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All Journal Proceedings of KNASTIK Semantik Jurnal Simetris Jurnal Sains dan Teknologi Prosiding SNATIF Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Informatika dan Teknik Elektro Terapan E-Dimas: Jurnal Pengabdian kepada Masyarakat Sistemasi: Jurnal Sistem Informasi Jurnal SOLMA JURNAL ILMIAH INFORMATIKA Jurnal Pengabdian Masyarakat AbdiMas Jurnal DISPROTEK EDUMATIC: Jurnal Pendidikan Informatika Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Jurnal SITECH : Sistem Informasi dan Teknologi Journal of Information Systems and Informatics Abdimas Toddopuli: Jurnal Pengabdian Pada Masyarakat JATI (Jurnal Mahasiswa Teknik Informatika) Jatilima : Jurnal Multimedia Dan Teknologi Informasi Jurnal Sistem Komputer dan Informatika (JSON) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Indonesian Journal of Technology, Informatics and Science (IJTIS) Jurnal UNITEK Jurnal Pendidikan dan Teknologi Indonesia Muria Jurnal Layanan Masyarakat Jurnal Locus Penelitian dan Pengabdian Bhakti Nagori (Jurnal Pengabdian kepada Masyarakat) Al-DYAS : Jurnal Inovasi dan Pengabdian kepada Masyarakat APPA : Jurnal Pengabdian Kepada Masyarakat Jurnal Sistem Informasi dan Manajemen International Journal of Artificial Intelligence and Science
Pratomo Setiaji
Universitas Muria Kudus
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
Real-Time Traffic Density and Anomaly Monitoring Using YOLOv8, OpenCV and Pattern Recognition for Smart City Applications in Demak Setiaji, Pratomo; Triyanto, Wiwit Agus; Nurhaliza, Maulin
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4867

Abstract

Urban traffic congestion is a persistent issue in medium-sized cities like Demak, leading to delays and potential accidents. This study presents the development of a real-time vehicle density and anomaly detection system using YOLOv8, combined with OpenCV for video analysis, to monitor traffic flow at strategic entry points of Demak City. The system classifies vehicles into four categories (cars, motorcycles, trucks, buses) and determines their direction by detecting crossing lines. A key feature is the recognition of vehicle patterns, particularly the detection of stopped vehicles, flagging anomalies after 30 seconds of stoppage, with tolerance for temporary detection losses. Traffic data is stored in CSV format, enabling periodic analysis and visualization via an interactive graphical user interface (GUI). Evaluation results show the YOLOv8n model achieves 92.5% precision, 88.3% recall, and 89.7% mean average precision (mAP@0.5), demonstrating improved accuracy and speed over previous YOLO versions. Additionally, the vehicle counting accuracy reaches 94.2% when compared with manual annotations. The proposed system provides a reliable solution for real-time traffic monitoring and early anomaly detection, supporting intelligent transportation systems (ITS) and enabling data-driven traffic management decisions. This research contributes to the advancement of real-time video analytics and pattern recognition for urban traffic control and serves as a scientific reference for the development of smart city infrastructures. Furthermore, this study strengthens the application of pattern recognition in intelligent anomaly detection, providing new insights for researchers in the fields of computer science and informatics.
Sentiment Analysis of Fizzo Novel Application Using Support Vector Machine and Naïve Bayes Algorithm with SEMMA Framework Pambudi, Satrio; Setiaji, Pratomo; Triyanto, Wiwit Agus
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4875

Abstract

The increasing popularity of digital reading platforms in Indonesia, such as Fizzo Novel, has generated many user reviews that can be analyzed to understand their satisfaction. This study analyzes user sentiment toward Fizzo Novel using the SEMMA (Sample, Explore, Modify, Model, Assess) framework, and compares the performance of the Support Vector Machine (SVM) and Naïve Bayes algorithms. A total of 139,759 reviews were collected from the Google Play Store through web scraping. The data was then processed through normalization, tokenization, lexicon-based sentiment labeling, and feature extraction using TF-IDF. To address class imbalance, the SMOTE technique was applied. The results showed that SVM achieved the highest accuracy, exceeding 96%, with a consistent F1-score across all sentiment classes. In contrast, Naïve Bayes recorded lower accuracy (75.82% before SMOTE and 73.63% after SMOTE), along with a decline in performance for the neutral class. SVM proved more reliable in handling large and imbalanced text data. Practically, the results of this study can help application developers such as Fizzo Novel in automatically understanding user opinions. With an accurate sentiment classification model, developers can monitor reviews in real-time, identify issues such as excessive advertising or an unpopular chapter division system, and design feature improvements based on real user needs. This research also provides a foundation for algorithm selection in future large-scale sentiment analysis projects and recommends SVM as the more appropriate choice in this context.
Co-Authors - Supriyono Adi Nugroho Adisaputra, Rizky Aditiya, Natasya Aditya, Ahsanu 'Amala Adiyono, Soni Agusta, Feriyan Alwani, Yazid Amaliyah, Fitriyah Amelia, Dwi Andy Prasetyo Utomo Anteng Widodo Aprilia Damayanti Arif Setiawan Artiarno, Andrean Maulana Aynullutfihana, Nanda Azzahro, Erina Diana Laily Fithri Eko Darmanto Fadhilah, Bayu Samudro Fajar Nugraha Fakhriyyah, Anis Ferianti, Lydya Ayu Hanif Rizqullah, Aufa Irfan Santiko Kurniasari, Nita Kusumawardani, Nada Laily Fithri, Diana Lisa Rachmawati Maula, Ahmad Inzul Muhammad Arifin Nia Zuliyana, Nia Noor Latifah Nugroho, Mochamad Bagus Iqbal Adi Nurhaliza, Maulin Pambudi, Satrio Prayogo, Adhie Putri Kurnia Handayani Putri, Rizka Ferbriliana R Rhoedy Setiawan Rachman, Muhammad Ulinnuha Ainun Noor Rahmawati, Yulinda Ramadlan As'ari, Wahyu Ramandani, Fitri Rani Setiawaty Rohmah, Putri Anjilis Romadhon, Zainur Semit, Danial Setiawan, Faris Apri Setyanto, Rangga Setyawan, Moh Dodi Suciningtyas - Suciningtyas - Suciningtyas Suciningtyas Suciningtyas Suciningtyas, Suciningtyas Suku Rahayu, Sri Intan Supriyono Supriyono Supriyono Supriyono Syafiul Muzid Tamaulina Br Sembiring Tengku Riza Zarzani N Wahyu Wibowo, Angga Widodo, Wahyu Kurniawan Ade Nur Wiwit Agus Triyanto Yoanas, Sahenda Yudie Irawan Zahra, Fatimah Az