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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 264 Documents
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Analysis of Students’ Perceptions of the Free Nutritious Food Program (MBG) Based on K-Means Clustering Nur Rahmi; Lorna Yertas Baisa; Andreas Leonardo Sumendap
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 9 No. 1 (2026): March 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

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Abstract

The Free Nutritious Food Program is a strategic policy to support students’ nutritional resilience and readiness to learn. This study examined students’ perceptions of the program and identified respondent profiles using the K-Means clustering algorithm. Data from 501 students were collected through a Likert-scale questionnaire and analyzed to determine distinct perception patterns. The results revealed five clusters with strong validity, indicated by a silhouette value of 0.917. Overall, 74.6% of respondents expressed positive perceptions, suggesting that the program has been well received and supports school nutrition. However, some groups reported concerns regarding menu variety and cleanliness at distribution points. These findings underscore the need for routine quality monitoring, standardized implementation procedures, and greater attention to service consistency. Future studies should also include objective indicators such as body mass index and school attendance to provide a more comprehensive evaluation of program impact
Comparative Study of Machine Learning Methods for Sentiment Analysis of TikTok Comments Related to Cyberbullying Celestina Florecita Mariwy; Lorna Yertas Baisa; Andreas Leonardo Sumendap
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 9 No. 1 (2026): March 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

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Abstract

The rapid growth of internet use in Indonesia has contributed to the rise of cyberbullying on TikTok, increasing the importance of automated sentiment analysis for digital safety. This study compares the performance of Support Vector Machine, K-Nearest Neighbors, and Naive Bayes in classifying sentiments in TikTok comments related to cyberbullying. The dataset was collected via web scraping and processed through several preprocessing stages, yielding 7,900 unique comments. Sentiment labeling used a lexicon-based approach, and the data were split into training and testing sets with an 80:20 ratio. Results show that 34.18% of comments were negative, indicating a notable level of harmful content. Among the three models, Support Vector Machine performed best with an accuracy of 91.5%, followed by Naive Bayes at 82.8% and K-Nearest Neighbors at 80.8%. These findings suggest Support Vector Machine is the most effective method for sentiment classification in this context and offer a useful reference for developing more accurate content moderation systems on social media.
Classification of Online Gambling Spam Comments on YouTube Using Support Vector Machine Umbu Anaagung Pariamalinya; Josua Josen A. Limbong; Julius Panda Putra Naibaho
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 9 No. 1 (2026): March 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

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Abstract

While digital transformation has established YouTube as a major communication platform, the site has also become vulnerable to online gambling spam in Indonesia. This study investigates the effectiveness of the Support Vector Machine (SVM) algorithm for automated spam detection as an alternative to manual moderation. A total of 9,169 comments were collected from gaming, education, and entertainment channels using the YouTube Data API v3 and were used to train and evaluate the model with an 80:20 data split. The experimental results show that SVM achieved an accuracy of 99.62% and an F1-score of 0.996, demonstrating strong capability in identifying spam comments written in informal and modified promotional language. The main contribution of this study is the development of a highly accurate and practical spam detection approach for Indonesian YouTube comments, which can support more efficient moderation systems. However, the model still has limitations in detecting sarcastic content. Therefore, future research should explore deep learning models such as BERT to improve contextual understanding and strengthen automated moderation in digital environments.
Public Sentiment Analysis of the Affan Kurniawan Social Issue: A Comparison of Naïve Bayes and SVM Algorithms Marsella Iriana Mamusung; Lorna Yertas Baisa; Andreas Leonardo Sumendap
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 9 No. 1 (2026): March 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

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Abstract

Social media X is a dynamic public space where opinions on social issues, including the Affan Kurniawan case, spread rapidly. This study aims to analyze sentiment distribution, compare the performance of Multinomial Naïve Bayes and Linear Support Vector Machine (LinearSVC), and evaluate classification consistency under a unified evaluation framework. Indonesian-language posts were collected using keyword-based crawling and cleaned from 10,624 to 7,431 valid records (28 August–2 September 2025). The data were preprocessed through normalization, tokenization, stopword removal, and stemming, and labeled into negative, neutral, and positive sentiments using a lexicon-based approach. The results show a dominance of negative sentiment (50.26%), followed by neutral (30.96%) and positive (18.77%). Using Bag-of-Words features and an 80:20 train–test split, LinearSVC outperformed Naïve Bayes with higher accuracy (0.826 vs 0.745) and macro F1-score (0.759 vs 0.579). This study highlights the effectiveness of SVM as a stronger baseline model for Indonesian sentiment classification on social media data.
Optimized Indonesia Language Preprocessing Framework for Gojek Reviews Sentiment Analysis Using Linear Support Vector Machine Jahda Rusti Putri; Hendra Wijaya; Ali Ibrahim; Ahmad Heryanto
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 9 No. 2 (2026): July 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

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Abstract

This study analyzes sentiment in Gojek application user reviews using Natural Language Processing (NLP) and machine learning techniques to classify sentiments into positive, negative, and neutral categories. A dataset of 8,091 Bahasa Indonesia reviews from Kaggle (Gojek version 4.8) was processed, yielding 5,685 valid instances after cleaning, with sentiment distribution of neutral (52.7%), positive (26.8%), and negative (20.3%). An optimized Indonesian preprocessing pipeline was developed, incorporating text cleaning, slang normalization using a curated 1,247-word mapping dictionary, tokenization, stemming via Sastrawi, and stopword removal. Feature extraction employed TF-IDF Vectorizer (max_features=10,000; n-gram=(1,2)). Four algorithms, Naïve Bayes, Linear SVM, Logistic Regression, and Random Forest (tuned) were evaluated using stratified 80:20 split. Linear SVM and Random Forest achieved the highest accuracy at 93% (weighted F1-score: 93%), followed by Logistic Regression (92%) and Naïve Bayes (68%). Ablation study confirmed that slang normalization contributed the greatest performance gain (4.8%). Keyword-based aspect extraction on negative reviews identified three priority improvement areas: customer service responsiveness (38%), pricing transparency (32%), and application stability (24%).
MATLAB-Based Performance Evaluation of Lightweight YOLO Models for Waste Object Detection Nadhirah Meidiasty Maharani; Dewi Permata Sari; Ibnu Maja; Destra Andika Pratama; Ozkar F. Homzah
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 9 No. 2 (2026): July 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

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Abstract

Accurate waste object detection is important for enabling efficient automated recycling and environmental management. While lightweight YOLO models are often managed in Python, integration and evaluation of the models in MATLAB remains a technical challenge due to limited support and documentation. This study intends to fill that gap by evaluating the performance of YOLOv5s, YOLOv7-Tiny, and YOLOv8n within the MATLAB environment for waste object detection using the TrashNet dataset. A semi-automatic labeling approach was employed, combining manual annotation with pseudo-labeling using a pretrained YOLOv8n model. The models were trained and exported to the ONNX format for MATLAB-based inference and analysis. Experimental results show that YOLOv8n achieved the highest mAP@0.5 of 0.954, while YOLOv5s demonstrated the most stable inference performance in MATLAB, consistently producing confidence scores above 90% and real-time speeds of up to 15.9 fps. In contrast, YOLOv7-Tiny achieved the fastest inference speed (up to 24.4 fps) but exhibited reduced classification consistency. Notably, YOLOv8n experienced confidence score degradation during MATLAB inference, suggesting post-processing discrepancies between native Python and ONNX-imported workflows. This research highlights MATLAB’s capability to serve as a functional evaluation platform for modern lightweight detectors and emphasizes its potential for expanding accessible AI applications in waste management systems.
Hybrid Deep Learning Approach Using SiEBERT and LSTM for Sentiment Analysis of E-Commerce Product Reviews Nadya Salsabila Mustafa; Radius Tanone
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 9 No. 2 (2026): July 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

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Abstract

The rapid growth of electronic commerce platforms has generated a large volume of user-generated product reviews, increasing the need for accurate sentiment analysis. This study proposes a hybrid deep learning architecture that combines a sentiment-optimized transformer, SiEBERT, as a frozen encoder with a Long Short-Term Memory (LSTM) network as a sequence classifier. The model is evaluated on an Amazon review dataset containing 34,626 instances labeled into positive and negative sentiments. Three configurations are compared: SiEBERT as a direct classifier, LSTM trained from scratch, and the proposed hybrid model. Experimental results show that SiEBERT achieves 73.72% accuracy, while LSTM reaches 57.00%. The hybrid model achieves the best performance with 93.76% accuracy and a weighted F1 score of 94.15%. These findings indicate that combining contextual embeddings with sequential modeling produces more effective sentiment representations. This study contributes by introducing a hybrid SiEBERT–LSTM architecture that addresses limitations of standalone models and significantly improves classification performance, particularly in handling imbalanced sentiment data
Sentiment Analysis of Free Nutritious Meal Policy on Platform X Using IndoBERT and DistilBERT Julios Gibral Ragananta; Radius Tanone
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 9 No. 2 (2026): July 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

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Abstract

The Free Nutritious Meal Program has sparked debate on the X platform, creating the need to map public sentiment toward the policy. This study aims to map public sentiment toward the MBG policy on X and compare the performance of two Indonesian NLP models, IndoBERT and DistilBERT, under both imbalanced and balanced data conditions. The corpus consists of public tweets collected from 13 August to 17 September 2025, followed by text cleaning and automatic labeling. Class imbalance is addressed through back-translation to obtain more even class proportions. Four scenarios are evaluated using accuracy, precision, recall, and F1-score. On the original imbalanced data, IndoBERT reaches 96.32% accuracy with a macro F1 score of 0.9565. After balancing, interclass performance improves with a macro F1 score of 0.9384. DistilBERT remains competitive and more efficient, with accuracy around 91% to 93%. These findings underline the importance of aligning model choice and balancing strategy with analytic objectives.
Authorized Occupant Detection System in Smart Rooms under Daylight and Nighttime Lighting Conditions Reza Fahlevi; Dewi Permata Sari; Ibnu Maja
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 9 No. 2 (2026): July 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

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Abstract

This study discusses the development of a legitimate occupant detection system in smart rooms using the YOLOv8 algorithm, tested under daytime and nighttime lighting conditions. The system is designed using a Raspberry Pi connected to a webcam for real-time monitoring. The aim of this study is to evaluate the system's performance under different light intensities. Data were obtained by capturing images during the day and night, which were then used as a training dataset for the YOLOv8 model. With a mAP@0.5 of 0.91 and precision, recall, and F1-score values of 0.90, 0.88, and 0.89, respectively, the evaluation findings demonstrate that the system operates effectively under ideal lighting conditions. This shows that the model can recognize things accurately and consistently in real time. However, performance drastically declines in low light, with mAP@0.5 falling to 0.68 and precision, recall, and F1-score falling to 0.70, 0.65, and 0.67, respectively. This indicates a rise in false and missed detections (FP and FN). Reduced image quality, including inadequate illumination, noise, and poor feature visibility, is the primary cause of this degradation. However, it has been demonstrated that using more light sources increases detection accuracy
Waste Identification Using a Hybrid Convolutional Neural Network and Vision Transformer on Visually Heterogeneous Images Muhammad Fauzan Adzim; Dewi Sri Susanti; Sigit Dwi Prabowo
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 9 No. 2 (2026): July 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

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Abstract

In image classification, convolutional neural networks (CNNs) focus on local patterns, whereas vision Transformers (ViTs) emphasize global context. Combining the two in a hybrid CNN-ViT model may yield a more comprehensive image representation. Waste image classification with visually heterogeneous characteristics can be used to effectively evaluate the performance of the hybrid CNN-ViT model. In addition, such classification supports the crucial need for accurate waste-type identification to enable effective waste management systems. This study investigates a hybrid CNN-ViT model for classifying 24705 organic and recyclable waste images. The workflow involves resizing, an 80:10:10 split, and data augmentation, with models trained for 50 epochs using BCE loss and the Adam optimizer. Evaluation is conducted at the best epoch, defined as the epoch with the highest validation accuracy. For comparison, CNN and ViT models are also trained and evaluated separately. On the test set, the hybrid CNN-ViT model achieves an accuracy of 91.54%, the CNN achieves 91.78%, and the ViT achieves 87.17%. These findings show that CNNs provide an effective and efficient baseline, while the hybrid CNN-ViT model delivers performance competitive with CNNs and is worth considering as a robust alternative for image-based waste classification tasks.

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