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All Journal TEKNIK Jurnal Teknologi dan Manajemen Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL LENTERA : Kajian Keagamaan, Keilmuan dan Teknologi JOURNAL OF APPLIED INFORMATICS AND COMPUTING Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurnal Sisfokom (Sistem Informasi dan Komputer) Technomedia Journal Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Capture : Jurnal Seni Media Rekam Building of Informatics, Technology and Science Jurnal ABDINUS : Jurnal Pengabdian Nusantara Jurnal Teknik Informatika (JUTIF) Business Economic, Communication, and Social Sciences Journal (BECOSS) Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ Jurnal Pendidikan dan Teknologi Indonesia Jurnal Locus Penelitian dan Pengabdian Eduvest - Journal of Universal Studies Finger : Jurnal Ilmiah Teknologi Pendidikan Jurnal Indonesia : Manajemen Informatika dan Komunikasi The Indonesian Journal of Computer Science
Santoso, Handri
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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
USER EXPERIENCE IN METAVERSE BUILDING TRAINING USING PHOENIX-FIRESTORM SOFTWARE Magdalena, Maria; Indrajit, Richardus Eko; Santoso, Handri; Sari, Muh Masri
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.3447

Abstract

This study aims to evaluate the effectiveness of training using Phoenix-Firestorm software in a 3D virtual environment (metaverse) for teachers, lecturers, and students. A total of 49 participants were involved in the online training consisting of seven sessions, facilitated through the Discord platform for voice communication. Each participant was given a virtual area of 35x35 meters for practice, with daily guidance via Discord chat. The training was designed to equip participants with basic skills in building 3D objects, including an understanding of the software and building techniques. After the training, a survey was conducted using a Likert scale of 1-9 to assess participants' understanding of navigation, software customization, virtual communication, and problem-solving. The survey results showed that the majority of participants found Phoenix-Firestorm relatively easy to use, although some challenges were reported regarding the complexity of the interface. These findings will be used as a basis for developing more effective and user-friendly training guidelines in the future, with a focus on improving accessibility and user experience in the context of technology-based learning. This study is in line with previous studies that show the potential of virtual worlds in education, as discussed by Jusuf (2023). Additionally, the use of virtual technology in education is also supported by research on the effectiveness of virtual learning environments, as explained by Wang et al (2022), that digital games contributed to a moderate overall effect size when compared with other instructional methods. These findings are expected to make a significant contribution to the development of innovative training methods in education in the digital era.
Comparison of Information Technology Governance Maturity Levels Based on COBIT 2019 at PT Kereta Commuter Indonesia in 2023 and 2024 Purwadi, Purwadi; Santoso, Handri
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5200

Abstract

This study aims to analyze and compare the maturity level of Information Technology (IT) governance at PT Kereta Commuter Indonesia (KCI) between 2023 and 2024 using the COBIT 2019 framework. The background of this study is based on the operational complexity of KCI which serves a high daily passenger volume, so that the information system becomes the backbone of the smooth transportation service. The method used is a descriptive-comparative case study with a mixed approach, through interviews, Likert scale questionnaires, and internal document reviews such as IT audit reports and government regulations. The results of the analysis show a significant and consistent increase, where the level of IT governance maturity which was previously at level 2 (Managed) and 3 (Defined) in 2023, increased to level 3 and 4 (Quantitatively Managed) in 2024. The most prominent improvements were seen in the strategic domain EDM01 (Ensure Governance Framework Setting) and the operational domain DSS01 (Manage Operations), which successfully reached level 4. This success reflects top management's commitment and ongoing internal evaluation in strengthening IT governance strategically and operationally. The research findings confirm that annual evaluations serve as an objective benchmark for identifying governance gaps, developing digital strategies, and determining future IT investment priorities. Overall, this study confirms that regular assessments can improve the effectiveness of data-driven IT transformation and ensure alignment of IT implementation with the company's business objectives.
TRANSFER LEARNING IMPLEMENTATION ON IMAGE RECOGNITION OF INDONESIAN TRADITIONAL HOUSES Firmansah, R Arif; Santoso, Handri; Anwar, Agus
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 6 (2023): JUTIF Volume 4, Number 6, Desember 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.6.767

Abstract

Indonesia is the largest archipelago in the world that has cultural diversity, one of Indonesia's cultural wealth is the architectural uniqueness of the types of traditional houses that come from different tribes and regions. in this era of digitalization, the younger generation of this nation must continue to preserve cultural wealth, one of which is by building a system that can document and provide learning about image recognition of the archipelago's traditional houses. Thanks to Artificial Intelligence Technology, it is possible to create a smart model that functions as an image recognition with system learning by working with a neural network called deep learning, which is supported by a transfer learning algorithm that can utilize previous models that have been trained, one of which is the MobileNetV2, Resnet50, VGG16 and Xception models as an effort to get a model with high accuracy with limited dataset conditions. So, the purpose as well as the update of this research is to build an image recognition model of Indonesian traditional houses with the transfer learning method. The methods and stages used are CRISP-DM (Cross Industry Standard Process for Data Mining), a standard used to build applications that aim to gain insight from a dataset, the image dataset used in this study was created with the image scraper technique from the internet. The conclusion of this research is that an image recognition model of Indonesian traditional houses is produced by training experiments from 5 transfer learning models that have been determined and the greatest accuracy is obtained, namely 0.96% of the MobileNetV2 transfer training method, the potential for further development for future research is to add more classes and amount of data and design a more detailed and detailed deployment model.
From Logs to Insights in the Pulp & Paper Industry: Generating Structured Alarm Reports Using LLMs and RAG Santoso, Handri; Wijaya, Oktavianus Hendry; Andriani, Febri; Prijantono, Sonny
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5225

Abstract

Effective alarm management is essential in industrial environments to ensure operational safety and minimize costly downtime. Traditional rule-based reporting systems often struggle to handle heterogeneous alarm log formats and the complexity of natural language queries, limiting their adaptability in real-world applications. To address these limitations, this study proposes a generative alarm reporting system that integrates Large Language Models (LLMs) with a Retrieval-Augmented Generation (RAG) framework. The system converts natural language queries into structured JSON filters, enabling efficient retrieval of contextual information from historical alarm logs. Three open-source LLMs—CodeLlama-7B, LLaMA 3.1-8B, and Mistral-7B—were locally deployed and evaluated using both quantitative and qualitative methods. Experimental results show that CodeLlama-7B achieved the best overall performance, with an Exact Match Accuracy of 0.80, a Field Match score of 93.8%, and a 0% Parse Failure Rate, outperforming the other models in reliability and structural consistency. Compared to conventional rule-based approaches, the proposed LLM-RAG integration demonstrates improved relevance, interpretability, and responsiveness in alarm reporting. This work represents the first systematic benchmarking of locally deployed open-source LLMs for industrial alarm management, providing a replicable framework and highlighting their potential to advance intelligent, real-time, and domain-specific reporting in the pulp and paper industry and beyond.
Co-Authors Agus Anwar Akbar, Achmad Fathurrazi Akbariani, Nurhaliza Vania Amanda, Djaja Andrew Andrew, Andrew Andriani, Febri Anggraini, Astria Febrian Aprilia, Novia Pramesti Dazki, Erick Devanda, Faustin Eko Hadianto, Eko Firmansah, R Arif Fransisca, Viviana Harlis Setiyowati HARYONO Heni Jusuf Hindarto, Djarot Ho, Patricia Indrajit, Eko ISWAHYUDI ISWAHYUDI Ito Wasito Karjadi, Daniel Avian Laurenso, Justin Lijaya, Hendra Lucia Sri Istiyowati Lupitha, Mariska Mareta, Arvin Maria Magdalena Mayatopani, Hendra Mohammad, Saiful Azhari Muhammad Alfathan Harriz Nana Sutarna Panna, Ariya Perdana, Nanda Praharto, Suwandhy Prawira, Khairul Thamrin Prijantono, Sonny Priswanto, Budi Purwadi Purwadi Purwadi Putri, Gladys Rahman, Ben Ramadhoni, Moehammad Richardus Eko Indrajit Rochadiani, Theresia Herlina Ruddin, Isra Sakti, Irwin Sandjaya, Julio Santoso, Billy Cahyo Saputra, Adi Dwifana Sari, Muh Masri Sembiring, Agustinus Sentosa, Steve Sucipto, Nadya Rudie Suhandi, Vartin Suherman, Endang Sumargo, Ruly Sze, Edward Tauhid, Tauhid Wedha, Alessandro Enriqco Putra Bayu Wedha, bayu Yasa Widjaja, Herman Wijaya, Oktavianus Hendry