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INDONESIA
Indonesian Journal of Applied Technology and Innovation Science
Published by Institut Riset dan Publikasi Indonesia
ISSN : 30327466     EISSN : 30327474     DOI : doi.org/10.57152
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
IJATIS: Indonesian Journal of Applied Technology and Innovation Science is a scientific journal published by the Institute of Research and Publication Indonesian (IRPI). The main focus of the IJATIS Journal is Engineering, Applied Technology, Informatics Engineering, and Computer Science. IJATIS is published 2 (two) times a year (February and August). IJATIS is written in English, consisting of 8 to 12 A4 pages, using Mendeley or Zotero reference management and similarity/ plagiarism below 20%. Manuscripts for IJATIS are submitted via the Open Journal Systems (OJS) in Microsoft Word (.doc or .docx) format. The IJATIS review process uses a Closed System (Double-Blind Reviews) with 2 reviewers per article. Articles are published in open access and are open to the public.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 37 Documents
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Prediction of Fetal Health Using Machine Learning Algorithms Dinda Mustika; Rindiani Suhadi Putri; M. Naufal Dzaky Alhady; Kharisma Ummi Khairunnisa; Arifah Nur Mahmudah
IJATIS: Indonesian Journal of Applied Technology and Innovation Science Vol. 3 No. 1 (2026): IJATIS February 2026
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v3i1.2496

Abstract

This study evaluates several machine learning algorithms for predicting fetal health conditions using cardiotocography (CTG) data. The dataset contains 2,126 records with 22 numerical features obtained from Kaggle and is classified into three categories: normal, suspect, and pathological. Four classification models Decision Tree, Random Forest, Support Vector Machine, and Logistic Regression were implemented and evaluated using two data split scenarios (80:20 and 70:30). Model performance was assessed using precision, recall, and F1-score. The results show that Random Forest achieves the best performance with an F1-score of 91% in both split scenarios, indicating stable and accurate classification compared with other models. The contribution of this study is to provide a comparative evaluation of classical machine learning algorithms for CTG-based fetal health prediction. The findings can support the development of decision-support tools to help medical personnel detect and monitor fetal health risks early.
Performance Comparison of Five Machine Learning Algorithms for Early Detection of Alzheimer's Disease Elsya Avivi; Rena Resdarima; Syabihul Khairy; Laksana Pratama Jaya Ningrat
IJATIS: Indonesian Journal of Applied Technology and Innovation Science Vol. 3 No. 1 (2026): IJATIS February 2026
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v3i1.2498

Abstract

Alzheimer's disease (AD) is the most common form of dementia and is characterized by progressive cognitive decline. Early detection of AD is crucial for earlier intervention, as there is currently no cure for this disease. This study evaluates the performance of five machine learning algorithms, namely Logistic Regression, Decision Tree, Support Vector Machine (SVM), Random Forest, and XGBoost for AD classification using a dataset of demographic information, lifestyle, medical factors, and cognitive symptoms of patients. The data was processed through pre-processing steps (data cleaning, missing value imputation, and feature selection) and model evaluation using k-fold cross-validation with a 70:30, 80:20, and 90:10 data split. Unlike several previous studies that only conducted partial evaluations, this study directly tested the performance (head-to-head) of five algorithms representing various classification paradigms.The model evaluation also focused on maximizing Recall (Sensitivity) to minimize the critical risk of false negative diagnoses in the early detection process. The results showed that the XGBoost algorithm performed best across all evaluation metrics. With an 80:20 data split, XGBoost achieved the highest performance with Accuracy, Precision, and Recall of 95.1%. These findings demonstrate the effectiveness of the XGBoost algorithm in classifying patients and support the development of faster and more objective medical decision support systems. These results have practical implications that the ML model has the potential to support clinical decision support systems for the early detection of Alzheimer's disease
Classification-Based Supervised Learning Algorithms for Accurate Prediction of Customer Churn in Banking Nora Waningsih; Alfi Surya Akbar; Shofia Ariska; Ri'lah Faizatul Husnayaini; Eflin Nurrin; Rosidur Ridho; Fauziah Tio Pratama Situmorang
IJATIS: Indonesian Journal of Applied Technology and Innovation Science Vol. 3 No. 1 (2026): IJATIS February 2026
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v3i1.2499

Abstract

The banking industry has become increasingly dynamic with the emergence of financial technology (fintech) companies that have significantly changed customer behavior and expectations. As competition intensifies, customer churn has become a critical issue because it directly affects a bank’s revenue, reputation, and long-term sustainability. Therefore, banks require effective analytical approaches to identify customers likely to leave and to develop appropriate retention strategies. This study aims to analyze and predict customer churn likelihood using a bank customer dataset by applying supervised machine learning classification techniques. Five algorithms were evaluated, namely Decision Tree, Random Forest, Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost). The models were trained and evaluated using a hold-out validation approach, and performance was assessed using accuracy as the primary evaluation metric. The experimental results show that Random Forest achieved the highest accuracy of 86%, outperforming the other algorithms, while the MLP model produced the lowest accuracy of 82%. These findings indicate that ensemble-based methods provide better performance for predicting bank customer churn. The results of this study can assist banks in identifying potential churn customers and in developing effective customer retention strategies. Future research may explore additional algorithms, advanced data preprocessing techniques, and larger datasets to further improve prediction performance.
Comparison of Machine Learning Algorithm Performance for Toddler Stunting Prediction Sophia Anjani; Nadirah Nadirah; Nur Qistina Binti Mohamad Iskandar; Mujahid Zinky; Ramzy Hammad Atmanagara; Rayhan Syahbani; Muhammad Marzuq; Muhammad Anis Fitri; Nur Khalis; Fajar Abiyyu Khairullah; Fawwaz Zanuar Alfarizy; Ahmad Fahiq Zauqol Kalam; Aldi Setiawan
IJATIS: Indonesian Journal of Applied Technology and Innovation Science Vol. 3 No. 1 (2026): IJATIS February 2026
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v3i1.2503

Abstract

Stunting is a chronic nutritional issue in toddlers that has long-term effects on children's physical growth and cognitive development. This study aims to compare the performance of four machine learning algorithms, namely Support Vector Machine (SVM), Random Forest (RF), Multilayer Perceptron (MLP), and Logistic Regression (LR), in classifying the nutritional status of toddlers. The research stages included data preprocessing, data division into training and test sets, model training, and evaluation using accuracy, precision, recall, F1-Score, a confusion matrix, and Area Under the Curve (AUC). The evaluation results showed that Random Forest achieved the best performance, with an accuracy of 94%, as well as precision, recall, and F1-score values above 90%, and an AUC value close to 1.00 across all nutritional status classes. This was followed by the MLP algorithm in second place, with an accuracy of 93.29%. The main contribution of this study is the identification of a high-performing, stable model for large-scale stunting detection, providing a strong foundation for developing decision-support systems for early detection in the public health sector.
Comparative Analysis of Machine Learning Algorithms for Predicting Heart Attack Habib Ahmad Tsaqif; Dimas Indra Kirana; Eka Efa Fariski
IJATIS: Indonesian Journal of Applied Technology and Innovation Science Vol. 3 No. 1 (2026): IJATIS February 2026
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v3i1.2514

Abstract

Early detection of heart attack risk is crucial for reducing mortality rates associated with cardiovascular diseases. This study aims to perform a comparative performance analysis of four machine learning algorithms: Decision Tree, Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) in classifying heart attack risk using a clinical dataset from Kaggle. The research methodology includes data preprocessing, data splitting using a 70:30 hold-out scheme, and model evaluation through a confusion matrix and standard classification metrics. The test results indicate that Random Forest provides the superior performance with the highest accuracy of 84%. Meanwhile, the SVM and XGBoost algorithms achieved 80% accuracy each, while the Decision Tree achieved the lowest at 70%. These findings confirm that ensemble-based models like Random Forests exhibit greater stability in handling complex clinical data patterns, making them highly promising for integration into early heart health warning systems.
Effective Machine Learning Schemes for Face Recognition in Higher-Dimensional Datasets Onibeju Basit Adeyinka; Akinyemi Moruff Oyelakin
IJATIS: Indonesian Journal of Applied Technology and Innovation Science Vol. 3 No. 2 (2026): IJATIS August 2026
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v3i2.2587

Abstract

Face recognition systems play a vital role in personal identification, particularly in security applications. However, high-dimensional image data often reduces the effectiveness of machine learning (ML) models by increasing computational complexity and training difficulty. This study aims to improve face recognition performance by combining dimensionality reduction and deep feature extraction techniques. Two benchmark datasets, Filtered LFW and a custom VGGFace2 dataset, were used to evaluate two ML-based models. The first model integrates Linear Discriminant Analysis (LDA), CNN (ResNet18), and K-Nearest Neighbors (KNN), while the second combines CNN (ResNet18) with Naïve Bayes (NB). Both models were trained, tested, and evaluated using accuracy, precision, recall, F1-score, and execution time. On the LFW dataset, the CNN-LDA-KNN model achieved the best performance, with 97.82% accuracy, 0.9799 precision, 0.9782 recall, 0.9782 F1-score, and 68.85 seconds evaluation time, outperforming the CNN-NB model (96.71% accuracy). Similarly, on the VGGFace2 dataset, CNN-LDA-KNN obtained 93.46% accuracy compared with 78.87% for CNN-NB. These findings demonstrate that integrating LDA with CNN-based feature extraction and KNN classification significantly enhances face recognition performance on high-dimensional image datasets while maintaining competitive computational efficiency.
Application of Deep Learning for the Classification of Brain Tumor Magnetic Resonance Imaging Images Rizki Andreas; Mustakim Mustakim; Ma. Angelica M. Qin; Safril Siregar
IJATIS: Indonesian Journal of Applied Technology and Innovation Science Vol. 3 No. 2 (2026): IJATIS August 2026
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v3i2.3089

Abstract

Accurate and timely classification of brain tumors from Magnetic Resonance Imaging (MRI) is essential for supporting clinical diagnosis and treatment planning. This study presents a comparative evaluation of transfer learning-based Convolutional Neural Network (CNN) architectures, including ResNet-50, DenseNet121, and EfficientNet-B2, for binary brain tumor MRI classification (Glioma and Meningioma). The dataset was augmented to improve model generalization, and the data were split into training and test sets using an 80:20 hold-out split. The models were trained using Adam and RMSProp optimizers with different learning rates, and their performance was evaluated using accuracy, precision, recall, and F1-score. Experimental results demonstrate that data augmentation significantly enhances classification performance across all evaluated architectures. Among the tested models, ResNet-50 with the RMSProp optimizer and a learning rate of 0.001 achieved the best performance, yielding the lowest training and validation losses and perfect classification results on the test set, with accuracy, precision, recall, and F1-score approaching 100%. DenseNet121 and EfficientNet-B2 also achieved excellent performance but were slightly inferior to ResNet-50. These findings indicate that transfer learning with ResNet-50 and RMSProp provides an effective and reliable solution for automated brain tumor MRI image classification

Page 4 of 4 | Total Record : 37

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