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Indonesian Journal of Artificial Intelligence and Data Mining
Published by Universitas Islam Negeri Sultan Syarif Kasim Riau
ISSN : 26143372     EISSN : 26146150     DOI : -
Core Subject : Science,
Computer Science & IT
Indonesian Journal of Artificial Intelligence and Data Mining (IJAIDM) is an electronic periodical publication published by Puzzle Research Data Technology (Predatech) Faculty of Science and Technology UIN Sultan Syarif Kasim Riau, Indonesia. IJAIDM provides online media to publish scientific articles from research in the field of Artificial Intelligence and Data Mining. IJAIDM will be published 2 (two) times a year, in March and September, each edition contains 7 (seven) articles. Articles may be written in English or Indonesia.
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Articles 233 Documents
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Comparative Study: Performance Comparison of You Only Look Once and Convolutional Neural Networks Algorithms in Human Object Detection Sari, Dewi Permata; Ramadhani, M. Akbar Tri; Abdurrahman, Abdurrahman
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i3.37676

Abstract

The evolution of object identification technologies, particularly for person detection applications, has increasingly accelerated due to the merger of deep learning and artificial intelligence with computer vision. This study intends to test the efficacy of two object detection algorithms, YOLOv8n and CNN MobileNetSSD, in identifying human objects in digital photos. A dataset of 12,334 human-labeled photos from the Roboflow platform was utilized to train the YOLOv8n model, while performance results for the CNN MobileNetSSD model were acquired from a prior article. The precision, recall, and F1-score of each model were examined. Experimental results reveal that YOLOv8n attains 94% precision, 92% recall, and a 92.9% F1-score, representing a considerable enhancement over MobileNetSSD. Conversely, MobileNetSSD got an F1-score of 85.2%, with a precision of 86.5% and a recall of 84.1%. The findings show that CNN MobileNetSSD is more ideal for non-time-sensitive or resource-limited scenarios; however, YOLOv8n is preferable for real-time human identification tasks due to its greater accuracy and faster inference. This comparative analysis is important for differentiating object detection models matched to certain application needs.
Acne Skin Detection System Using You Only Look Once (YOLOV8) Based on Artificial Intelligence Sabara, Gally; Abdurrahman, Abdurrahman; Sari, Dewi Permata; Kurniawan, Aprila
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.37217

Abstract

Acne is one of the most common skin problems among teenagers and young adults, and early detection is essential to prevent progression and long-term skin damage. This study aims to develop a real-time acne detection system utilizing the YOLOv8 deep learning algorithm, integrated with a Raspberry Pi and webcam, and supported by Telegram-based notifications for user monitoring. The dataset comprises 4,092 annotated facial images representing three types of acne: papule, pustule, and nodule. Model training was conducted in Google Colab with appropriate hyperparameter adjustments. The evaluation results show that the model performs well in detecting papule and pustule acne types, with correct predictions of 258 and 222 samples, respectively, in the confusion matrix, although misclassification remains high for comedones and background classes. The Precision–Confidence Curve indicates that the model achieves a perfect precision score of 1.00 at a confidence threshold of 0.929, while the F1–Confidence Curve shows an optimal F1-score of 0.73 at a confidence level of 0.39, demonstrating the best balance between precision and recall. Real-time testing further confirms that the system can detect papules with high confidence (88%), but confidence levels for comedones (31%) and nodules (29%) remain low due to visual similarity and non-ideal lighting conditions. Overall, the results indicate that the YOLOv8-based system is capable of performing real-time acne detection with acceptable accuracy. However, further improvements in dataset diversity and annotation quality are required to enhance performance, particularly for comedone detection.
A RAG-Based Academic Information Chatbot Using Lightweight LLaMA and Indo-Sencence-BERT Saman, Muhamad; Alfarisy, Gusti Ahmad Fanshuri; Amelia, Rizky; Fadhliana, Nisa Rizqiza
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i3.38150

Abstract

In the current digital era, Institut Teknologi Kalimantan (ITK) encounters challenges in delivering academic information that is fast, accurate, and easily accessible to students, lecturers, and academic staff. Access to important information such as administrative procedures, report writing guidelines, and academic policies remains largely reliant on manual systems and static handbooks. To address this issue, this study investigates a chatbot system utilizing the Retrieval-Augmented Generation (RAG) framework through LLaMA model. The chatbot combines semantic retrieval and natural language generation to provide relevant and accurate answers based on existing academic documents. Evaluation was conducted on two lightweight LlaMA models: 1.5 and 3B parameters. Furthermore, different embedding vector also evaluated along with Indo-Sentence-BERT as well as the chunking size. The most optimal configuration was achieved using LLaMA 3B as the generative model and Indo-Sentence-BERT as the retriever, with a chunk size of 200 tokens and an overlap of 10 tokens. This setup achieved a RAGAS score of approximately 0.9, a competitive MRR of 0.5, and response latency under 1 second. Although LLaMA 1B recorded a higher MRR (0.6), its low RAGAS score made it less favorable. Overall, the LLaMA 3B and Indo-Sentence-BERT configuration is recommended to enhance the efficiency of academic information retrieval at ITK.

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