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All Journal Journal of Information Systems and Informatics Jurnal Abdimastek (Pengabdian Masyarakat Berbasis Teknologi)
M. Yoka Fathoni
Telkom University
Humanities Computer Science & IT Education Library & Information Science

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DIGITALISASI RANTAI PASOK PAKAN MANDIRI BERBASIS ARTIFICIAL INTELLIGENCE UNTUK OPTIMALISASI DISTRIBUSI MAGGOT PADA PETERNAK AYAM PETELUR DI DESA CIKAKAK Mahazam Afrad; M. Yoka Fathoni; Arif Riyandi; Alfin Hikmaturokhman; Tanzil Aziim; Maghda Luqyana; Rindi Indah Lestari; Citra Kumala Dewi
Jurnal Pengabdian Masyarakat Berbasis Teknologi Vol 7 No 01 (2026): Volume 7, Nomor 1, Mei 2026
Publisher : ISB Atma Luhur

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Abstract

Tingginya biaya pakan komersial serta belum optimalnya pengelolaan dan distribusi maggot menjadi kendala dalam pengembangan pakan mandiri bagi peternak ayam petelur di Desa Cikakak. Program pengabdian masyarakat ini dilaksanakan untuk mendukung digitalisasi rantai pasok maggot melalui implementasi sistem MaggotChain. Kegiatan dilakukan melalui tahapan identifikasi permasalahan, perumusan solusi, penerapan sistem, pelatihan kepada masyarakat, serta evaluasi menggunakan kuesioner. Sistem digital berhasil diterapkan sebagai media pengelolaan stok, distribusi, dan pemantauan maggot sehingga memudahkan koordinasi antara pemasok dan peternak. Hasil evaluasi menunjukan bahwa 94% tanggapan peserta berada pada kategori setuju dan sangat setuju, yang menunjukkan bahwa program telah diterima dengan baik serta sesuai dengan kebutuhan masyarakat. Program ini diharapkan dapat mendukung pengelolaan rantai pasok maggot yang lebih efektif dan berkelanjutan di Desa Cikakak
Machine Learning-Based Multi-Class Scholarship Classification with a Streamlit Prototype Siti Nabila Ariza Fitri; Rona Nisa Sofia Amriza; M. Yoka Fathoni
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1673

Abstract

The scholarship selection process at a private university is still conducted manually by reviewing scholarship applicant documents individually, resulting in a time-consuming process and potential inconsistencies in evaluation. This study aims to develop a multi-class Machine Learning-based classification model to support scholarship classification based on recipient criteria and evaluating model performance using accuracy, precision, recall, F1-score, and confusion matrix metrics. This study applied the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework consisting of six stages Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation, and Deployment. The dataset consists of 408 historical scholarship recipient records categorized into four scholarship classes. A key contribution of this study is the integration of the CRISP-DM methodology with feature importance analysis using Random Forest Feature Importances, class balancing using SMOTE, and machine learning classifiers, including Random Forest, SVM, and Naïve Bayes, within a unified predictive modelling framework. Based on the evaluation results, the Support Vector Machine algorithm achieved the best performance with an accuracy of 77% and a macro F1-score of 63%, followed by Random Forest at 76% and Naïve Bayes at 70%. The SVM model was then implemented as a Streamlit-based web to support scholarship recommendations and is not intended as a final scholarship approval system. This research contributes to the development of an efficient, data-driven scholarship classification support system for higher education institutions.
Co-Authors Alfin Hikmaturokhman Arif Riyandi Citra Kumala Dewi Maghda Luqyana Mahazam Afrad Rindi Indah Lestari Rona Nisa Sofia Amriza Siti Nabila Ariza Fitri Tanzil Aziim