Analisis Klasifikasi Kesiapan Digital Desa Menggunakan Decision Tree dan Pemetaan Spasial
DOI:
https://doi.org/10.47065/bulletincsr.v5i5.741Keywords:
Decision Tree; Digital Readiness; Digital Village; Machine Learning; Spatial MappingAbstract
Digital transformation at the village level is a strategic element in promoting equitable development and improving public service delivery. However, the level of digital readiness across regions remains uneven. This study aims to classify the digital readiness of villages in West Java Province by utilizing data from Open Data Jabar (opendata.jabarprov.go.id) related to the number of digital villages, internet access, and village development strata. A Decision Tree classification algorithm was employed to categorize regions into two readiness classes: high and low. The modeling results indicate that the number of self-reliant (mandiri) villages and the percentage of villages with internet access are the most influential variables in the classification. Although internet infrastructure is available in most areas, it does not always correspond to the level of village digitalization. Districts with high internet access but a low number of self-reliant villages are still classified as having low readiness. The model achieved an accuracy of 83%, although its performance in identifying the high readiness class was limited due to class imbalance in the dataset. Spatial visualization was also used to highlight regional disparities in digital readiness. This study provides an early contribution to digital readiness mapping of villages using a machine learning approach in Indonesia.
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