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All Journal Jurnal Ilmiah Administrasi Pemerintahan Daerah JEKP (Jurnal Ekonomi dan Keuangan Publik)
Muh Rivandy Setiawan
087888511474
Humanities Economics, Econometrics & Finance Social Sciences

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INTEGRATION OF DATA SCIENCE AND AI ENGINEERING FOR CLUSTERING AND FORECASTING OF WEST JAVA REGIONAL BUDGET 2015–2024 Muh Rivandy Setiawan; Melianus Mesakh Taebenu
Jurnal Ilmiah Administrasi Pemerintahan Daerah Vol 17 No 2 (2025): Regional and Local Government Resources
Publisher : Post Graduate School of Government Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33701/jiapd.v17i2.5629

Abstract

The management of regional revenue and expenditure budgets (APBD) plays a critical role in supporting local economic development. However, conventional evaluations often fail to capture complex patterns and long-term fiscal dynamics. This study integrates Data Science and AI Engineering approaches to analyze APBD data of municipalities and regencies in West Java Province for the 2015–2024 period. The workflow begins with data preparation, including cleaning, normalization, and the construction of derived variables such as year-on-year growth, the ratio of personnel to capital expenditure, and surplus/deficit status. Exploratory Data Analysis (EDA) was conducted through trend visualization and distribution analysis across regions. Clustering was carried out using the K-Means algorithm and compared with alternative methods such as DBSCAN and hierarchical clustering, evaluated by Silhouette Score and Davies-Bouldin Index. For forecasting, the study employed time-series models ranging from ARIMA and Prophet to advanced LSTM, with accuracy measured by RMSE and MAPE. The findings reveal substantial disparities across regions, such as clusters of high-growth yet recurrent deficit areas, as well as fiscally stable but small-capacity regions. Forecasts for 2025–2026 provide projections of revenue and expenditure, which serve as evidence-based guidance for regional fiscal planning. The main contribution of this research lies in offering a data-driven framework that not only explains historical fiscal performance but also delivers actionable policy recommendations for local governments in West Java.
Descriptive and Predictive Analysis of Village Fund Allocation in Bandung Regency in 2025 Integrating the Roles of Data Analysts and Data Scientists: Analisis Deskriptif dan Prediktif Alokasi Dana Desa di Kabupaten Bandung pada Tahun 2025_ Mengintegrasikan Peran Analis Data dan Ilmuwan Data Muh Rivandy Setiawan
JEKP (Jurnal Ekonomi dan Keuangan Publik) Vol. 12 No. 2 (2025): JEKP (Jurnal Ekonomi dan Keuangan Publik)
Publisher : Fakultas Manajemen Pemerintahan, Institut Pemerintahan Dalam Negeri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33701/jekp.v12i2.5716

Abstract

ABSTRACT This study aims to analyze the allocation of Village Funds in Bandung Regency in 2025 by integrating descriptive and predictive approaches through the roles of Data Analyst and Data Scientist. Official data from the Ministry of Finance (2025), covering all villages in Bandung Regency, served as the primary dataset. The descriptive approach focused on identifying distribution patterns, inter-village disparities, and differences in performance-based allocations. Meanwhile, the predictive approach employed clustering techniques to uncover hidden structures within the allocation patterns. The findings reveal that a portion of villages received performance-based allocations with a fixed value of IDR 396.180.329.000, which increased their total allocation by approximately 25% compared to villages without such allocations. Cluster analysis identified three main distribution patterns, reflecting variations in fiscal characteristics and village capacities. Geographic visualization further highlighted disparities across sub-districts, with certain areas receiving relatively higher allocations than others. These results underscore the importance of integrating descriptive and predictive analytics in public policy studies, particularly in the management of Village Funds. Practically, the study provides empirical evidence to support local governments in formulating allocation policies that are more transparent, equitable, and data-driven. Furthermore, the findings demonstrate the potential of data analytics to enhance fiscal governance at the village level, contributing to more effective and accountable resource distribution. Keywords: Village Funds, Performance Allocation, Descriptive Analysis, Clustering, West Java
Co-Authors Melianus Mesakh Taebenu