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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 RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Faktor Exacta 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 Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Teknik Informatika (JUTIF) Journal of Information Technology Ampera Indonesian Journal of Technology, Informatics and Science (IJTIS) Jurnal UNITEK Jurnal Pendidikan dan Teknologi Indonesia Bima Abdi: Jurnal Pengabdian Masyarakat Muria Jurnal Layanan Masyarakat Jurnal Locus Penelitian dan Pengabdian Abdi Cendekia: Jurnal Pengabdian 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
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Komparasi Metode You Only Look Once Versi 8 (Yolov8) Untuk Sistem Deteksi Gender Berdasarkan Citra Wajah Fakhriyyah, Anis; Triyanto, Wiwit Agus; Setiaji, Pratomo
Jurnal SITECH : Sistem Informasi dan Teknologi Vol 8, No 1 (2025): JURNAL SITECH VOLUME 8 NO 1 TAHUN 2025
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/sitech.v8i1.15340

Abstract

Perkembangan teknologi dan kemudahan akses data mendorong peningkatan kebutuhan sistem berbasis kecerdasan buatan, terutama dalam bidang pengolahan citra wajah. Data wajah menjadi salah satu jenis data personal yang mudah diperoleh dan banyak digunakan dalam penelitian, terutama dalam identifikasi gender secara otomatis. Identifikasi ini bersifat efisien, non-invasif, cocok diterapkan pada sistem digital untuk meningkatkan keamanan dan pengalaman pengguna. Salah satu metode yang efektif untuk deteksi wajah dan klasifikasi gender secara real-time adalah YOLO (You Only Look Once). Pada penelitian ini nantinya menggunakan metode You Only Look Once versi 8 (YOLOv8) untuk pendeteksian objek dengan mengimplementasikan tiga sub versi didalamnya yaitu nano (YOLOv8n), small (YOLOv8s), medium (YOLOv8m) untuk mendeteksi dan mengklasifikasikan gender berdasarkan citra wajah. Setiap subversi memiliki karakteristik tersendiri dalam hal kecepatan, akurasi, dan kebutuhan komputasi. Penelitian ini bertujuan untuk membandingkan performa ketiganya untuk memperoleh model deteksi gender yang paling optimal. Dengan pendekatan ini diharapkan dapat mendukung pengembangan sistem cerdas yang mampu mengidentifikasi jenis kelamin secara otomatis dan akurat.
Deteksi dan Perhitungan Kendaraan Parkir di Terminal Wisata Bakalan Krapyak Menggunakan Algoritma Yolo Suku Rahayu, Sri Intan; Setiaji, Pratomo; Triyanto, Wiwit Agus
Jurnal Locus Penelitian dan Pengabdian Vol. 4 No. 8 (2025): JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v4i8.4453

Abstract

Pendahuluan: Penelitian ini membahas “Deteksi dan Perhitungan Kendaraan Parkir di Terminal Wisata Bakalan Krapyak Menggunakan Algpritma Yolo. Tujuan: Membangun sistem yang mampu mendeteksi dan menghitung jumlah kendaraan yang terparkir secara otomatis melalui video CCTV menggunakan algoritma YOLOv8 dan pelacak SORT dalam antarmuka aplikasi berbasis Streamlit. Metode: Penelitian ini menerapkan algoritma YOLOv8 untuk mendeteksi kendaraan dan metode SORT untuk mencegah penghitungan ganda. Deteksi dilakukan pada setiap frame video CCTV untuk mengidentifikasi bus, minibus, mobil, dan motor. Sistem dibangun dengan Python, menggunakan OpenCV, NumPy, dan Streamlit, serta menampilkan hasil deteksi berupa jumlah dan jenis kendaraan. Hasil: Sistem mampu mendeteksi dan menghitung kendaraan berdasarkan jenisnya secara otomatis, output berupa jumlah kendaraan tiap jenis dalam urutan yang ditentukan. Visualisasi real-time menampilkan bounding box dan label pada video. Kesimpulan: Sistem deteksi kendaraan menggunakan YOLOv8 dan SORT yang dibangun dalam platform Streamlit terbukti efektif dalam menghitung jumlah kendaraan parkir secara real-time. Sistem ini meningkatkan efisiensi pengawasan area parkir dan mengurangi ketergantungan pada pencatatan manual. Dapat dikembangkan lebih lanjut dengan integrasi database dan laporan statistik otomatis.
PENERAPAN YOLOv5 UNTUK SISTEM DETEKSI DAN MONITORING LAHAN PARKIR OTOMATIS Putri, Rizka Ferbriliana; Triyanto, Wiwit Agus; Setiaji, Pratomo
JURSIMA Vol 12 No 3 (2025): Volume 12 Nomor 3 2025
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v12i3.1172

Abstract

Pertumbuhan kendaraan di wilayah perkotaan menimbulkan permasalahan keterbatasan lahan parkir dan waktu pencarian tempat parkir yang lama. Penelitian ini bertujuan untuk merancang dan menerapkan sistem yang dapat secara otomatis mendeteksi dan memantau ketersediaan lahan parkir dengan memanfaatkan algoritma YOLOv5 serta citra yang diambil dari kamera drone. Metode yang digunakan mencakup akuisisi data citra melalui rekaman drone dari dua sudut pandang berbeda (atas dan samping), pelabelan data, pelatihan model deteksi objek, serta klasifikasi status slot parkir (kosong atau terisi). Evaluasi sistem dilakukan dengan mengukur precision, recall, accuracy, dan mAP@0.5. Hasil pengujian menunjukkan bahwa sudut pandang memengaruhi akurasi deteksi: pada sudut pandang samping, sistem memperoleh precision 100%, recall 75,86%, dan mAP@0.5 sebesar 75,86%, sedangkan pada sudut atas recall dan mAP@0.5 turun menjadi 35,29% dan 35,00%. Visualisasi Confusion Matrix dan Precision-Recall Curve mendukung hasil ini. Sistem yang dibangun terbukti mampu mendeteksi dan memantau ketersediaan lahan parkir secara real-time dengan visualisasi pada dashboard digital. Pemanfaatan kamera drone memberikan kemampuan untuk menjangkau area yang lebih luas dan fleksibel dibandingkan dengan penggunaan kamera statis. Dengan demikian, sistem ini memiliki potensi untuk menjadi solusi praktis dalam pengembangan smart parking berbasis deep learning di ruang publik. Kata Kunci: YOLOv5, deteksi kendaraan, smart parking, kamera drone, deep learning.
K-Means Clustering untuk Segmentasi Pelanggan: Mengungkap Pola Pembelian Strategi Pemasaran pada Sektor Ritel Artiarno, Andrean Maulana; Setiaji, Pratomo; Nugraha, Fajar
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 2 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i2.30336

Abstract

Digital transformation has posed new challenges for retail companies in understanding consumer behavior due to the increasing volume of data and continuously changing preferences. This study aims to uncover purchasing patterns among retail customers and to provide data-driven marketing strategies through customer segmentation using the K-Means Clustering algorithm. This research adopts a quantitative exploratory approach using 3,900 synthetic entries from the Kaggle platform, representing retail transactions. The analysis focuses on variables such as age, gender, product category, location, purchase amount, and transaction frequency. The analytical process includes data preprocessing, dimensionality reduction using PCA, and segmentation with the K-Means algorithm. The optimal number of clusters was determined using the Elbow Method and Silhouette Score, while the quality of the clustering was evaluated using internal metrics, namely the Calinski-Harabasz Score (491.47) and the Davies-Bouldin Score (2.02). These values indicate a well-structured and reliable clustering result. Our findings reveal five distinct customer segments with varying characteristics, ranging from teenagers with small and periodic purchases to high-value adult customers who transact infrequently. These insights serve as the foundation for developing marketing strategies such as loyalty programs, seasonal promotions, and exclusive approaches.
Sistem Klasifikasi Kematangan Apel Fuji berdasarkan Warna menggunakan KNN untuk Sortasi Otomatis Maula, Ahmad Inzul; Triyanto, Wiwit Agus; Setiaji, Pratomo
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 2 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i2.31243

Abstract

Manual fruit sorting typically relies on workers' visual observation to assess ripeness. This assessment is heavily influenced by individual experience and lighting conditions, often leading to inaccuracies. Furthermore, manual methods are time-consuming, increase the risk of misclassification, and reduce operational efficiency. Our research aims to develop a color-based Fuji apple ripeness classification application using the K-Nearest Neighbor algorithm that combines RGB and HSV features. Our research is developmental research using the Waterfall model, consisting of requirements analysis, design, implementation, testing, and maintenance. We used 240 fuji apple images sourced from images taken in the Kudus area. Our findings are an automatic classification application capable of classifying apple images into three ripeness levels: unripe, semi-ripe, and ripe. The evaluation results showed an accuracy of 93.75% with balanced precision, recall, and f1-score across all classes, confirming the system's stable performance without any indication of bias. Testing results using the black-box method in three scenarios opening the application, uploading an image, and reclassifying proved that all features performed as expected. The implication is that this application is ready for use in camera-based sorting in horticultural production lines and can be developed for other fruit classifications, supporting widespread post-harvest digitalization.
KLASIFIKASI EKSPRESI EMOSI WAJAH BAHAGIA DAN TIDAK BAHAGIA MENGGUNAKAN ARSITEKTUR MOBILENETV2 BERBASIS DEEP LEARNING Zahra, Fatimah Az; Setiaji, Pratomo; Triyanto, Wiwit Agus
Jurnal SITECH : Sistem Informasi dan Teknologi Vol 8, No 1 (2025): JURNAL SITECH VOLUME 8 NO 1 TAHUN 2025
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/sitech.v8i1.15546

Abstract

Penelitian ini bertujuan membangun sistem klasifikasi ekspresi wajah dua kelas (happy dan not happy) menggunakan arsitektur Convolutional Neuran Network (CNN) berbasis MobileNetV2 yang ringan dan efisien. Dataset yang digunakan merupakan gabungan dari FER2013, Pinterest, dan Roboflow, yang telah melalui proses augmentasi dan preprocessing. Model dilatih menggunakan metode 5-Fold Cross Validation untuk memperoleh evaluasi yang lebih stabil dan menyeluruh. Hasil penelitian menunjukkan bahwa model mencapai rata-rata akurasi validasi sebesar 81,49%, dengan nilai precision, recall, dan F1-score yang seimbang. Model kemudian diimplementasikan dalam sistem web berbasis Flask, memungkinkan pengguna mengunggah gambar dan memperoleh hasil klasifikasi dalam bentuk label teks. Pengujian menggunakan gambar wajah pribadi menunjukkan bahwa sistem memiliki kemampuan generalisasi yang baik pada data nyata di luar data latih. Penelitian ini menunjukkan bahwa arsitektur MobileNetV2 dapat diandalkan untuk tugas klasifikasi ekspresi wajah dua kelas berbasis gambar statis dan berpotensi dikembangkan lebih lanjut untuk aplikasi praktik di bidang pendidikan, interaksi manusia-komputer, dan layanan publik.
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.
OPTIMASI SISTEM INFORMASI PENGELOLAAN DATA KESEHATAN PEGAWAI PADA UNIT P3K PT KEBON AGUNG PG TRANGKIL MENGGUNAKAN METODE AGILE DENGAN PENDEKATAN SCRUM Rahmawati, Yulinda; Setiawan, R. Rhoedy; Irawan, Yudie; Setiaji, Pratomo
JURNAL ILMIAH INFORMATIKA Vol 13 No 02 (2025): Jurnal Ilmiah Informatika (JIF)
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/jif.v13i02.10194

Abstract

This study is entitled “Optimization of Employee Health Data Management Information System at P3K Unit PT Keon Agung PG Trangkil Using Agile Method with Scrum Approach”. This study aims todevelop an integrated and flexible informastions system to manage employee healrt data. With agile method with Scrum approach, the system is developed in stages through Sprint, adjusted ased on user feedback. The system is designed to record examination data, classify results into mild and severe diseases, and process referrals for severe disease cases. The development results show increased efficiency, accuracy of recording, and support management in making decisions related to employee welfare
Multi-Platform System Development for Violence Complaint Services using the CodeIgniter Framework Rohmah, Putri Anjilis; Setiaji, Pratomo; Muzid, Syafiul
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i6.5528

Abstract

Violence against women and children remains a prevalent social issue in Indonesia, including in Kudus Regency. The lack of fast, secure, and easily accessible reporting facilities is one of the factors contributing to the low reporting rate, leaving many cases unaddressed. This study aims to design and develop a web-based violence complaint information system using the CodeIgniter framework with a Model-View-Controller (MVC) architecture to ensure a more structured, secure, and efficient system. The development method follows the waterfall model, consisting of requirements analysis, design, implementation, integration, and system testing. The system provides key features such as an online reporting form, automated notifications to officers, real-time report status tracking, and case progress recording by authorized personnel. Black-box testing conducted by one reporter and three staff members of the Kudus Social Service (Dinas Sosial P3AP2KB) on six main features across four different scenarios resulted in a total of 96 test cases, achieving a functional success rate of 98.9%. One failure was identified in file upload validation, where the system still allowed unsupported file formats. Nevertheless, all other features functioned properly, and the system was proven responsive across devices. This reliability supports faster reporting and case handling, enabling victims to report more easily while allowing relevant institutions to respond quickly, accurately, and transparently.
Co-Authors - Supriyono Adi Nugroho Adisaputra, Rizky Aditiya, Natasya Aditya, Ahsanu 'Amala Aila Virda Amaliyah, Fitriyah Amelia, Dwi Andy Prasetyo Utomo Angga Wahyu Wibowo Anis Fakhriyyah Anteng Widodo Aprilia Damayanti Arif Setiawan Artiarno, Andrean Maulana Ary Kania Sya'diah Aufa Hanif Rizqullah Aynullutfihana, Nanda Azzahro, Erina Bagus Aris Setiawan Bayu Samudro Fadhilah Dewi Masitoh Diana Laily Fithri Eko Darmanto Faby Melia Shanni Fajar Nugraha Fakhriyyah, Anis faridah ayu shefia Fatimah Az Zahra Ferianti, Lydya Ayu Feriyan Agusta Ilham Dwi Pranoto Indri Putri Irfan Santiko Kurniasari, Nita Laily Fithri, Diana Lisa Rachmawati Maula, Ahmad Inzul Mila Rosyada Mochammad Imron Awalludin Moh Sendi Febriyanto Muhammad Arifin Muhammad Ulinnuha Ainun Noor Rachman Nada Kusumawardani Najwa Hanindya Putri Nanda Aulia Salsa Bila 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 Rahmawati, Yulinda Ramadlan As'ari, Wahyu Rani Setiawaty Revanda Putri Rahmadani Ridho Agiel Syahputra Siallagan Rohmah, Putri Anjilis Romadhon, Zainur Rosalva Denisia Yulia Yahya Semit, Danial Setiawan, Faris Apri Setyanto, Rangga Setyawan, Moh Dodi Soni Adiyono 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 Yutia Nia Nesicha Zahra, Fatimah Az Zuyyina Syarifa Yahya