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All Journal Building of Informatics, Technology and Science JPM: JURNAL PENGABDIAN MASYARAKAT
Afrendi, Mohammad
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Aerospace Engineering Humanities Astronomy Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Medicine & Pharmacology Public Health Social Sciences Transportation Other

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Optimalisasi Pelatihan Transparansi Keuangan Masjid Melalui Implementasi Aplikasi Kas Digital Abdul Karim; Bangun, Budianto; Prayetno, Sugeng Prayetno; Afrendi, Mohammad
JPM: Jurnal Pengabdian Masyarakat Vol. 6 No. 1 (2025): Juli 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jpm.v6i1.2448

Abstract

Transparent and accountable financial management is essential for maintaining congregational trust and enhancing participation in religious and social activities. However, many mosques still manage their finances manually, making them prone to recording errors and lack of transparency. This community service activity aims to optimize financial transparency in mosques through the implementation of the eKasMasjid digital cash application, accessible online via http://ekasmasjid.com. The methods involved include socialization, training for mosque administrators on how to use the application, and continuous mentoring and evaluation during implementation. The results show that the eKasMasjid application significantly improves financial recording efficiency, reduces transaction errors by up to 85%, and facilitates financial reporting that can be accessed openly by the congregation. A survey of mosque congregants revealed that 89% expressed increased trust in the mosque’s financial management, and 76% felt more motivated to donate regularly. In conclusion, the use of digital technology through the eKasMasjid application contributes positively to establishing a more transparent, accountable, and participatory mosque financial governance system.
Optimasi Prediksi Harga Sawit Menggunakan Teknik Stacking Algoritma Machine Learning dan Deep Learning dengan SMOTE Karim, Abdul; Bangun, Budianto; Prayetno, Sugeng; Afrendi, Mohammad
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7239

Abstract

The prediction of palm oil prices plays a strategic role in decision-making within the agribusiness sector, particularly in addressing market volatility and imbalanced historical data distribution. This study aims to optimize the accuracy of palm oil price prediction by applying a stacking approach that combines machine learning and deep learning algorithms, while integrating the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance issues. Three main models were employed in this study: Random Forest, Long Short-Term Memory (LSTM), and a model enhanced with SMOTE. The evaluation was conducted using accuracy, precision, recall, and F1-score metrics, supported by confusion matrix analysis. The results indicate that the model integrated with SMOTE outperforms the others, achieving an accuracy of 0.5447, precision of 0.5512, recall of 0.5447, and F1-score of 0.5462. This model also demonstrates a more balanced classification performance compared to the LSTM and Random Forest models. These findings confirm that the application of oversampling techniques such as SMOTE, when combined with appropriate algorithms, can significantly enhance predictive performance in imbalanced datasets. The study contributes to the development of predictive models for commodity prices based on historical data and opens opportunities for further exploration of more adaptive hybrid methods in future research.
Co-Authors Abdul Karim Abdul Karim Bangun, Budianto Prayetno, Sugeng Prayetno, Sugeng Prayetno