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All Journal Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) Indonesian Journal of Data and Science
I Putu Yoga Indrawan
Institut Bisnis dan Teknologi Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Mathematics

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Sentiment Analysis of Student Comments on Facilities and Infrastructure at Instiki Using Retrieval Augmented Generation Ni Putu Juliana Dewi; I Kadek Dwi Gandika Supartha; I Putu Yoga Indrawan; Ketut Jaya Atmaja
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.377

Abstract

This research was conducted to analyze the sentiment of student comments on infrastructure facilities at the Indonesian Institute of Business and Technology (INSTIKI) to overcome the problem of comment analysis that was previously done manually. The data used is in the form of student comments in 2024. The method used in this study is Retrieval Augmented Generation (RAG) with data labeling using Lexicon-Based. The test was carried out on three Large Language Models (LLMs), namely indobenchmark/indobert-base-p1, TinyLlama/TinyLlama-1.1B-Chat-v1.0, and w11wo/indonesian-roberta-base-sentiment-classifier. The test results showed that the indobenchmark/indobert-base-p1 model produced the highest accuracy of 80% in both test sessions compared to other models. The TinyLlama/TinyLlama-1.1B-Chat-v1.0 model produced 60% accuracy in session 1 and 65% in session 2, while the w11wo/indonesian-roberta-base-sentiment-classifier model produced 60% accuracy in both test sessions. The difference in the performance of these three LLMs shows that the model's understanding of Indonesian can affect the results of sentiment predictions.
PERBANDINGAN MODEL DEEP LEARNING UNTUK PENERJEMAHAN BAHASA ISYARAT SIBI BERBASIS MOBILE I Putu Yoga Indrawan; Christina Purnama Yati; Ni Luh Wiwik Sri Rahayu
JTIKA (Jurnal Teknik Informatika, Komputer dan Aplikasinya) Vol 8 No 1 (2026): Maret 2026
Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jtika.v8i1.573

Abstract

Komunikasi antara penyandang tunarungu dan masyarakat umum di Indonesia masih terbatas akibat rendahnya pemahaman terhadap Sistem Bahasa Isyarat Indonesia (SIBI) yang secara resmi diakui oleh pemerintah. Penelitian ini bertujuan untuk mengevaluasi performa tiga model deteksi objek berbasis deep learning MobileNetV2-SSD, MobileNetV2-RetinaNet, dan YOLOv11 dalam mendeteksi bahasa isyarat SIBI, serta merekomendasikan model terbaik untuk implementasi di perangkat Android. Sistem dirancang dengan fokus pada efisiensi agar dapat digunakan secara optimal di perangkat mobile. Hasil evaluasi menunjukkan bahwa MobileNetV2-SSD memberikan performa terbaik dengan mean Average Precision (mAP) sebesar 99,7% dan kecepatan 9 frame per second (FPS). YOLOv11 memperoleh mAP sebesar 89,8% dan 5 FPS, meskipun mengalami fluktuasi pada validation loss. Sementara itu, MobileNetV2-RetinaNet awalnya mencatat mAP sebesar 38,8%, namun meningkat hingga 87,69% pada rasio dataset 70:15:15. Meskipun akurasinya membaik, model ini tetap kurang ideal karena kecepatan inferensi hanya mencapai 2 FPS.Penelitian ini diharapkan dapat menjadi kontribusi awal dalam pengembangan teknologi penerjemah bahasa isyarat yang inklusif dan efisien, guna meningkatkan aksesibilitas komunikasi bagi penyandang tunarungu di Indonesia.
Performance Comparasion of DenseNet-121 and MobileNetV2 for Cacao Fruit Disease Image Classification Kadek Rizki Ariawan; Anak Agung Gde Ekayana; I Putu Yoga Indrawan; Komang Redy Winatha; I Nyoman Anom Fajaraditya Setiawan
Indonesian Journal of Data and Science Vol. 6 No. 1 (2025): Indonesian Journal of Data and Science
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i1.233

Abstract

Introduction: Cacao fruit diseases significantly impact cocoa yield and quality in Indonesia. Early detection is critical, yet traditional methods are often time-consuming and error-prone due to the visual similarity of disease symptoms. This study aims to compare the performance of two Convolutional Neural Network (CNN) architectures—DenseNet-121 and MobileNetV2—for automated image classification of cacao fruit diseases. Methods: The dataset comprises 8000 images augmented from 2000 original photos taken in cocoa plantations in Bali, categorized into four classes: fruit rot, fruit-sucking pests, pod borers, and healthy fruits. Both CNN models were trained using the Adam optimizer, a learning rate of 0.001, and a dropout rate of 0.4. The input images were resized to 224×224 pixels. Evaluation metrics included accuracy, precision, recall, and F1-score. Results: DenseNet-121 outperformed MobileNetV2 across all metrics. DenseNet-121 achieved an accuracy of 94.50%, precision of 94.75%, recall of 94.25%, and F1-score of 94.50%. In comparison, MobileNetV2 reached an accuracy of 93.88%. Although MobileNetV2 offered faster training time and lower model complexity, DenseNet-121 demonstrated superior feature extraction and stability, supported by its deeper architecture and greater parameter capacity. Conclusions: DenseNet-121 is more effective than MobileNetV2 in classifying cacao fruit diseases, providing higher accuracy and robustness. Despite requiring more computational resources, it is better suited for developing a web-based cocoa disease detection tool to assist farmers in timely and accurate disease identification.
Co-Authors Anak Agung Gde Ekayana Atmaja, Ketut Jaya Christina Purnama Yati I Kadek Dwi Gandika Supartha I Nyoman Anom Fajaraditya Setiawan Kadek Rizki Ariawan Komang Redy Winatha Ni Luh Wiwik Sri Rahayu Ni Putu Juliana Dewi