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ComTech: Computer, Mathematics and Engineering Applications
Published by Universitas Bina Nusantara
ISSN : 20871244     EISSN : 2476907X     DOI : -
Core Subject : Science, Education, Engineering,
Computer Science & IT Engineering Mathematics
The journal invites professionals in the world of education, research, and entrepreneurship to participate in disseminating ideas, concepts, new theories, or science development in the field of Information Systems, Architecture, Civil Engineering, Computer Engineering, Industrial Engineering, Food Technology, Computer Science, Mathematics, and Statistics through this scientific journal.
Arjuna Subject : -
Articles 6 Documents
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Search results for , issue "Vol. 17 No. 1 (2026): ComTech" : 6 Documents clear
Investigating Prospective Athletic Athletes: Classifiers, Benchmarking, and Post-Hoc XAI Analysis Ibnu Febry Kurniawan; A'yunin Sofro; Danang Ariyanto; Junaidi Budi Prihanto; Dimas Avian Maulana
ComTech: Computer, Mathematics and Engineering Applications Vol. 17 No. 1 (2026): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v17i1.13224

Abstract

Identifying highly potential athletes is a critical yet inherently challenging process that requires comprehensive analysis of diverse factors, including physiological attributes, demographic characteristics, and social influences. This multifaceted process requires meticulous evaluation of extensive datasets to ensure both accuracy and fairness in talent identification protocols. The complexity stems from the interconnected nature of the determinants of athletic performance, where physical capabilities intersect with psychological resilience, social support systems, and environmental factors. In recent years, machine learning (ML) algorithms gain prominence in decision-making processes, offering unprecedented opportunities to uncover subtle patterns and relationships within athlete data that might otherwise remain hidden. This study systematically benchmarks the performance of several state-of-the-art ML classifiers using a novel, self-collected dataset of athlete candidates. Furthermore, an explainable AI (XAI) technique, Shapley Additive Explanations (SHAP), is applied to interpret model decisions and provide meaningful insights into key predictive factors. Experimental results demonstrate that Gradient Boosting achieves superior predictive performance (F1) across the 10-fold sets, with a mean value of 0.46. SHAP analysis reveals the critical importance of anthropometric measurements and social group features in influencing prediction outcomes. These findings collectively underscore the substantial potential of ML to revolutionize talent identification in sports while emphasizing the importance of model interpretability in fostering trust and acceptance of AIdriven decision-making processes.
Explainable Machine Learning Models SHAP-based for Feature Importance Affecting Stunting Prevalence Asysta Amalia Pasaribu; Nur Fitriyani Sahamony; Khairil Anwar Notodiputro; Bagus Sartono
ComTech: Computer, Mathematics and Engineering Applications Vol. 17 No. 1 (2026): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v17i1.13732

Abstract

Stunting is a form of chronic nutritional deficiency in toddlers and remains a major public health concern due to its impact on child growth and development. Efforts to reduce its prevalence continue to be strengthened in Indonesia, particularly in Sumatra Province. This study aims to evaluate the accuracy of a logistic regression model and three machine learning models—decision tree, random forest, and Support Vector Machine (SVM)—in classifying stunting prevalence. The response variable is the prevalence of stunting among toddlers and is categorized into two classes: exceeding the national target and not exceeding it, based on the 2024 national threshold. Although classification models can provide accurate predictions, they often lack interpretability. Therefore, this study applies the Shapley Additive exPlanations (SHAP) method to the best-performing machine learning model to identify the key factors influencing stunting. The use of Shapley values is justified through the uniqueness theorem, which establishes it as the only attribution method that satisfies desirable fairness properties. SHAP values explain the model by referencing both the trained model and the underlying data. The results show that the random forest model achieves the highest accuracy (90.00%) and outperforms the other models. SHAP analysis reveals that Underweight is the most influential predictor contributing to stunting prevalence in Sumatra Province. These findings highlight the importance of machine learning interpretability in supporting policy decisions to reduce stunting.
Balinese Language Classification on Social Media using Multinomial Naive Bayes Method with TF-IDF Putu Widyantara Artanta Wibawa; Cokorda Pramartha; I Gusti Ngurah Anom Cahyadi Putra; Luh Gede Astuti
ComTech: Computer, Mathematics and Engineering Applications Vol. 17 No. 1 (2026): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v17i1.14132

Abstract

Balinese is a local language that is widely used and spoken by Balinese people, including on social media platforms. However, the nuances of its politeness levels are often lost in informal digital communication, and there is a significant lack of computational models that automatically classify these levels, particularly for low-resource languages such as Balinese. The primary objective of this study is to evaluate the performance of the Multinomial Naive Bayes method combined with Term Frequency–Inverse Document Frequency (TFIDF) feature extraction, Chi-square feature selection, and the Synthetic Minority Oversampling Technique (SMOTE) in classifying Balinese language levels. The dataset used in this study consists of 1,314 annotated social media posts and comments, primarily sourced from Instagram. A Balinese language expert performs the annotation, categorizing the texts into six levels that represent varying degrees of politeness and formality. These levels include alus singgih (polite, used for respecting others), alus sor (polite, used for self-humbling), alus mider (polite, used for both respecting others and self-humbling), alus madia (an intermediate level of politeness), basa andap (casual, commonly used in everyday life), and basa kasar (impolite, often used during arguments or toward animals). The experimental results show that the model achieves 96.53% accuracy on the training data and 61.45% accuracy on the test data. In addition, hyperparameter tuning reveals that the Multinomial Naive Bayes model with 2,720 selected features and SMOTE oversampling achieves 91.78% accuracy, significantly outperforming the baseline model without feature selection or oversampling, which achieves only 64.93% accuracy.
Forecasting Food Prices in East Java Using Stacking Ensemble Learning via K-MEANS Aviolla Terza Damaliana; Amri Muhaimin; Nabilah Selayanti; Shafira Amanda Putri; Muhammad Nasrudin
ComTech: Computer, Mathematics and Engineering Applications Vol. 17 No. 1 (2026): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v17i1.14218

Abstract

Food commodities are essential in developing countries such as Indonesia, and the government regulates food commodity prices in every province. However, price instability issues persist in certain provinces, creating challenges for effective policy control. Data science and statistical techniques play an important role in supporting the government’s efforts to monitor and manage food commodity prices. This study proposes the Stackelberg-K-Means method to predict the commodity price index in East Java. The proposed method is a collaborative framework that combines cluster analysis and stacking ensemble learning for time-series prediction. Cluster analysis is conducted first using Dynamic Time Warping as the distance measure, which is suitable for time-series data, resulting in two clusters for each commodity: rice, oil, and flour. The stacking model consists of base learners and a meta-learner. The base learner models include Ridge Regression, Random Forest, and Support Vector Regression, while the meta-learner uses Light Gradient Boosting. Parameter optimization is performed using grid search, and the proposed method is evaluated against AutoARIMA implemented in Python using both training and testing data. The results show that the proposed method outperforms the ARIMA model across all three error metrics: MAPE, MAE, and RMSE. For flour commodities, the scores are 0.042% versus 0.328%, 4.715 versus 37.57, and 6.34 versus 523.99, respectively. For rice commodities, the scores are 0.261% compared to 0.392%, 31.585 compared to 48.142, and 41.92 compared to 56.068. For oil commodities, the scores are 0.185% compared to 0.250%, 33.02 compared to 47.571, and 39.35 compared to 56.060.
Evaluating Application Integration Success Using DeLone McLean and CSF Model Al Aziizu Putra Hendriana; Tanty Oktavia
ComTech: Computer, Mathematics and Engineering Applications Vol. 17 No. 1 (2026): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v17i1.14513

Abstract

Digital transformation in the financial industry encourages organizations to adopt application integration systems to enhance operational efficiency and effectiveness. However, many information system implementation projects fail to meet expectations, particularly in guarantee institutions characterized by complex business processes. This study evaluates the success of an application integration system project in a guarantee company by applying an integrated framework that combines the DeLone and McLean (D&M) (2003) Information System Success Model with Critical Success Factors (CSF). By explicitly positioning CSF variables as antecedents of system quality, information quality, and service quality, this study extends the conventional use of the D&M model by incorporating managerial and organizational perspectives into the assessment of integration success. A quantitative approach is employed using survey data collected from 120 users of the Penjaminan Application Integration (PAI) system at PT XYZ, which are analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that CSF variables—namely top management support, internal communication, user training, and project risk management—significantly influence system quality, information quality, and service quality. Furthermore, these three quality dimensions have a significant effect on intention to use and user satisfaction, which in turn impact perceived net benefits for the organization. In conclusion, integrating managerial and system quality perspectives provides a more comprehensive understanding of application integration project success. These findings offer practical insights for improving IT project implementation strategies in the guarantee sector and in other industries with similar organizational and operational characteristics.
Comparative Performance Analysis of Object-Oriented Programming and Data-Oriented Programming in TensorFlow Mangapul Siahaan; Jefriyanto Chandra; Muhamad Dody Firmansyah
ComTech: Computer, Mathematics and Engineering Applications Vol. 17 No. 1 (2026): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v17i1.14648

Abstract

The rapid advancement of deep learning significantly increases computational demands, making performance optimization essential for model scalability and deployment. While numerous studies optimize neural network architectures, the effect of different programming paradigms on computational efficiency remains insufficiently explored. This study aims to compare Object-Oriented Programming (OOP) and Data-Oriented Programming (DOP) paradigms in TensorFlow-based deep learning workflows, focusing on their performance across four processing phases: build, compile, train, and evaluate, under a controlled experimental environment with repeated iterations and systematic measurements. Both paradigms are implemented using identical Convolutional Neural Network (CNN) architectures trained on the CIFAR-100 image dataset over thirty controlled experimental iterations. A custom profiler integrating the Python System and Process Utilities (psutil) and NVIDIA Management Library (pynvml) monitors real-time system performance, capturing CPU and GPU utilization as well as memory usage. The results reveal that DOP achieves better resource efficiency with lower memory usage (549.98 MB versus 676.25 MB), higher GPU utilization (64.68% versus 61.08%), and faster evaluation execution (1.50 seconds versus 2.59 seconds), while also attaining higher model accuracy (32.38% versus 28.08%). In contrast, OOP benefits from TensorFlow’s Sequential API optimizations, resulting in faster training times but greater CPU and memory consumption. These findings highlight that DOP provides superior runtime efficiency and offers practical benefits for performancecritical deep learning applications.

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