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All Journal Prosiding SNATIKA Vol 01 (2011) Telematika Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal CoreIT Applied Information System and Management Technomedia Journal Aptisi Transactions on Technopreneurship (ATT) Building of Informatics, Technology and Science IJIIS: International Journal of Informatics and Information Systems Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) ADI Bisnis Digital Interdisiplin (ABDI Jurnal) Journal of Applied Data Sciences Jurnal Pendidikan dan Teknologi Indonesia International Journal of Engineering, Science and Information Technology International Journal for Applied Information Management Jurnal Abdi Masyarakat Indonesia Journal of Social Research Scientific Journal of Informatics Journal of Digital Market and Digital Currency Journal of Current Research in Blockchain International Journal Research on Metaverse
Taqwa Hariguna
STMIK AMIKOM Purwokerto
Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
AN EVALUATION OF THE SUCCESSFUL IMPLEMENTATION OF THE INFORMATION SYSTEM PLATFORM MERDEKA MENGAJAR USING HUMAN ORGANIZATION TECHNOLOGY FIT MODEL APPROACH Abidin, Uun; Hariguna, Taqwa; Barkah, Azhari Shouni
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.2025.6.1.4282

Abstract

The implementation of technology in education has great potential to improve the quality of learning that supports the implementation of the Merdeka curriculum. The Merdeka Mengajar platform (MMP) is designed to help educators by providing various features including self-development, inspiration and teaching. Uneven ICT infrastructure and teachers' personal abilities are problems in the implementation of the MMP, so it is necessary to analyze the success of the implementation of the MMP. The purpose of this study is to analyze the success of the implementation of the information system for the Merdeka Mengajar Platform by adopting the Hot Fit Model by expanding the Technology component with the ICT Infrastructure variable, expanding the Human component with the personal competence variable, expanding the organizational component with the organizational culture variable and the training & learning variable which can affect the successful implementation of the MMP. The data obtained were 328 respondents who were analyzed using SmartPLS 3.2.9. The analysis results obtained the proposed conceptual model has an accuracy of 58.6%. Net benefits are influenced by system use, user satisfaction, personal competence, structure, environment, organizational culture, and training & learning. Service quality, system quality, information quality, and ICT infrastructure have a positive impact on system use and user satisfaction.
Comparison of Accuracy and Computation Time for Predicting Earthquake Magnitude in Java Island Yuniarto, Abdul Hakim Prima; Hariguna, Taqwa; Nawangnugraeni, Devi Astri
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5044

Abstract

Java Island has numerous active faults, making earthquake magnitude prediction a crucial component of disaster mitigation efforts. This study conducted a rigorous comparative analysis of four machine learning algorithms—Random Forest, Neural Network, Linear Regression, and Support Vector Machine—to determine their effectiveness in this specific task. The methodology employed involved systematic hyperparameter optimization for each model to ensure a fair and robust evaluation, with performance measured by Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and training time. The results showed that all three nonlinear models significantly outperformed Linear Regression. Random Forest achieved the highest accuracy (RMSE 0.5445), but Support Vector Machine and Neural Network demonstrated very competitive and nearly equal performance. The study concluded that while Random Forest has a slight advantage, several state-of-the-art models are highly capable of addressing this problem after appropriate optimization. This underscores the critical role of methodical tuning and implies that model selection in practical applications depends on a trade-off between modest improvements in accuracy and computational efficiency.
Enhancing Accessibility in Local Government Data Portals via Retrieval- Augmented Generation: A Case Study on Satu Data Indonesia in Banyumas Regency Hadie, Agus Nur; Tahyudin, Imam; Hariguna, Taqwa
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5153

Abstract

Public access to local government data in Indonesia, such as that in the Satu Data Indonesia portal for Banyumas Regency, is severely hampered by outdated search interfaces and the technical complexity of handling heterogeneous data formats like PDF, Excel, and CSV. This research directly addresses this accessibility gap by designing, developing, and evaluating an intelligent question-answering system. We introduce a novel application of a Retrieval- Augmented Generation (RAG) architecture tailored for Indonesian local government data. The core novelty lies in our methodology for handling heterogeneous data formats (PDF, Excel, CSV) by integrating a low-code orchestrator (n8n) with a high-performance vector database (pgvector), a practical solution for a common public sector challenge. The system utilizes the text-embedding-3-large model for semantic understanding and gpt-4.1 for generating grounded, factual answers. The system's effectiveness was rigorously validated, achieving a perfect 100% score across accuracy, precision, recall, and F1-score on defined test cases. Crucially, usability testing with end-users confirmed the system is perceived as significantly more efficient and user-friendly than manual data searching. The primary impact of this work is a validated, replicable blueprint for local governments to democratize public information. By transforming complex data retrieval into an intuitive conversation, this research offers a practical AI solution to enhance governmental transparency and citizen engagement.
Optimizing Early Network Intrusion Detection: A Comparison of LSTM and LinearSVC with SMOTE on Imbalanced Data Nugroho, Khabib Adi; Hariguna, Taqwa; Barkah, Azhari Shouni
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.4672

Abstract

This study aims to improve network intrusion detection systems (IDS) by addressing class imbalance in the CICIDS 2017 dataset. It compares the effectiveness of Long Short-Term Memory (LSTM) networks and Linear Support Vector Classifier (LinearSVC) in detecting intrusions, with a focus on the impact of Synthetic Minority Over-sampling Technique (SMOTE) for balancing the dataset. The dataset was preprocessed by removing irrelevant features, handling missing values, and applying Min-Max normalization. SMOTE was applied to balance the training dataset. Results showed that LSTM outperformed LinearSVC, especially in recall and F1-score, after applying SMOTE. This research highlights the benefits of combining LSTM with SMOTE to address class imbalance in IDS and emphasizes the importance of temporal sequence models like LSTM for detecting network intrusions. Future work could involve using the full dataset, exploring advanced feature engineering, and implementing more complex architectures to further enhance performance. This research underscores the critical need for improving network security by addressing the challenges of class imbalance in intrusion detection systems, which is vital for ensuring the real-time identification and mitigation of sophisticated cyber threats in the ever-evolving landscape of network security.
User Experience Analysis of Learning Management System (LMS) SINAU to Support Learning with MERDEKA Flow Using UX Curve Method Yarsasi, Sri; Tahyudin, Imam; Hariguna, Taqwa
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.4579

Abstract

The rapid development of information technology has driven transformation in education, including the use of Learning Management Systems (LMS) to facilitate independent and flexible learning aligned with the Merdeka Curriculum. This study aims to evaluate the user experience (UX) of the Sinau LMS at SMA Negeri 1 Sidareja using the UX Curve method, which tracks changes in user perceptions over time. The research involved 20 grade XII students who had used the LMS for at least three months. Data were collected through initial questionnaires, interviews, UX curve drawings, and final questionnaires, focusing on five main UX aspects: General UX, Attractiveness, Ease of Use, Utility, and Degree of Usage. The analysis of 100 curves revealed that more than half of the respondents experienced a decline in user experience quality, particularly in Ease of Use, General UX, and Degree of Usage, due to issues such as an unattractive interface, navigation challenges, and limited feature relevance. Conversely, a minority showed improved perceptions as they adapted and became more familiar with the system. These findings highlight the need for continuous improvement of the LMS's interface design and features to enhance user satisfaction and learning effectiveness. The study contributes theoretically by demonstrating the application of the UX Curve in educational systems and practically by providing recommendations for refining LMS development to better support the Merdeka Curriculum.
Co-Authors Abbas, Intesar Abdul Azis Abdul Hakim Prima Yuniarto Abidin, Uun Adi Suryaputra Paramita Al-Rawahna, Ammar Salamh Mujali Anton Satria Prabuwono Asikin, Zaenal Azhari Shouni Barkah Baihaqi, Wiga Maulana Bangkit, Jeffri Prayitno Ben-Othman, Jalel Berlilana Berlilana Chen, Shih Chih Chen, Shih-Chih Cheng, Tsang-Hsiang Dhanar Intan Surya Saputra Eko Winarto El Emary, Ibrahiem M. M. Fandy Setyo Utomo Fitriani, Radifa Rahma Giat Karyono Hadie, Agus Nur Hananto, Andhika Rafi Harahap, Eka Purnama Heru Supriyanto Hidayat, Muhammad Taufik Nur Husni Teja Sukmana Hussain, Refat Taleb Imam Tahyudin Izumi, Calvina Jawad, Haider Mahmood Johan Reimon Batmetan Kim, Jong Il Mohammed, Ismail Abdulaziz Muflikhatun, Siti Muhamad Yusup NAWANGNUGRAENI, DEVI ASTRI Nazwan, Nazwan Nugroho, Khabib Adi Nurwanti, Aulia Othman, Jalel Ben Pratama, Satrya Fajri Priyadi, Agung PUJI LESTARI Qurotul Aini R Rizal Isnanto Rahayu, Silvia Anggun Ramadani, Nevita Cahaya Riawan, Fendi Rozak, Rofik Abdul Ruangkanjanases, Athapol Sabah, Sura Salsabila, Salsabila Sarmini Sarmini Setiawan, Ito Subhan Mahendrasyah, Muhammad Sugianto, Dwi Sumiati, Ai Irma Susanti, Nindi Nofi Syahrizal, Hendrawan Tikaningsih, Ades Tri Wahyuningsih Uswatun Hasanah Wahid, Arif Mu'amar Waluyo, Retno Yarsasi, Sri Yi, Ding