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INDONESIA
Indonesian Journal on Computing (Indo-JC)
Published by Universitas Telkom
ISSN : 24609056     EISSN : 24609056     DOI : https://doi.org/10.34818/INDOJC
Core Subject : Science, Education, Engineering,
Computer Science & IT Control & Systems Engineering Education Engineering
Indonesian Journal on Computing (Indo-JC) is an open access scientific journal intended to bring together researchers and practitioners dealing with the general field of computing. Indo-JC is published by School of Computing, Telkom University (Indonesia). The journal coverage includes, but is not limited to: Networks, security, and Computer systems. Network architectures, Network protocols, Network services, Cryptography, Formal methods, network security, Systems security, and embedded System, Software engineering. Software system structures, Contextual software domains, Software creation and management, Software notations and tools, and Software functional properties Theory of computation, and Computing methodologies. Models of computation, Computational complexity, Game Theory, Symbolic and algebraic manipulation, Parallel computing methodologies, Artificial intelligence, Machine learning, Modeling and simulation, Computer vision, and Mathematics of computing Information technology. Data management systems, Information storage systems, Information systems applications, web technology, and Information retrieval Human-centered computing and Applied computing. Human computer interaction (HCI), Collaborative and social computing, Ubiquitous and mobile computing, Visualization and Accessibility, and applied computing
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 10 Documents
 1 
The Impact of Ransomware on Indonesia’s National Data Security: Case Study of Kominfo Data Leaks Rahmat Rambe; Fairuz Fernanda Hermawan
Indonesian Journal on Computing (Indo-JC) Vol. 10 No. 1 (2025): August, 2025
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/indojc.v10i1.8976

Abstract

Ransomware poses a growing threat to national data security, especially in Indonesia, where government agencies have experienced serious data breaches. This study examines the June 2024 ransomware attack on Indonesia’s Ministry of Communication and Informatics (Kominfo) through a systematic literature review (SLR) of 1,200 articles from Semantic Scholar, Scopus, and IEEE Xplore (2015–2024), narrowing to 45 relevant studies. Findings highlight critical vulnerabilities, including weak technical infrastructure, inadequate backup systems, low password security, poor inter-agency coordination, and a shortage of trained cybersecurity professionals. Governance issues such as ineffective regulation and corruption in procurement further increased systemic risk. Current literature shows limited relevance to Indonesia’s context, as most studies originate from high-income countries. The study recommends strengthening cybersecurity regulations aligned with frameworks like the GDPR, and improving workforce capabilities through targeted training. Cross-sector and international collaboration are also key to building resilience. These strategies are essential to enhance data protection and prevent future breaches.
Optimizing Gravity Forward Modeling through OpenMP Parallel Approach: A Case Study in Bawean Island Indra Gunawan; Susanti Alawiyah
Indonesian Journal on Computing (Indo-JC) Vol. 10 No. 1 (2025): August, 2025
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/indojc.v10i1.9068

Abstract

This study investigates the optimization of Gravity Forward Modeling (GFM) by implementing an OpenMP parallel computing approach, focusing on the synthetic model and topography of Bawean Island. The research addresses computational limitations in traditional GFM by utilizing parallel processing techniques to enhance efficiency and resolution. By modeling subterranean layers with rectangular prisms and using the Okabe equation for calculations, the study significantly improves computational speed and resource management. The practical application on Bawean Island includes a strategic distribution of observation stations to facilitate Bouguer Anomaly (BA) computations, providing a clearer understanding of subsurface density anomalies. Notably, the study demonstrates a 600% increase in efficiency for the synthetic case when OpenMP with 8 threads is applied to the utilized architecture. For the field model, computations spanning 400 square kilometers on Bawean Island, encompassing over 1.3 billion GFM calculations, are completed in just 143 seconds. Additionally, the gravity terrain values on Bawean Island exhibit a range of up to 65 mGal, which closely correlates with the high-resolution topographic map. These findings highlight the considerable advantages of parallel computing in enhancing the efficiency and feasibility of complex geophysical modeling tasks, offering substantial improvements for large-scale geophysical exploration.
Hyperparameter Optimization Analysis of MultinomialNB and Logistic Regression in Multi‑Feature Text‑Based Film Genre Classification Shabrio Cahyo Wardoyo; Umniy Salamah
Indonesian Journal on Computing (Indo-JC) Vol. 10 No. 1 (2025): August, 2025
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/indojc.v10i1.9100

Abstract

This study aims to analyze and compare the performance of two text classification algorithms Multinomial Naive Bayes (MNB) and Logistic Regression (LR)—for film genre classification using multi-feature text data, both with and without hyperparameter optimization. Film genres play a crucial role in digital content recommendation systems; however, manual classification is subjective and time-consuming. The dataset, obtained from Letterboxd via Kaggle, includes film titles, descriptions, and themes. After preprocessing and text normalization (tokenization, lemmatization, and stemming), the text data were transformed into numerical features using the TF-IDF method. Two modeling scenarios were applied: the first using default parameters, and the second employing GridSearchCV to find the optimal hyperparameter settings. Model performance was evaluated using accuracy, precision, recall, and F1-score. The results indicate that the optimized LR model achieved the highest accuracy of 0.847, followed by the optimized MNB model with an accuracy of 0.837. This study concludes that hyperparameter optimization significantly improves model performance and that LR outperforms MNB in the context of multi-feature text-based genre classification.
Sentiment Analysis of the Mobile Legends: Bang Bang Application Using a Hybrid CNN-LSTM Model Eric Nur Rahman; Yuliant Sibaroni
Indonesian Journal on Computing (Indo-JC) Vol. 10 No. 1 (2025): August, 2025
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/indojc.v10i1.9687

Abstract

The increasing number of user reviews on the Google Play Store is a challenge in understanding user opinions and experiences with apps. One of the most discussed apps is Mobile Legends: Bang Bang (MLBB), a popular game with millions of downloads and reviews from Indonesian users. The problem faced is the limitation of conventional sentiment analysis models in understanding sentences and context simultaneously, making it less than optimal in analyzing user sentiment. This study proposed a comprehensive sentiment analysis system for MLBB application reviews, utilizing a hybrid CNN-LSTM architecture with a systematic optimization approach. A dataset comprising 30,000 balanced Indonesian user reviews was extracted from the Google Play Store using web scraping techniques and then processed through an extensive pre-processing pipeline, which included data cleaning, case folding, stopword removal, and stemming. Five experimental scenarios were conducted to optimize model performance through feature engineering and algorithmic enhancement. The baseline CNN-LSTM model achieved 71.97% accuracy, which was progressively improved through TF-IDF vectorization with optimal N-gram (1,2) configuration, max features optimization reaching 10,000 features, FastText embedding feature expansion using a 300-dimensional Indonesian pre-trained model, and optimizer selection experiments across five algorithms. The final optimized hybrid CNN-LSTM model, using the RMSprop, demonstrated a breakthrough performance of 88.84% accuracy with remarkable consistency (standard deviation of 0.000754), representing a 23.4% improvement over the baseline. This research contributes to the field of sentiment analysis, especially for game applications, by proving that a combined approach can produce a more accurate and reliable system for understanding user opinions.
Visualizing Functional Emotions: Mapping Counseling Responses from Text to Virtual Facial Expressions Rifki Padilah; Rifki Wijaya; Shaufiah
Indonesian Journal on Computing (Indo-JC) Vol. 10 No. 1 (2025): August, 2025
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/indojc.v10i1.9707

Abstract

This research develops an innovative virtual counseling system by integrating text-based emotion classification with visual representation to address the problem of early marriage in Lombok. The system leverages the sophisticated IndoRoBERTa model to accurately classify conselor responses into five functional emotion categories relevant to the counseling context: Enthusiasm, Gentleness, Analytical, Inspirational, and Cautionary. The limitations of conventional counseling services in rural areas serve as the primary justification for developing this responsive and accessible technological solution. Evaluation results demonstrate that the IndoRoBERTa model achieves a highly competitive accuracy rate of 89% after being trained on an expanded dataset, an achievement that significantly surpasses previous architectures. In conclusion, this IndoRoBERTa-based system is not only technically viable but also effective as a tool for providing initial empathetic support. Its capability to translate textual emotions into non-verbal visual cues makes it a promising technological solution to bridge the gap in current counseling services.
Speech to Text Correction for Indonesian Early Marriage Counseling Chatbots Using IndoRoBERTa and Mistral-7B Firdhaus Dwi Sukma; Rifki Wijaya; Ade Romadhony
Indonesian Journal on Computing (Indo-JC) Vol. 10 No. 1 (2025): August, 2025
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/indojc.v10i1.9708

Abstract

Early marriage among individuals of immature age continues to draw significant attention in Lombok. As of 2021, the prevalence rate stands at 16.59%, indicating that this social issue remains unresolved within the region's community dynamics. Limited access to counseling services particularly in rural areas poses a significant barrier to prevention efforts. This study introduces a virtual counseling chatbot designed to detect and correct Indonesian language text errors during user interactions. The system integrates IndoRoBERTa for error detection and Mistral-7B-Instruct to refine speech to text transcriptions. IndoRoBERTa was trained on synthetic datasets to classify user input as accurate or incorrect, while Mistral-7B-Instruct generates context aware corrections. Achieving an accuracy rate of 98.90%, IndoRoBERTa outperformed benchmark models such as BERT and RNN. The proposed chatbot offers an adaptive and accessible digital solution, especially for communities with limited access to conventional counseling services. This approach highlights the potential of AI-driven tools to support early intervention strategies and reduce the incidence of child marriage in underserved regions.
Implementation of IndoRoBERTa to Improve the Clarity of the Context of Homograph Words in the Text-to-Speech System for Education Chatbot Early Marriage in Lombok Fikri Rahmanda Noor; Rifki Wijaya; Ade Romadhony
Indonesian Journal on Computing (Indo-JC) Vol. 10 No. 2 (2026): February, 2026
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/indojc.v10i2.9709

Abstract

This study presents the implementation of IndoRoBERTa, a pre-trained Indonesian language model, to improve the contextual clarity of homograph words in Text-to-Speech (TTS) systems, particularly for virtual chatbot applications addressing early marriage education in Lombok. The proposed system integrates IndoRoBERTa into the TTS pipeline to classify the context of homographs prior to grapheme-to-phoneme (G2P) conversion, ensuring accurate pronunciation based on meaning. The research was conducted in two fine-tuning phases: the first utilized 500 manually labeled conversational samples, achieving 96% test accuracy, while the second expanded the dataset with 2,000 auto-labeled samples and yielded 88% accuracy. Evaluation metrics including precision, recall, and F1-score demonstrated the model’s effectiveness across 20 homograph categories. Despite strong results, the study acknowledges limitations in data authenticity and challenges in underrepresented classes. Future work is recommended to incorporate real-world dialogue data and enhance the system’s generalization in more complex linguistic settings. This research contributes to the advancement of Indonesian NLP in TTS systems, particularly in socially impactful educational contexts.
Emotion Recognition from Text and Gesture Generation for an Early Marriage Counseling Chatbot in Lombok Using BERT Adam Zahran Ramadhdan; Rifki Wijaya; Shaufiah
Indonesian Journal on Computing (Indo-JC) Vol. 10 No. 1 (2025): August, 2025
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/indojc.v10i1.9710

Abstract

Early marriage remains a pressing issue among adolescents in Lombok, Indonesia, influenced by cultural norms, educational barriers, and economic challenges. This study develops an emotion classification and reason identification framework for a virtual counseling chatbot to support prevention efforts. Five functional emotion categories ‘Enthusiastic’, ‘Gentle’, ‘Analytical’, ‘Inspirational’, and ‘Cautionary’ were defined to capture counseling tones. The system leverages IndoBERT with a two-phase fine-tuning strategy. Phase 1 used a balanced dataset of 2,000 samples and achieved a macro F1-score of 0.95, while Phase 2 refined the model using 10,000 imbalanced pseudo-labeled samples, yielding a macro F1-score of 0.88 and improved sensitivity to minority classes. In addition, a semantic similarity-based reason identification module was implemented to classify user inputs into Education, Economy, Religion, or Culture categories, enhancing context awareness beyond simple keyword matching. Performance evaluation employed accuracy, precision, recall, and F1-score, supported by confusion matrices and training plots for generalization analysis. A descriptive emotion-to-gesture mapping was also designed to link each emotion category with static body pose visualizations, providing a conceptual basis for future multimodal applications.
The The Impact of Features, Usability, and Perceived Benefits of Digital Financial Applications on Satisfaction and Loyalty of Generation Z University Students Rieka Damayanti; Irawati Irawati
Indonesian Journal on Computing (Indo-JC) Vol. 10 No. 2 (2026): February, 2026
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/indojc.v10i2.9800

Abstract

This study compares three personal finance apps—Money Pocket, Catatan Keuangan, and Money Lover—used by Indonesian Generation Z students. An online purposive survey yielded 88 valid responses (Money Pocket n = 28; Catatan Keuangan n = 42; Money Lover n = 18). Reliability was acceptable (Cronbach’s α = 0.87–0.94). Mean scores (1–5) ranged from 3.90 to 4.20. One-way ANOVA identified a significant difference only in feature completeness (F = 4.12, p = 0.020); Tukey post-hoc tests showed Money Pocket > Catatan Keuangan (mean diff. = 0.33, p = 0.018). No significant inter-app differences were found for ease of use, perceived usefulness, satisfaction, or continuance usage intention. Overall satisfaction (3.96–4.12) and continuance usage intention (3.92–4.09) indicate moderate to high user approval. Developers should prioritize feature integration—bank synchronization, automated budgeting, bill reminders, and spending analytics—to enhance completeness and retention among student users.
Employee Attendance System Based on Face Recognition and Liveness Detection Using MagFace Muhammad Idris; Rifki Wijaya; Tjokorda Agung Budi Wirayuda
Indonesian Journal on Computing (Indo-JC) Vol. 10 No. 2 (2026): February, 2026
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/indojc.v10i2.10294

Abstract

Face recognition-based attendance systems are vulnerable to spoofing attacks without effective liveness detection. This study proposes an employee attendance system that integrates CNN-based liveness detection with MagFace-based face recognition to enhance security. The liveness module serves as a preliminary filter to distinguish live faces from spoof attempts before identity verification. Experimental results show that the liveness detection module achieved accuracies of 98%, 96.28%, and 87.27% on training, validation, and testing datasets, respectively, with a False Positive Rate (FPR) of 6.0% on the testing dataset. The MagFace-based recognition module achieved an accuracy of 95.24%, with a False Acceptance Rate (FAR) of 4.64% and an Equal Error Rate (EER) of approximately 4.76%. These results indicate that the proposed system is suitable for employee attendance applications. However, the liveness detection module is intended as a baseline prototype and is not yet designed for high-security biometric authentication scenarios.

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