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All Journal Jurnal Simetris Jurnal Sosioteknologi Jurnas Nasional Teknologi dan Sistem Informasi INFORMAL: Informatics Journal Journal of Animation & Games Studies JOIV : International Journal on Informatics Visualization International Journal of Artificial Intelligence Research INTEGER: Journal of Information Technology Jurnal Komputer Terapan Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Indonesian Journal of Artificial Intelligence and Data Mining Jurnal ULTIMATICS SISFO KOMPUTIKA - Jurnal Sistem Komputer Buana Information Technology and Computer Sciences (BIT and CS) INTEGRITAS : Jurnal Pengabdian Media Informatika Indonesian Journal of Electrical Engineering and Computer Science Journal of Innovation Information Technology and Application (JINITA) Abdiformatika: Jurnal Pengabdian Masyarakat Informatika Jurnal Nasional Teknik Elektro dan Teknologi Informasi Ilmu Komputer untuk Masyarakat Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) Journal of Applied Agricultural Science and Technology Jurnal Pengabdian Masyarakat Bidang Sosial dan Humaniora Proceeding International Applied Business and Engineering Conference JITER-PM (Jurnal Inovasi Terapan - Pengabdian Masyarakat) PAPUMA: Journal of Comunnity Services JGGAG (Journal of Games, Game Art, and Gamification) Jurnal Pengabdian Masyarakat IPTEKS Jurnal Elektronika dan Telekomunikasi
Furqon, Muhammad Ariful
Departemen Informatika, Universitas Jember
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Pendampingan Petani Melalui Aplikasi Smart Farming “GermasTani” di Desa Sukorejo Kabupaten Jember Ariful Furqon, Muhammad; Kusmiati, Ati; Puspaningrum, Diah
JITER-PM (Jurnal Inovasi Terapan - Pengabdian Masyarakat) Vol. 3 No. 4 (2025): JITER-PM
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/jiter-pm.v3i4.6839

Abstract

Salah satu desa di Kabupaten Jember yang masih menggunakan cara usahatani konvensional adalah Desa Sukorejo Kecamatan Bangsalsari. Kegiatan usahatani dilakukan secara turun temurun dan konvensional tanpa menggunakan Good Agricultural Practice. Berbagai macam permasalahan muncul seperti waktu tanam kurang tepat, pemupukan dilakukan kurang berimbang, dan penentuan harga hasil panen oleh tengkulak. Oleh karena itu dalam kegiatan pengabdian ini dikembangkan sebuah sistem smart farming berbasis mobile yang diberikan nama “GermasTani”. Tujuan kegiatan pengabdian adalah meningkatkan kemandirian digital petani melalui pendekatan teknologi dan pendampingan partisipatif. Metode yang digunakan meliputi pelatihan penggunaan aplikasi, pendampingan intensif selama enam bulan, pembentukan Kelompok Tani Digital. Dari hasil kegiatan dapat disimpulkan bahwa aplikasi “GermasTani” dapat meningkatkan literasi digital, bahkan di kalangan petani lanjut usia, berkat desain antarmuka yang inklusif.
Rice Deep Knowledge Graph-Based Expert System: An Intelligent Solution for Identifying Rice Pests and Diseases Muhammad Ariful Furqon; Muhamad Arief Hidayat; Windi Eka Yulia Retnani; Gayatri Dwi Santika
Journal of Applied Agricultural Science and Technology Vol. 10 No. 1 (2026): Journal of Applied Agricultural Science and Technology
Publisher : Green Engineering Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55043/jaast.v10i1.332

Abstract

Accurate diagnosis of rice pests and diseases is essential but often challenging using traditional methods, which are time-consuming and prone to human error. In this study, we propose the Rice Deep Knowledge Graph (RiceDKG) Expert System, which integrates deep learning techniques, particularly Long Short Term Memory (LSTM), with a Knowledge Graph to enhance symptom pattern-based diagnosis accuracy. This hybrid approach captures relationships among rice plant symptoms while leveraging systematically constructed domain knowledge. The system was evaluated on a dataset of 25 test cases, encompassing various symptoms such as brown spots, leaf curling, and fungal damage. Evaluation results demonstrate an overall accuracy of 84%, with 21 out of 25 cases correctly diagnosed, compared to expert evaluations. These findings indicate that integrating LSTM with knowledge graphs improves the system's ability to handle diverse diagnostic scenarios.
Peningkatan Layanan Warga Sebagai Pendukung Tercapainya Sdg's Desa Melalui Perbaikan Layanan Internet Service Desa Diah Ayu Retnani Wulandari; Albert Dewanata Mahrahillah; Muhammad ‘Ariful Furqon; Benny Ridwan Susanto4
Jurnal Pengabdian Masyarakat IPTEKS Vol. 9 No. 1 (2023): JURNAL PENGABDIAN MASYARAKAT IPTEKS
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/jpmi.v9i1.474

Abstract

Internet saat ini telah dirasakan banyak manfaatnya oleh masyarakat dusun Calok dengan berlangganan internet RT/RW berbasis fiber optic. Permasalahan yang dihadapi warga dusun Calok terkait sering mengalami gangguan dalam menggunakan layanan internet. Permasalahan ini diselesaikan dengan melakukan kegiatan pengabdian dengan metode participatory rural appraisal untuk meningkatkan keterlibatan warga sehingga sesuai kebutuhan warga. Dimulai dari analisis kendala penggunaan internet melalui observasi kepada pengguna dan monitoring infrastruktur jaringan dan analisa kebutuhan pelanggan baru kemudian melakukan perbaikan sekaligus melakukan pengembangan jaringan. Pengembangan jaringan dilakukan dengan Teknik bottom up yaitu jaringan dibangun berdasarkan kebutuhan sehingga spesifikasi yang diberikan tidak sama. Kegiatan pengabdian ini diakhiri dengan melakukan analisis kinerja jaringan menggunakan parameter Quality of Service (QOS). Dari Hasil Pengukuran QOS pada 5 percobaan didapat nilai rata – rata indeks throughput sebesar 3,8 dengan kategori sangat baik, nilai indeks 4 pada Packet Loss dengan kategori sangat baik dan nilai pada delay sebesar 4 dengan kategori sangat baik. .
Implementation of Bidirectional Encoder Representations from Transformers in a Content-based Music Recommendation System for Digital Music Platform Users Fadil Abdillah Suyudi; Muhammad Ariful Furqon; Qurrota A'yuni Ar Ruhimat
Jurnal Elektronika dan Telekomunikasi Vol. 25 No. 1 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.660

Abstract

Digital music platform users today have unlimited access to millions of songs from various genres and artists through music streaming services. However, with so many music platforms available, users often need help finding songs that suit their preferences. This study presents a music recommendation system that utilizes lyrical analysis to provide users with relevant song suggestions based on selected lyrics. The system employs a two-pronged approach: the Term Frequency-Inverse Document Frequency (TF-IDF) method for initial feature extraction and the IndoBERT model for advanced contextual representation of song lyrics. A dataset of 8,944 Indonesian language songs was compiled using scraping techniques from various sources. The recommendation process is driven by cosine similarity calculations between the lyrics of the selected songs and the entire dataset, enabling the identification of songs with similar themes and messages. Model evaluation through a five-fold Multi-Class Cross-Validation (MCCV) approach yielded promising results, indicating high precision, recall, and F1 scores. The study results show that the system built can provide recommendations with good precision performance with Precision@k values varying between 0.7965 to 0.8371, Recall@k values ranging from 0.8017 to 0.8204, and F1-score@k values varying between 0.8083 up to 0.8190. Overall, the model shows strength in providing accurate recommendations and good performance stability
Agraph neural network framework for vascular streak dieback recognition Slamin Slamin; Rizky Alfanio Atmoko; Antonius Cahya Prihandoko; Muhammad Ariful Furqon; Qurrota A’yuni Ar Ruhimat; Annisa Fitri Maghiroh Harvyanti; Bayu Taruna Widjaja Putra; Roslan Hasni
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 1: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i1.pp194-204

Abstract

Vascular streak dieback (VSD) is one of the most destructive diseases affecting cocoa production in Southeast Asia, including Indonesia, where early visual symptoms are often subtle and spatially distributed across the leaf sur face. Conventional image-based disease recognition approaches, particularly those relying solely on convolutional neural networks (CNNs), are effective in extracting local visual features but remain limited in modeling long-range structural relationships such as venation disruption and lesion spread. To ad dress this limitation, this study investigates a hybrid CNN-graph neural network (CNN-GNN) framework for automated VSD recognition from cocoa leaf im ages. A primary dataset consisting of 1,000 RGB images collected directly from cocoa plantations in Jember Regency was used to reflect realistic field condi tions. In the proposed approach, CNNs are employedfor local feature extraction, while graph-based representations enable GNNs to capture global relational pat terns through message passing. Experimental results demonstrate stable learning behavior and strong classification performance, achieving a maximum validation accuracy of 95.2% and an area under the curve (AUC) of approximately 0.94. Further analysis shows balanced precision and recall across classes, indicating reliable discrimination between Sehat and VSD-infected leaves. These findings suggest that hybrid CNN-GNN modeling provides an effective strategy for cap turing both local and distributed structural characteristics of VSD symptoms and highlights the potential of graph-based reasoning to complement convolutional feature learning in plant disease diagnostics.
Arabica Coffee Sales Forecasting Using ARIMA Neural Network (Case Study: KOTEM Bondowoso): Author's Country: Indonesia Muhammad Ariful Furqon; Saudi Efendi; Yanuar Nurdiansyah
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 7 No. 1 (2026): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/5a61hr55

Abstract

Kopi Tembakau (KOTEM) Bondowoso is a local coffee producer specializing in Arabica ground coffee, sourced directly from nearby farmer cooperatives. A major hurdle they face is accurately forecasting sales, a critical factor for optimizing production and inventory. To tackle this, a hybrid forecasting model blending ARIMA (for linear/seasonal trends) and Neural Networks (for non-linear patterns) was developed. The study analyzed KOTEM’s sales data from September 2019 to August 2022, preprocessed to address non-stationarity via differencing and normalization. Results revealed the hybrid model outperformed standalone ARIMA, achieving a 1.0% MAPE (vs. ARIMA’s 1.3%). It also better captured sales volatility and seasonal shifts, offering more dependable forecasts. ARIMA-NN could significantly enhance KOTEM production scheduling and stock management.
Systematic Literature Review : Machine Learning and Deep Learning Aproaches for Crop Yield Prediction Using Remote Sensing Data Muhammad Fikri Haikal; Muhammad Ariful Furqon
Media Informatika Vol 25 No 2 (2026)
Publisher : P3M STMIK LIKMI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37595/mediainfo.v25i2.496

Abstract

Accurate crop yield prediction is essential for improving agricultural productivity and food security. This study presents a Systematic Literature Review (SLR) on machine learning and deep learning approaches for crop yield prediction using remote sensing data. Following PRISMA guidelines, 20 high-quality studies published between 2015 and 2025 were analyzed. The results highlight the growing dominance of deep and hybrid models, particularly when multi-source remote sensing data and phenological information are integrated.
Pengembangan Game Edukasi Vocabulary Untuk Siswa Sekolah Dasar Menggunakan Metode Multimedia Development Life Cycle Afandy Maulana Pangestu; Muhammad Ariful Furqon; Gama Wisnu Fajarianto
Journal of Animation and Games Studies Vol 12, No 1 (2026): April 2026
Publisher : Institut Seni Indonesia Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24821/jags.v12i1.13952

Abstract

Pembelajaran bahasa Inggris di sekolah dasar sering menghadapi tantangan karena siswa merasa bosan dengan metode pengajaran konvensional dan kurangnya penguasaan kosakata. Penelitian ini bertujuan mengembangkan game edukasi berbasis Android untuk membantu siswa meningkatkan kosakata bahasa Inggris secara menyenangkan. Penelitian menggunakan metode multimedia development life cycle (MDLC) yang terdiri dari enam tahap: konsep, desain, pengumpulan materi, perakitan, pengujian, dan distribusi. Subjek penelitian adalah siswa kelas 4 UPTD SPF SD Negeri Tamanan 1, Bondowoso. Game yang dikembangkan bernama Vocab.Me, sebuah game pada platform Android. Hasil pengujian beta menggunakan metode User Acceptance Test menunjukkan game diterima dengan baik oleh siswa, dengan aspek daya tarik 94%, relevansi 96%, kepercayaan diri 97%, dan kepuasan 98%. Pretest dan posttest menunjukkan peningkatan hasil sebesar 9,7015%. Evaluasi keseluruhan menghasilkan skor 96,33%, yang berarti game edukasi Vocab.Me sangat diterima oleh siswa kelas 4 UPTD SPF SD Negeri Tamanan 1.  
Perbandingan Seleksi Fitur Sequential, Chi-Square, dan Embedded Pada Klasifikasi Penyakit Kanker Payudara Menggunakan Algoritma Random Forest Yudha Alif Auliya; Muhammad ‘Ariful Furqon; Nico Wibiyanto
INTEGER: Journal of Information Technology Vol 11, No 1 (2026): Maret
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.integer.2026.v11i1.7984

Abstract

Cancer is typically linked to malignant tumors that can metastasize to extensive body tissues. Breast cancer arises from the uncontrolled proliferation of breast cells, resulting in the formation of benign and malignant tumors. Breast cancer presents various indicators, including small, round, and soft lumps associated with benign breast conditions and non-cancerous growths. In contrast, malignant breast cancer presents as asymmetrical, irregular, painful, and various other manifestations. If untreated, the tumor may metastasize and present a fatal risk. This study intends to evaluate the efficacy of Sequential Feature Selection, Chi-Square, and Embedded methods in classifying breast cancer, alongside implementing hyperparameter optimization via grid search on the random forest algorithm. This study utilizes the Wisconsin Breast Cancer dataset from the UCI Machine Learning Repository, comprising 569 data entries, 30 attributes, and 1 class label. The performance of the model is assessed using a Confusion matrix, which quantifies accuracy, precision, recall, and F1-score. The test results were derived from twenty testing schemes employing a combination of data splitting, cross-validation, and hyperparameter tuning via grid search. The optimal performance outcomes were achieved using the random forest model, which was subjected to hyperparameter tuning alongside SFS feature selection. The integration of 20 features yielded an accuracy of 97.37%, precision of 95.83%, recall of 97.87%, and an F1 score of 96.84%. The employed prediction model demonstrates effective performance in identifying both positive and negative classes. The model accurately predicted the true negative class in 66 instances. The model accurately identified the true positive class in 46 instances. One instance involved the model predicting a false positive class, while another instance involved the model predicting a false negative class. These results demonstrate that the model exhibits a high degree of accuracy with negligible prediction errors.
Co-Authors Afandy Maulana Pangestu Ahmad Sonny Pranata Albert Dewanata Mahrahillah Alifian Sukma, Alifian Annisa Fitri Maghiroh Harvyanti Apol Pribadi Ar Ruhimat, Qurrota A'yuni Ari Cahaya Puspitaningrum Ati Kusmiati Balthasar Watunglawar Bayu Taruna Widjaja Putra Benny Ridwan Susanto4 Deana Nathania Damayanti Deny Hermansyah Dwi Wijonarko Eki Saputra Eki Saputra El Maidah, Nova Endah Septa Sintiya Fadil Abdillah Suyudi Fajariyanto, Gama Wisnu Fazrol Rozi, Fazrol Firmansyah, Diksy Media Gama Wisnu Fajarianto Gama Wisnu Fajarianto Gayatri Dwi Santika Gun Faisal Harry Soepandi Herman Cahyo Diartho Hidayat, Muhamad Arief Iqbal Al Mahdi Irawan, Hendry Sakti Kartika, Vera Katarina Leba Komaria, Vida Leba, Katarina Madani, Anis Mochamad Bagoes Alfarazi Mohammad Ovi Sanjaya Mohammad Zarkasi Muhamad Arief Hidayat Muhammad Fikri Haikal Muhammad Zuama Al Amin Najwa, Nina Fadilah Naura, Lintang Arsa Nico Wibiyanto Nina Najwa Nova El Maidah Nur Aini Rakhmawati Okfalisa Oktavia, Nelly Priza Pandunata Puspaningrum, Diah Putri, Ikfira Agustina Qurrota A'yuni Ar Ruhimat Qurrota A’yuni Ar Ruhimat Rakhmawati, Nur A retnani, windy eka yulia Rita Sari Rizky Alfanio Atmoko Rizqi Alvian, Muhammad Bagus Roslan Hasni S Slamin Saiful Bukhori Saudi Efendi Sayekti Harits Suryawan Setiawan, Juni Suyudi, Fadil Abdillah Vega Kartika Sari Vera Kartika Windi Eka Yulia Retnani Yanuandika Akbar Yanuar Nurdiansyah, Yanuar Yanuarti, Rizky Yosari Dwi Fadhillah Yudha Alif Auliya Yudha Alif Auliya Zarkasi, Mohamad Zarkasi, Mohammad