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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal on Computing (Indo-JC) JURNAL MEDIA INFORMATIKA BUDIDARMA INTEK: Jurnal Penelitian Jurnal Teknik Informatika (JUTIF) Almufi Jurnal Pengabdian kepada Masyarakat eProceedings of Engineering Community Service Seminar and Community Engagement (COSECANT) Indonesian Journal on Computing (Indo-JC)
Rifki Wijaya
Telkom University, Bandung
Religion Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Integration of Thermal Images and Agricultural Data for Multi-Class Classification of Palm Seed Origin using MobileNet Nurrahman, Yusuf Abidin; Wijaya, Rifki; Wirayuda, Tjokorda Agung Budi
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.2026.7.1.4879

Abstract

This research develops a palm kernel origin classification model by combining thermal images and numerical agricultural data using MobileNet architecture. The quality of palm kernels is highly influenced by origin and environmental conditions, but manual visual identification is difficult. Therefore, a machine learning-based approach is applied to improve classification accuracy. The dataset consists of 7.257 thermal images representing 73 seed origin classes, as well as supporting data in the form of soil, fruit, and socioeconomic information collected from plantations in Aceh, Indonesia. The MobileNet model was tested in two scenarios: using only thermal images, as well as a combination of thermal images with agricultural data. Results show that data integration provides significant performance improvement. The best model was obtained from MobileNet V3-Large with the optimal hyperparameter configuration (batch size 16, learning rate 0.001, and optimizer Adam), resulting in test accuracy of 99.04%, validation 97.25%, and training 98.77%. This finding opens up opportunities for the application of real-time classification technology in the plantation environment, especially to support precision and sustainable agriculture.
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.
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.
Co-Authors Adam Zahran Ramadhdan Ade Romadhony Anugerah, Sri Mulyani Ardiansyah, Muhammad Fikri Raihan Citra Pangestu Fakhruddin, Muhammad Rafi Fikri Rahmanda Noor Firdhaus Dwi Sukma Gamma Kosala Gia Septiana Wulandari Ginting, Dewi Swarni Br Khalid Kiki Dwi Prasetyo Komara, Riza Aria Lukman Nurwahidin M Darfyma Putra Moch Arif Bijaksana Muhammad Idris Muhammad Rahman Ali Natha, Si Gede Ngurah Chandra Adi Naufal , Dziban Nur, Zikri Kholifah Nurrahman, Yusuf Abidin Padilah, Rifki Prasti Eko Yunanto Putti, Fasya Hanifah Putu Harry Gunawan Rachdian Habi Yahya Ramadhan, Adam Zahran Rambe , Muhammad Anugra Rizky Rifki Padilah Setyawan, Hendy Shaufiah Shaufiah . Tito Waluyo Tito Waluyo Purboyo Tjokorda Agung Budi Wirayuda Warih Maharani