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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) JOIV : International Journal on Informatics Visualization Altasia : Jurnal Pariwisata Indonesia
Rangkuti, Abdul Haris
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Humanities Computer Science & IT Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media

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Enhancing Vision-Based Vehicle Detection and Counting Systems with the Darknet Algorithm and CNN Model Rangkuti, Abdul Haris; Athala, Varyl Hasbi
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2550

Abstract

This study focuses on developing an algorithm that accurately calculates the volume of vehicles passing through a busy crossroads in Indonesia using object recognition. The high density of vehicles and their proximity often pose a challenge when distinguishing between vehicle types using a camera. Therefore, the proposed algorithm is designed to assign a unique identity (ID) to each vehicle and other objects, such as pedestrians, ensuring that volume calculations are not repeated. The objective is to provide an equitable comparison of road density and the total number of detected vehicles, enabling the determination of whether the road is crowded. To accomplish this, the algorithm incorporates the Non-Max Suppression function, which displays bounding boxes around objects with confidence values and counts the objects within each box. Even when objects are nearby, the algorithm tracks them effectively, thanks to the support of the Darknet Algorithm. The main capabilities of this algorithm for improving vehicle detection include enhanced accuracy, speed, and generalization ability. Typically, it is used in conjunction with the You Only Look Once (YOLO) object detection framework. Five convolutional neural network models are tested to assess the algorithm's accuracy: YOLOv3, YOLOv4, CrResNext50, DenseNet201-YOLOv4, and YOLOv7-tiny. The training process utilizes the Darknet Algorithm. The best-performing models, YOLOv3 and YOLOv4, achieve exceptional accuracy and F1 scores of up to 99%. They are followed by CrResNext50 and DenseNet201-YOLOv4, which achieve accuracy rates of 92% and 98% and F1 scores of 94% and 98%, respectively. The YOLOv7-tiny model achieves an accuracy rate and F1 score of 86% and 88%, respectively. Overall, the results demonstrate the algorithm's success in accurately detecting and calculating the volume of vehicles and other objects in a busy intersection. This makes it a valuable tool for regional government decision-making.
Identification of Indonesian Traditional Foods Using Machine Learning and Supported by Segmentation Methods Rangkuti, Abdul Haris; Kerta, Johan Muliadi; Mogot, Roderik Yohanes; Athala, Varyl Hasbi
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2545

Abstract

Traditional food is essential in preserving cultural heritage and is a vital part of Indonesian cuisine. In this research, we implement a methodology to identify the traditional Indonesian food using machine learning algorithms supported by various segmentation methods. This research aims to provide an efficient and accurate approach to classifying traditional foods, which can contribute to promoting and preserving Indonesia's culinary heritage. To conduct this research, we conducted experiments on 34 types of conventional Indonesian food originating from various provinces in Indonesia. The analysis of food images involved several segmentation algorithms, including Sobel, Prewitt, Robert, Scharr, and Canny filters. After the segmentation process, we proceeded with feature extraction and classification using traditional machine learning algorithms such as the Random Forest algorithm, Decision Tree, and derivatives of the SVM algorithm. These algorithms aimed to recognize the 34 types of traditional food. After conducting several experiments, we found that Random Forest with Robert's segmentation method was the highest-performance algorithm. It produced extraordinarily accurate results on the test dataset, with an accuracy performance of 85.52%, recall of 84.63%, precision of 83.77%, and an f1 score of 82.49%. Additionally, the best-performing algorithms with execution time averaged less than 1 minute. Another experimental result showed that the Random Forest algorithm with the Canny operator achieved an accuracy of 81.51%, recall of 84.97%, precision of 86.8%, and an f1 score of 85.61% on the test dataset. Furthermore, the Random Forest algorithm with the Sobel operator achieved accuracy results of 78.4%, recall of 65.3%, precision of 62.3%, and an f1 score of 63.71%.  In the SVM algorithms derivative, the Sigmoid SVM combined with the Scharr operator achieved the highest performance in its category across all classification metrics. In conclusion, this research offers valuable insights into classifying traditional Indonesian dishes using traditional machine learning algorithms. Simultaneously, this research aims to promote the appropriate and effective preservation and recognition of traditional Indonesian food.
Upaya Pengembangan Daerah Wisata: Strategi Pengembangan UKM Indonesia Melalui Kerjasama Perguruan Tinggi dengan Pemerintah Sudrajat, Jajat; Rangkuti, Abdul Haris; Tjokrowerdojo, Adrianto Wibowo; Morika, Doni; Wijanto, Natalia Marijani
Altasia Jurnal Pariwisata Indonesia Vol. 5 No. 2 (2023): Jurnal ALTASIA (Agustus)
Publisher : Program Studi Pariwisata - Universitas Internasional Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37253/altasia.v5i2.7896

Abstract

Menindaklanjuti dan mendukung Peraturan Presiden Republik Indonesia Nomor 2 Tahun 2022 (Perpres 2/2022) tentang Pembinaan Kewirausahaan Nasional Tahun 2021 – 2024. Permasalahannya Perpres ini berlaku sampai tahun 2024, sehingga Kementerian Koperasi dan Usaha Kecil dan Menengah (KEMENKOPUKM) Republik Indonesia mengundang perguruan tinggi negeri maupun perguruan tinggi swasta yang tergabung dalam Aliansi Program Studi Kewirausahaan Indonesia (APSKI) untuk mengadakan Workshop Kewirausahaan Pusat Kewirausahaan Nasional. Tujuan dari penelitian ini adalah untuk memberikan masukan kepada pemerintah dalam membuat Kebijakan Pengembangan Kewirausahaan Nasional dalam Rencana Pembangunan Pemerintah melalui KEMENKOPUKM. Metode penelitian yang digunakan dalam penelitian ini adalah metode Research and Development (R&D) melalui Focus Group Discussion dengan 59 perguruan tinggi yang tergabung dalam APSKI. Hasil dari penelitian ini adalah APSKI mendorong Program Studi Kewirausahaan untuk melakukan branding kewirausahaan dan menjadikan pertumbuhan kewirausahaan sebagai salah satu Indikator Kegiatan Utama. Sebagai studi kasus dalam penelitian ini, Prodi Kewirausahaan Binus Bandung bekerjasama dengan Kelurahan Batununggal Kota Bandung untuk menata dan melengkapi Taman Kota Kelurahan Batununggal sebagai salahsatu solusi membranding UKM di sekitar Taman Kota dan mendukung pengembangkan infrastruktur wisata kuliner Kota Bandung, sebagai model pengembangan ekosistem kewirausahaan dan pengembangan Pariwisata Kota Bandung. Hasil penelitian di taman Kelurahan Batununggal Kota Bandung, banyak infastruktur yang harus ditingkatkan pemeliharaannya, sehingga pengunjung akan lebih nyaman dan UKM yang tertata di sekitar taman kota akan meningkatkan kunjungan wisatawan.
Enhancing touchless smart locker systems through advanced facial recognition technology: a convolutional neural network model approach Rangkuti, Abdul Haris; Tanuar, Evawaty; Yapson, Febriant; Sijoatmodjo, Felix Octavio; Athala, Varyl Hasbi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3262-3273

Abstract

As the world recovers from COVID-19, demand for contactless systems is increasing, promising safety and convenience. Touchless technology, particularly public locker security systems that use facial recognition and hand detection, is advancing rapidly. The system minimizes physical contact, increasing user safety. It uses advanced models such as multi-task cascaded convolutional networks (MTCNN) and RetinaFace, FaceNet512, ArcFace, and visual geometry group (VGG)-Face for face detection and recognition, with a combination of RetinaFace, ArcFace, and L2 norm Euclidean or cosine as the most effective distance metric method, where the accuracy reaches 96 and 90%. 'Yourvault', an application demonstrating this efficient security feature, provides notifications for mask detection, facial authenticity and locker status, offering a solution to the problem of convenience and security of public spaces. Future research could investigate the impact of photo age on facial recognition accuracy, potentially making touchless systems more efficient. In general, the application of this technology is an important step towards a safer and more comfortable world after the pandemic. This model approach can be followed up with more optimal facial recognition.
Automated Detection of Molting Crabs Using YOLO: Enhancing Efficiency in Soft-Shell Crab Aquaculture Saputra, Dany Eka; Rangkuti, Abdul Haris; Dwi Putra, Sulistyo Emantoko; Daru Kusuma, Purba; Kurniawan, Albert; Gabriela, Melanie
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3468

Abstract

Crab molting detection is a crucial process in aquaculture, particularly to produce soft-shell crabs, which are considered a delicacy in many markets. Traditional methods of manually monitoring crabs for molting are labor-intensive and susceptible to human error. To address this challenge, this study examines the application of the YOLO (You Only Look Once) object detection model for automating the detection of molting crabs. YOLO is renowned for its capability to perform real-time object detection, making it an ideal choice for this application. Our research focuses on developing a YOLO-based system that accurately identifies molting crabs from videos or images captured in farming environments. The model was trained on a comprehensive dataset comprising images of crabs at various stages of molting, ensuring robustness against environmental variations and different lighting conditions commonly encountered in aquaculture settings. The results indicate that the YOLO model achieves high accuracy in detecting molting crabs, significantly enhancing the efficiency and reliability of the detection process compared to manual observation and other machine learning approaches. These advancements facilitate timely intervention and harvesting, which are critical for optimizing the quality and yield of soft-shell crabs. In our experiments, the recognition of the crab molting process was categorized into three classes: the molting crab, the crab skin, and the newly molted crab. Overall, the YOLOv8 and YOLOv11 models demonstrated impressive performance, achieving an average accuracy of 96% to 98%. This research on molting crab detection has proven successful and can be further extended to include other types of crabs.
Co-Authors Albert Kurniawan, Albert Athala, Varyl Hasbi Dwi Putra, Sulistyo Emantoko Gabriela, Melanie Jajat sudrajat Johan Muliadi Kerta, Johan Muliadi Mogot, Roderik Yohanes Morika, Doni Purba Daru Kusuma Saputra, Dany Eka Sijoatmodjo, Felix Octavio Tanuar, Evawaty Tjokrowerdojo, Adrianto Wibowo Wijanto, Natalia Marijani Yapson, Febriant