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All Journal Journal of Information Systems and Informatics IJCOSIN : Indonesian Journal of Community Service and Innovation
Rona Nisa Sofia Amriza
Telkom University
Computer Science & IT Education Other

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Implementasi Sistem Pengelolaan Bisnis dan Laporan Keuangan Digital di Bumdes Sangkara Desa Panembangan Rona Nisa Sofia Amriza; Toni Anwar; Sukmadiningtyas; Intan Azizah; Tanzil Azim; Chhoun Seachhing
Indonesian Journal of Community Service and Innovation Vol. 6 No. 1 (2026): April 2026
Publisher : LPPM IT Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/ijcosin.v6i1.10503

Abstract

BUMDes Sangkara Desa Panembangan memiliki peran strategis sebagai motor penggerak ekonomi masyarakat desa melalui unit usaha wisata edukasi mina padi, tubing, dan internet desa, namun pengelolaan bisnis dan keuangan masih menghadapi kendala mendasar berupa pencatatan manual, keterbatasan kompetensi akuntansi pengurus, serta ketiadaan sistem pelaporan sesuai standar, yang berdampak pada rendahnya transparansi dan akuntabilitas serta menurunkan kepercayaan stakeholder; padahal pencatatan keuangan yang sehat merupakan tolok ukur utama dalam kelayakan penambahan modal sebagaimana praktik di koperasi Merah Putih; untuk menjawab permasalahan tersebut, program pengabdian masyarakat ini menawarkan implementasi sistem pengelolaan bisnis dan laporan keuangan digital melalui pelatihan akuntansi sederhana, literasi digital, penerapan aplikasi pencatatan VestNet, pendampingan teknis penyusunan laporan, serta evaluasi berkelanjutan dengan partisipasi aktif mitra, sehingga diharapkan mampu menciptakan tata kelola BUMDes yang lebih profesional, transparan, dan akuntabel, memperluas peluang akses permodalan eksternal, serta meningkatkan kesejahteraan masyarakat Panembangan secara berkelanjutan.
PCOS Classification Using Random Forest, Recursive Feature Elimination, and Explainable AI Syifa Ayu Salsabila Putri; Rona Nisa Sofia Amriza
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1603

Abstract

Ovary Syndrome (PCOS) is an endocrine-related condition predominantly affecting women during their childbearing years who experience delayed diagnosis due to the limitations of conventional methods that require laboratory tests and imaging procedures that are relatively costly and time-consuming. This study develops a PCOS classification model based on a clinical dataset of 541 patients with 42 clinical attributes using the random forest algorithm with Recursive Feature Elimination (RFE) feature selection and an Explainable AI (XAI) approach. The research pipeline comprised several sequential stages: problem identification, data collection, preprocessing, data splitting, feature selection, model training and testing, evaluation, and SHAP-based explainability analysis. Performance was evaluated using Accuracy, Precision, Recall, and F1-score, and compared between two models, namely RF+CF and RF+RFE, where RF+RFE was identified as the best-performing model. The XAI approach using SHAP (SHapley Additive exPlanations) was applied to identify and explain the contribution of clinical variables to the classification results. The best model, RF+RFE, achieved an accuracy of 92.66%, precision of 93.75%, recall of 83.33%, and F1-score of 88.24%, demonstrating superior performance compared to RF+CF. As this study relies on a single dataset, broader validation across multiple centers is recommended before clinical deployment. This model is intended as a screening-support approach and has not been validated as a clinical diagnostic tool. The findings are anticipated to serve as a foundation for building data-driven early screening tools and clinical decision-making support systems.
Co-Authors Chhoun Seachhing Intan Azizah Sukmadiningtyas Syifa Ayu Salsabila Putri Tanzil Azim Toni Anwar