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All Journal Jurnal Simetris Jurnal Sosioteknologi Jurnas Nasional Teknologi dan Sistem Informasi INFORMAL: Informatics Journal JOIV : International Journal on Informatics Visualization International Journal of Artificial Intelligence Research Jurnal Komputer Terapan Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Elektronika dan Telekomunikasi Jurnal ULTIMATICS SISFO KOMPUTIKA - Jurnal Sistem Komputer INTEGRITAS : Jurnal Pengabdian 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) Jurnal Pengabdian Masyarakat Bidang Sosial dan Humaniora Proceeding International Applied Business and Engineering Conference PAPUMA: Journal of Comunnity Services JGGAG (Journal of Games, Game Art, and Gamification)
Furqon, Muhammad Ariful
Departemen Informatika, Universitas Jember
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry 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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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PEMBERDAYAAN MASYARAKAT DESA ANTIROGO MELALUI PENINGKATAN KAPASITAS KELEMBAGAAN DESA BERMUTU Kusmiati, Ati; Puspaningrum, Diah; Furqon, Muhammad Ariful; Yanuarti, Rizky; Diartho, Herman Cahyo
INTEGRITAS : Jurnal Pengabdian Vol 9 No 1 (2025): JANUARI - JULI
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat - Universitas Abdurachman Saleh Situbondo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36841/integritas.v9i1.6075

Abstract

Pembangunan berkelanjutan dalam lingkup pedesaan adalah upaya ekstensif dalam mewujudkan pembangunan desa dimana seluruh masyarakat desa harus merasakan dan menikmati hasil dari manfaat SDGs. Pemberdayaan Masyarakat melalui Implementasi SDG’s Desa Berbasis Potensi Lokal dapat menjadi pendekatan efektif untuk keberlanjutan Pembangunan desa termasuk di Kelurahan Antirogo Kecamatan Sumbersari Kabupaten Jember. Salah satu potensi lokal yang dimiliki adalah kelembagaan Kampung Keluarga Berkualitas (KKB). Namun aktivitas KKB kurang optimal dan partisipasi aktif anggotanya masih sedikit. Oleh karenanya penting untuk meningkatkan kapasitas kelembagaan tersebut untuk mencapai Indeks Ketahanan Sosial (IKS) sehingga tujuan SDG’s Desa No 3 (Desa Sehat dan Sejahtera) dapat terwujud. Adapun tujuan kegiatan pengabdian ini yaitu meningkatkan kapasitas kelembagaan Kampung Keluarga Berkualitas (KKB). Metode yang digunakan dalam pengabdian ini adalah Focus Group Discussion (FGD). Hasil yang diperoleh dalam pengabdian ini antara lain (1) peningkatan kapasitas kelembagaan Kampung Keluarga Berkualitas (KKB) melalui penyuluhan kepada Tribina untuk meningkatkan ketahanan keluarga dan dan status kesehatan masyarakat secara keseluruhan; (2) adanya promosi Lingkungan Desa yang Sehat dan Bersih dengan memperindah bantaran Sungai melalui kegiatan pengecatan dan menjaga kebersihan Sungai. Kegiatan ini sangat mendukung keberhasilan program Open Defecation Free (ODF) sehingga diharapkan adanya perubahan perilaku Masyarakat.
Prediksi Penjualan Kopi Bubuk Menggunakan Extreme Learning Machine (Studi Kasus: Kafe Tjap Daoen Bondowoso) Furqon, Muhammad Ariful; Madani, Anis; Nurdiansyah, Yanuar; Fajarianto, Gama Wisnu
Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) 2025: SNESTIK V
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/p.snestik.2025.7066

Abstract

Cafe Tjap Daoen belum menerapkan metode yang mendukung peramalan penjualan, sehingga keputusan terkait penjualan hanya didasarkan pada data periode sebelumnya. Hal ini menyebabkan terjadinya underproduksi saat permintaan tinggi dan overproduksi ketika permintaan menurun. Penelitian ini menganalisis dua produk utama, yaitu Arabica Specialty Coffee dan Arabica Wine, menggunakan data penjualan dari tahun 2019 hingga 2022. Algoritma Extreme Learning Machine (ELM) dipilih karena kinerjanya yang sangat baik dalam memprediksi data deret waktu. Hasil penelitian menunjukkan bahwa algoritma ELM mampu menghasilkan nilai Mean Absolute Percentage Error (MAPE) sebesar 1,8450%. Sementara itu, Arabica Wine menghasilkan MAPE sebesar 10,373%. Penelitian ini menunjukkan bahwa algoritma ELM efektif dalam meningkatkan akurasi peramalan penjualan untuk kedua produk tersebut. @font-face {font-family:"Cambria Math"; panose-1:2 4 5 3 5 4 6 3 2 4; mso-font-charset:0; mso-generic-font-family:roman; mso-font-pitch:variable; mso-font-signature:-536870145 1107305727 0 0 415 0;}@font-face {font-family:Calibri; panose-1:2 15 5 2 2 2 4 3 2 4; mso-font-charset:0; mso-generic-font-family:swiss; mso-font-pitch:variable; mso-font-signature:-469750017 -1040178053 9 0 511 0;}p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-unhide:no; mso-style-qformat:yes; mso-style-parent:""; margin:0cm; mso-pagination:widow-orphan; font-size:12.0pt; mso-bidi-font-size:11.0pt; font-family:"Times New Roman",serif; mso-fareast-font-family:Calibri; mso-ansi-language:IN;}.MsoChpDefault {mso-style-type:export-only; mso-default-props:yes; font-size:10.0pt; mso-ansi-font-size:10.0pt; mso-bidi-font-size:10.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-fareast-font-family:Calibri; mso-hansi-font-family:Calibri; mso-font-kerning:0pt; mso-ligatures:none; mso-fareast-language:EN-ID;}div.WordSection1 {page:WordSection1;}
Deteksi Berita Hoaks Berbahasa Indonesia Menggunakan One-Dimensional Convolutional Neural Network Muhammad Zuama Al Amin; Muhammad Ariful Furqon; Dwi Wijonarko
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 2: Mei 2025
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v14i2.19050

Abstract

The rapid advancement of information technology has enabled global information dissemination and led to a surge in hoax news, particularly in Indonesia. Hoax news poses a significant risk of spreading disinformation, potentially influencing public opinion, social stability, and security. Therefore, an effective technology-based solution is required to detect and identify hoaxes. This study aims to develop and optimize a one-dimensional convolutional neural network (1D-CNN) model to detect hoax news with high accuracy. The dataset comprised 12,151 articles, including 5,276 valid news items and 6,875 hoax news items, collected from reliable sources and anti-hoax platforms. The text preprocessing stages included data cleaning, case folding, punctuation removal, number removal, and stopword removal. The textual data were processed through tokenization and padding stages for model training preparation. The proposed 1D-CNN architecture integrated embedding, Conv1D, batch normalization, globalmaxpooling1d, dense, and dropout layers to enhance generalization capabilities and reduce the risk of overfitting. The model was trained using the Adam optimizer and its performance was evaluated using 10-fold cross-validation. Experimental results showed that the model achieved an average accuracy, precision, recall, and F1 score of 97.74%, 97.75%, 97.74%, and 97.73%, respectively. The developed model outperformed previous methods, namely the convolutional neural network–bidirectional long short-term memory (CNN-BiLSTM), gated recurrent unit (GRU), and conventional methods such as naïve Bayes or support vector machine (SVM), in terms of accuracy and training efficiency. This study demonstrates that the model has a reliable capability in identifying hoax news, both in terms of detection accuracy and performance consistency.
Implementation of Bidirectional Encoder Representations from Transformers in a Content-based Music Recommendation System for Digital Music Platform Users Suyudi, Fadil Abdillah; Furqon, Muhammad Ariful; Ar Ruhimat, Qurrota A'yuni
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
Rancang Bangun Sistem E-Commerce untuk Usaha Penjualan Elektronik Najwa, Nina Fadilah; Furqon, Muhammad Ariful; Kartika, Vera
Jurnal Nasional Teknologi dan Sistem Informasi Vol 8 No 1 (2022): April 2022
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v8i1.2022.34-43

Abstract

BM Elektronik yang menjual berbagai macam alat elektronik. Namun dalam prosesnya, BM Elektronik mengalami beberapa masalah seperti kesalahan dalam mengolah data barang maupun transaksi, sering kehilangan bukti transaksi, tidak dapat menghitung jumlah persediaan di toko, hingga kurang luasnya jangkauan pasar ke pelanggan. Untuk mengatasi permasalahan tersebut, perancangan sistem informasi penjualan berbasis e-commerce dapat membantu proses pencatatan data barang maupun transaksi dan membantu pelanggan dalam mencari informasi produk yang ada di toko. Sistem Informasi Penjualan Berbasis E-commerce dengan menggunakan metode persediaan barang First In First Out (FIFO) dan dibangun menggunakan bahasa pemrograman Hypertex Processor (PHP) dan basis data MySQL. Hasil uji coba fungsionalitas menggunakan metode User Acceptance Testing (UAT) yang terdiri dari 42 butir uji dan telah disetujui oleh client. Uji coba terhadap sistem menggunakan metode Usability Testing dengan menyebarkan kuisioner kepada pelanggan toko, dengan hasil persentase 83,75%.
Pelatihan Pemrograman Visual Kodular Bagi Siswa SMPS Mitra Patrang Jember Furqon, Muhammad Ariful; Hidayat, Muhamad Arief; Pandunata, Priza; Zarkasi, Mohammad; Nurdiansyah, Yanuar; Leba, Katarina
Abdiformatika: Jurnal Pengabdian Masyarakat Informatika Vol. 4 No. 1 (2024): Mei 2024 - Abdiformatika: Jurnal Pengabdian Masyarakat Informatika
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/abdiformatika.v4i1.211

Abstract

Pelatihan pemrograman visual menggunakan platform Kodular di SMPS Mitra Patrang Jember bertujuan untuk meningkatkan pemahaman siswa terhadap konsep pemrograman komputer. Metode yang digunakan dalam kegiatan pengabdian ini mencakup: (1) spesifikasi tujuan dan identifikasi masalah; (2) desain pelatihan; (3) implementasi pelatihan; serta (4) evaluasi dan umpan balik. Desain pelatihan yang terstruktur melibatkan partisipasi siswa dalam serangkaian sesi yang mencakup konsep dasar pemrograman visual dan penggunaan platform Kodular. Hasil menunjukkan peningkatan signifikan dalam pemahaman siswa setelah pelatihan, dengan mayoritas menyatakan kepuasan dan minat yang tinggi. Pelatihan ini efektif dalam meningkatkan pemahaman pemrograman visual dan merangsang minat siswa dalam teknologi. Studi ini memberikan kontribusi penting dalam memperluas pemahaman tentang pendekatan pembelajaran inovatif dalam konteks pendidikan sekolah menengah.
Knowledge Graph Construction for Rice Pests and Diseases Furqon, Muhammad Ariful; Bukhori, Saiful
International Journal of Artificial Intelligence Research Vol 7, No 1 (2023): June 2023
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.1022

Abstract

The agricultural industry in Indonesia confronts the simultaneous task of augmenting food production to satisfy escalating demand while proficiently handling crop losses caused by pests and diseases.  This study introduces a novel approach that leverages knowledge graphs to transform traditional, expert-based knowledge into a dynamic and interconnected system for addressing these agricultural challenges. The study delineates constructing a comprehensive knowledge graph, commencing with data extraction with SPARQL queries, and progressing to ontology design, object property and datatype property specification, and instance generation. The resultant knowledge graph not only serves as an organized archive for pest and disease information but also gives a systematic framework for the integration, analysis, and decision-making of data in agriculture. This knowledge graph adds to the broader junction of data science and agriculture by improving the diagnosis, prevention, and control of rice diseases.
Implementation of Finite State Machine to Determine The Behaviour of Non-Playabale Character in Leadership Simulation Game Rizqi Alvian, Muhammad Bagus; Bukhori, Saiful; Furqon, Muhammad ‘Ariful
Journal of Games, Game Art, and Gamification Vol. 9 No. 1 (2024)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/jggag.v9i1.10894

Abstract

In today's era, games are widely enjoyed by the Indonesian society, and one of them is simulation games. Simulation games have many advantages, including allowing players to experiment freely and encouraging them to learn. Therefore, the use of simulation games can be utilized as a training medium, such as leadership training. There are five levels of leadership based on The 5 Levels of Leadership: position, permission, production, people development, and pinnacle. Direct practice is necessary in training these levels of leadership through the implementation of Artificial Intelligence (AI) in simulation games. One of the AIs used for this implementation is the Finite State Machine (FSM). FSM will be implemented in Non-Playable Characters (NPCs) to determine behavior that is adjusted to the 5 levels of leadership. There are three State Machines (SM) applied to NPCs: Core Game SM, Movement SM, and Status SM. The use of FSM in NPCs results in dynamic NPC behavior in terms of physical movement and changes in NPC status according to 5 Levels of Leadership
Analisi Data Eksploratori Kritis untuk Dataset Prediksi Stroke Ariful Furqon, Muhammad Arif; Najwa, Nina Fadilah; Zarkasi, Mohamad; Pandunata, Priza; Fajariyanto, Gama Wisnu
Jurnal Komputer Terapan Vol 10 No 1 (2024): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/jkt.v10i1.6307

Abstract

Stroke is a significant global health concern, requiring an in-depth understanding of the complex factors contributing to its occurrence. Age, body mass index (BMI), and average glucose levels are critical factors in stroke etiology. This study employed exploratory data analysis techniques to investigate the relationships between variables in a stroke prediction dataset. The research methodology included (1) dataset description, (2) data preprocessing, (3) exploratory data analysis, and (4) interpretation. Descriptive statistical analysis provided insights into the dataset's composition and variability, while data preprocessing techniques handled missing values and facilitated feature extraction. Based on exploratory data analysis, significant relationships were found between age, hypertension, heart disease, average glucose levels, and stroke. However, BMI showed a less significant role in stroke. These findings contribute to a better understanding of the factors contributing to stroke risk and may aid in developing more effective prevention strategies.
Sentiment Analysis of Universitas Jember’s Sister for Student Application Using Gaussian Naive Bayes and N-Gram Mochamad Bagoes Alfarazi; Muhammad 'Ariful Furqon; Harry Soepandi
Journal of Innovation Information Technology and Application (JINITA) Vol 6 No 2 (2024): JINITA, December 2024
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v6i2.2400

Abstract

This research aims to classify sentiment in reviews of the Universitas Jember Sister for Student application on Google Play Store, a vital student platform. The primary challenge tackled is the automated identification of positive and negative user sentiments. The study employs the Gaussian Naive Bayes method for classification and uses N-Gram techniques for sentiment analysis. The dataset consists of 1097 reviews, with 673 negative and 424 positive reviews, after removing 86 neutral spam reviews. The data is divided into 80% training data (877 reviews) and 20% test data (220 reviews). Gaussian Naive Bayes is used for modeling and combined with TF-IDF vectorization. The findings reveal that the Gaussian Naive Bayes model achieves an accuracy of 68%, precision of 68%, and recall of 71% on the test data. N-Gram analysis shows frequent occurrences of words like "bisa", "bagus", and "aplikasi" in positive sentiments, while "bisa", "hp", and "absen" are prevalent in negative sentiments. The study concludes that the Gaussian Naive Bayes model effectively classifies sentiment in application reviews, with the potential for further performance improvements.
Co-Authors Ahmad Sonny Pranata Alifian Sukma, Alifian Apol Pribadi Ar Ruhimat, Qurrota A'yuni Ari Cahaya Puspitaningrum Ati Kusmiati Balthasar Watunglawar Deana Nathania Damayanti Deny Hermansyah Dwi Wijonarko Eki Saputra Eki Saputra El Maidah, Nova Endah Septa Sintiya Fajariyanto, Gama Wisnu Fazrol Rozi, Fazrol Firmansyah, Diksy Media Gama Wisnu Fajarianto Gayatri Dwi Santika Gun Faisal Harry Soepandi Herman Cahyo Diartho Hidayat, Muhamad Arief Irawan, Hendry Sakti Kartika, Vera Katarina Leba Komaria, Vida Leba, Katarina Madani, Anis Mochamad Bagoes Alfarazi Mohammad Ovi Sanjaya Mohammad Zarkasi Muhammad Zuama Al Amin Najwa, Nina Fadilah Naura, Lintang Arsa Nina Najwa Nova El Maidah Nur Aini Rakhmawati Okfalisa Oktavia, Nelly Priza Pandunata Puspaningrum, Diah Putri, Ikfira Agustina Rakhmawati, Nur A retnani, windy eka yulia Rita Sari Rizqi Alvian, Muhammad Bagus Saiful Bukhori 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 Zarkasi, Mohamad Zarkasi, Mohammad