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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Building of Informatics, Technology and Science JATI (Jurnal Mahasiswa Teknik Informatika) Journal of Computer Networks, Architecture and High Performance Computing
Rahman, Ben
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering

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Object Detection Using YOLOv5 and OpenCV Subagja , Mifta; Rahman, Ben
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 13 No. 1 (2025): Maret 2025
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v13i1.10772

Abstract

Object detection is one of the main tasks in computer vision, aimed at recognizing and localizing objects in images or videos. In this study, we utilize the YOLOv5 model, which is well known for its efficiency in realtime object detection. We implement this method with the help of the OpenCV library for image processing. This research aims to evaluate the performance of YOLOv5 in detecting objects in various types of images, including landscape photos, cat photos, and traffic light images with vehicles. The model is trained using optimization methods with the Adam optimizer and assessed through metrics like accuracy, precision, recall, and IoU. The results indicate that YOLOv5 can detect objects with high accuracy and fast inference time, making it an ideal solution for various applications such as security monitoring, video analysis, and automatic recognition systems. The advantage of YOLOv5 over traditional methods such as histogram equalization and thresholding lies in its ability to perform realtime detection with optimal computational efficiency. Thus, this study demonstrates that YOLOv5 is a suitable choice for implementing deep learningbased object detection systems.
Point of Sale Framework-Based Code Igniter and Model View Controller Using Lighthouse Testing Syaifudin, Muhammad; Fauziah, Fauziah; Rahman, Ben
Journal of Computer Networks, Architecture and High Performance Computing Vol. 3 No. 2 (2021): Journal of Computer Networks, Architecture and High Performance Computing, July
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v3i2.1006

Abstract

The success of companies engaged in the field of distributors is not only from good and smart human resources, but how ready the company's way to provide tools or programs supporting the work of employees. Some of the problems are the administration system between the company's internal divisions, so there are restrictions on the processing of input data, processes, outputs that are still manual. Web Application is made to facilitate employees to conduct administrative activities with the access role of each user admin division, for example inventory data, creation of sales invoices, road letters, procurement forms and bookkeeping. In addition, The Web View application is already responsive in this sense can adjust the device PC / Tablet / Mobile, then for visualization of data calculation already using cart / data graphics in real time, and there is also a feature preview, download and print JPEG, PNG, PDF, Barcode and QR code. Web Testing This application with black box method and shows it is running well and no error method, so it can be declared passed. Web Creation This application involves the LTE 2.0 Admin Template with bootstrap CSS, for programmer frameworks using MVC model view controllers that exist in the Code Igniter 3.0 framework so that it is faster and easier to implement or developed in other field companies or in the manufacturing industry. Tests using lighthouse for perform by 94% showed that the application designed to have a good performance for display results, and its access value of 60%, indicating that the process related to responsiveness between users is appropriate both using PC / Tablet / Mobile
PEMANFAATAN MACHINE LEARNING DALAM MENGOPTIMALKAN STRATEGI PENJUALAN BERBASIS E-COMMERCE UNTUK MENINGKATKAN PENDAPATAN UMK Budiman, Tegar; Rahman, Ben
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 3 (2025): JATI Vol. 9 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i3.13463

Abstract

Usaha Mikro Kecil (UMK) di Kota Tangerang Selatan memiliki peran penting dalam perekonomian lokal, dengan kontribusi terhadap pertumbuhan ekonomi sebesar 2,62% pada tahun 2024. Namun, pelaku UMK masih menghadapi tantangan dalam permodalan, perizinan, dan adaptasi terhadap digitalisasi. Penelitian ini berfokus pada pengaruh e-commerce terhadap strategi penjualan UMK di Kota Tangerang Selatan dengan pendekatan kuantitatif berbasis data. Data dikumpulkan melalui survei kepada pelaku UMK dan dianalisis menggunakan Random Forest Regressor, yang menunjukkan bahwa e-commerce berkontribusi sebesar 89,9% terhadap strategi penjualan. Hasil penelitian menunjukkan peningkatan rata-rata pendapatan UMK dari 30.74 menjadi 63.34 setelah menggunakan e-commerce, dengan strategi personal selling sebagai pendekatan paling efektif. Persebaran UMK menunjukkan Ciputat sebagai pusat aktivitas tertinggi, sedangkan Serpong memiliki jumlah UMK paling sedikit. Penelitian ini diharapkan menjadi referensi bagi pengambilan kebijakan yang mendukung optimalisasi strategi digital bagi UMK di Kota Tangerang Selatan.
Enhancing Coffee Marketing Strategies through Multi-Criteria Decision-Making Andryana, Septi; Wahyuddin, Mohammad Iwan; Rahman, Ben; Putra, Abdul Rahman Wijaya; Mantoro, Teddy
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3282

Abstract

Coffee is a globally preferred beverage, and Indonesia, as a major supplier, provides a wide variety of high-quality coffee varieties with unique characteristics from each region. East Manggarai, East Nusa Tenggara, Indonesia, produces Colol coffee, a high-quality variety with unexplored market potential. The marketing of Colol coffee faces significant challenges, including limited accessibility, lack of market information, and inadequate logistics infrastructure. A comprehensive marketing strategy necessitates the consideration of numerous criteria, which generate a range of alternative decisions to identify the marketing area. This study proposes a framework to optimize the marketing strategy of Colol coffee using the MCDM (Multi-Criteria Decision-making) approach, which integrates AHP, SMARTER, and TOPSIS methods. This framework is applied to rank marketing areas in 38 provinces in Indonesia based on five criteria, namely, accessibility, market potential, logistics, environmental conditions, and safety. The results show that the MCDM method can increase the effectiveness of marketing strategies. The top three alternative coffee marketing regions are Papua, East Kalimantan, and South Papua, with eigenvalues of 0.0569, 0.0424, and 0.0421. With incomplete data, in some marketing areas, it is a challenge to integrate multiple MCDM methods to have a better ranking that represents the real world of marketing strategy. This study supports the enhancement of the digital economy in the agricultural sector. It provides a meaningful understanding of the application of MCDM in marketing agricultural products, with far-reaching implications for marketing strategies in similar sectors.
Co-Authors Abdul Karim Adinda, Saskia Ahyuna Ahyuna, Ahyuna Aris Gunaryati, Aris Budiman, Tegar Fauziah Fauziah Fifto Nugroho Gustian, Yonathan Wijaya Handayani, Adelia Putri Hindarto, Djarot I Wayan Sugianta Nirawana Iskandar Fitri, Iskandar Jayanti, Riza Dwi Muhammad Syaifudin Putra, Abdul Rahman Wijaya Santoso, Handri Saputra, Adi Dwifana Septi Andryana Shakaramiro, Muhammad Ariel Sjah, William Surya Subagja , Mifta Suherman, Endang Teddy Mantoro Wahyuddin, Mohammad Iwan Wedha, Alessandro Benito Putra Bayu Wedha, bayu Yasa