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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Teknik Elektro AGRIVITA, Journal of Agricultural Science JURNAL KESEHATAN LINGKUNGAN (Journal of Environmental Health) Edu Komputika Journal Jurnal Teknologi dan Sistem Komputer Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer International Journal of Artificial Intelligence Research JMM (Jurnal Masyarakat Mandiri) Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer SAP (Susunan Artikel Pendidikan) Jurnal Riset Rekayasa Elektro Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Journal of Applied Data Sciences Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Informatika Polinema (JIP) International Journal Research on Metaverse Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika
Budi Sunarko
Universitas Negeri Semarang
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Library & Information Science Mechanical Engineering Public Health Social Sciences Other

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Ray Tracing-Based Modeling of Bifacial Photovoltaic Systems in Greenhouse Agrivoltaics Endang Widiyawati; Subiyanto Subiyanto; Siti Ridloah; Budi Sunarko; Bagaskoro Saputro; Rizky Ajie Aprilianto; Mario Norman Syah; Abdurrakhaman Hamid Al-Azhari; Deyndrawan Sutrisno; Aisya Fathimah; Apriansyah Wibowo; I Gede Bagus Jayendra
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 15 No. 2 (2026): April 2026
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtepl.v15i2.510-524

Abstract

This work presents an enhanced ray tracing-based modeling framework to optimize bifacial photovoltaic energy generation and crop productivity within greenhouse environments. The proposed framework integrates a ray tracing-based optical and electrical model to simulate light dynamics and energy generation within greenhouse structures. The optical model incorporates Uniform Distribution of rear-irradiance (UF) and Non-Uniform Distribution of rear-irradiance (NUF) principles to simulate irradiance distribution, shading, and reflection, using Light Saturation Point (LSP) and Photosynthetically Active Radiation (PAR) measurements. The electrical model estimates energy yield using the LambertW function based on incident and transmitted light through photovoltaic arrays. Five types of greenhouse structures using plastic and SG80 materials are analyzed to assess their impact on system performance under various conditions. The evaluation showed that integrating bPV increased rear-side energy captured by 25-30%. The optimal configuration was achieved by combining a plastic cover with a checkerboard pattern, resulting in up to 5% higher performance than the 35° tilt setup and offering enhanced light distribution uniformity. Although the average soil irradiance of 170.801 W/m² slightly exceeded the light saturation threshold of 164.7 W/m², it remained within a safe range that supports efficient photosynthesis without causing photoinhibition.
Computer vision pipeline implementation: Acne lesion segmentation with SwinUnet and multi-class classification with swin transformer Syarif Romadloni; Asri Kurnia Ramadhani; Fafian Ihsan Saputra; Humam Nasywa Fawazi; Muhammad 'Ainun Naja; Budi Sunarko
TEKNOSAINS : Jurnal Sains, Teknologi dan Informatika Vol 13 No 2 (2026): TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika
Publisher : LPPMPK- Universitas Muhammadiyah Cileungsi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37373/tekno.v13i2.2114

Abstract

Acne is a common dermatological problem and often requires accurate lesion identification to support the diagnosis and appropriate treatment. Advances in deep learning and computer vision technologies offer opportunities to develop automated systems capable of detecting and classifying acne lesions more objectively and efficiently. This study aims to develop a two-stage computer vision pipeline for automatic acne lesion detection and classification by integrating the Swin-UNet model for semantic segmentation and the Swin Transformer for multi-class classification. The approach used is a Transformer-based cascaded pipeline architecture, where the segmentation results are used as a guide for Regions of Interest (ROIs) in the classification stage so that the classification process focuses on relevant lesion areas. To address class imbalance and improve model generalization, a combination of Weighted Random Sampling, Mixup data augmentation, and Sharpness-Aware Minimization (SAM) algorithms are applied. The evaluation process is carried out using a dataset strictly separated into training, validation, and testing data. Experimental results showed that the segmentation model achieved a Dice coefficient of 0.9885 and an Intersection over Union (IoU) of 0.9788. Meanwhile, the classification model achieved an accuracy of 96.24% with an F1-score of 0.9629. These findings demonstrate that the proposed system is effective in identifying and classifying acne lesions with precision. Therefore, this approach has the potential to serve as the basis for developing a more accurate and reliable deep learning-based dermatology diagnostic support system.
Pengujian Kinerja Sistem Pendeteksi Kebocoran Kompor Gas LPG Berbasis Internet of Things pada Kondisi Lingkungan yang Berbeda Anggraini Mulwinda; Budi Sunarko; Riana Defi Mahadji Putri; Agus Suryanto; Dhea Puspitasari
Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer Vol. 6 No. 1 (2026): Maret
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/juritek.v6i1.6657

Abstract

Liquefied Petroleum Gas (LPG) serves as the primary cooking fuel in Indonesia, with around 88.59% of households relying on gas energy for their daily cooking tasks. The significant reliance on LPG raises the threat of gas leaks, which can cause fires and jeopardize the safety of users. Consequently, there is a need for a dependable early detection system that can deliver swift alerts and facilitate remote monitoring. This research aims to create and develop a gas leakage detection system utilizing Internet of Things (IoT) technology, incorporating an MQ-2 gas sensor and an ESP32 microcontroller. The methodology used in this study follows a Research and Development (R&D) approach, which includes stages of planning, production, and evaluation. The system features local alarms comprised of LEDs and a buzzer, alongside real-time monitoring and notification capabilities through the Blynk application. Experimental results show that the MQ-2 sensor is able to detect LPG gas concentrations in the range of approximately 410 PPM to over 2000 PPM. Performance evaluation indicates that the system responds faster and more consistently in a closed-space environment, with an average response time of ±3.8 seconds, compared to an open-space environment with an average response time of ±5.8 seconds. These findings demonstrate that environmental conditions significantly affect system performance, and the proposed system is effective as an early warning tool for LPG gas leakage in household environments.
Identification of the Sub-motifs of Batik Kawung Using Deep Learning Budi Sunarko; Subiyanto Subiyanto; Hari Wibayanto Wibawanto; Alfanza Rizky Zakaria Zakaria; Alifian Alifian; Naufal Muhammad Muhammad; Yudha Andriano Rismawan Rismawan
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i2.5818

Abstract

Batik is one of Indonesia’s cultural heritages, with motifs that are both diverse and intricate. The Kawung motif, characterized by repetitive circular patterns, is divided into sub-motifs such as Kawung Bribil, Kawung Sen, and Kawung Picis. Automatic classification of these sub-motifs is important for digital preservation but remains difficult due to subtle inter-class similarities. The aim of this research is to analyze the performance of VGG, ResNet, and DenseNet and determine the most effective CNN architecture in classifying the sub-motifs of Batik Kawung. The research method is a convolutional neural network-based image classification approach using a dataset of 300 Kawung Batik images evenly distributed across three classes. Preprocessing steps included grayscale conversion, resizing to 256 × 256 pixels, Canny edge detection, and normalization to the range [0,1]. The dataset was randomly split into 210 training, 60 validation, and 30 testing images. The results of this research are that VGG achieved the highest training accuracy of 97%, but only 67% on the testing set, indicating a tendency to overfit. In contrast, DenseNet achieved the best generalization performance with a testing accuracy of 80%, surpassing both VGG and ResNet. At the class level, DenseNet161 demonstrated consistent performance across all Kawung sub-motifs, with precision ranging from 67% to 91% and F1-scores between 71% and 95%. These results suggest that DenseNet161 not only performed effectively during training but also generalized well to unseen data, establishing it as the most robust architecture for sub-motif Batik Kawung classification. The results underscore the effectiveness of CNNs, particularly DenseNet, in classifying subtle batik sub-motifs. This research contributes to develope a reliable automated system for identifying Kawung batik, leveraging modern technology to support the preservation of Indonesia’s cultural heritage.
Co-Authors Abdurrakhaman Hamid Al-Azhari Agung Adi Firdaus Agus Suryanto Aisya Fathimah Alfa Faridh Suni Alfanza Rizky Zakaria Zakaria Alfian Arsyad Alifian Alifian Aminuddin, Raihah Anan Nugroho Ananda, Briska Putra Anggraini Mulwinda Apriansyah Wibowo Ardiansyah, Muhammad Irfan Asri Kurnia Ramadhani Baehaqi Bagaskoro Saputro Baroroh, Luluk Taufiqul Citra Nur Aini Deyndrawan Sutrisno Dhea Puspitasari Djuniadi Djuniadi Dwi Oktaviyanti, Dwi Endang Widiyawati Fafian Ihsan Saputra Hakiki, Muhammad Khikam Hanrian Rossa Hari Wibawanto Hari Wibayanto Wibawanto Hudallah, Noor Humam Nasywa Fawazi I Gede Bagus Jayendra Ibrahim, Shafaf Khakim Assidiqi Nur Hudaya MARIO NORMAN SYAH Md Ghani, Nor Azura Mohd Fuad, Nur Afiqah Muhammad 'Ainun Naja Muhammad, Naufal Mukhamad Farkhan Naufal Muhammad Muhammad Noor Hudallah Noor Hudallah Nugroho, Anan Nugroho, Anan Nur Iksan Rahmawati, Desi Ria Wulansarie, Ria Riana Defi Mahadji Putri Riana Defi Mahadji Putri Rina Rachmawati Rizky Ajie Aprilianto Savitri, Dina Wulung Siti Ridloah Subiyanto Subiyanto Supraptono, Eko Suswitaningrum, Ema Syahroni Hidayat Syarif Romadloni Uswatun Hasanah USWATUN HASANAH Uswatun Hasanah Vera Noviana Sulistyawan Wiwik Handayani Yudha Andriano Rismawan Yudha Andriano Rismawan Rismawan