cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Mustakim
Contact Email
Mustakim
Phone
-
Journal Mail Official
ijaidm@uin-suska.ac.id
Editorial Address
-
Location
Kab. kampar,
Riau
INDONESIA
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.
Arjuna Subject : -
Articles 264 Documents
 23   24   25   26   27 
Implementation of EfficientNet-B0-Based Convolutional Neural Network Architecture for Classification of Digital Images of Traditional Spices Muhamad Rafi Raihan Akbar; Bedy Purnama
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 9 No. 2 (2026): July 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Traditional Indonesian spice identification has historically depended on human expertise, a process prone to subjective error and limited scalability. This study evaluates the use of an EfficientNet-B0-based Convolutional Neural Network, incorporating transfer learning and fine-tuning, to automatically classify digital images representing 31 categories of traditional Indonesian spices. The Indonesian Spices Dataset, containing 6,510 images from Kaggle, was divided into 80% training, 10% validation, and 10% testing sets. Data augmentation techniques, such as random horizontal flipping and rotation, were implemented to enhance model generalization and reduce overfitting. The model was trained for over 20 epochs using the AdamW optimizer with cosine learning rate scheduling. Results indicate that the proposed model achieved a test accuracy of 97%, with macro average precision, recall, and F1-score also at 97%. The minimal difference between training and validation accuracy demonstrates robust generalization to unseen data. The model is computationally efficient and suitable for deployment on edge devices, supporting applications in agribusiness for automated spice identification, quality control, and education
Yoga Pose Classification Using Body Landmark-Based Pose Estimation with Mediapipe and Machine Learning Approach Audrey Nasywaa Harimaydina; Bedy Purnama
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 9 No. 2 (2026): July 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Practicing yoga independently without professional supervision can lead to incorrect postures, increasing the risk of injury and reducing exercise effectiveness. Although various studies have utilized pose estimation and machine learning techniques for yoga pose classification, most focus on a single feature representation. This study proposes a static image-based yoga pose classification system using MediaPipe to extract 33 body landmarks, which are transformed into geometric features consisting of landmark coordinates, joint angles, and inter-body point distances. The novelty of this study lies in the systematic evaluation of these features, both individually and in combination, within a Random Forest classification framework. The dataset consists of 1,531 images representing five yoga pose classes: Downward Dog, Goddess, Plank, Tree, and Warrior II. The Random Forest model was optimized using hyperparameter tuning and cross-validation. Experimental results show that combining landmark, angle, and distance features achieved the best performance, with an accuracy of 95.02% and an F1-score of 0.9501. The model also demonstrated stable performance during cross-validation, with accuracy ranging from 0.9436 to 0.9608. The results show that combining multiple geometric feature representations improves yoga pose classification performance while maintaining computational efficiency, supporting safer and more effective self-guided yoga practice
Machine Learning-Based Early Prediction of Stunting Risk in Toddlers in Balangan Regency Using the AdaBoost Algorithm Yulia Nita; Maya Gian Sister; Achmad Solichin
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 9 No. 2 (2026): July 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Stunting is a serious public health problem, particularly in Balangan Regency, where the prevalence in 2022 reached 29.8%, exceeding the national average of 21.6%. This condition highlights the importance of data-driven early detection to support targeted prevention efforts. This study aimed to develop an early stunting risk classification model using historical toddler healthcare data and evaluate the performance of several Machine Learning classification algorithms. Unlike previous studies that generally focused on a limited number of classification methods, this study provides a comprehensive comparison of eight Machine Learning algorithms, namely Naive Bayes, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest, AdaBoost, XGBoost, LightGBM, and CatBoost, combined with Synthetic Minority Oversampling Technique (SMOTE) and Recursive Feature Elimination (RFE) to improve classification performance. The dataset was divided into 80% training data and 20% testing data. Model evaluation was conducted using 5-fold cross-validation with accuracy, precision, recall, and F1-score metrics. The results showed that AdaBoost achieved the best performance with an accuracy of 99.39%, precision of 100%, recall of 98.78%, and F1-score of 99.39% during cross-validation. Further evaluation on testing data produced an accuracy of 90.20%, precision of 90.57%, recall of 92.36%, and F1-score of 91.39%. The selected model was implemented into a Streamlit-based web application to support both manual and bulk prediction. The developed system can assist healthcare institutions and healthcare workers in conducting accurate and efficient early stunting risk detection, enabling more targeted interventions and data-driven decision-making. Therefore, this study provides both methodological and practical contributions to support stunting prevention efforts.
Hybrid LSTM-XGBoost Model for Lightning Strike Frequency Forecasting Using Spatio-Temporal Features Frama Andika; Muhammad Nawawi; Masayu Anisah
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 9 No. 2 (2026): July 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Lightning strikes pose significant risks to human safety, infrastructure, and aviation systems, making accurate frequency forecasting essential for early warning and disaster mitigation. However, existing approaches often fail to capture both the temporal dependencies and nonlinear spatial patterns inherent in real-time lightning data. This study proposes a hybrid model integrating Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) for lightning strike frequency forecasting using spatio-temporal features. The LSTM component was employed to extract sequential temporal dependencies from time-series lightning data, while XGBoost was utilized to model complex nonlinear relationships among spatial features, including latitude, longitude, and region. The dataset comprised 742,543 real-time lightning strike records with seven features: timestamp, coordinates, region, multi-point distance score (MDS), multi-channel gradient (MCG), and status. Feature engineering was performed to construct temporal aggregations and spatial encodings as model inputs. The proposed hybrid model was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). The experimental results demonstrate that the hybrid LSTM-XGBoost model outperformed the standalone LSTM and XGBoost baselines across all evaluation metrics, confirming the effectiveness of integrating deep learning and ensemble methods for spatio-temporal lightning forecasting. The novelty of this study lies in the application of a sequential hybrid LSTM-XGBoost pipeline specifically designed for real-time spatio-temporal lightning strike frequency forecasting, an area that remains underexplored in the existing literature. The findings suggest that the proposed model holds strong practical potential for integration into operational lightning early warning systems, thereby contributing to enhanced disaster mitigation and risk assessment across aviation, infrastructure, and public safety sectors.

Page 27 of 27 | Total Record : 264

 23   24   25   26   27 

Filter by Year

2018 2026


Reset
Filter By Issues
All Issue Vol. 9 No. 2 (2026): July 2026 Vol. 9 No. 1 (2026): March 2026 Vol. 8 No. 3 (2025): November 2025 Vol 8, No 2 (2025): July 2025 Vol 8, No 1 (2025): March 2025 Vol 7, No 2 (2024): September 2024 Vol 7, No 1 (2024): March 2024 Vol 6, No 2 (2023): September 2023 Vol 6, No 1 (2023): Maret 2023 Vol 5, No 2 (2022): September 2022 Vol 5, No 1 (2022): March 2022 Vol 4, No 2 (2021): September 2021 Vol 4, No 1 (2021): March 2021 Vol 3, No 2 (2020): Spetember 2020 Vol 3, No 1 (2020): March 2020 Vol 2, No 2 (2019): September 2019 Vol 2, No 1 (2019): March 2019 Vol 1, No 2 (2018): September 2018 Vol 1, No 1 (2018): March 2018 More Issue