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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)
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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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
Palm Oil Seed Origin Classification Based on Thermal Images and Agricultural Data Using Convolutional Neural Network Natha, Si Gede Ngurah Chandra Adi; Wirayuda, Tjokorda Agung Budi; Wijaya, Rifki
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.2025.6.4.4880

Abstract

The traceability of palm oil seed origins plays a vital role in ensuring transparency, legality, and sustainability across the palm oil supply chain. Recent advances in deep learning have created new opportunities to improve classification systems by leveraging both visual and contextual data. This study proposes a deep learning-based model for classifying the origin of palm oil seeds by integrating thermal imagery with agricultural data. Two convolutional neural network (CNN) architectures, ResNet50 and MobileNet, were evaluated under three experimental setups: using only thermal images, combining thermal images with agricultural features (socio-economic, soil, and spectral fruit characteristics), and applying hyperparameter tuning to the best-performing model. The results show that ResNet50 consistently outperformed MobileNet, particularly in multimodal configurations. The highest performance was achieved using ResNet50 with the Adam optimizer, a learning rate of 0.001, and a batch size of 16, resulting in training accuracy of 99.75%, validation accuracy of 99.92%, and test accuracy of 100.00%. Evaluation metrics confirmed the model’s robustness with precision, recall, and F1-score all reaching 100.00%. This research highlights the significant potential of combining thermal imagery and agricultural data in CNN-based models for accurate and reliable classification of palm oil seed origins. The approach can support traceability systems in the palm oil industry, offering a scalable and data-driven solution for ensuring supply chain integrity and sustainability.
Mapping Gestures Based on Text Emotion Classification for a Virtual Chatbot for Early Marriage Consultation in Lombok Using RoBERTa Model Ramadhan, Adam Zahran; Wijaya, Rifki; Shaufiah, Shaufiah
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.2025.6.5.5038

Abstract

To address the persistent issue of early marriage among Indonesian adolescents, this study proposes a virtual counseling chatbot that classifies emotional cues in text using a fine-tuned IndoRoBERTa model. The emotion classification framework is designed to support counseling-based prevention efforts by moving beyond basic sentiment analysis and adopting five functional emotional categories such as ‘Enthusiastic’, ‘Gentle’, ‘Analytical’, ‘Inspirational’, and ‘Cautionary’ to align with psychological counseling styles. Built on fine-tuned IndoRoBERTa architecture, the model was trained in two phases: first with 2,500 manually validated samples yielding 92.8% accuracy, and then with 12,500 auto-labeled entries, resulting in 91.3% accuracy. Performance was assessed using accuracy, precision, recall, and F1-score. A gesture mapping layer was also integrated to enhance empathetic response generation. Each emotion label was paired with a predefined body gesture, grounded in counseling theory, to support future development of multimodal virtual agents capable of expressing emotions both textually and physically. The novelty lies in combining context-aware emotion classification with gesture mapping, enabling future development of expressive, culturally relevant, and empathetic virtual chatbot agents.
Mapping Facial Expressions Based on Text for Virtual Counseling Chatbot Using IndoBERT Model Padilah, Rifki; Wijaya, Rifki; Shaufiah, Shaufiah
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.2025.6.5.5039

Abstract

Early marriage in Lombok remains a serious issue, with a prevalence rate of 16.59% in 2021, the second highest in Indonesia. Limited access to counseling services, especially in rural areas, poses a significant prevention challenge. This study developed a virtual counseling chatbot system capable of mapping text-based emotions to facial expressions to improve the effectiveness of counseling for early marriage prevention. The methodology involved training an IndoBERT model on a synthetic dataset to analyze conversation texts. The model was designed to classify user input into five functional emotion categories: Enthusiasm, Gentleness, Analytical, Inspirational, and Cautionary. Performance evaluation revealed that the IndoBERT model achieved an outstanding accuracy of 94% in its final phase. This result significantly surpassed other models evaluated, such as CNN (71.6%) and KNN (79%), confirming the superiority of the chosen approach The study concludes that the high-accuracy IndoBERT model is a robust foundation for empathetic virtual agents. This research provides a significant contribution to the fields of Affective Computing and Human-Computer Interaction by demonstrating an effective framework for mapping nuanced, functional emotions from Indonesian text to facial expressions. The proposed system not only offers a scalable technological solution for mental health challenges like early marriage prevention but also highlights the impact of advanced, context-aware NLP models in creating more human-like and empathetic user interactions.
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.
Co-Authors Anugerah, Sri Mulyani Ardiansyah, Muhammad Fikri Raihan Citra Pangestu Fakhruddin, Muhammad Rafi 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 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 Setyawan, Hendy Shaufiah . Tito Waluyo Tito Waluyo Purboyo Tjokorda Agung Budi Wirayuda Warih Maharani