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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 25 Documents
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Search results for , issue "Vol 8, No 3 (2025): November 2025" : 25 Documents clear
Real-Time Access Control System with YOLOv11-Based Face and Blink Detection Rifani, Namira Nur; Kusumanto, RD.; Husni, Nyayu Latifah
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.v8i3.36812

Abstract

This study presents a real-time smart access control system that combines facial recognition with blink-based liveness detection to strengthen security and reduce spoofing risks. The main purpose is to provide a lightweight and efficient method that verifies both identity and physical presence in real time. The system employs two YOLOv11 models: one for detecting facial regions and another for distinguishing eye states through “open” and “closed” transitions. Identity verification is carried out by comparing facial embeddings using Euclidean distance. A private dataset was collected for facial images, while blink data was obtained from a public source, both annotated in YOLO format. After 100 epochs, the face detection model achieved 0.999 precision, 1.000 recall, 0.995 mAP50, and 0.868 mAP50–90, while the blink detection model recorded 0.959 precision, 0.962 recall, 0.967 mAP50, and 0.678 mAP50–90. These outcomes confirm that the objectives were achieved, demonstrating a practical and reliable biometric authentication solution with integrated liveness verification. The system also offers scalability for future multi-modal applications.
Carbon Emission Trends (1999–2022): Forecasting Association of Southeast Asian Nations (ASEAN)'s Future Using a Hybrid Approach to Support Zero-Emission Policies Dhika, Muhammad Rama; Lestari, Sri
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.v8i3.36685

Abstract

Carbon Dioxide (CO₂) emissions are a primary driver of global climate change, with the energy sector being the dominant contributor. Southeast Asia, experiencing rapid economic growth, faces significant increases in CO₂ emissions due to high energy consumption. This study proposes a hybrid Autoregressive Integrated Moving Average (ARIMA)-XGBoost approach to predict CO₂ emissions in Association of Southeast Asian Nations (ASEAN) countries from 2023 to 2035, overcoming limitations of traditional linear models by combining machine learning (XGBoost) and time-series analysis ARIMA. Results demonstrate high accuracy (R² = 0.98) with the identification of key factors, including Gross Domestic Product (GDP), population, and total greenhouse gas (GHG) emissions. For instance, Indonesia's emissions are predicted to rise from 841.84 MtCO₂ (2023) to 2197.36 MtCO₂ (2035), while Brunei's emissions decrease from 10.86 MtCO₂ to 9.57 MtCO₂. Residual analysis and k-fold cross-validation confirm model robustness. These findings underscore the need for differentiated policies, such as renewable energy transitions in high-growth emission countries (Indonesia, Philippines) and regulatory strengthening in stable-trend nations (Brunei, Laos). The study provides methodological contributions to data-driven emission forecasting and evidence-based policy recommendations for the Association of Southeast Asian Nations (ASEAN) climate change mitigation.
Discovering Prescription Patterns in Type 2 Diabetes Based on Demographic Attributes Using Association Rules Yani, Putri; Hikmah, Maulida; Mahdiana, Deni
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.v8i3.38082

Abstract

Type 2 diabetes mellitus (T2DM) is a chronic disease that requires effective long-term therapeutic management. Appropriate and continuous treatment is crucial to prevent complications and improve patients’ quality of life. In clinical practice, prescription patterns vary significantly and are influenced by demographic and clinical characteristics. This study aimed to analyze prescription patterns of T2DM patients based on demographic and clinical attributes, and to identify frequently co-prescribed drug combinations using the Apriori algorithm. A total of 3,500 prescription records were obtained from RSUD H. Damanhuri Barabai. The analysis was conducted in two stages: (1) association between demographic factors (age, gender, blood pressure) and prescribed drugs, and (2) association among drugs regardless of patient demographics. With minimum support of 3%, confidence thresholds of 60% and 35%, and lift greater than 1.5, fifteen valid rules were identified in the demographic-to-drug analysis, and nine rules in the drug combination analysis. Strong patterns were observed, such as the prescription of Empagliflozin and Insulin Degludec for hypertensive patients aged 40–49, and the co-prescription of Acarbose and Glimepiride. These findings demonstrated that the Apriori algorithm was effective in identifying meaningful prescription patterns. Beyond methodological contributions, the results provide practical value for hospitals by supporting pharmacy managers in drug procurement planning, optimizing stock management, and designing distribution strategies that anticipate patient needs based on prescription trends.
Deep Support Vector Data Description for Anomaly Detection in Credit Insurance Claim Processes Ramadhana, Sari; Nababan, Erna Budhiarti; Sitompul, Opim Salim
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.v8i3.38134

Abstract

This study evaluates Deep Support Vector Data Description (Deep-SVDD) for anomaly detection in credit insurance claim submissions processed through host-to-host systems. The model addresses irregularities such as duplicate claims, inconsistent values, and delayed reporting by learning normal claim behavior in a latent space and applying calibrated thresholds. Using a dataset of 5,000 claims with mixed-type variables, Deep-SVDD achieved strong performance on the validation set, with high precision, recall, and ROC-AUC. Confusion matrix and Recall@K analyses confirmed low false alarms and effective anomaly ranking, capturing a substantial portion of anomalies among top-ranked claims. These results demonstrate Deep-SVDD’s potential as a scalable and efficient early detection layer, improving transparency and reliability in credit insurance claim verification.
Advanced Machine Learning Implementation for Early Detection and Prediction of Alzheimer's Disease Silalahi, Christian Petrus; Lestari, Sri
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.v8i3.38004

Abstract

Early detection of Alzheimer's disease is essential for more effective patient care. This study explores the application of Machine Learning (ML) algorithms in detecting Alzheimer's disease by analyzing influential factors, such as demographic profile, medical history, and clinical examination results. Five ML methods, namely Deep Learning, Random Forest, Decision Tree, Naïve Bayes, and Logistic Regression, are used to classify Alzheimer's disease cases. In addition, the study used RFE and BPSO methods for feature selection with the aim of improving model performance. The evaluation was conducted using cross-fold validation and split-validation techniques, with performance measured in terms of accuracy, precision, recall, and F1-score. The results showed that the Random Forest algorithm combined with BPSO achieved the best performance, with 99% accuracy and high precision and recall values, surpassing other methods. These findings demonstrate that integrating feature selection significantly improves classification quality and confirms the practical potential of ML models as reliable tools for the early detection of Alzheimer's disease, thereby assisting clinicians in diagnostic decision-making and enhancing patient care.
Early Detection of Hepatitis Disease Using Machine Learning Algorithms Sister, Maya Gian; Nita, Yulia; Solichin, Achmad
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.v8i3.38084

Abstract

Hepatitis is an inflammation of the liver caused by viral infections, autoimmune disorders, or exposure to toxic substances. Hepatitis B and C are major public health concerns because they may progress to cirrhosis or liver cancer. In Indonesia, the transmission rate remains high, primarily through blood contact, unsterile needles, transfusions, and maternal delivery. Limited public awareness, coupled with the often asymptomatic nature of hepatitis, leads to delayed detection, which increases the risk of severe complications and mortality. Therefore, early detection is crucial to minimizing the disease burden.This study proposes a risk prediction model for hepatitis using non-laboratory clinical data and machine learning methods. Eight classification algorithms were compared, Naïve Bayes, K-Nearest Neighbor (K-NN), Random Forest, Support Vector Machine (SVM), Decision Tree, AdaBoost, XGBoost, CatBoost, and LightGBM. Model performance was evaluated through K-fold cross-validation using accuracy, precision, recall, F1-score, and AUC. The results show that the SVM with a linear kernel achieved the highest performance, with 87% accuracy and balanced F1-scores across all classes. The model successfully classified four categories, Acute Hepatitis, Chronic Hepatitis, Liver Abscess, and Parasitic/Viral Infections. These findings highlight the potential of machine learning to improve early detection of hepatitis effectively and efficiently.
Big Data Analytics for Predicting Depression Risk in Generation Z: Integrating Self-Organizing Maps and Long Short-Term Memory Sinaga, Joy Nasten; Nuraina, Nuraina; Sinaga, Frans Mikael; Kelvin, Kelvin; Nurhayati, Nurhayati
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.v8i3.38011

Abstract

Mental health issues among Generation Z are rising, with depression being one of the most significant challenges. Leveraging the capabilities of big data analytics and artificial intelligence, this study proposes a hybrid method combining Self-Organizing Maps (SOM) and Long Short-Term Memory (LSTM) networks to predict depression risk based on behavioral data. The SOM algorithm is utilized for clustering high-dimensional input data to uncover hidden patterns, while the LSTM network is employed to capture sequential dependencies over time. Data were collected from various digital platforms, processed, and analyzed to train and validate the proposed model. Results show that the SOM-LSTM framework significantly improves the accuracy and reliability of early depression risk detection compared to conventional models. This study contributes a scalable and adaptable model for mental health prediction that can assist in timely interventions for Generation Z
AI-Based Prediction of Fatalities in Flight Accidents: Insights from 75 Years of Aviation Accident Records Ramadhan, Muhammad Ridho; Faadhilah, Avelia Fairuz; Roniyansyah, Roniyansyah; Juanara, Elmo
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.v8i3.37948

Abstract

Aviation accidents have reached a plateau in safety improvements since the late 1990s, emphasizing the need for advanced analytical approaches. This study utilizes a data-driven framework with Artificial Intelligence (AI) on a comprehensive dataset encompassing 75 years of global aviation accidents. This allows identification of long-term safety patterns often overlooked in studies restricted to specific regions or flight phases. The study aims to analyze long-term trends and predict future aviation accidents using Machine Learning (ML) classification models. This study involved web scraping Aviation Safety Network (ASN) database to compile the dataset, followed by Exploratory Data Analysis (EDA) to obtain insights. Support Vector Machine (SVM), Random Forest (RF), and Categorical Naive Bayes were employed for fatality prediction. EDA results show while the number of fatal accidents has declined, scheduled passenger service and En route flight phase show the highest occurrences proportionally. Furthermore, the maneuvering flight phase and military service have maximum likelihood of a fatal outcome. The predictive models achieved accuracies of approximately 79-80%. The SVM model, with the highest F1-score (79.85%), proved to be the most balanced in terms of specificity for non-fatal incidents and sensitivity for fatal ones. This result provides safety practitioners with a reliable framework for evidence-based decision making. 
Prediction of Student Academic Stress Levels Using the Decision Tree Algorithm and Particle Swarm Optimization Dzakiyyah, Syifa Ghina; Mahdiana, Deni
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.v8i3.38081

Abstract

Academic stress was recognized as a major challenge for university students because it negatively affected learning outcomes, mental health, and overall well-being. The purpose of this research was to develop and validate a predictive model of student academic stress levels and to evaluate whether optimization techniques improved the performance of a baseline classifier. Data were collected from 413 active students of Universitas Sapta Mandiri from the 2022 and 2023 cohorts using the Perception of Academic Stress (PAS) scale, which consisted of 18 indicators, together with demographic, academic, and psychosocial attributes. The Decision Tree (DT) algorithm was selected for its interpretability and transparency in multi-class classification. To improve generalization, its parameters were optimized using Particle Swarm Optimization (PSO) with 10 particles and 20 iterations. The baseline model achieved an accuracy of 93 percent, with the highest recall observed in the low-stress group. After optimization, the accuracy increased to 95 percent, and the recall for the high-stress group reached 0.96, indicating greater sensitivity to students at risk. These results confirmed that the research objectives were achieved, as the integration of DT with PSO enhanced both accuracy and class balance. The proposed model was consistent with the intended purpose of supporting early detection and timely academic and psychological interventions in higher education institutions.
Early Detection of Dengue Hemorrhagic Fever Using Patient Medical Data with Ensemble Learning Methods Saleh, Achmad; Mukhtar, Ridha; Rusdah, Rusdah
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.v8i3.38088

Abstract

Dengue Hemorrhagic Fever (DHF) remains a major public health concern in Indonesia and worldwide, where delayed diagnosis increases the risk of severe complications and mortality. Conventional laboratory-based diagnostics are time-consuming and often less accessible in resource-limited healthcare settings. This study aims to develop an early detection model for DHF using only initial clinical symptoms and demographic data extracted from electronic medical records at RSUD Brigjend H. Hasan Basry Kandangan. A total of 649 patient records (352 DHF cases and 297 non-dengue) were analyzed using the CRISP-DM framework. Five ensemble learning algorithms Random Forest, Bagging, AdaBoost, and Gradient Boosted Tree were evaluated across 80:20, 70:30, and 60:40 data splits and validated using 5-fold and 10-fold cross-validation. Random Forest consistently delivered the best and most stable performance, achieving up to 90.00 % accuracy and 0.967 AUC in the 80:20 split and mean accuracies of 88.91 % (5-fold) and 88.29 % (10-fold) in cross-validation. Further hyperparameter tuning enhanced model stability and prevented overfitting. The findings confirm that initial clinical symptoms and demographic attributes can reliably identify DHF cases early, enabling faster and more affordable screening prior to laboratory confirmation. This machine learning based decision-support model has the potential to significantly improve early clinical management of dengue fever.

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