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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 International Journal of Advances in Artificial Intelligence and Machine Learning 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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User-Friendly Interface and Comprehensive Features for Hostel Management Kolan, Helini; Mungi, Keerthana; Somayajula, Lekhana; Achanta, Harshitha; Edulakanti, Vaishnavi; Haryanto, Haryanto
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 2 No. 3 (2025): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v2i3.456

Abstract

Background of study: Hostel administration in academic institutions has traditionally relied on manual processes such as paper-based record keeping, in-person registration, and ad-hoc maintenance communication. These fragmented practices often lead to inefficiencies, delays, miscommunication, and data inaccuracy. As student populations grow and operational demands increase, institutions require modernized systems that integrate automation, usability, and real-time information management to improve service delivery and resource allocation. However, existing solutions frequently lack comprehensive features, scalability, or user-centric design, indicating a clear gap in the availability of accessible and robust digital hostel management platforms.Aims: This study aims to design and implement a user-friendly, web-based Hostel Management System (HMS) that consolidates key administrative operations including student registration, room allocation, maintenance reporting, and occupancy tracking within a unified interface. The scope encompasses database design, workflow automation, interface usability, security provisions, and system evaluation through functional demonstrations.Methods: The system was developed following an Agile methodology, enabling iterative refinement based on user feedback. Dataset acquisition involved collecting student, room, facility, and maintenance information, followed by preprocessing steps such as data cleaning, normalization, and categorization to ensure accuracy. The architecture employed modular design principles, a web-based interface for multi-device accessibility, and security measures such as encrypted storage and role-based access control. Functional testing, integration testing, and user acceptance trials validated system performance and reliability.Result: The implemented HMS successfully automated core hostel processes improved real-time data access, and significantly reduced manual workload for administrative staff. Features such as automated room allocation, maintenance request tracking, virtual hostel viewing, and dashboard-based monitoring demonstrated high usability and operational effectiveness. User feedback indicated enhanced transparency, faster response times, and improved overall efficiency in hostel management.Conclusion: The proposed system provides a scalable, secure, and intuitive solution that modernizes hostel operations. By integrating comprehensive features within a user-friendly platform, the HMS enhances administrative productivity and student satisfaction. Its modular architecture and cloud-ready design position it for future enhancements, including AI-driven analytics, mobile integration, and predictive resource planning.
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.
MARKET SENTIMENT WITH ARTIFICIAL INTELLIGENCE: A REVOLUTION IN THE DIGITAL ECONOMY Lucky Mahesa Yahya; Silvia Ekasari; Haryanto
INTERNATIONAL JOURNAL OF FINANCIAL ECONOMICS Vol. 2 No. 4 (2025): APRIL
Publisher : CV. Adiba Aisha Amira

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Abstract

The digital age has brought about a massive transformation in many sectors, including market sentiment analysis. The existence of big data from the internet, especially social media and online reviews, requires advanced technology to process and analyze it. Artificial Intelligence (AI) with Natural Language Processing (NLP) and machine learning capabilities are key in this revolution, especially in identifying and interpreting public sentiment towards products, services, or brands. The research method used is literature by looking for references that are in accordance with the research context. The research findings show that the integration of AI in market sentiment analysis has significant potential in improving the understanding of consumer sentiment. AI not only accelerates the process of analyzing vast data, but also increases the accuracy in interpreting sentiments and emotions. In particular, the use of machine learning models has enabled the adaptation and continuous improvement of sentiment analysis performance, providing deeper and more predictive insights into market trends.
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): Forthcoming Issue
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.
Co-Authors Achanta, Harshitha 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 Dhidhi Pambudi Edulakanti, Vaishnavi F.R Desiana Kardha Fadly Fadly Hadenan Towpek Hafidh Munawir Hartati Dyah Wahyuni, Hartati Dyah Hartini Hartini Haryo Basuki Husna Sarirah Husin Husna Sarirah Husin Isadora Nugroho Jefri Rieski Triyanto Kolan, Helini Kristono Kristono Kristono Kristono Kurniawan, Muhammad Hafiz Loso Judijanto Lucky Mahesa Yahya Moh. Imron Rosidi Muhammad Abdul Aziz Muhammad Fadhil Mungi, Keerthana Prathama Diva Wiranata Ratna Herawati Ratna Herawati Roffi Rengga Waryatko Silvia Ekasari Siti Maesaroh Siti Nandiroh siti Nurindah Sari Somayajula, Lekhana Sugeng Widada Wahyuningsih, Hartati Dyah Widyo Ari Utomo