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All Journal JPM: JURNAL PENGABDIAN MASYARAKAT International Journal of Community Service (IJCS)
Peranginangin, Andreas P
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Penguatan Kapasitas Aparatur Desa di Medan Johor melalui Pelatihan Manajemen Informatika dan Tata Kelola SDM yang Akuntabel Purnama Sari, Dian; Muhammad Syahrizal; Rizky Indra Saputra; Sari, Vina Winda; Peranginangin, Andreas P
JPM: Jurnal Pengabdian Masyarakat Vol. 6 No. 3 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jpm.v6i3.3028

Abstract

This community service program aims to strengthen the capacity of village officials in Medan Johor District through training in informatics management and accountable human resource governance. The training was conducted using an interactive workshop and practice-based mentoring approach, focusing on digital information systems, administrative data management, and the implementation of public accountability principles in village governance. A total of 20 village officials participated in the program, and evaluations were carried out using structured pre- and post-training surveys. The results indicate a significant improvement in informatics skills (from an average score of 2.8 to 4.3), understanding of accountability (from 3.0 to 4.5), and work efficiency (from 2.9 to 4.2). Moreover, 92% of participants reported satisfaction with the program implementation. These findings demonstrate that the integration of digital training and public accountability ethics can effectively enhance local governance toward a more transparent, efficient, and participatory system. Nevertheless, limited training duration and digital infrastructure remain challenges for long-term implementation. This program serves as a model for digital-based capacity development of village officials in North Sumatra.
Integrating Machine Learning into Portfolio Optimization: A Hybrid ARIMA–GARCH Predictive Model with Genetic Algorithm Peranginangin, Andreas P
International Journal of Community Service (IJCS) Vol. 5 No. 1 (2026): January-June
Publisher : PT Inovasi Pratama Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55299/ijcs.v5i1.1824

Abstract

This article proposes a hybrid quantitative framework that integrates statistical time‑series modeling and evolutionary machine learning for portfolio optimization. The approach combines an ARIMA–GARCH model to jointly estimate conditional mean and volatility of asset returns with a Genetic Algorithm (GA) that searches for optimal portfolio weights on the basis of model‑implied return–risk profiles. The study adopts a secondary‑data quantitative design, synthesising evidence from prior empirical applications of ARIMA–GARCH forecasting and GA‑based portfolio optimization in equity markets, including the S&P500 index and Indonesian LQ45 constituents. Descriptive analysis confirms strong volatility clustering and leptokurtosis in daily stock index returns, justifying the use of GARCH‑type volatility models. Empirical results from the literature show that hybrid ARIMA–GARCH models significantly outperform standalone ARIMA and buy‑and‑hold strategies in terms of forecasting error and risk‑adjusted performance, while GA‑optimized portfolios achieve superior risk–return trade‑offs compared with traditional mean–variance optimization. These findings support the conceptual integration of ARIMA–GARCH forecasts and GA‑based allocation as a promising direction for portfolio construction, particularly in emerging markets such as Indonesia. The article concludes with implications for portfolio managers, regulators, and higher‑education curricula in quantitative finance and data‑driven investment.
Co-Authors Muhammad Syahrizal Purnama Sari, Dian Rizky Indra Saputra Vina Winda Sari