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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic SISFOTENIKA Journal of Information Technology and Computer Science (JOINTECS) JURNAL MEDIA INFORMATIKA BUDIDARMA JITK (Jurnal Ilmu Pengetahuan dan Komputer) INTECOMS: Journal of Information Technology and Computer Science Ensiklopedia of Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Jurnal Mantik Aisyah Journal of Informatics and Electrical Engineering Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Teknik Informatika C.I.T. Medicom Journal of Computer System and Informatics (JoSYC) Journal of Computer Networks, Architecture and High Performance Computing Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ Jurnal Sains Teknologi dalam Pemberdayaan Masyarakat Jurnal Ilmiah Wahana Pendidikan KLIK: Kajian Ilmiah Informatika dan Komputer
Ben Rahman
Universitas Nasional
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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Object Detection Using YOLOv5 and OpenCV Mifta Subagja; Ben Rahman
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.
Facial Age Estimation on Asian Faces Using SE-ResNeXt50 and Skin Texture Analysis Hanni Deswita; Ben Rahman; Andrianingsih Andrianingsih; Agus Iskandar
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 14 No. 1 (2026): March 2026
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v14i1.12039

Abstract

Image-based facial age estimation is becoming an important component in biometrics and digital dermatology, but many deep learning approaches still rely on global facial features, making them less sensitive to micro changes on the skin surface, particularly on Asian faces which have distinct ageing patterns. This research offers a novel contribution by integrating SE-ResNeXt50 with skin texture analysis to produce more accurate and interpretable age estimations. The dataset used is APPA-REAL, which consists of 7,612 age-labeled Asian face images. After face detection, skin area cropping, size standardisation, and intensity normalisation, visual features were extracted using SE-ResNeXt50, which utilises a channel attention mechanism through Squeeze-and-Excitation blocks to amplify subtle ageing signals. In parallel, this study adds skin texture analysis based on quantitative indicators, namely wrinkle index, tone unevenness, shine proxy, and brightness, to represent the skin microstructure correlated with ageing. The performance of the method is evaluated using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results show that the combination of an attention-based deep network and skin texture indicators can improve the consistency of age prediction and provide a clearer basis for interpreting changes in skin texture on Asian faces. This finding strengthens the potential for developing an age estimation system that is not only precise but also relevant for digital skin monitoring applications and ageing evaluation.
K-Means for IT Asset Segmentation and Demand Forecasting Using Double Exponential Smoothing (DES) Anita aprilianti; Ben Rahman
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 14 No. 1 (2026): March 2026
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v14i1.12129

Abstract

IT asset management was a crucial aspect in supporting the smooth operation of a company. Poorly planned asset procurement resulted in asset shortages or excesses, which impacted cost efficiency. Frequent problems included the lack of asset grouping based on needs and difficulties in forecasting future IT asset demand. This study aimed to group IT assets using the K-Means method and to forecast IT asset demand using the Double Exponential Smoothing method. Asset grouping was used to assist companies in determining asset procurement priorities. This study used historical IT asset demand data for the period January 2024 to February 2025. The K-Means method was applied to group assets into three categories: submitted, need to consider, and not submitted. The Double Exponential Smoothing method was employed to forecast future IT asset demand by measuring the error rate using Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE). The results showed that the K-Means method helped companies determine IT asset management priorities, while the Double Exponential Smoothing method produced asset demand forecasts with low error rates, thereby supporting more accurate IT asset procurement planning.
Co-Authors Adam Firmansyah Adelia Putri Handayani Agung Triayudi Agus Iskandar Ahmad Aldi Andres Ardiansah Aldya Bagas Prahastyo Andrianingsih Angga Pradihta Anita Aprilianti Anita Setyowati Srie Gunarti Anita Setyowati Srie Gunarti Ardiyanto Wantudi Aris Gunaryati Ary Dwi Pantoro Bernardito Jordan Budi Dwi Kurniawan Cut Dinda Rizki Amirillah Daud Situmorang Fata Nidaul Khasanah Fatha Alsidqi Husaini Fauziah Fauziah Gilang Irfansyah Guntur Wicaksono Hanni Deswita Hasian, Irene Herlawati Herlawati Imam Abdurrahman Irwan Raharja Isfauzi Hadi Nugroho Iskandar Fitri Iskandar Fitri Jeperson Hutahaean Kiai Agus Priyaharto Mulia I M Iwan Wahyudin Maimunah Maimunah Mifta Subagja Mochamad Naufal Shofy Muhammad Ariel Shakaramiro Muhammad Raihan Azis Muhammad Ridwan Muhammad Syaifudin Mursyid Salim Nifea Kusumawardhani Nofri Yudi Arifin Novialdy Novialdy Nurul Mega Saraswati Rachmad Andri Atmoko Rafif Ramadhan Al Yarda Rahmadya Trias Handayanto Renita Oktaviani Irawan Ricky Andri Widayat Rifqi Firmansyah Rima Ruktiari Rito Cipta sigitta Hariyono Rizki Kurnia Romantika Sirait Saskia Adinda Septi Andriyana Septi Andryana Seta Samsiana Seta Samsiana Ucok Darussalam Ucuk Darusalam Ucuk Darusallam Ucuk Darussalam Wafiqah Setyawati Wahyuni Wahid Al Jufri Yusril Adam Mahendra