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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
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Improving Software Defect Prediction Using a Combination of Ant Colony Optimization-based Feature Selection and Ensemble Technique Retnani, Windi Eka Yulia; Furqon, Muhammad 'Ariful; Setiawan, Juni
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 4, November 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i4.2038

Abstract

Software defect prediction plays a vital role in enhancing software quality and minimizing maintenance costs. This study aims to improve software defect prediction by employing a combination of Ant Colony Optimization (ACO) for feature selection and ensemble techniques, particularly Gradient Boosting. This research utilized three NASA MDP datasets: MC1, KC1, and PC2, to evaluate the performance of four machine learning algorithms: Random Forest, Support Vector Machine (SVM), Decision Tree, and Naïve Bayes. The data preprocessing comprised handling class imbalance using SMOTE and converting categorical data into numerical representations. The results indicate that the integration of ACO and Gradient Boosting significantly enhances the accuracy of all four algorithms. Notably, the Random Forest algorithm achieved the highest accuracy of 99% on the MC1 dataset. The findings suggest that combining ACO-based feature selection with ensemble techniques can effectively boost the performance of software defect prediction models, offering a robust approach for early detection of potential software defects and contributing to improved software reliability and efficiency.
Penerapan Metode Fuzzy Time Series Cheng Pada Peramalan Inflasi di Indonesia Putri, Ikfira Agustina; El Maidah, Nova; Ariful Furqon, Muhammad
Komputika : Jurnal Sistem Komputer Vol 13 No 2 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i2.12108

Abstract

Inflasi ialah kenaikan harga dan barang dalam suatu periode tertentu yang pertumbuhannya selalu diusahakan tetap rendah dan stabil demi mewujudkan kesejahteraan masyarakat. Fluktuasi inflasi yang tinggi memiliki pengaruh besar terhadap perekonomian suatu negara, diperlukan adanya peramalan yang bisa digunakan sebagai acuan bagi Pemerintah dan Bank Sentral untuk mencegah terjadinya inflasi yang tinggi sekaligus menjaga stabilitas harga di masa depan. Selain itu, peramalan inflasi dapat membantu pelaku ekonomi dalam pengambilan keputusan. Peramalan dapat dilakukan dengan berbagai metode, salah satunya Fuzzy Time Series Cheng. Data inflasi yang digunakan pada penelitian ini diperoleh dari website Bank Indonesia dari Januari 2003 hingga September 2023 dengan periode data bulanan sebanyak 249 data. Hasil prediksi untuk periode 9 bulan yaitu 5,54% untuk inflasi tertinggi dan 2,92% untuk inflasi terendah. Berdasarkan pengujian yang telah dilakukan, diperoleh nilai error MAPE sebesar 9,54% dengan kategori nilai MAPE sangat baik.
Segmentasi Citra Tanda Tangan Menggunakan Fitur Titik SURF (Speeded Up Robust Features) dan Klasifikasi Jaringan Syaraf Tiruan hidayat, muhamad arief; retnani, windy eka yulia; Firmansyah, Diksy Media; Santika, Gayatri Dwi; Furqon, Muhammad ‘Ariful
INFORMAL: Informatics Journal Vol 9 No 3 (2024): Informatics Journal (INFORMAL)
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v9i3.53514

Abstract

Signature image classification is an important field of image processing. One of the stages of signature classification is segmentation. The segmentation process aims to detect image pixels that are part of the signature and separate them from text or logo pixels in a document image. There is a signature segmentation technique using interest points extracted using the SURF (Speeded Up Robust Features) algorithm [1] In this technique, a connected component pixel will be considered part of the signature if it has more SURF points in common with the database connected component pixel signature. Compared to the similarity with the database connected component non-signature pixels. This method is able to provide good accuracy results for signature pixel segmentation. However, the recall value is relatively low, namely 56%. This is because many connected component logos are considered as connected component signatures. In this study, signature segmentation was carried out using SURF points by adding two things: 1) using internal connected component characteristics as additional classification atributs: extent, solidity, ratio, and circularity 2) using an Artificial Neural Network classification algorithm to assist the segmentation process. The test results show that the proposed method improves segmentation quality by an average of 20.7% for an increase in accuracy, an average of 22.4% for an increase in precision, and an average of 18.6% for an increase in recall. When compared with the results reported in (Ahmed et al., 2012), the recall has increased by 38.3% - 42.8%
Implementation of Finite State Machine to Determine The Behaviour of Non-Playabale Character in Leadership Simulation Game Muhammad Bagus Rizqi Alvian; Saiful Bukhori; Muhammad ‘Ariful Furqon
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
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%.
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 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 Madani, Anis Mochamad Bagoes Alfarazi Mohamad Zarkasi Mohammad Ovi Sanjaya Mohammad Zarkasi Muhamad Arief Hidayat Muhammad Bagus Rizqi Alvian 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 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