
844 |
Samalagi |
C19 |
894 |
Silale |
U44 |
944 |
Tanah Rata |
SI48 |
994 |
Tual |
U59 |
|
845 |
Samang |
OB93 |
895 |
Silohan |
C283 |
945 |
Tananahu |
U91 |
995 |
Tubir Wasiwang |
NC134 |
|
846 |
Sameth |
SI44 |
896 |
Simi |
C67 |
946 |
Tanimbar Kei |
OB477 |
996 |
Tuburlay |
OB480 |
|
847 |
Sanahu |
C197 |
897 |
Sinairusi |
OB307 |
947 |
Taniwel |
C204 |
997 |
Tuburngil |
OB481 |
|
848 |
Sangliat Dol |
OB172 |
898 |
Siopot |
C68 |
948 |
Tanjung Karang |
C25 |
998 |
Tubyal |
OB217 |
|
849 |
Sangliat Krawain |
OB173 |
899 |
Sirbante |
OB469 |
949 |
Tansi Ambon |
C288 |
999 |
Tuha |
SI154 |
|
850 |
Sanleko |
C20 |
900 |
Siri Sori Amalatu |
U89 |
950 |
Taruy |
C289 |
1000 |
Tuhaha |
SI51 |
Appendix Table 1. Village typology in Maluku Province- part F
|
NO. |
VILLAGE |
CODE |
NO. |
VILLAGE |
CODE |
NO. |
VILLAGE |
CODE |
NO. |
VILLAGE |
CODE |
|
1001 |
Tulehu |
U93 |
1051 |
Waefusi |
C72 |
1101 |
Wafol |
OB494 |
1151 |
Waplau |
C37 |
|
1002 |
Tum |
C291 |
1052 |
Waegeren |
NC21 |
1102 |
Wahai |
U95 |
1152 |
Waprea |
C38 |
|
1003 |
Tumbur |
OB177 |
1053 |
Waeha |
C73 |
1103 |
Wahangula-Ngula |
OB106 |
1153 |
Wapsalit |
NC35 |
|
1004 |
Tunas Ilur |
SI155 |
1054 |
Waehaka |
C74 |
1104 |
Wahaolon |
NC54 |
1154 |
Warabal |
OB112 |
|
1005 |
Tungu |
OB102 |
1055 |
Waehata |
NC22 |
1105 |
Wahayum |
OB107 |
1155 |
Waraka |
C162 |
|
1006 |
Tunguwatu |
OB103 |
1056 |
Waekasar |
U2 |
1106 |
Waifual |
OB108 |
1156 |
Waraloin |
C213 |
|
1007 |
Tuniwara |
SI68 |
1057 |
Waekatin |
NC49 |
1107 |
Waihaong |
U51 |
1157 |
Warasiwa |
C163 |
|
1008 |
Tunsai |
C292 |
1058 |
Waekeka |
C75 |
1108 |
Waihatu |
U109 |
1158 |
Waras-Waras |
C295 |
|
1009 |
Tutrean |
OB482 |
1059 |
Waeken |
NC50 |
1109 |
Waiheru |
U52 |
1159 |
Warbal |
OB498 |
|
1010 |
Tutukembong |
OB178 |
1060 |
Waekerta |
NC23 |
1110 |
Waihoka |
U53 |
1160 |
Waria |
OB113 |
|
1011 |
Tutukey |
OB316 |
1061 |
Waekose |
C27 |
1111 |
Waiketam Baru |
NC136 |
1161 |
Warialau |
OB114 |
|
1012 |
Tutunametal |
OB179 |
1062 |
Waelana-lana |
NC24 |
1112 |
Wailay |
OB109 |
1162 |
Warjukur |
OB115 |
|
1013 |
Tutuwaru |
OB317 |
1063 |
Waelapia |
C28 |
1113 |
Wailoping |
U96 |
1163 |
Warkar |
OB218 |
|
1014 |
Tutuwawang |
OB318 |
1064 |
Waeleman |
NC25 |
1114 |
Wailulu |
C159 |
1164 |
Warloy |
OB116 |
|
1015 |
Uat |
OB483 |
1065 |
Waelihang |
NC26 |
1115 |
Waimital |
U110 |
1165 |
Waru |
C296 |
|
1016 |
Ubung |
C26 |
1066 |
Waelikut |
C76 |
1116 |
Waimusal |
NC90 |
1166 |
Waru |
U97 |
|
1017 |
Udar |
OB484 |
1067 |
Waelo |
NC27 |
1117 |
Waimusi |
NC91 |
1167 |
Warwut |
OB499 |
|
1018 |
Uf |
OB485 |
1068 |
Waelo |
NC51 |
1118 |
Wain |
OB495 |
1168 |
Wasarili |
OB324 |
|
1019 |
Uhak |
OB319 |
1069 |
Waemala |
C77 |
1119 |
Wain Baru |
OB496 |
1169 |
Wasbaka |
NC36 |
|
1020 |
Ujir |
OB104 |
1070 |
Waemangit |
C29 |
1120 |
Wainitu |
U54 |
1170 |
Wasi |
NC37 |
|
1021 |
Ulahahan |
C157 |
1071 |
Waemasing |
C78 |
1121 |
Waipirit |
C208 |
1171 |
Wasia |
C214 |
|
1022 |
Ulima |
SI8 |
1072 |
Waematakabo |
NC135 |
1122 |
Waiputih |
NC92 |
1172 |
Waspait |
C39 |
|
1023 |
Ullath |
SI52 |
1073 |
Waemite |
NC28 |
1123 |
Waisalan |
SI159 |
1173 |
Wassu |
SI56 |
|
1024 |
Undur |
C293 |
1074 |
Waemiting |
C30 |
1124 |
Waisamet |
NC137 |
1174 |
Watidal |
OB183 |
|
1025 |
Uneth |
NC48 |
1075 |
Waemorat |
C31 |
1125 |
Waisamu |
C209 |
1175 |
Watkidat |
OB500 |
|
1026 |
Upuhupun |
OB320 |
1076 |
Waemulang |
C79 |
1126 |
Waisarisa |
C210 |
1176 |
Watlaar |
OB501 |
|
1027 |
Ur |
OB486 |
1077 |
Waenalut |
C80 |
1127 |
Waitila |
NC93 |
1177 |
Watludan |
NC95 |
|
1028 |
Ur Pulau |
OB487 |
1078 |
Waenamaolon |
NC52 |
1128 |
Waitonipa |
NC94 |
1178 |
Watmasa |
OB184 |
|
1029 |
Uraur |
NC120 |
1079 |
Waenetat |
U3 |
1129 |
Wakal |
C160 |
1179 |
Watmuri |
OB185 |
|
1030 |
Ureng |
U94 |
1080 |
Waenibe |
C32 |
1130 |
Wakarleli |
U62 |
1180 |
Watngil |
OB502 |
|
1031 |
Urimessing |
U48 |
1081 |
Waenono |
C81 |
1131 |
Wakasihu |
C161 |
1181 |
Watngon |
OB503 |
|
1032 |
Urimessing |
U49 |
1082 |
Waepandan |
C82 |
1132 |
Wakol |
OB497 |
1182 |
Watsin |
OB504 |
|
1033 |
Uring Tutra |
SI53 |
1083 |
Waeperang |
C33 |
1133 |
Wakolo |
C211 |
1183 |
Watu Watu |
C297 |
|
1034 |
Uritetu |
U50 |
1084 |
Waepotih |
C34 |
1134 |
Wakpapapi |
OB322 |
1184 |
Watuar |
OB505 |
|
1035 |
Urung |
SI156 |
1085 |
Waepure |
C35 |
1135 |
Wakua |
OB110 |
1185 |
Watui |
NC122 |
|
1036 |
Usliapan |
NC89 |
1086 |
Waer |
OB492 |
1136 |
Walakone |
C212 |
1186 |
Waturu |
OB186 |
|
1037 |
Ustutun |
OB321 |
1087 |
Waer |
SI54 |
1137 |
Walang Tenga |
C294 |
1187 |
Watuwei |
OB325 |
|
1038 |
Usun Kataloka |
SI157 |
1088 |
Waerat |
OB493 |
1138 |
Walbele |
C87 |
1188 |
Waur |
OB506 |
|
1039 |
Utta |
SI158 |
1089 |
Waereman |
NC29 |
1139 |
Walerang |
OB182 |
1189 |
Waur Tahit |
OB507 |
|
1040 |
Uwat |
OB488 |
1090 |
Waereman |
NC53 |
1140 |
Wali |
C88 |
1190 |
Way Asih |
NC96 |
|
1041 |
Uwat Reyaan |
OB489 |
1091 |
Waesala |
C207 |
1141 |
Walling Spanciby |
SI55 |
1191 |
Wayame |
U55 |
|
1042 |
Uwat Wear |
OB490 |
1092 |
Waesili |
C83 |
1142 |
Walunghelat |
NC55 |
1192 |
Wearlilir |
OB508 |
|
1043 |
Uwen Pantai |
C206 |
1093 |
Waetawa |
C84 |
1143 |
Wamana Baru |
NC33 |
1193 |
Weduar |
OB509 |
|
1044 |
Uweth |
NC121 |
1094 |
Waeteba |
C85 |
1144 |
Wamkana |
C89 |
1194 |
Weduar Fer |
OB510 |
|
1045 |
Waai |
C158 |
1095 |
Waetele |
NC30 |
1145 |
Wamlana |
U4 |
1195 |
Weer Frawaf |
OB511 |
|
1046 |
Wab |
OB491 |
1096 |
Waetina |
NC31 |
1146 |
Wamsisi |
C90 |
1196 |
Weer Ohoiker |
OB512 |
|
1047 |
Wabar |
OB180 |
1097 |
Waeturen |
C86 |
1147 |
Wanakarta |
U5 |
1197 |
Weer Ohoinam |
OB513 |
|
1048 |
Wabloy |
NC19 |
1098 |
Waeura |
C36 |
1148 |
Wanareja |
NC34 |
1198 |
Welora |
OB326 |
|
1049 |
Wadankou |
OB181 |
1099 |
Wafan |
OB105 |
1149 |
Wangel |
OB111 |
1199 |
Welutu |
OB187 |
|
1050 |
Waedanga |
NC20 |
1100 |
Waflan |
NC32 |
1150 |
Wanuwui |
OB323 |
1200 |
Werain |
OB188 |
Appendix Table 1. Village typology in Maluku Province- part G
|
NO. |
VILLAGE |
CODE |
NO. |
VILLAGE |
CODE |
NO. |
VILLAGE |
CODE |
|
1201 |
Weratan |
OB189 |
1212 |
Wolu |
C164 |
1223 |
Yafila |
NC98 |
|
1202 |
Werinama |
C298 |
1213 |
Wonosari |
U98 |
1224 |
Yainuelo |
C165 |
|
1203 |
Werka |
OB514 |
1214 |
Wonreli |
OB329 |
1225 |
Yaltubung |
OB331 |
|
1204 |
Wermaf |
OB515 |
1215 |
Wotay |
NC97 |
1226 |
Yamalatu |
C166 |
|
1205 |
Wermatang |
OB190 |
1216 |
Wowonda |
OB191 |
1227 |
Yamluli |
OB332 |
|
1206 |
Werwaru |
OB327 |
1217 |
Wulmasa |
OB192 |
1228 |
Yamtel |
OB519 |
|
1207 |
Wewali |
C91 |
1218 |
Wulur |
OB330 |
1229 |
Yamtimur |
OB520 |
|
1208 |
Widit |
NC38 |
1219 |
Wulurat |
OB517 |
1230 |
Yapas |
OB219 |
|
1209 |
Wiratan |
OB328 |
1220 |
Wunin Eldedora |
SI160 |
1231 |
Yaputih |
C167 |
|
1210 |
Wirin |
OB516 |
1221 |
Wunlah |
OB193 |
1232 |
Yatoke |
OB333 |
|
1211 |
Wokam |
OB117 |
1222 |
Yafavun |
OB518 |
1233 |
Yatwav |
OB521 |
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