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All Journal Jurnal Sains Dan Teknologi (SAINTEKBU) JOURNAL OF SCIENCE AND SOCIAL RESEARCH Building of Informatics, Technology and Science Jatilima : Jurnal Multimedia Dan Teknologi Informasi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Instal : Jurnal Komputer Jurnal Minfo Polgan (JMP) Jurnal Nasional Teknologi Komputer Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Jurnal Hasil Pengabdian Masyarakat (JURIBMAS) International Conference on Sciences Development and Technology Journal of Accounting Research, Utility Finance and Digital Assets (JARUDA) Jurnal Manajemen Sistem Informasi Jurnal Ilmu Komputer dan Teknik Informatika Journal of Electrical Engineering Research Proceedings of The International Conference on Computer Science, Engineering, Social Sciences, and Multidisciplinary Studies
Rizal, Chairul
Universitas Pembangunan Panca Budi
Religion Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences

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Interactive Museum Innovation with Digital Technology to Enhance Education and Preserve Cultural Heritage in Indonesia Rizal, Chairul; Erni Marlina Saari
Proceedings of The International Conference on Computer Science, Engineering, Social Science, and Multi-Disciplinary Studies Vol. 1 (2025)
Publisher : CV Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/cessmuds.v1.17

Abstract

Museums play an important role in preserving cultural heritage and educating the public. However, the changing behavior of the younger generation, who are more interested in digital media, has led to a further decline in visits to traditional museums. This research aims to design a digital technology-based interactive museum prototype as an effort to enhance education and cultural preservation in Indonesia. This research method uses a Research and Development (R&D) approach, which includes the stages of needs analysis, system design, prototype development, and user testing. The research instruments include the System Usability Scale (SUS) and semi-structured interviews with 30 respondents. The research findings indicate that the interactive museum prototype achieved an average SUS score of 75.6, placing it in the excellent category, and received positive feedback regarding increased visitor engagement in understanding cultural collections. This research contributes to the development of a digital museum model that meets the needs of Indonesian society.
Application of Machine Learning in Computer Hardware Failure Detection Systems Irwan, Irwan; Supiyandi, Supiyandi; Rizal, Chairul
Proceedings of The International Conference on Computer Science, Engineering, Social Science, and Multi-Disciplinary Studies Vol. 1 (2025)
Publisher : CV Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/cessmuds.v1.71

Abstract

The rapid advancement of computer systems has increased the complexity and performance demands of computer hardware, leading to higher risks of hardware failure. Early detection of hardware faults is crucial to ensure system reliability, reduce downtime, and minimize maintenance costs. This proceeding discusses the application of Machine Learning (ML) techniques in computer hardware failure detection systems as an intelligent and adaptive solution. Machine Learning enables automated analysis of large volumes of hardware monitoring data, such as temperature, voltage, power consumption, and error logs, to identify patterns that indicate potential failures. Various ML approaches, including supervised learning, unsupervised learning, and anomaly detection methods, can be utilized to predict hardware malfunctions before critical failures occur. Compared to traditional rule based monitoring systems, ML based detection systems offer higher accuracy, scalability, and the ability to adapt to dynamic hardware environments. Furthermore, the integration of Machine Learning with hardware sensors and monitoring tools enhances real time fault detection and supports predictive maintenance strategies. This paper highlights the role, advantages, and challenges of applying Machine Learning in computer hardware failure detection systems, including issues related to data quality, model interpretability, and computational overhead. Overall, the application of Machine Learning provides a promising approach to improving the reliability and efficiency of modern computer hardware systems.
Co-Authors AA Sudharmawan, AA Abdul Karim Armasari, Selly Barany Fachri Chatarina Umbul Wahyuni Dedi Purwanto, Dedi Deni Apriadi Eka Putra Eka, Muhammad Eka, Muhammad Eko Hariyanto Elisa Erni Marlina Saari Fachri, Barany Fakhruddin Nasution, Akhmad Haris Al-Muridhillah, Sultan Haryadi, Patrialman Hendry Hendry . Heri Kurniawan, Heri Hermansyah Hermansyah Irwan Irwan Irwan Khairunnisa Khairunnisa Kurniawan, Oktavian Putra Leni Marlina LINDA WAHYUNI Listya Ningtiyas, Hastanti Maria Stefanova Br Manik, Icha Mochammad Imron Awalludin Muhammad Amin Muhammad Eka Muhammad Hasanuddin, Muhammad Muhammad Iqbal Muhammad Noor Hasan Siregar Muhammad Syahputra Novelan Muhammad Zen, Muhammad Muslem R, Imam Nasution, Yusuf Ramadhan Nasution, Yusuf Ramadhan Nuranisah Nuranisah Pasaribu, Saddam Husen Romadon Rahmayani, Putri Ricky Ramadhan Harahap Rismayanti Rismayanti Saidi Ramadan Siregar Sanjaya, Dian Siregar, Habibie Fahlevy Sitha, Nur Syifa'u Siti Khodijah Subhan Subhan Suherman, Endang Supiyandi Supiyandi Syamsiar, Syamsiar Tambunan, M. Johan Pratama Tirta, Deazei Wirayuda, Satria Yusma, Yanti Yusuf Ramadhan Nasution Zhafira, Raihan Novalda Ziky, Ziky Zulham Zulham