Indonesian Journal on Computing (Indo-JC)
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
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<![CDATA[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 ]]>
<![CDATA[Fikri Rahmanda Noor]]>;
<![CDATA[Rifki Wijaya]]>;
<![CDATA[Ade Romadhony]]>
<![CDATA[ Indonesian Journal on Computing (Indo-JC) Vol. 10 No. 2 (2026): February, 2026 ]]>
Publisher : <![CDATA[Telkom University ]]>
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DOI: 10.21108/indojc.v10i2.9709
<![CDATA[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.]]>
<![CDATA[The The Impact of Features, Usability, and Perceived Benefits of Digital Financial Applications on Satisfaction and Loyalty of Generation Z University Students ]]>
<![CDATA[Rieka Damayanti]]>;
<![CDATA[Irawati Irawati]]>
<![CDATA[ Indonesian Journal on Computing (Indo-JC) Vol. 10 No. 2 (2026): February, 2026 ]]>
Publisher : <![CDATA[Telkom University ]]>
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DOI: 10.21108/indojc.v10i2.9800
<![CDATA[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.]]>
<![CDATA[Employee Attendance System Based on Face Recognition and Liveness Detection Using MagFace ]]>
<![CDATA[Muhammad Idris]]>;
<![CDATA[Rifki Wijaya]]>;
<![CDATA[Tjokorda Agung Budi Wirayuda]]>
<![CDATA[ Indonesian Journal on Computing (Indo-JC) Vol. 10 No. 2 (2026): February, 2026 ]]>
Publisher : <![CDATA[Telkom University ]]>
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DOI: 10.21108/indojc.v10i2.10294
<![CDATA[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.]]>
