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All Journal Jurnal TIMES JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Mantik Penusa INTECOMS: Journal of Information Technology and Computer Science The IJICS (International Journal of Informatics and Computer Science) JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Riset Informatika ComTech: Computer, Mathematics and Engineering Applications Jurnal Mantik bit-Tech JATI (Jurnal Mahasiswa Teknik Informatika) Journal of Intelligent Decision Support System (IDSS) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Instal : Jurnal Komputer Bulletin of Information Technology (BIT) Jurnal Minfo Polgan (JMP) BEES: Bulletin of Electrical and Electronics Engineering Jurnal Nasional Teknologi Komputer Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Technologica Jurnal Hasil Pengabdian Masyarakat (JURIBMAS) Journal of Artificial Intelligence and Digital Business Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Jurnal Testing dan Implementasi Sistem Informasi Elimensi Journal of Electrical Engineering Journal of Algorithmic Digital Engineering and Networks The Journal of Information Technology, Computer Science, and Electrical Engineering
Muhammad Iqbal
Universitas Pembangunan Panca Budi
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Social Sciences

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Prediction Of Book Borrowing Patterns Using Lstm-Xgboost For Collection Planning At Uinsu Library Andri Ismail Sitepu; Zulham Sitorus; Muhammad Iqbal
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 8 No. 1 (2026): September (2026)
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v8i1.10190

Abstract

University libraries face challenges in evidence-based collection planning due to the underutilization of borrowing circulation data. This study proposes a hybrid prediction model that combines Extreme Gradient Boosting (XGBoost) and Long Short-Term Memory (LSTM) to predict book borrowing patterns at the Library of Universitas Islam Negeri Sumatera Utara (UINSU). The proposed approach employs XGBoost to learn relationships among borrowing-related features and generate prediction outputs, which are subsequently used as additional input features for the LSTM model to capture temporal borrowing patterns. The dataset consists of 3,961 physical book borrowing transactions collected between 2024 and 2025. After preprocessing and feature engineering, the data were chronologically sorted and divided into 80% training data and 20% testing data using a time-based split to preserve the temporal sequence. Model performance was evaluated on the testing data using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Experimental results demonstrate that the proposed hybrid XGBoost–LSTM model achieved an RMSE of 0.3879 and an MAE of 0.1868, indicating good predictive performance with relatively low error rates. The comparison between actual and predicted borrowing values further shows that the proposed model is capable of capturing borrowing trends effectively. The developed model can serve as the foundation for a Decision Support System (DSS) to support data-driven collection planning and acquisition strategies in academic libraries.
Pemberdayaan UMKM Pascabencana melalui Pemasaran Digital Terintegrasi Blockchain dalam Ekosistem Marketplace Desa Indra Nasution; Muhammad Iqbal
Jurnal Nasional Teknologi Komputer Vol 6 No 1 (2026): Januari 2026
Publisher : CV. Hawari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61306/jnastek.v6i1.342

Abstract

Bencana banjir yang melanda wilayah Tapanuli Selatan pada tahun 2025 berdampak signifikan terhadap keberlanjutan sosial dan ekonomi masyarakat Desa Tolang Julu, khususnya pada sektor Usaha Mikro, Kecil, dan Menengah (UMKM) kuliner lokal. Pascabencana, pelaku UMKM menghadapi keterbatasan sarana produksi, rendahnya inovasi produk, sempitnya jangkauan pemasaran, serta dampak psikososial yang menurunkan motivasi berusaha. Penelitian ini bertujuan untuk mengkaji model pemberdayaan UMKM kuliner lokal pascabencana melalui pengembangan inovasi produk frozen food dan penerapan pemasaran digital terintegrasi teknologi blockchain dalam ekosistem marketplace desa. Metode penelitian menggunakan pendekatan partisipatif dan deskriptif dengan tahapan preparasi objek dan sistem, pelatihan dan pendampingan produksi frozen food berbasis bahan pangan lokal, serta penerapan pemasaran digital berbasis desa. Teknik karakterisasi dilakukan terhadap aspek produksi, pemasaran, dan dampak sosial-ekonomi melalui observasi, wawancara, dokumentasi, serta replikasi kegiatan produksi dan pemasaran. Hasil penelitian menunjukkan bahwa penerapan inovasi frozen food mampu meningkatkan kapasitas produksi dan daya simpan produk UMKM kuliner lokal. Pemasaran digital melalui marketplace desa yang didukung sistem pencatatan transaksi berbasis blockchain menghasilkan pengelolaan pemasaran yang lebih tertata, transparan, dan mudah ditelusuri. Selain itu, keterlibatan aktif kelompok perempuan dan keluarga penyintas dalam kegiatan produktif berkontribusi terhadap pemulihan sosial dan psikososial masyarakat pascabencana. Penelitian ini menyimpulkan bahwa integrasi inovasi produk, pemasaran digital, dan teknologi blockchain berpotensi menjadi model pemberdayaan UMKM desa yang adaptif, inklusif, dan berkelanjutan dalam mendukung pemulihan ekonomi pascabencana, serta dapat direplikasi pada desa lain dengan karakteristik serupa.
PERANCANGAN SISTEM PEMBAYARAN MAKANAN DAN MINUMAN BERBASIS WEB PADA CAFE RUMAH SI DALANG MENGGUNAKAN METODE R&D Dwi Rizky Ramadhani; Muhammad Iqbal; Abdul Khaliq
Jurnal Nasional Teknologi Komputer Vol 6 No 2 (2026): April 2026
Publisher : CV. Hawari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61306/jnastek.v6i2.416

Abstract

The rapid advancement of information technology has significantly influenced various sectors, including payment methods in the culinary business. Manual payment processes often result in delays, inaccuracies in recording, and lack of transparency. This study aims to design and implement a webbased payment system for Cafe Rumah Si Dalang using the Research and Development (R&D) approach. The research process includes requirement analysis, system design, implementation, and testing. The findings indicate that the developed system can streamline the payment process, enhance accuracy, and minimize human errors. Its user-friendly interface allows both cashiers and customers to use the system efficiently. This research is expected to serve as a digital payment solution for culinary businesses and provide a reference for future development, including QRIS integration and automated financial reporting features.
ANALISIS ALGORITMA RANDOM FOREST DAN SURVIVAL ANALYSIS TERHADAP FAKTOR YANG MEMPENGARUHI WAKTU PENYELESAIAN PERKARA DI PENGADILAN AGAMA LUBUK PAKAM KELAS 1A - DELI SERDANG Nur Azizah Harahap; Muhammad Iqbal; Rian Farta Wijaya
Jurnal Nasional Teknologi Komputer Vol 6 No 4 (2026): Oktober 2026
Publisher : CV. Hawari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61306/jnastek.v6i4.570

Abstract

The increasing number of cases in religious courts poses challenges in maintaining consistency in case resolution time. This study aims to analyze factors affecting case resolution time using Random Forest and Survival Analysis methods.The dataset consists of case records from the Religious Court of Lubuk Pakam Class IA, Deli Serdang, covering March 2025 to March 2026. Random Forest is used to classify case duration and identify dominant variables, while Survival Analysis with the Cox Proportional Hazards model is applied to examine the effect of variables on resolution time.The results show that the Random Forest model achieves an accuracy of 66%, with the number of hearings identified as the most dominant factor (feature importance = 0.634). Survival Analysis indicates that the number of hearings has a hazard ratio of 0.78, suggesting that an increase in hearings tends to prolong case resolution time. The concordance value of 0.70 indicates a reasonably good predictive performance.These findings indicate that operational factors, particularly the number of hearings and the presence of parties, significantly influence case resolution time.
Analisis Prediksi Tahfiz Qur'an Santri Mengunakan Algoritma K-means dan Naive Bayes Studi Kasus Pondok Pesantren Darul Makmur Al-Hafiz Aceh Tenggara Aidul Safii; Zulham Sitorus; Muhammad Iqbal
Jurnal Nasional Teknologi Komputer Vol 6 No 4 (2026): Oktober 2026
Publisher : CV. Hawari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61306/jnastek.v6i4.632

Abstract

Program tahfiz Pondok Pesantren Darul Makmur Al-Hafiz Aceh Tenggara berharap setiap santri dapat menghafal tiga puluh juz. Namun, pembinaan yang konsisten tidak efektif karena capaian hafalan santri sangat beragam. Studi ini mengelompokkan siswa berdasarkan karakteristik capaian tahfiz dan menggunakan algoritma K-Means dan Naive Bayes untuk memprediksi tingkat hafalan mereka pada akhir masa pendidikan. Data penelitian terdiri dari 211 siswa dari kelas 7 hingga 11. Proses penelitian meliputi pembersihan data, transformasi jumlah hafalan ke satuan juz, penciptaan atribut turunan untuk laju hafalan (juz per tahun), normalisasi antara min dan max, penggunaan metode Elbow untuk menghitung jumlah klaster, dan ulangi perhitungan jarak Euclidean hingga konvergen. Hasil penelitian menunjukkan bahwa algoritma konvergen pada iterasi keempat dengan nilai Sum of Squared Error (SSE) sebesar 2,3707 dan koefisien Silhouette sebesar 0,628, yang menunjukkan bahwa algoritma memiliki struktur klaster yang wajar. Santri dibagi menjadi tiga kelompok klister: Rendah 149 santri (70,6%) menghafal 1,08 juz dan laju 0,52 juz per tahun; Sedang 53 santri (25,1%) menghafal 5,96 juz dan laju 2,09 juz per tahun; dan Tinggi 9 santri (4,3%) menghafal 18,56 juz dan laju 4,87 juz per tahun. Model menggunakan empat kategori hafalan, jenjang kelas, nilai muhadhoroh, dan jenis kelamin. Data diklasifikasikan secara sistematis menjadi 169 data latih dan 42 data uji, dengan perbandingan sekitar 80:20. Untuk menghindari peluang nol, peluang prior dan kemungkinan dihitung dengan koreksi Laplace. Selanjutnya, kelas dipilih berdasarkan nilai posterior terbesar. Hasil pengujian menunjukkan bahwa model sangat andal dalam mengenali santri kategori Rendah (recall 96,30%) dan Tinggi (recall 100%), dengan F1-score makro sebesar 74,85%. Kategori Sedang masih sering tertukar (recall 53,85%) karena interval kategori hafalan yang luas. Hasilnya menunjukkan bahwa Naive Bayes adalah alat yang tepat untuk digunakan saat merencanakan pembinaan tahfiz di pesantren.
Design of Web-Based Dengue Disease Detection Intelligent System by Applying Naive Bayes Method in Belian District Batam Sastika Yolanda 12; Rahmadani; Muhammad Iqbal
Journal of Electrical Engineering Vol. 4 No. 01 (2026): Elimensi : Journal of Electrical Engineering
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/elimensi.v4i01.684

Abstract

Dengue fever remains a serious public health issue in tropical regions such as Indonesia, with the Belian District in Batam experiencing periodic outbreaks that demand effective early detection systems. This research aims to design and implement a web-based intelligent system that utilizes the Naive Bayes classification method to detect potential dengue fever cases based on user-input symptoms. The system was developed using web technologies (HTML, CSS, JavaScript, PHP/Python, and MySQL) and allows users—both medical personnel and the general public—to input symptoms and receive real-time probabilistic predictions. The dataset used in this study consisted of 200 records of patient medical history, divided into training and testing sets. Performance evaluation of the model using accuracy, precision, recall, and F1-score yielded promising results: 87% accuracy, 85% precision, 89% recall, and 87% F1-score. The system also features data management modules for symptoms, diseases, rules, users, and consultation history, supporting structured diagnostic processes. The results show that the Naive Bayes method effectively supports intelligent, data-driven decision-making for early dengue detection and can serve as a valuable screening tool, especially in areas with limited medical resources.
Analisis Metode Support Vector Machine (SVM) Dan Random Forest Terhadap Perilaku Dan Prestasi Siswa Berbasis Kurikulum Merdeka Di SMK Negeri 1 Stabat I Ibrahim; Muhammad Iqbal; K Khairul
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 10, No 1 (2025): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v10i1.888

Abstract

This study analyzes the influence of student behavior on academic achievement at SMKN 1 Stabat, using a quantitative approach with simple linear regression analysis on 245 tenth-grade students. The results show a positive and significant influence, with a coefficient of determination (R²) value of 0.272, indicating that student behavior contributes 27.2% to academic achievement. Discipline and responsibility are the most influential behavioral aspects, where students with high discipline tend to be more consistent in learning and completing assignments. The implications of this study highlight the importance of character building and positive behavior development within the school environment through collaboration among teachers, parents, and school administrators. Although not the sole determinant of success, good behavior such as discipline, responsibility, and active learning has been proven to enhance student learning outcomes. Therefore, efforts to improve the quality of education should consider not only cognitive aspects but also the development of constructive behavior through guidance programs and the strengthening of intrinsic motivation.
Analisis Metode FMEA Dan SPC Pada Proyeksi Losses Produksi Dan Prediksi Perawatan Pompa Minyak Di PT.Pertamina Ep Zona 1 Rantau Field Kuala Simpang Bambang Sugito; Muhammad Iqbal; Zulham Sitorus
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 10, No 1 (2025): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v10i1.889

Abstract

Leaks in oil pumping systems are among the main causes of production losses and reduced operational efficiency in the upstream oil and gas industry. This study aims to identify dominant failure modes, monitor process stability, and predict pump leakage risk using an integrated approach that combines Failure Mode and Effect Analysis (FMEA), Statistical Process Control (SPC), and the Support Vector Machine (SVM) algorithm. The research was conducted at PT Pertamina EP Zone 1 Rantau Field using operational data from the year 2024. FMEA results show that leakage due to illegal tapping and corrosion are the most critical failures, with Risk Priority Numbers (RPN) of 216 and 180, respectively. SPC analysis using X̄-R control charts revealed weekly process fluctuations, indicating potential variability in operations. To predict leakage risk, an SVM model was trained using technical pump features such as pressure, temperature, vibration, and pipe joint age. Class imbalance was addressed using the Synthetic Minority Oversampling Technique (SMOTE), and model evaluation yielded an accuracy of 95.48%. The integration of these three methods has proven effective in supporting data-driven predictive maintenance strategies. It reduces unplanned downtime, minimizes production losses, and enhances the reliability of oil pumping systems.
Analisys of Trends And Patterns of Hololive ID Vtubers on Content Variation for Monetization Optimization Using Multiple Linear Regression Algorithm Ananda Aulia; Muhammad Iqbal; K Khairul
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 10, No 1 (2025): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v10i1.885

Abstract

YouTube, as a new form of mass media in the advancement of increasingly sophisticated technology, caters to a diverse audience seeking daily information. Many YouTube users leverage this platform as a medium for creativity and income generation, such as through video blogs, educational videos, gaming videos, and various other content. Not all YouTubers can express themselves directly or through livestreaming; some use virtual 2D or 3D characters created with computer software, utilizing technologies such as Face Tracking and Hand Tracking. Hololive Production is a Japanese VTuber agency owned by the Japanese technology company Cover Corporation. Hololive Indonesia (Hololive ID) was established to produce Indonesian-language VTuber content and has garnered significant attention from fans in Indonesia and worldwide. VTuber content primarily revolves around video game topics, indicating a strong interest among VTuber fans in gaming-related content. Moreover, the analysis results show that multiplayer games enabling interaction among VTubers or even with viewers are highly favored, further supporting the popularity of gaming topics among VTuber enthusiasts. The Multiple Linear Regression algorithm has been widely used for sales predictions related to buyer patterns and trends. The performance measurement is influenced by the amount of data and the number of attributes.
Implementasi Algoritma Naïve Bayes Dalam Menganalisis Jumlah Live Stream VTuber Skem Ananda Aulia; Miftah Rusydi Tanjung; Muhammad Iqbal; Rian Farta Wijaya
Bulletin of Information Technology (BIT) Vol 4 No 4: Desember 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v4i4.899

Abstract

YouTube is evidence of the development of leading digital technology in the media and entertainment sector. YouTuber is a term for those who create content or broadcast live on YouTube media. Not all YouTubers are able to express themselves directly in the content they create. For this reason, Virtual YouTubers were created, which are 2-dimensional or 3-dimensional virtual characters created using computer software to make it easier for YouTubers to interact with viewers. This research aims to analyze the live broadcasts of VTuber HoloH3ro, namely Kaela Kovalskia, Vestia Zeta, and Kobo Kanaeru, which contain schemes during the live streaming. With a large amount of data for each live stream time, a Naïve Bayes algorithm is needed through the use of range and frequency features, so accuracy results will be obtained which will be tested using the Confusion Matrix. The dataset used was taken from the kaggle.com site regarding the scheme live stream schedule conducted by HoloH3ro.
Co-Authors , Arpan Abdul Khaliq Abdul Muin Nasution Ade Guna Suteja Ahkyar Khadafi Aidul Safii Akmal Pratama Harahap Amin, Muhammad Ananda Aulia Ananda Aulia Andi Ernawati Andi Ernawati Andri Ismail Sitepu Andysah Putera Utama Siahaan Anzas Ibezato Zalukhu Anzas Ibezato Zalukhu Ardiansyah Ardiansyah Astri Aprilia Pratiwi Atja Ramadhan Ayu Ofta Sari Bagas Alivia M Bambang Sugito Boy Rizky Akbar Chairul Rizal Dadang Iskandar Mulyana` Darmeli Nasution Dika Dika Dimas Armadansyah Dwi Rizky Ramadhani DWI SURYANTO Dwika Ardya Efendi, Syahril Eko Hariyanto Eva Mufida Padilla Firdaus Firdaus Hafizh Sallam Hafni Hafni Hindra Syahputra I Ibrahim Indra Nasution Irvanizam Irvanizam Irwan Irwan Irwan Syahputra Jelly Rolleys Sitompul Jimmy Iqbal Wiranata Siregar K Khairul Kairul Fahmi Khairul Khairunnisa Khairunnisa Kurnia Wira Syahputra Leni Marlina M. Azhari Rizko Maha Valne Datin Maida Indrayani Maisya Fitri Anugrah Marsini Sibuea Mhd Arfan Sitorus Miftah Rusydi Tanjung Muhammad Adli Afdika Muhammad Eka Muhammad Irfan Sarif Muhammad Irfan Syarif Muhammad Israr Fathoni Muhammad Noor Hasan Siregar Muhammad Syahputra Novelan Muhammad Zen Nadya Septiani Nadya Nelviony Parhusip Niska, Debi Yandra Nur Azizah Harahap Nurlina Sari Harahap Puspita Wanny Putra, Randi Rian Rahma Yuni Simanullang Rahmadani Rian Farta Wijaya Rian Farta Wijaya Wijaya Rindi Antika Robin Antoni Rudi Hermawan Sapna Indah Br Ginting Sartika Siburian Sastika Yolanda 12 Siska Mayasari Rambe Siti Hapsoh Lubis Siti Nurhaliza Sofyan Siti Nurhaliza Sofyan Sitorus, Zulham Sri Wahyuni Suhardiansyah Sumarno Supiyandi Supiyandi Susilawati Yahya Syaiful Asmi Hasibuan Syaiful Rahman Lubis Wina Aulia Zulham Zulham Sitorus