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All Journal Jurnal Simantec Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Conference on Innovation and Application of Science and Technology (CIASTECH) Applied Technology and Computing Science Journal Scientific Journal of Informatics E-Link: Jurnal Teknik Elektro dan Informatika G-Tech : Jurnal Teknologi Terapan Prosiding Conference on Research and Community Services Jurnal Teknik Informatika (JUTIF) Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) As-Sidanah : Jurnal Pengabdian Masyarakat Jurnal Teknologi dan Manajemen Industri Terapan Adopsi Teknologi dan Sistem Informasi JUSTIFY : Jurnal Sistem Informasi Ibrahimy "JAMASTIKA" Jurnal Mahasiswa Teknik Informatika International Journal of Health and Information System (IJHIS) Gudang Jurnal Multidisiplin Ilmu Nusantara Journal of Computers and its Applications INTER TECH Jurnal Riset Teknik Komputer JOKI: Journal of Computing and Informatics Abhakte Journal Of Global Computer Science
Ahmad Homaidi
Universitas Ibrahimy
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Sistem Informasi Survey Kepuasan Masyarakat Berbasis Web Pada Badan Kepegawaian Dan Pengembangan Sumber Daya Manusia Kabupaten Bondowoso Nori Nur Fasratul Aini; Zaehol Fatah; Ahmad Homaidi
Journal Of Global Computer Science Vol. 1 No. 2 (2025): JGCS - AUGUST
Publisher : PT. Padang Tekno Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59435/jgcs.v1i2.2025.34

Abstract

Survey Kepuasan Masyarakat di Badan Kepegawaian dan Pengembangan Sumber Daya Manusia (BKPSDM) Kabupaten Bondowoso seringkali terdapat kendala dalam mengumpulkan data kepuasan masyarakat terkait layanan publik yang diberikan oleh instansi BKPSDM Kabupaten Bondowoso. Metode survey konvensional yang menggunakan kuesioner manual seringkali membutuhkan waktu, tenaga, dan sumber daya yang signifikan. Selain itu, data yang diperoleh dari survey tersebut sering tidak tersedia secara real-time dan sulit untuk diolah secara efisien. Dalam penelitian ini bertujuan untuk meningkatkan akurasi dan kendala data kepuasan masyarakat. kuesioner online akan meminimalkan kesalahan penulisan dan memungkinkan validasi data secara langsung. Penelitian ini menghasilkan nilai survey kepuasan masyarakat terhadap pelayanan pada Badan Kepegawaian dan Pengembangan Sumber Daya Manusia.
Mobile-Based Alumni Management System for Digital Engagement in Educational Institutions Ahmad Homaidi; Prapti Deshmukh
JOKI: Jurnal Komputasi dan Informatika Vol 3 No 1 (2026): June 2026
Publisher : CV. Laskar Karya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65678/joki.v3i1.376

Abstract

Alumni management plays an important role in maintaining institutional relationships, supporting information exchange, and strengthening collaboration between graduates and educational institutions. However, many institutions still manage alumni data manually, resulting in inefficient data processing, limited communication, and difficulties in maintaining long-term engagement. This study proposes a mobile-based alumni management system designed to improve data management efficiency and enhance communication between alumni and institutions. The system integrates alumni registration, profile management, information sharing, and interactive communication within a centralized platform accessible through mobile devices and a web-based administrative panel. The development process included requirement analysis, system design, implementation, and system testing to ensure functionality and usability. Functional testing confirmed that all features operated as expected, while user acceptance evaluation involving alumni participants indicated a feasibility score of 92.5%, categorized as very good. These results demonstrate that the proposed system improves accessibility, administrative efficiency, and alumni engagement compared with manual management methods. The developed platform provides a practical and scalable solution for managing alumni data and communication in educational institutions. Future enhancements may include integration of advanced analytics, expanded communication features, and broader institutional deployment to support sustainable alumni engagement and digital transformation in education management.
Model Klasifikasi Hybrid Berbasis PSO-KNN untuk Akurasi Diagnosis Penyakit Hepatitis Sunardi Sunardi; Jarot Dwi Prasetyo; Hermanto Hermanto; Ach. Zubairi; Ahmad Homaidi; Irma Yunita; Lukman Fakih Lidimilah
JUSTIFY : Jurnal Sistem Informasi Ibrahimy Vol. 5 No. 1 (2026): JUSTIFY : Jurnal Sistem Informasi Ibrahimy
Publisher : Fakultas Sains dan Teknologi, Universitas Ibrahimy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/justify.v5i1.10322

Abstract

Diagnosis dini penyakit hepatitis masih menghadapi tantangan akibat kompleksitas data medis, tingginya variasi hasil laboratorium, serta keberadaan atribut yang tidak relevan sehingga menurunkan kinerja algoritma K-Nearest Neighbor (K-NN). Penelitian ini bertujuan mengembangkan model hibrida Particle Swarm Optimization–K-Nearest Neighbor (PSO-KNN) untuk mengoptimalkan bobot fitur dan parameter K-NN secara simultan guna meningkatkan akurasi klasifikasi penyakit hepatitis. Metode yang digunakan meliputi preprocessing dataset HCV, normalisasi Min-Max, pembagian data latih dan uji sebesar 80:20, serta optimasi menggunakan PSO dengan 20 partikel dan 30 iterasi. Evaluasi dilakukan menggunakan metrik accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa PSO-KNN meningkatkan akurasi dari 95,1% menjadi 96,7%, presisi dari 88,9% menjadi 100%, recall dari 61,5% menjadi 69,2%, dan F1-score dari 72,7% menjadi 81,8%. Model juga berhasil menghilangkan false positive dan menurunkan false negative. Bobot fitur menunjukkan bahwa ALT, AST, GGT, dan Bilirubin merupakan biomarker yang paling berpengaruh dalam klasifikasi. Dengan demikian, model PSO-KNN terbukti mampu meningkatkan performa klasifikasi, efisiensi komputasi, serta interpretabilitas diagnosis hepatitis, sehingga berpotensi mendukung pengambilan keputusan klinis secara lebih akurat.
Perbandingan Kinerja Algoritma K-Nearest Neighbor (KNN) Dan Naive Bayes Dalam Pengklasifikasian Skor Ujian Mahasiswa Berdasarkan Faktor Akademik Dan Non-Akademik Mohammad Sujatmiko; Zaehol Fatah; Ahmad Homaidi
Adopsi Teknologi dan Sistem Informasi (ATASI) Vol. 5 No. 2 (2026): Adopsi Teknologi dan Sistem Informasi (ATASI)
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/atasi.v5i2.4652

Abstract

Student academic achievement is an important indicator for assessing the success of the learning process in higher education. Students’ exam scores are influenced not only by academic factors, such as study hours, class attendance, study methods, exam difficulty, and course, but also by non-academic factors, such as age, gender, internet access, sleep duration, sleep quality, and learning facilities. This study aims to compare the performance of the K-Nearest Neighbor (KNN) and Naïve Bayes algorithms in classifying students’ exam scores into two categories: Poor and Good. The dataset used in this study consists of 5,716 records with 12 attributes. The research process includes data preprocessing, target transformation, data splitting into training and testing sets, algorithm implementation, and performance evaluation using accuracy, precision, recall, and classification error. The results show that Naïve Bayes achieved an accuracy of 83.02%, precision of 82.85%, recall of 82.79%, and classification error of 16.98%, making it more effective than KNN in classifying students’ exam scores.
Co-Authors Abd Ghofur Abd. Ghofur Abdul Jalil ABDUS SAMAD Abu Dzarrin Al Ghifari Ach. Zubairi Ach. Zubairi Ach. Zubairi Ahmad Ambari Ahmad Jalaludin, Ahmad Ahmad Lutfi Ahmad Lutfi Ahmad Lutfi Ahmad Muflih Wafir S.A Ahmad Yogianto Akhlis Munazilin Ammar Farisi Atika Lina Bahtiarullah, Febri Basufi Baijuri, Achmad Damayanti, Alfina Damayanti Dwitya Sitaresmi Suharjo Edwin Wira Liyanto Efendi, Ahmad Fadil Dwi Eko Fendy Hermawan Fahreza Adams Lazuardy Fatah, Zaehol Fatah, Zaehol Fauzan Firdaus Firman Santoso Firmansyah Widiarto Prabowo Ganang Aji Pambudhi Hali Mukid Hari Santoso Hermanto Hermanto , Hermanto Hermanto Hermanto Hermanto Hermanto Hermanto IKA INDAH LESTARI Irma Yunita Irma Yunita irma yunita Irma Yunita Irma Yunita Irma Yunita Jarot Dwi Jarot Dwi Prasetyo Jarot Dwi Prasetyo Jarot Dwi Prasetyo Jarot Dwi Prasetyo Jarot Dwi Prasetyo Lidimilah, Lukman Fakih Lidimillah, Lukman Fakih Lina, Atika Lukman Fakih Lukman Fakih Lidimilah Lukman Fakih Lidimilah Lukman Fakih Lidimillah Lutfi, Zainul Mawaddah, Maulidatul Medi Sugiarto Mohammad Sujatmiko Muhamad Ilhan mansiz Muhammad Dzikry Afandi Muhammad Ramadhani Muwasatil Muhtajin Nabila Nabila Nico Irawan Nori Nur Fasratul Aini Nur Azizah Nur Azizah Prapti Deshmukh Prasetyo, Jarot Dwi Ria Nufika Rofiatul Munawaroh Rohiqim Mahtum Saleh, Taufik Santoso, Firman Siti Nur Aizah Sobri, Miftahus Sunardi Sunardi Syahrul Ibad Taufik Saleh Taufik Saleh Yanto Yusfi Chusnul Raufah Zaehol Fatah Zahroh, Siti Zainal Arifin Zainur Rohman Zendy Robi Junianto Zubairi, Ach. Zulfa Faradila