Submitted:
15 December 2024
Posted:
16 December 2024
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Abstract
Indigenous institutions may play a vital role in sustainable development at the local level by serving the people’s interests and supporting their livelihood. By spreading structured questionnaires to the households in our research area, this research aims to find the determinant factor of the utilization of community institutions, especially the indigenous institutions. Per household, 26 features, including demographic, psycho-social, economic, and location variables, were collected to study the predictability of the utilization of the community institutions. The results show that the location variables are the most crucial for explaining the utilization of the community institutions in times of need.
Keywords:
1. Introduction
2. Materials and Methods
2.1. Materials
2.2. Method
- The importance of independent variables for prediction institutions’ utilization. The Random Forest approach figures out the importance of the variables in predicting the utilization of the community institutions, by calculating the accuracy of the prediction by the Random Forest approach based on all independent variables minus the variable of concern. The difference in accuracy between the prediction of the complete Random Forest and that of Random Forest minus the variable of concern is called the importance value. Variable importance was calculated ten times in order to assess the variability of the importance due to random steps within the Random Forest approach. The independent variables with an importance variance not including zero were regarded as having an importance value that is significantly higher than zero, and were called non-zero variable. The higher the importance value, the larger the predictive power of the variable.
- The average importance per variable category was to capture which category best explained the utilization of institutions. By averaging the importance value of variables in each category and checking with a permutation test whether it is significantly higher than the average of a random sample of the variables, we would know which category significantly influenced the utilization of institutions. The permutation test is conducted by taking a fixed number of variables equal to the number in that category randomly out of all variables (ie., for category one, nine out of 26 variables; for category two, ten out of 26 variables; for category three, four out of 26 variables; and for category four, three out of 26 variables), calculate the average, and repeat this 1000 times to get the distribution of the random average. When the actual average of the importance of the variables in the category is higher than the 95 per-centiles of that distribution, the average is considered significantly higher than the random average. The permutation test in this form is an exact one-sided test.
3. Results
3.1. The Correlation Test (Cramer’s V and Chi-Square)
3.2. The importance of variables for prediction institutions’ utilization
3.3. The average importance per variable category value
4. Discussion
4.1. General Results
4.2. The importance of variables for prediction institutions’ utilization
4.3. The average importance per variable category value
5. Conclusion
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| Name of the village | Type of area | Geographic area of Wonosobo | Number of households |
|---|---|---|---|
| Kejajar | Highland/Rural | Northern area | 51 |
| Kalibeber | Lowland/Sub-urban | Central area | 50 |
| Wonosobo Barat | Lowland/Urban | Central area | 46 |
| Sojokerto | Lowland/Rural | Western area | 52 |
| Total number of households | 199 | ||
| Village | Utilization of the community institutions | ||||||
|---|---|---|---|---|---|---|---|
| Village Name | Indigenous | Transitional | Modern | Total | |||
| N | % | N | % | N | % | N | |
| Kejajar | 41 | 80.4 | 2 | 3.9 | 8 | 15.7 | 51 |
| Kalibeber | 9 | 18.0 | 31 | 62.0 | 10 | 20.0 | 50 |
| Wonosobo Barat | 1 | 2.2 | 16 | 34.8 | 29 | 63.0 | 46 |
| Sojokerto | 13 | 25.0 | 12 | 23.1 | 27 | 51.9 | 52 |
| Total | 64 | 32.2 | 61 | 30.6 | 74 | 37.2 | 199 |
| Category | 95 percentile | n | Mean | Sign |
|---|---|---|---|---|
| Socio-Demography | 0.013497 | 9 | 0.001094 | |
| Psycho-Social | 0.012797 | 10 | 0.009814 | |
| Economic | 0.017877 | 4 | 0.009062 | |
| Location | 0.020554 | 3 | 0.023986 | * |
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