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All Journal TEKNIK INFORMATIKA JTEV (Jurnal Teknik Elektro dan Vokasional Jurnal Pengabdian Kepada Masyarakat Sakai Sambayan Jurnal Pengabdian Kepada Masyarakat (Indonesian Journal of Community Engagement) JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknik Informatika UNIKA Santo Thomas Jurnal TIPS : Jurnal Teknologi Informasi dan Komputer Politeknik Sekayu IJISTECH (International Journal Of Information System & Technology) Jurnal Pendidikan dan Konseling Jurnal Berdaya Mandiri Journal of Intelligent Decision Support System (IDSS) JUKI : Jurnal Komputer dan Informatika Suluah Bendang: Jurnal Ilmiah Pengabdian Kepada Masyarakat Jurnal Teknik Informatika (JUTIF) IJISTECH Jurnal Teknologi dan Sistem Tertanam Parta: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Pengabdian Masyarakat Bhinneka Electrician : Jurnal Rekayasa dan Teknologi Elektro Jurnal Pengabdian Pada Masyarakat Jurnal Relawan dan Pengabdian Masyarakat REDI TeknoKreatif: Jurnal Pengabdian kepada Masyarakat
Afriansyah, Aidil
Informatics Engineering Study Program, Institut Teknologi Sumatera
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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Optimizing Driving Completeness Prediction Models: A Comparative Study of YOLOv7 and Naive Bayes at Institut Teknologi Sumatera Algifari, Muhammad Habib; Ashari, Ilham Firman; Nugroho, Eko Dwi; Afriansyah, Aidil; Vebriyanto, Mario
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.6761

Abstract

The number of vehicles in Indonesia is increasing every year. The number of motor vehicle accidents in 2022 will be more than 100,000. It is hoped that several regulations regarding motorbike rider equipment will increase awareness of rider safety. By utilizing image recognition technology developed with artificial intelligence, it is possible to create digital image processing models or images that are fast and accurate for detecting driving equipment. The object detection model developed uses a dataset in the form of images of motorists who want to enter ITERA through the main gate. The object detection model will also be integrated with the classification model to create a program that can detect motorbike rider equipment, such as mirrors, helmets, not wearing a helmet, shoes, not wearing shoes, open clothes, and closed clothes. After detecting motorized rider equipment in the classification area, the results will be transferred to a classification model to determine the level of safety for motorized riders, either insufficient or sufficient safety. The test results show that the optimal object detection model was found at an epoch value of 70 with a batch-size of 16, producing a mAP value of 0.8914. The optimal classification model uses the naive Bayes method which has been trained with a dataset of 62 data and achieves an accuracy of 94%.
Development of YOLO-Based Mobile Application for Detection of Defect Types in Robusta Coffee Beans Nugroho, Eko Dwi; Verdiana, Miranti; Algifari, Muhammad Habib; Afriansyah, Aidil; Firmansyah, Hafiz Budi; Rizkita, Alya Khairunnisa; Winarta, Richard Arya; Gunawan, David
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8886

Abstract

Improving the quality of Robusta coffee beans is a crucial challenge in the coffee industry to ensure that consumers receive high-quality products. However, the identification of defects in coffee beans is still largely performed manually, making the process error-prone and time-consuming. This study aims to develop a YOLO-based mobile application to detect defects in Robusta coffee beans quickly and accurately. The method employed in this study is YOLO, a deep learning-based object detection algorithm known for its real-time object detection capabilities. The application was tested using a dataset of Robusta coffee beans containing various defects, such as broken, black, and wrinkled beans. The test results indicate that the application achieves high detection accuracy, with the black bean class achieving 95.3% accuracy, while the moldy or bleached bean class records the lowest accuracy at 62.2%. This application is expected to assist farmers and coffee industry stakeholders in improving the quality of Robusta coffee beans and enhancing the efficiency of the sorting process.
Co-Authors Agung Mahadi Putra Perdana Akram Abdillah, Attar Alfajrin, Achmad Chalid Afif Algifari, Muhammad Habib Andika Setiawan Andre Febrianto Anggraini, Leslie Annisa, Resty Apri Kurniawansyah Arham, Laode Bagaskara, Radhinka Chalid, Achmad AA Dani Hamdani Hamdani David Gunawan, David Duwi Harianto Duwi Hariyanto Dwi Nugroho, Eko Eko Dwi Nugroho Eko Nugroho Ermida Silitonga Fajar Paundra Febrianto, Andre Firmansyah, Hafiz Budi Firmansyah, Hafiz Budi Habib Algifari, Muhammad Ichwani, Arief Idris, Mohamad Ilham Firman Ashari Kartadilaga, Muhammad Qaessar Kisna Pertiwi Kisna Pertiwi, Kisna Lathifa Putri Afisna Lorini, Valerio M. Rizky Hikmatullah M. Yafi Fahmi Miranti Verdiana Mufidah, Zunanik Muhammad Nadhif Athalla Naufal Raki Nela Agustin Kurnianingsih Nur'azmi, Eka Praseptiawan, Mugi Radhinka Bagaskara Rahman Indra Kesuma Resty Annisa Resty Annisa Resty Annisa Annisa Rinaldi, Daniel Rio Ariestia Pradipta Rizkita, Alya Khairunnisa Sabar Sabar Sabar Solly Aryza Sophia Nouriska Untoro, Meida Cahyo Utoro, Meida Cahyo Vebriyanto, Mario Verdiana, Miranti Verlina Agustine Winarta, Richard Arya Winda Yulita Yoga Naufal Ray Putro Yulita, Winda