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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
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 Public Health Social Sciences Other

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Identification of the Sub-motifs of Batik Kawung Using Deep Learning Sunarko, Budi; Subiyanto, Subiyanto; Wibawanto, Hari Wibayanto; Zakaria, Alfanza Rizky Zakaria; Alifian, Alifian; Muhammad, Naufal Muhammad; Rismawan, Yudha Andriano 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.
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.
Co-Authors Abdurrakhaman Hamid Al-Azhari Agung Adi Firdaus Aisya Fathimah Alfa Faridh Suni Alfian Arsyad Alifian, Alifian Aminuddin, Raihah Anan Nugroho Ananda, Briska Putra Anggraini Mulwinda Apriansyah Wibowo Ardiansyah, Muhammad Irfan Baehaqi Bagaskoro Saputro Baroroh, Luluk Taufiqul Citra Nur Aini Deyndrawan Sutrisno Djuniadi Djuniadi Dwi Oktaviyanti, Dwi Endang Widiyawati Hakiki, Muhammad Khikam Hanrian Rossa Hari Wibawanto Hudallah, Noor I Gede Bagus Jayendra Ibrahim, Shafaf Khakim Assidiqi Nur Hudaya MARIO NORMAN SYAH Md Ghani, Nor Azura Mohd Fuad, Nur Afiqah Muhammad, Naufal Muhammad, Naufal Muhammad Mukhamad Farkhan Noor Hudallah Noor Hudallah Nugroho, Anan Nugroho, Anan Nur Iksan Rahmawati, Desi Ria Wulansarie, Ria Riana Defi Mahadji Putri Rina Rachmawati Rismawan, Yudha Andriano Rismawan Rizky Ajie Aprilianto Savitri, Dina Wulung Siti Ridloah Supraptono, Eko Suswitaningrum, Ema Syahroni Hidayat Uswatun Hasanah Uswatun Hasanah USWATUN HASANAH Vera Noviana Sulistyawan Wibawanto, Hari Wibayanto Wiwik Handayani Yudha Andriano Rismawan Zakaria, Alfanza Rizky Zakaria