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All Journal Gaung Informatika Jurnal Ilmiah Teknik Industri INFORMATIKA Aptisi Transactions on Technopreneurship (ATT) Jurnal Ilmiah Sinus Go Infotech: Jurnal Ilmiah STMIK AUB JICS : Journal Of International Community Service Jurnal Pendidikan Teknologi Informasi dan Komunikasi Prosiding Seminar Nasional Indonesia INTERNATIONAL JOURNAL OF FINANCIAL ECONOMICS (IJEFE) Journal of Sustainable Software Engineering and Information Systems
Haryanto, Haryanto
Program Studi Sistem Komputer, Universitas Dharma AUB Surakarta
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Improving the Classification Accuracy of Parang Batik Motifs with High Visual Similarity Through the Integration of GLCM and MobileNetV2 Haryanto; Husna Sarirah Husin
Journal of Sustainable Software Engineering and Information Systems Vol. 2 No. 1 (2026): Journal of Sustainable Software Engineering and Information Systems
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jsseis.v2i1.158

Abstract

Background: Despite its high aesthetic value, automatic classification of Parang Surakarta batik is difficult due to the extreme textural similarities between sub-motifs. Standard CNN architectures, including MobileNetV2often fail to detect the subtle textural details that distinguish each variation of the motif. Aims: This study develops a hybrid classification model that combines manual and automated spatial texture features to improve identification accuracy on motifs with high visual similarity. Methods: Using a dataset that has been expanded to 120 original images (40 per class) which is then augmented to a total of 1,200 images to ensure stronger model generalization. This methodology hybrid GLCM-MobileNetV2architecture through transfer learning techniques. Features from both methods are combined through feature fusion before being classified using a Dense layer. Result: The hybrid GLCM-MobileNetV2model achieved an accuracy of 99%. This performance outperformed the pure MobileNetV2 method (66.67%) and GLCM-SVM (85%), demonstrating that texture features provide significant discriminatory power against similar repetitive patterns. Conclusion: The integration of GLCM and MobileNetV2 is highly effective for classifying visually similar batik motifs, achieving a superior accuracy of 99% compared to the pure MobileNetV2 (66.67%). This hybrid approach provides a robust and efficient solution for digital cultural preservation on mobile devices.
A Lightweight Hybrid GLCM–MobileNetV2 Model for Batik Motif Recognition in Digital Cultural Learning Environments Haryanto Haryanto; Husna Sarirah Husin; Hartini Hartini; F.R Desiana Kardha; Widyo Ari Utomo
JENTIK : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Vol. 5 No. 1 (2026): Jurnal Pendidikan Teknologi Informasi dan Komunikasi
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jentik.v5i1.637

Abstract

Background: Automatic identification of Surakarta Parang batik motifs presents significant challenges due to the high visual similarity among sub-motifs, where conventional Convolutional Neural Network (CNN) architectures often fail to capture fine-grained texture characteristics.Purpose of Study: A lightweight hybrid model is proposed to integrate 24 GLCM-derived texture features with MobileNetV2 spatial descriptors through a feature fusion strategy to improve motif classification accuracy.Methodology: The proposed methodology employs a hybrid feature extraction strategy, where 24 texture descriptors consisting of six statistical parameters (Contrast, Correlation, Homogeneity, Dissimilarity, ASM, and Energy) calculated across four orientations (0°, 45°, 90°, and 135°) with 1,280 deep spatial features obtained from the MobileNetV2 backbone.Main Findings: Experimental results demonstrate that the proposed hybrid model achieves an accuracy of 99%, representing a substantial performance gain over the baseline MobileNetV2 model (66.67%) and the GLCM-SVM approach (85%). These results indicate that the integration of statistical texture descriptors and deep spatial features notably enhances the recognition of complex batik patterns. Furthermore, the findings suggest that this feature fusion approach is highly effective in resolving the intricate geometric similarities of Parang sub-motifs, providing a more reliable and efficient alternative to standard deep learning models for fine-grained classification tasks.Novelty/Originality of This Study: The novelty of this study lies in the implementation of a feature fusion strategy that compensates for the limitations of lightweight CNNs in texture recognition by incorporating classical statistical descriptors, specifically tailored for the intricate patterns of Parang batik.
AI-BASED DECISION MAKING IN MACRO AND MICROECONOMICS: TOWARD OPTIMAL EFFICIENCY Loso Judijanto; Bahrun Thalib; Haryanto; Al-Amin
Prosiding Seminar Nasional Indonesia Vol. 1 No. 3 (2024): Prosiding Seminar Nasional Indonesia
Publisher : CV. Adiba Aisha Amira

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Abstract

In the last decade, Artificial Intelligence (AI) has moved from being a futuristic concept to a critical component of economic decision-making. The use of AI has been extended to various aspects of the economy, ranging from strategic decision-making at the firm level to macroeconomic policy at the government level. This study aims to examine the impact of AI on decision-making in macro and microeconomics, and understand how optimal efficiency can be achieved through the implementation of this technology. The study conducted in this research utilizes the literature research method. The results of this study show that AI has the potential to increase economic growth due to increased productivity and operational efficiency. At the macro level, AI contributes to more accurate policy planning and efficient resource management. At the micro level, AI supports businesses in gaining competitive advantage through supply chain optimization, personalization of service offerings, and better customer data management. However, the findings also emphasize the importance of addressing ethical, privacy, and accessibility challenges to ensure that the benefits of AI are widely and equitably enjoyed.
Co-Authors Afifah Nur Pratiwi Afrizal Akhsin Widi Putra Al-Amin Al-Amin Aldri Frinaldi Ambar Saputri Anwar, Muhammad Rehan Baehaqi Bahrun Thalib Bambang Priyatmoko Budhi Sumboro Cahyo Nugroho, Ernes Dessyana Kardha F.R Desiana Kardha Hadenan Towpek Hafidh Munawir Hartati Dyah Wahyuni, Hartati Dyah Hartini Hartini Haryo Basuki Husna Sarirah Husin Husna Sarirah Husin Isadora Nugroho Jefri Rieski Triyanto Kristono Kristono Kristono Kristono Loso Judijanto Lucky Mahesa Yahya Moh. Imron Rosidi Muhammad Abdul Aziz Muhammad Fadhil Prathama Diva Wiranata Ratna Herawati Ratna Herawati Roffi Rengga Waryatko Silvia Ekasari Siti Maesaroh Siti Nandiroh siti Nurindah Sari Sugeng Widada Wahyuningsih, Hartati Dyah Widyo Ari Utomo