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All Journal Telematika : Jurnal Informatika dan Teknologi Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Pilar Nusa Mandiri Jurnal Bina Praja Bappenas Working Papers Jurnal Teknik Informatika (JUTIF) Buletin Poltanesa Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Malcom: Indonesian Journal of Machine Learning and Computer Science Lokomotif Abdimas: Jurnal Pengabdian kepada Masyarakat BACA: Jurnal Dokumentasi dan Informasi
Rianto, Yan
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences

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Journal : Malcom: Indonesian Journal of Machine Learning and Computer Science

 1 
Revolutionizing Corporate Event Planning with AI: A Cost-Efficiency Strategy for BuatEvent.id Supriyadi, Muhammad; Rianto, Yan
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.1929

Abstract

BuatEvent.id leverages an AI-driven platform for event planning, powered by Gemini.ai—a sophisticated NLP model with an accuracy rate of 92.5%. The system integrates multiple technologies, including PHP, Python, Golang, Flutter, and MySQL, to automate essential processes, achieving a 25% improvement in planning precision. This study aims to evaluate the role of AI in enhancing budget management and corporate event customization. By addressing the inefficiencies of conventional event planning, this platform optimizes workflows, enhances overall productivity, and offers a seamless user experience customized to cater to a wide range of client requirements. The results demonstrate a 92.5% accuracy in processing user queries and a 25% increase in event planning efficiency, highlighting the platform’s ability to deliver cost-effective and personalized solutions. These figures were obtained through internal testing using a dataset of 200 annotated user queries. The platform primarily targets corporate events, including workshops, product launches, and business meetings.For example, the system was successfully deployed during a corporate training event in Jakarta, where it reduced planning time by 30%.
AI-Powered: Leveraging Teachable Machine for Real-time Scanner Marcelly, Frizca Fellicita; Rianto, Yan
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.1931

Abstract

Effective inventory control is essential in optimizing profitability through cost control and efficiency expectations. Conventional inventory techniques frequently find it difficult to adjust to the fast-changing restaurant setting, resulting in surplus stock, inventory deficits, and unnecessary food waste. Nonetheless, a notable shift is approaching, as the incorporation of artificial intelligence (AI) may help address this issue. AI-powered inventory management systems help restaurants optimize stock levels, reduce waste, and predict demand more accurately, leading to improved efficiency and increased profitability. This study explores how AI-driven inventory management enhances efficiency, reduces waste, and automates restocking in the restaurant sector, with a particular focus on TastyGo's integration of Teachable Machine and TensorFlow Lite. The suggested solution uses picture recognition for real-time inventory tracking, and machine learning models to predict demand and replenishment automation. TastyGo can expedite supply chain management, save waste through predictive analytics, and improve its inventory by employing these AI techniques. This study shows how AI-driven solutions may boost decision-making, reduce food waste, and greatly increase operational efficiency, all of which can result in higher profitability. The findings highlight how AI technologies have the potential to revolutionize conventional inventory management systems in the restaurant industry.
Real-Time Road Damage Detection on Mobile Devices using TensorFlow Lite and Teachable Machine Nova, Lusindah; Rianto, Yan
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.1933

Abstract

This study presents a mobile-based road damage detection system using Teachable Machine and TensorFlow Lite to support real-time monitoring and efficient infrastructure maintenance. The system identifies road damage types such as cracks, potholes, and uneven surfaces. The RDD2020 dataset is used for model training, with preprocessing steps including augmentation, normalization, and resizing. A Convolutional Neural Network (CNN) model is trained through Teachable Machine for ease of customization. TensorFlow Lite is employed for on-device inference, with optimization techniques like quantization and pruning applied to improve speed and reduce model size. The system is evaluated using precision, recall, F1-score, and accuracy metrics under varying lighting and weather conditions. The final model is deployed in a mobile app using TensorFlow Lite Interpreter for efficient performance. Experimental results show high detection accuracy, with a precision of X% and F1-score of Y% (insert actual values). This approach offers a lightweight, cost-effective solution for road maintenance authorities and urban planners. Future enhancements include dataset expansion, integration with mapping tools, and improved robustness in diverse environments. Overall, the proposed system enables real-time, accurate road damage detection and supports smarter, eco-friendly infrastructure management.
Co-Authors Abdullah, Ifnu Abdul Aziz Abdurrahman, Muhammad Farhan Aldi, Kenny Antonius Yadi Kuntoro Cipta Endyana Dedi Priansyah Emma Rochima Fabiyanto, Dedik Fahlapi, Riza Gata , Windu Hanggoro, Dimas Banu Dwi Ida Widianingsih Laela Kurniawati, Laela Lindung Parningotan Manik Marcelly, Frizca Fellicita Muhammad Firmansyah, Muhammad Nasir Hamzah Nova, Lusindah Nugraha, Thomas Fajar Parningotan Manik , Lindung Prakoso Bhairawa Putera Pratama Putra, Zico Prawira, Nurul Adi Rika Wulandari Sadikin, Rifki Septiani, Riska Kurnia Sinta Ningrum Sunendar, Nendi Supriyadi, Muhammad Suryanto Suryanto