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All Journal ComEngApp : Computer Engineering and Applications Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JURNAL SISTEM INFORMASI BISNIS Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer JUITA : Jurnal Informatika Jurnal Teknologi dan Sistem Komputer Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Sains dan Informatika Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Mnemonic Jurnal Teknik Informatika (JUTIF) Jurnal Informatika Polinema (JIP) Scientific Journal of Informatics Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Irwan Budiman
Fakultas MIPA, Universitas Lambung Mangkurat Banjarbaru, Kalimantan Selatan
Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Health Professions Library & Information Science Materials Science & Nanotechnology Mathematics

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The Effectiveness of Data Imputations on Myocardial Infarction Complication Classification Using Machine Learning Approach with Hyperparameter Tuning Mazdadi, Muhammad Itqan; Saragih, Triando Hamonangan; Budiman, Irwan; Farmadi, Andi; Tajali, Ahmad
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i3.29479

Abstract

Complications from Myocardial Infarction (MI) represent a critical medical emergency caused by the blockage of blood flow to the heart muscle, primarily due to a blood clot in a coronary artery narrowed by atherosclerotic plaque. Diagnosing MI involves physical examination, electrocardiogram (ECG) evaluation, blood sample analysis for specific heart enzyme levels, and imaging techniques such as coronary angiography. Proactively predicting acute myocardial complications can mitigate adverse outcomes, and this study focuses on early prediction using classification methods. Machine learning algorithms such as Support Vector Machine (SVM), Random Forest, and XGBoost were employed to classify patient medical records accurately. Techniques like K-Nearest Neighbors (KNN) imputation, Iterative imputation, and Miss Forest were used to handle incomplete datasets, preserving vital information. Hyperparameter optimization, crucial for model performance, was performed using Bayesian Optimization, which minimizes the objective function by modeling past evaluations. The contribution to this study is to see how much influence data imputation has on classification using machine learning methods on missing data and to see how much influence the optimization method has when performing hyperparameter tuning. Results demonstrated that the Iterative Imputation method yielded excellent performance with SVM and XGBoost algorithms. SVM achieved 100% accuracy, precision, sensitivity, F1 score, and AUC. XGBoost reached 99.4% accuracy, 100% precision, 79.6% sensitivity, an F1 score of 88.7%, and an AUC of 0.898. KNN Imputation with SVM showed results similar to Iterative Imputation with SVM, while Random Forest exhibited poor classification outcomes due to data imbalance, causing overfitting.
Implementation of Ant Colony Optimization in Obesity Level Classification Using Random Forest Wardana, Muhammad Difha; Budiman, Irwan; Indriani, Fatma; Nugrahadi, Dodon Turianto; Saputro, Setyo Wahyu; Rozaq, Hasri Akbar Awal; Yıldız, Oktay
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4696

Abstract

Obesity is a pressing global health issue characterized by excessive body fat accumulation and associated risks of chronic diseases. This study investigates the integration of Ant Colony Optimization (ACO) for feature selection in obesity-level classification using Random Forests. Results demonstrate that feature selection significantly improves classification accuracy, rising from 94.49% to 96.17% when using ten features selected by ACO. Despite limitations, such as challenges in tuning parameters like alpha (α), beta (β), and evaporation rate in ACO techniques, the study provides valuable insights into developing a more efficient obesity classification system. The proposed approach outperforms other algorithms, including KNN (78.98%), CNN (82.00%), Decision Tree (94.00%), and MLP (95.06%), emphasizing the importance of feature selection methods like ACO in enhancing model performance. This research addresses a critical gap in intelligent healthcare systems by providing the first comprehensive study of ACO-based feature selection specifically for obesity classification, contributing significantly to medical informatics and computer science. The findings have immediate practical implications for developing automated diagnostic tools that can assist healthcare professionals in early obesity detection and intervention, potentially reducing healthcare costs through improved diagnostic efficiency and supporting digital health transformation in clinical settings. Furthermore, the study highlights the broader applicability of ACO in various classification tasks, suggesting that similar techniques could be used to address other complex health issues, ultimately improving diagnostic accuracy and patient outcomes.
Accurate Skin Tone Classification for Foundation Shade Matching using GLCM Features-K-Nearest Neighbor Algorithm Syahputra, Muhammad Reza; Mazdadi, Muhammad Itqan; Budiman, Irwan; Farmadi, Andi; Saputro, Setyo Wahyu; Rozaq, Hasri Akbar Awal; Sutaji, Deni
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4723

Abstract

Foundation shade matching remains a significant challenge in the beauty industry, particularly in Indonesia where consumers exhibit three distinct skin tone categories: ivory white, amber yellow, and tan. Manual foundation selection often results in mismatched shades, leading to customer dissatisfaction. This study presents a novel automated skin tone classification system combining Gray Level Co-Occurrence Matrix (GLCM) feature extraction with the K-Nearest Neighbor (KNN) algorithm. The GLCM method extracts four key texture features (contrast, homogeneity, energy, and entropy) from facial images, while KNN performs classification. A comprehensive dataset of 963 facial images was used, with 770 training and 193 test samples collected under controlled lighting conditions. After testing K values from 1 to 15, the optimal K=1 achieved 75.65% accuracy. Compared to baseline color histogram methods (60% accuracy), our GLCM-KNN approach demonstrates 15.65% improvement in classification performance. This research contributes to computer vision applications in beauty technology, enabling the development of mobile applications for virtual foundation try-on and personalized product recommendations. The findings have significant implications for the cosmetics industry, particularly for automated cosmetic shade matching systems and enhanced customer experience in online beauty retail. Further research is recommended to explore deep learning approaches and expand dataset diversity to improve accuracy.
Evaluation of User Experience in the Banjarbaru Disdukcapil Public Service Application Using User Experience Questionnaire and System Usability Scale Martalisa, Asri; Wahyu Saputro, Setyo; Turianto Nugrahadi, Dodon; Abadi, Friska; Budiman, Irwan
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.13780

Abstract

Purpose: Dukcapil Banjarbaru is an online-based government agency application used for various public services. According to the complaint report from Disdukcapil Banjarbaru, several users have reported similar problems and difficulties. The application has received a rating of 3.3 stars from approximately 24.000 users on the Google Play Store. Therefore, researchers conducted a user experience analysis using the UEQ methods and a usability evaluation using the SUS methods. Methods: This research analyzes user experience in applications using the UEQ to identify issues faced by users and evaluate usability through the System Usability Scale. The UEQ method is chosen for its efficiency and simplicity in assessing user experience within an application. The SUS method is employed because it is an effective approach for obtaining reliable statistical data and generating clear and accurate scores. Result: The UEQ benchmark results show that the scales for Attractiveness (1.59), Efficiency (1.68), Accuracy (1.66), and Stimulation (1.54) are categorized as "Good." The scales for clarity (1.37) and novelty (0.80) are classified as "Above Average." Meanwhile, the SUS score of 65 positions the application within the "acceptable" category for the acceptability range, the "D" category on the grade scale, and the "OK" category for adjective ratings. This indicates that while the Banjarbaru Dukcapil application has good usability, it requires improvements based on the total SUS score, which reveals several critical areas with scores below the average (258.4). Novelty: In this research, solutions for improvements are provided to Disdukcapil based on each aspect to improve the quality of the application, thereby offering better services to users.
Application of Adaboost Algorithm with SMOTE and Optuna Techniques in Sleep Disorder Classification Anshory, Muhammad Naufal; Mazdadi, Muhammad Itqan; Saragih, Triando Hamonangan; Budiman, Irwan; Saputro, Setyo Wahyu
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.99

Abstract

Data imbalance is a serious challenge in developing machine learning models for sleep disorder classification. When models are trained on an uneven distribution of classes, classification performance for minority classes such as insomnia and sleep apnea is often low. As a result, the overall accuracy may seem elevated, yet the sensitivity to important cases to be weak. Therefore, this research aims to design and develop a robust sleep disorder classification model with the AdaBoost algorithm, with improved performance through the integration of two main approaches, namely data balancing technique utilizing SMOTE and hyperparameter optimization using Optuna. This research contributes by showing that the combination of the two approaches can significantly improve model performance, not only in terms of global accuracy, but also accuracy on previously overlooked minority classes. The dataset utilized is the Sleep Health and Lifestyle Dataset which consists of 374 synthesized data and is divided into three categories: insomnia, sleep apnea, and none. This method stages include data preprocessing, data division using train-test split (80:20), application of SMOTE to balance the class distribution, hyperparameter tuning using Optuna, and model training with the AdaBoost algorithm. Evaluation was performed using classification metrics: accuracy, precision, recall, and F1-score. Results showed that mix of SMOTE and Optuna yielded the best results, accuracy 90.6%, F1-score 0.83871 for insomnia, and 0.81250 for sleep apnea. This performance was consistently superior to scenarios with no SMOTE or no tuning. This confirms the importance of using combination strategies to obtain fair and accurate classification on medical data. Future research is recommended to use real datasets as well as test the capabilities of this research on other models such as XGBoost or LightGBM.
Improving nutrient prediction models with polynomial and ratio features and mRMR selection Indriani, Fatma; Budiman, Irwan; Kartini, Dwi; Handayani, Lilies
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.9189

Abstract

Due to limited space and regulations, food labels often lack information on micronutrients, i.e., vitamins and minerals. Accurately predicting missing these micronutrient data is essential yet challenging. This study explores the feasibility of using machine learning to predict these missing nutrients based on a limited reported nutrient (protein and carbs). Using the Tabel Komposisi Pangan Indonesia (TKPI) dataset, we evaluated the performance of 12 diverse classifiers to predict binary classes ("low" or "high") for 13 target micronutrients. Random forest emerged as the best performing classifier with an average accuracy of 0.7421 across all target nutrients. Additionally, we introduced feature engineering techniques by incorporating polynomial and ratio features to enhance model performance. Minimum redundancy maximum relevance (mRMR) feature selection was then applied to identify the most informative features. This approach boosted the average accuracy of the random forest classifier to 0.7591. These findings highlight the efficacy of feature engineering and selection in enhancing nutrient prediction models, demonstrating the potential to improve consumer knowledge about unknown nutrients in food.
Early Fusion of CNN Features for Multimodal Biometric Authentication from ECG and Fingerprint Using MLP, LSTM, GCN, and GAT Priyatama, Muhammad Abdhi; Nugrahadi, Dodon Turianto; Budiman, Irwan; Farmadi, Andi; Faisal, Mohammad Reza; Purnama, Bedy; Adi, Puput Dani Prasetyo; Ngo, Luu Duc
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5299

Abstract

Traditional authentication methods such as PINs and passwords remain vulnerable to theft and hacking, demanding more secure alternatives. Biometric approaches address these weaknesses, yet unimodal systems like fingerprints or facial recognition are still prone to spoofing and environmental disturbances. This study aims to enhance biometric reliability through a multimodal framework integrating electrocardiogram (ECG) signals and fingerprint images. Fingerprint features were extracted using three deep convolutional networks—VGG16, ResNet50, and DenseNet121—while ECG signals were segmented around the first R-peak to produce feature vectors of varying dimensions. Both modalities were fused at the feature level using early fusion and classified with four deep learning algorithms: Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Graph Convolutional Network (GCN), and Graph Attention Network (GAT). Experimental results demonstrated that the combination of VGG16 + LSTM and ResNet50 + LSTM achieved the highest identification accuracy of 98.75 %, while DenseNet121 + MLP yielded comparable performance. MLP and LSTM consistently outperformed GCN and GAT, confirming the suitability of sequential and feed-forward models for fused feature embeddings. By employing R-peak-based ECG segmentation and CNN-driven fingerprint features, the proposed system significantly improves classification stability and robustness. This multimodal biometric design strengthens protection against spoofing and impersonation, providing a scalable and secure authentication solution for high-security applications such as digital payments, healthcare, and IoT devices.
Comparative Performance Evaluation of Linear, Bagging, and Boosting Models Using BorutaSHAP for Software Defect Prediction on NASA MDP Datasets Kartika, Najla Putri; Herteno, Rudy; Budiman, Irwan; Nugrahadi, Dodon Turianto; Abadi, Friska; Ahmad, Umar Ali; Faisal, Mohammad Reza
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5393

Abstract

Software defect prediction aims to identify potentially defective modules early on in order to improve software reliability and reduce maintenance costs. However, challenges such as high feature dimensions, irrelevant metrics, and class imbalance often reduce the performance of prediction models. This research aims to compare the performance of three classification model groups—linear, bagging, and boosting—combined with the BorutaSHAP feature selection method to improve prediction stability and interpretability. A total of twelve datasets from the NASA Metrics Data Program (MDP) were used as test references. The research stages included data preprocessing, class balancing using the Synthetic Minority Oversampling Technique (SMOTE), feature selection with BorutaSHAP, and model training using five algorithms, namely Logistic Regression, Linear SVC, Random Forest, Extra Trees, and XGBoost. The evaluation was conducted with Stratified 5-Fold Cross-Validation using the F1-score and Area Under the Curve (AUC) metrics. The experimental results showed that tree-based ensemble models provided the most consistent performance, with Extra Trees recording the highest average AUC of 0.794 ± 0.05, followed by Random Forest (0.783 ± 0.06). The XGBoost model provided the best results on the PC4 dataset (AUC = 0.937 ± 0.008), demonstrating its ability to handle complex data patterns. These findings prove that BorutaSHAP is effective in filtering relevant features, improving classification reliability, and strengthening transparency and interpretability in the Explainable Artificial Intelligence (XAI) framework for software quality improvement.
Enhancing Classification of Self-Reported Monkeypox Symptoms on Social Media Using Term Frequency-Inverse Document Frequency Features and Graph Attention Networks Rizian, Rizailo Akfa; Budiman, Irwan; Faisal, Mohammad Reza; Kartini, Dwi; Indriani, Fatma; Ahmad, Umar Ali
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5482

Abstract

Early detection of infectious diseases plays a crucial role in minimizing their spread and enabling timely intervention. In the digital era, social media has emerged as a valuable source of real-time health information, where individuals often share self-reported symptoms that can serve as early warning signals for disease outbreaks. However, textual data from social media is typically unstructured, noisy, and contextually diverse, posing challenges for conventional text classification methods. This study proposes a hybrid model combining Term Frequency–Inverse Document Frequency (TF-IDF) feature representation with a Graph Attention Network (GAT) to enhance the early detection of Monkeypox-related self-reported symptoms on Indonesian social media. A dataset of 3,200 tweets was collected through Tweet-Harvest and subsequently preprocessed and manually labeled, producing a balanced distribution between positive (51%) and negative (49%) samples. TF-IDF vectors were used to construct a document similarity graph via the k-Nearest Neighbors (k-NN) method with cosine similarity, enabling GAT to leverage both textual and relational information across posts. The model’s performance was evaluated using accuracy, precision, recall, and macro-F1, with macro-F1 serving as the primary indicator. The proposed TF-IDF + GAT model achieved 93.07% accuracy and a macro-F1 score of 93.06%, outperforming baseline classifiers such as CNN (92.16% macro-F1), SVM (85.73%), Logistic Regression (84.89%). These findings demonstrate the effectiveness of integrating classical text representations with graph-based neural architectures for improving social media based disease surveillance and supporting early epidemic response strategies.
Peningkatan Akurasi Model Boosting pada Prediksi Kesehatan Tidur Menggunakan Optuna Mazdadi, Muhammad Itqan; Saragih, Triando Hamonangan; Budiman, Irwan; Anshory, Muhammad Naufal
Jurnal Informatika Polinema Vol. 12 No. 2 (2026): Vol. 12 No. 2 (2026)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v12i2.8878

Abstract

Kualitas tidur memiliki peran penting dalam menjaga kesehatan fisik maupun mental, sementara gangguan tidur dapat meningkatkan risiko berbagai penyakit kronis. Perkembangan machine learning membuka peluang untuk melakukan prediksi kesehatan tidur secara lebih akurat melalui pemanfaatan data gaya hidup. Penelitian ini berfokus pada penerapan algoritma boosting, yaitu XGBoost, LightGBM, AdaBoost, dan GradientBoosting, dengan dukungan teknik hyperparameter tuning berbasis Optuna untuk meningkatkan akurasi prediksi. Dataset yang digunakan adalah Sleep Health and Lifestyle Dataset yang memuat variabel demografis, kebiasaan hidup, serta kondisi tidur. Tahapan penelitian meliputi praproses data, pembagian data latih dan uji, pelatihan model, optimasi hyperparameter menggunakan Optuna dengan metode Tree-structured Parzen Estimator (TPE), serta evaluasi model menggunakan metrik akurasi. Hasil eksperimen menunjukkan bahwa tuning dengan Optuna memberikan peningkatan akurasi pada beberapa model, khususnya LightGBM dan AdaBoost, dengan nilai akurasi mencapai 93,3% dan 90,7%. Sementara itu, XGBoost dan GradientBoosting menunjukkan performa stabil dengan akurasi tetap tinggi baik sebelum maupun sesudah tuning. Temuan ini menegaskan bahwa efektivitas tuning bergantung pada karakteristik algoritma yang digunakan. Secara keseluruhan, penelitian ini membuktikan bahwa Optuna dapat menjadi solusi efektif dalam meningkatkan kinerja model boosting untuk prediksi kesehatan tidur. Sebagai arah penelitian lanjutan, disarankan penggunaan metrik evaluasi yang lebih beragam, penerapan teknik penyeimbangan data, serta eksplorasi integrasi dengan metode deep learning untuk memperkaya hasil analisis.
Co-Authors Adi Mu'Ammar, Rifqi Adi, Puput Dani Prasetyo Ahdyani, Annisa Salsabila Ahmad Tajali Al Ghifari, Muhammad Akmal Al Habesyah, Noor Zalekha Andi Farmadi Anshory, Muhammad Naufal Antoh, Soterio Azizah, Azkiya Nur Azizah, Siti Roziana Bedy Purnama Deni Sutaji Dodon Turianto Nugrahadi Dwi Kartini, Dwi Faisal, M. Reza Fathmah, Siti Fatma Indriani Friska Abadi Halim, Kevin Yudhaprawira Halimah Halimah Herteno, Rudy Jumadi Mabe Parenreng Kartika, Najla Putri Lilies Handayani Mahmud Mahmud Martalisa, Asri Muhammad Alkaff Muhammad Itqan Mazdadi Muhammad Reza Faisal, Muhammad Reza Muliadi Muliadi MULIADI -, MULIADI Ngo, Luu Duc Noor Hidayah Nurdiansyah Nurdiansyah Prastya, Septyan Eka Priyatama, Muhammad Abdhi Radityo Adi Nugroho Rizian, Rizailo Akfa Rizky, Muhammad Hevny Rozaq, Hasri Akbar Awal Rudy Herteno Said, Muhammad Al Ichsan Nur Rizqi Saputro, Setyo Wahyu Saragih, Triando Hamonangan Syahputra, Muhammad Reza Tajali, Ahmad Umar Ali Ahmad Wahyu Caesarendra Wahyu Saputro, Setyo Wardana, Muhammad Difha Winda Agustina YILDIZ, Oktay Yunida, Rahmi