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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika E-Dimas: Jurnal Pengabdian kepada Masyarakat JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Pengembangan Rekayasa dan Teknologi Jurnal Surya Masyarakat JSiI (Jurnal Sistem Informasi) ScientiCO : Computer Science and Informatics Journal Journal of Information Systems and Informatics Aiti: Jurnal Teknologi Informasi Jurnal Surya Masyarakat Jurnal Riptek SEMINAR NASIONAL PENELITIAN DAN PENGABDIAN KEPADA MASYARAKAT Jurnal INFOTEL Tematik Jurnal Informatika: Jurnal Pengembangan IT Signal and Image Processing Letters International Journal of Artificial Intelligence and Science Jurnal DIMASTIK
Aria Hendrawan, Aria
Universitas Semarang
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Nursing Public Health Social Sciences Transportation Other

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Vehicle Detection and Tracking using Coarse-to-Fine Module and Spatial Pyramid Pooling–Fast with Deep Sort Saputri, Anita Nur Widdia; Hendrawan, Aria; Khoiriyah, Rofiatul
Signal and Image Processing Letters Vol 7, No 2 (2025)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v7i2.118

Abstract

Semarang City, a rapidly growing urban area in Indonesia, faces significant traffic challenges stemming from the widespread use of motorcycles, an inefficient public transportation system, and accelerated urban development. These factors contribute to congestion and complicate traffic management efforts. To address this issue and enhance monitoring capabilities, this study develops an automatic vehicle detection system utilizing the YOLOv8 algorithm, applied to CCTV footage obtained from TILIK SEMAR, a local traffic surveillance initiative. The research methodology encompasses several key stages: data collection from real-world traffic scenarios, meticulous annotation of vehicle types, model training using the YOLOv8 framework, and performance evaluation conducted at two distinct locations in Semarang—Banyumanik and Thamrin Pandanaran. The trained model achieved an impressive average accuracy, measured as mean Average Precision (mAP50), exceeding 97%, with a rapid processing time of 4.2 milliseconds per image, making it suitable for real-time applications. Among vehicle categories, the highest detection accuracies were recorded for buses at 99.3% and box trucks at 99.5%, reflecting the model’s robustness for larger vehicles. However, motorcycles presented a challenge, with a lower mAP50-95 score of 64.3%, attributed to variations in shape, size, and lighting conditions. Overall, the system successfully identified 96.77% of 3,036 vehicles across the test dataset, demonstrating strong generalization across diverse traffic conditions. These findings validate YOLOv8 as an effective tool for real-time traffic monitoring in urban settings. Future enhancements will focus on expanding dataset diversity and improving performance under challenging environmental factors, such as adverse weather or low-light scenarios, to further refine the system’s reliability.
The Comparative Analysis Of Multi-Criteria Decision-Making Methods (MCDM) In Priorities Of Industrial Location Development Pinem, Agusta Praba Ristadi; Hendrawan, Aria; Wakhidah, Nur
JURNAL INFOTEL Vol 16 No 4 (2024): November 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i4.1099

Abstract

The process of prioritizing the development of an industrial area's site is a matter that necessitates a mature approach. The establishment of an industrial region has significant social implications for the surrounding locality. However, it is also necessary to take into account the availability of variables that facilitate the functioning of such an industrial zone. The goal of the study "A Comparative Analysis of Multi-Criteria Decision Making Methods (MCDM) for Determining the Priority of Industrial Area Location Development" is to compare and contrast different MCDM methods in the context of deciding which industrial area locations should be developed first. A case study was undertaken, examining various possible industrial sites for future development. Multiple approaches, namely MOORA, WASPAS, ARAS, COPRAS, and AHP, are employed to ascertain the prioritization of industrial area development locations. This study presents a comparative analysis of each approach by using the Spearman Rank correlation and utilizing the factual data obtained from the Department of Capital Plantation and Integrated One Door Services (DPMPTSP). The external research is anticipated to involve a comprehensive review of the literature on the efficacy of Multiple Criteria Decision Making (MCDM) methods. This research has the potential to assist both governmental bodies and private entities in establishing priorities for the development of industrial areas, taking into account prevailing circumstances and conditions while also considering various significant factors and criteria.
RASPBERRY PI DENGAN MODUL KAMERA DAN MOTION SENSOR SEBAGAI SOLUSI CCTV LAB FTIK UNIV. SEMARANG Pramono, Basworo Ardi; Hendrawan, Aria; Daru, April Firman
Jurnal Pengembangan Rekayasa dan Teknologi Vol. 2 No. 1 (2018): Mei (2018)
Publisher : Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/jprt.v14i1.1213

Abstract

Pengawasan tempat/ruangan dalam pekerjaan merupakan hal   penting, dimana dengan adanya CCTV pemantau kita bisa melihat kondisi suatu tempat dengan bantuan kamera pemantau (CCTV). Selama ini untuk perekaman CCTV mengunakan DVR (Digital Video Recording) dimana perangkat ini cenderung mahal dan belum menjangkau semua kalangan. Terlebih lagi jika CCTV merekam secara terus menerus 24 jam sehari selama 1 bulan, tentu harus menyediakan kapasitas ruang harddisk yang besar.Ruang Lab FTIK menjadi objek penelitian dimana mengunakan perangkat raspberry pi dengan modul kamera dan sensor motion detection dimana perangkat PC Mini Raspberry   Pi hanya akan merekam kondisi ruangan hanya pada saat terdeteksi suatu gerakan pada ruang Lab FTIK. Raspberry pi sendiri adalah sebuah komputer mini, sistem operasi Raspberry bisa bermacam-macam, salah satunya adalah Linux Debian yang telah dipaket minikan. Dengan itu diharapkan mampu mengurangi beban media penyimpanan. Penggunaan Raspberry Pi dalam hal pengawasan/monitoring tempat atau ruangan   memerlukan biaya yang murah dan efektif dalam pendayagunaan.
PEMODELAN PENENTUAN KREDIT SIMPAN PINJAM MENGGUNAKAN METODE ADDITIVE RATIO ASSESSMENT (ARAS) Maulana, Charis; Hendrawan, Aria; Pinem, Agusta Praba Ristadi
Jurnal Pengembangan Rekayasa dan Teknologi Vol. 3 No. 1 (2019): Mei (2019)
Publisher : Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/jprt.v15i1.1483

Abstract

Beberapa koperasi dalam memberikan pinjaman ke anggotanya sangat bergantung pada masing-masing pemberi keputusan dan bobot penilaian yang berbeda untuk setiap kriteria. Berbeda dengan pinjaman di bank, pinjaman pada koperasi memiliki kriteria yang mengacu pada aturan tiap koperasi. Hal ini menjadi menarik untuk dilakukan penelitian dengan menerapkan metode Additive Ratio Assessment dalam suatu sistem pendukung keputusan, sehingga dapat membantu dalam menentukan penerima pinjaman koperasi untuk menghindari kredit macet. Sistem Pendukung Keputusan (SPK) adalah sistem yang dapat membantu seseorang, dalam mengambil suatu keputusan yang akurat dan tepat sasaran. Banyak permasalahan yang dapat diselesaikan dengan menggunakan SPK, contohnya membangun model sistem pendukung keputusan penentuan anggota koperasi potensial dalam pengajuan pinjaman untuk menghasilkan informasi anggota koperasi potensial untuk menghindari kredit macet.  
LSTM Forecasting and K-Means Clustering for Passenger Mobility Management at Bus Terminals Khairunnisa, Hasna Rizqia; Hendrawan, Aria
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1159

Abstract

Rapid urban population growth has increased the need for efficient public transportation systems, particularly at bus terminals as major mobility hubs. To address operational challenges such as traffic congestion and limited infrastructure, this study proposes an innovative data-driven approach. A hybrid model is applied, integrating Long Short-Term Memory (LSTM) for passenger volume forecasting and K-Means Clustering for mobility pattern segmentation at the Jepara Bus Terminal. Monthly passenger data was utilized, and the K-Means method was applied to group monthly mobility patterns into three categories: low, medium, and high. The optimal cluster selection (k=3) was based on the highest Silhouette score of 0.785, providing clear seasonal insights. Analysis results indicate that September is the peak mobility period, while months like January and February fall into the low category. Furthermore, an LSTM model was trained to predict future passenger volumes. The model's performance was carefully validated and proven accurate, with a Mean Squared Error (MSE) of 0.0304 and a Root Mean Squared Error (RMSE) of 0.1745. These findings confirm that the model is reliable in capturing complex passenger movement patterns. Overall, this study concludes that the combination of LSTM and K-Means is an effective solution for supporting proactive decision-making. The results of this study can assist terminal managers in optimizing resource allocation and formulating more adaptive operational strategies, thereby contributing to the development of a more responsive and efficient intelligent transportation system.
Implementasi Sistem Administrasi di Unit Pelaksana Teknis Pusat Pengembangan Publikasi Ilmiah Dosen Universitas Semarang berbasis Website Pradana, Muhamamd Surya Jati; Hendrawan, Aria; Christioko, B.Very; Pinem, Agusta Praba Ristadi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 3: Juni 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021864089

Abstract

Pusat Pengembangan Publikasi Ilmiah Dosen (P3ID) ialah Unit Pelaksana Teknis (UPT) yang dibentuk di Universitas Semarang guna melakukan pengelolaan arsip surat yang berkaitan dengan jurnal. Kegiatan pengelolaan dan pengarsipan surat jurnal di P3ID masih dilakukan dengan cara manual, yang memungkinkan terjadinya kesalahan dalam pencatatan surat terlebih bila surat yang diarsipkan jumlahnya banyak. Dengan alasan tersebut, maka penelitian dilakukan untuk mengimplementasikan algoritma Sequential Search dalam proses pencarian arsip surat di P3ID ini. Metode yang digunakan dalam pengembangan sistem ialah metode Rapid Application Development (RAD), metode yang menekankan pada pengembangan sistem dengan jangka waktu yang singkat. Dari hasil implementasi pada sistem Administrasi berbasis Website ini, sistem berjalan dengan baik. Proses pencarian yang dilengkapi dengan algoritma Sequential Search ini juga dapat menampilkan hasil yang dicari berdasarkan kata kunci perihal. AbstractLecturer’s Scientific Publication Development Center is a Technical Implementation Unit established at the University of Semarang to manage the archive of letters related to journals. The management and archiving activities of journal letters in P3ID are still done by manual means, which allows for errors in the recording of letters especially when the letters archived in large numbers. For this reason, research was conducted to implement the Sequential Search algorithm in the process of searching for mail archives in this P3ID. The method used in system development is the Rapid Application Development (RAD) method, a method that emphasizes system development for a short period of time. From the implementation results on this Website-based Administration system, the system is running well. The search process equipped with sequential search algorithm can also display the results searched based on the subject keyword.
Vehicle Detection on The Traffic Using Detection Transformer (DETR) Algorithm Khoiriyah, Rofiatul; Hendrawan, Aria
International Journal of Artificial Intelligence and Science Vol. 1 No. 1 (2024): September
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v1.i1.4

Abstract

Object detection is a computer vision technique aimed at detecting and identifying objects in images or videos. In recent years, with advancements in Machine Learning and Deep Learning, object detection has made significant progress in various fields such as healthcare, security, and transportation. The DETR algorithm is a novel approach in object detection that combines transformer architecture with attention techniques to address object detection challenges. This research applies the DETR algorithm with ResNet backbone for vehicle detection on the roads, involving 6 object classes: Car, Truck, Bus, Motorcycle, Pickup Car, and Truck Box. Four training experiments were conducted: DETR-ResNet50, DETR-ResNet101, DETR-DC5-ResNet50, and DETR-DC5-ResNet101. The implementation results show that DETR-DC5 improves the accuracy of vehicle detection. DETR-DC5 with ResNet-101 achieved the highest score for AP50, which is 0.957. However, it should be noted that DETR-DC5 with ResNet-50 managed to maintain overall AP stability, with a lower parameter of 35.5. The model's outcomes in this study can be effectively applied for vehicle detection on the roads.
Traffic Vehicle Detection Using Faster R-CNN: A Comparative Analysis of Backbone Architectures Hakim, Luqman; Hendrawan, Aria; Khoiriyah, Rofiatul
International Journal of Artificial Intelligence and Science Vol. 1 No. 1 (2024): September
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v1.i1.5

Abstract

Object detection is a crucial task in computer vision, where advanced deep learning models have shown significant improvements over traditional methods. In this study, the Faster R-CNN algorithm is applied to a traffic dataset containing six vehicle categories: Bus, Car, Motorcycle, Pick Up Car, Truck, and Truck Box. The novelty of the research lies in the comparison of four backbone architectures ResNet50, ResNet50V2, MobileNetV3 Large, and MobileNetV3 Large 320 evaluated for their performance in vehicle detection at IoU thresholds of 0.5 and 0.75. The results reveal that ResNet50 provided the best overall performance, achieving mAP scores of 0.966 at IoU 0.5 and 0.887 at IoU 0.75, offering a balanced trade-off between precision and recall. ResNet50V2 and MobileNetV3 Large also performed well, with mAP scores of 0.945 and 0.870 for ResNet50V2, and 0.969 and 0.843 for MobileNetV3 Large, respectively. However, MobileNetV3 Large 320 showed the lowest detection performance, with mAP scores of 0.857 at IoU 0.5 and 0.551 at IoU 0.75. These findings provide useful insights into the suitability of different architectures for vehicle detection tasks, particularly in traffic surveillance applications.
A Hybrid Image Processing Approach for Real-Time Face Recognition in Attendance Monitoring Agho, Davit Cany; Hendrawan, Aria
International Journal of Artificial Intelligence and Science Vol. 2 No. 1 (2025): March
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v2.i1.17

Abstract

In the era of digital transformation, institutions are increasingly adopting automation to enhance administrative efficiency, particularly in human resource management. At Tanggirejo Village Hall, a critically low employee attendance rate of 46.45% in January 2024 exposed the limitations of manual attendance systems, which are prone to errors and manipulation. This study proposes a face recognition-based attendance system utilizing OpenCV’s Haar Cascade Classifier for face detection and the Local Binary Pattern Histogram (LBPH) for face recognition. A total of 500 grayscale facial images from 10 employees were collected and processed to train and test the system. Evaluation using a Confusion Matrix revealed an accuracy of 72%, precision of 93%, and recall of 75%. While a 27% error rate was observed—primarily due to lighting inconsistencies and limited training data—the system performed reliably in real-time scenarios. The integration of these lightweight algorithms allows for fast and accurate identification, suitable for resource-constrained environments. This solution not only addresses the local attendance challenges but also presents a scalable, automated model that can be adopted by similar institutions seeking to improve productivity and operational transparency through real-time employee monitoring.
A Systematic Review of Deep Learning for Intelligent Transportation Systems with Analysis and Perspectives Hendrawan, Aria; Gernowo, Rahmat; Nurhayati, Oky Dwi; Dewi, Christine
JURNAL INFOTEL Vol 16 No 2 (2024): May 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i2.1085

Abstract

This study presents a systematic review of deep learning for intelligent transportation systems. Statistics are used to find the most cited articles, and the number of articles and quotes are used to find the most productive and influential authors, institutions, and countries or regions. Key topics and patterns of change are discovered using the authors’ keywords, and the most common issues and themes are revealed using flow maps and showing the corresponding trends. A co-occurrence keyword network is also developed to present the research landscape and hotspots in the field. The results explain how publications have changed over the past seven years. Researchers can use this study to have a deeper understanding of the current state and future trends in the role of deep learning in intelligent transportation systems.
Co-Authors Adiuntoro, Alwan Agho, Davit Cany Agusta Praba Ristadi Pinem April Firman Daru Awaliyah, Rafika Farkhul Basworo Ardi Pramono Cholil, Saifur Rohman Christine Dewi Christioko, B.Very Dimas Dwi Budiarjo Febrian Wahyu Christanto Henny Indriyawati Huizen, Lenny Margaretta Khairunnisa, Hasna Rizqia Khoiriyah, Rofiatul Khoirudin Khoirudin Khoirudin Khoirudin, Khoirudin Luqman Hakim Margaretta Huizen, Lenny Maulana, Charis Maulida, Innayah Nur Wakhidah Oky Dwi Nurhayati Pinem, Agusta Praba Ristadi Pradana, Muhamamd Surya Jati Prind Triajeng Pungkasanti, Prind Triajeng Pulung Nurtantio Andono Rahmat Gernowo Rohman Cholil, Saifur Saifur Rohman Cholil Saputri, Anita Nur Widdia Sekar Sari, Mega Siti Asmiatun, Siti Soiful Hadi Susanto Susanto Titin Winarti Vensy Vydia Whisnumurti Adhiwibowo Wicaksana, Dinar Anggit