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All Journal Jurnal Teknologi dan Manajemen Informatika CommIT (Communication & Information Technology) Jurnal TIMES Sinkron : Jurnal dan Penelitian Teknik Informatika JIKO (Jurnal Informatika dan Komputer) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jiko (Jurnal Informatika dan komputer) JURIKOM (Jurnal Riset Komputer) JURTEKSI Building of Informatics, Technology and Science Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Ilmiah Smart Journal of Applied Computer Science and Technology (JACOST) Bulletin of Computer Science Research Journal of Artificial Intelligence and Engineering Applications (JAIEA) Majalah Ilmiah METHODA TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Journal of Computing Theories and Applications QOSIM: Jurnal Pendidikan, Sosial & Humaniora Jurnal Ilmiah Smart Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer
Robet Robet
STMIK TIME
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences

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Comparative Analysis of Loss Functions for Predicting Autoimmunity from Molecular Descriptors Using Deep Learning Gunawan, Candra; Robet, Robet; Hendri, Hendri
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8581

Abstract

Drug-induced autoimmunity (DIA) presents a complex obstacle in pharmacological safety due to its rare occurrence and unpredictable manifestation, often compounded by class imbalance in clinical datasets. This study investigates the influence of three loss functions, Binary Cross-Entropy (BCE), Focal Loss, and Dice Loss, on the performance of deep learning architectures comprising Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and 2-Layer Neural Network (SimpleNN). Models were trained using numerical molecular descriptors from the publicly available DIA dataset. The architectures were chosen based on their complementary properties: MLP is suitable for high-dimensional tabular descriptor data, CNN was examined to explore whether 1D convolutions can capture localized feature interactions among correlated descriptors, and 2-Layer Neural Network served as a lightweight baseline for comparison. A stratified 5-fold cross-validation strategy was employed to ensure statistical robustness. The results demonstrate that the MLP model, optimized with Focal Loss, consistently delivered the highest classification performance, achieving average scores of 94% accuracy, 93% precision, 95% recall, 94% F1-score, and an AUC of 0.97. In contrast, CNN and SimpleNN architectures yielded less favorable outcomes under the same loss configurations. These findings highlight the importance of aligning loss function choice with model complexity in the context of imbalanced biomedical data. The insights from this work contribute to the development of more reliable computational frameworks for early-phase immunogenicity screening and support the advancement of precision pharmacovigilance strategies.
COMPARISON OF DECISION TREE AND RANDOM FOREST ALGORITHMS FOR ASTHMA Lase, Wisriani; Robet, Robet; Hendri, Hendri
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 12 No. 1 (2025): Desember 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v12i1.4192

Abstract

Abstract: Asthma is a chronic respiratory disease that affects millions of people worldwide, making early detection crucial to prevent complications. This study aims to compare the performance of the Decision Tree and Random Forest algorithms in classifying asthma based on clinical symptom data. The data were processed through feature selection and model training stages, then evaluated using accuracy, precision, recall, and F1-score.The experimental analysis revealed that the Random Forest algorithm surpassed the Decision Tree in all metrics, achieving 95.19% accuracy, 90.43% precision, 95.00% recall, and 93.00% F1-score. In contrast, the Decision Tree obtained 89.14% accuracy, 90.60% precision, 88.70% recall, and 89.70% F1-score. These results suggest that Random Forest is more robust and dependable, especially in managing complex and imbalanced medical datasets. Keywords: asthma detection; decision tree; random forest; machine learning. Abstrak: Asma merupakan penyakit pernapasan kronis yang memengaruhi jutaan orang di seluruh dunia sehingga deteksi dini sangat penting untuk mencegah komplikasi. Penelitian ini bertujuan membandingkan kinerja algoritma Decision Tree dan Random Forest dalam mengklasifikasikan asma berdasarkan data gejala klinis. Data diproses melalui tahapan seleksi fitur dan pelatihan model, kemudian dievaluasi menggunakan akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa Random Forest memberikan performa terbaik dengan akurasi 90.43%, presisi 95.00%, recall 95.00%, dan F1-score 93.00%. Sebaliknya, Decision Tree memperoleh akurasi 89.14%, presisi 90.60%, recall 88.70%, dan F1-score 89.70%. Hasil ini menunjukkan bahwa Random Forest lebih kuat dan dapat diandalkan, terutama dalam mengelola kumpulan data medis yang kompleks dan tidak seimbang. Kata kunci: deteksi asma; decision tree; random forest; pembelajaran mesin.
Comparative Analysis of XGBoost, KNN, and SVM Algorithms for Heart Disease Prediction Using SMOTE-Tomek Balancing Yuliana, Yuliana; Robet, Robet; Hoki, Leony
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15469

Abstract

Heart disease remains one of the leading causes of death worldwide, making early detection crucial for improving patient outcomes. This study aims to evaluate and compare the performance of several machine learning algorithms in detecting heart disease using the 2015 BRFSS dataset, which includes responses from 253,680 individuals. The three algorithms examined are Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The data preprocessing steps involved feature encoding, class imbalance handling using the Synthetic Minority Over-sampling Technique combined with Tomek Links (SMOTE-Tomek), and hyperparameter tuning through RandomizedSearchCV. The models were assessed on a hold-out validation set using several metrics, including accuracy, Receiver Operating Characteristic-Area Under the Curve (ROC-AUC), F1-score, precision, and recall. The results demonstrated that XGBoost achieved the highest performance, with an accuracy of 94%, a ROC-AUC score of 0.98, and an F1-score of 0.94. In comparison, KNN achieved an accuracy of 87% (ROC-AUC 0.95), while SVM attained an accuracy of 79% (ROC-AUC 0.86). These findings suggest that XGBoost is a robust model for large-scale heart disease classification and holds potential for implementation in clinical decision support systems.
IoT Sensor Data Analysis for Early Fire Detection Using Dynamic Threshold Br Tarigan, Widia; Robet, Robet; Tarigan, Feriani Astuti
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15478

Abstract

Early fire detection using Internet of Things (IoT) technology plays a vital role in minimizing potential material losses and casualties. Conventional systems generally still rely on static thresholds that are less adaptive to environmental dynamics, leading to high false alarm rates and delayed detection. This study proposes a dynamic threshold approach based on a hybrid method of Fuzzy Logic–Random Forest–Adaptive Z-Score and compares it with the static threshold method. Testing was conducted using publicly available secondary datasets, and the algorithms were implemented and tested in Jupyter Notebook. Evaluation was performed using accuracy, false alarm rate (FAR), detection time, F1-score, precision, and recall metrics. The test results show that the dynamic threshold method provides better performance with an increase in accuracy from 59.5% to 74.8%, a decrease in FAR from 31.1% to 14.3%, and a reduction in detection time from 21 seconds to 0 seconds. In addition, the F1-score increased from 0.459 to 0.638, precision from 0.473 to 0.716, and recall from 0.446 to 0.575. These results show that the dynamic threshold approach is more adaptive and reliable in IoT-based fire detection systems than conventional static threshold methods.
Comparison of XGBoost and Naive Bayes Models in Type 2 Diabetes Prediction with RFE Feature Selection Barus, Hanisa putri; Robet; Feriani Astuti Tarigan
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15509

Abstract

Type 2 diabetes mellitus is a chronic disease with an increasing prevalence rate that can cause serious complications if not detected early. The application of machine learning algorithms can aid prediction, but selecting the right model and features greatly determines the accuracy of the results. This study aims to compare the performance of the Extreme Gradient Boosting (XGBoost) and Naive Bayes algorithms in predicting type 2 diabetes with and without Recursive Feature Elimination (RFE) feature selection. The data used were from the UCI Machine Learning Repository, comprising 768 samples and eight clinical features. The research process included data preprocessing, dividing the data into 614 training data and 154 testing data, applying RFE to select the most influential features, model training, and evaluation using accuracy, precision, recall, F1-score, and AUC. The results show that Naive Bayes without RFE achieves 70.77% accuracy, 0.57377 precision, 0.648148 recall, F1-score 0.608696, and 0.772778 AUC, while Naive Bayes with RFE increases the accuracy to 74.02% and the AUC to 0.793333. Meanwhile, XGBoost with RFE provided the best results with an accuracy of 74.67%, precision of 0.653061, recall of 0.592593, F1-score of 0.621359, and the highest AUC of 0.804259. Besides, applying RFE also improves the computational efficiency. These findings indicate that applying RFE significantly improves classification and computation time performance. The practical implication is that this model could aid early detection of diabetes in clinical settings. Further research can be conducted by optimizing parameters and using more diverse datasets.
Comparative Analysis of Four Machine Learning Algorithms for Smoke Detection Using SMOTE-Rebalanced Sensor Data Liecero, Marcus; Robet, Robet; Hendrik, Jackri
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15546

Abstract

Smoke detection plays a critical role in preventing fire-related hazards, particularly in intelligent monitoring and early warning systems. Conventional smoke sensors often exhibit limited responsiveness in dynamic environmental conditions, prompting the adoption of IoT-based sensor data combined with machine learning techniques. This study presents a comparative evaluation of four supervised classification algorithms, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, and Gradient Boosting, using the Smoke Detection Dataset from Kaggle. The methodology integrates SMOTE to address class imbalance and Z-score normalization for feature standardization. Hyperparameter tuning was performed using GridSearchCV with 5-fold cross-validation, and model performance was assessed based on accuracy and execution time. Experimental results show that KNN achieved the highest accuracy (98.33%) with the lowest execution time (0.0327 s), whereas Decision Tree recorded the lowest accuracy (84.17%) but remained computationally fast (0.0406 s). Random Forest and Gradient Boosting demonstrated strong predictive capability (97.22% and 96.94%, respectively), but at higher computational costs (1.4338 s and 8.3819 s, respectively). Almost all models achieved perfect scores (1.00) for precision, recall, and F1-score following SMOTE-based balancing, except KNN which obtained slightly lower values (0.99). The findings indicate a trade-off between predictive performance and computational efficiency, suggesting that lightweight models such as KNN are better suited for real-time IoT-based smoke detection. In contrast, ensemble models may be more appropriate for backend analysis. This research contributes an integrated evaluation framework that combines data rebalancing, multi-model benchmarking, and time-based performance analysis, providing practical insights for the development of responsive and scalable early smoke detection systems.
Comparative Study of Baseline and CBAM-Enhanced ResNet50 and MobileNetV2 for Indonesian Rupiah Banknote Classification Alvin, Alvin; Robet, Robet; Feriani, Feriani Astuti Tarigan
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15558

Abstract

This study investigates the performance of Convolutional Neural Network (CNN) architectures enhanced with Convolutional Block Attention Module (CBAM) for Indonesian banknote classification. Although attention mechanisms have shown strong potential in improving fine-grained visual recognition, their effectiveness for the classification of banknotes with fine textures and similar color patterns remains underexplored, forming a key research gap addressed in this work. Four architectures, ResNet50, ResNet50+CBAM, MobileNetV2, and MobileNetV2+CBAM, were evaluated using K-Fold cross-validation on a dataset of 1,281 images representing seven banknote denominations. Experimental results show that ResNet50 achieves strong baseline performance with a weighted Train accuracy of 99.14% and a Val accuracy of 96.72%, while the integration of CBAM further improves feature discrimination, with ResNet50+CBAM obtaining the highest average accuracy across all folds with a weighted Train accuracy of 100% and a Val accuracy of 99.45%. MobileNetV2 showed lower performance due to its lightweight capacity with a Train accuracy of 91.88% and a decrease in Val accuracy of 85.71%. However, the addition of CBAM provided measurable improvements and greater stability with a Train accuracy of 99.61% and Val accuracy of 92.82%. Overall, CBAM improved CNN’s ability to focus on spatial information and salient channels, resulting in more reliable classification. ResNet50+CBAM emerged as the best-performing model, offering the best balance between accuracy and consistency. These findings support the development of reliable computer vision systems for financial technology applications, including automatic banknote recognition, counterfeit detection, and secure transaction verification.
Klasifikasi Multikelas Tingkat Diabetes Berdasarkan Indikator Kesehatan Pasien Menggunakan Strategi One-vs-Rest Panjaitan, Tabitha Martha Agustine; Robet; Octara Pribadi
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i2.8985

Abstract

Diabetes is a non-communicable disease with a steadily increasing global prevalence. It often remains undiagnosed in its early stages, particularly during the prediabetic phase, which typically lacks noticeable symptoms. This study aims to develop a multi-class classification model to predict diabetes severity levels non-diabetic, prediabetic, and diabetic based on patient health indicators. A One-vs-Rest (OvR) strategy was employed, training each class against a combination of the others. The dataset was derived from the 2015 National Health Survey, comprising over 250,000 patient records with features such as blood pressure, body mass index, cholesterol levels, history of heart disease, and physical activity. Two machine learning algorithms, Logistic Regression and Random Forest, were applied to train the models. Class imbalance was addressed using the Synthetic Minority Over-sampling Technique (SMOTE). Evaluation metrics included accuracy, precision, recall, F1-score, and confusion matrix. The results show that the Random Forest model achieved an average accuracy of 93% and consistently high F1-scores, particularly for the prediabetic class of 98%. The most influential predictors were high blood pressure, obesity, and insufficient physical activity. This study contributes to the development of a reliable and efficient data-driven system for early diabetes risk detection.
Analisis Komparatif Model Regresi Machine Learning untuk Prediksi Prestasi Akademik Siswa dengan Optimasi Hyperparameter Hose, Fernando; Robet, Robet; Hendri, Hendri
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9240

Abstract

Low accuracy in the early identification of at-risk students often hinders timely academic intervention. This study analyzes and compares seven machine learning algorithms to predict student academic achievement, aiming to provide a foundation for a reliable early warning model. The dataset includes 2.392 students with 15 features covering demographics, learning behavior, and environmental support. Model training was performed using GridSearchCV optimization combined with stratified cross-validation to mitigate overfitting.Performance was evaluated using MAE, RMSE, and R². The results show CatBoost performed the best R² = 0,774; RMSE = 0,581; MAE = 0,306) followed by LightGBM (R² = 0,771) and Gradient Boosting (R² = 0,767), while MLP showed the lowest performance. Feature importance analysis placed GPA as the dominant predictor, followed by absenteeism and weekly study time. These findings affirm the superiority of boosting-based models in capturing complex nonlinear relationships and provide a practical framework for educational institutions to build data-driven early warning systems.
Comprehensive Comparison of TF-IDF and Word2Vec in Product Sentiment Classification Using Machine Learning Models Sinaga, Asra Gretya; Robet, Robet; Pribadi, Octara
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11582

Abstract

Sentiment analysis supports data-driven decisions by turning product reviews into reliable polarity labels. We compare four text representations, TF-IDF, TF-IDF reduced via SVD, Word2Vec (trained from scratch), and a hybrid TF-IDF(SVD-300). Word2Vec, for sentiment classification of Indonesian Shopee product reviews from Kaggle (~2.5k texts). After normalization (with optional emoji handling and Indonesian stemming), ratings are mapped to binary sentiment (≤2 negative, ≥4 positive; 3 discarded). Each representation is evaluated with Logistic Regression, Support Vector Machines (linear/RBF), Naive Bayes, and Random Forest under stratified 5-fold cross-validation. TF-IDF with Logistic Regression (C=1.0) yields the best results (F1-macro = 0.816 ± 0.026; Accuracy = 0.816 ± 0.026), with LinearSVC as a strong runner-up. Word2Vec (scratch) performs lower, consistent with limited data being insufficient to learn stable embeddings, while the hybrid representation offers only modest gains over Word2Vec and does not surpass TF-IDF. These findings indicate that TF-IDF is the most reliable and consistent representation for small, short-text review datasets, and they underscore the impact of feature design on downstream classification performance.
Co-Authors Agus Maringan Siahaan Agus Maringan Siahaan Alvin Alvin Alvin Alvin, Alvin Andi Andi Andy Andy Andy Andy Ari Rizkita Ari Rizkita Arnold Adimabua Ojugo Barus, Hanisa putri Bayu Teta Bimo Haryo Setyoko Br Tarigan, Widia CANDRA GUNAWAN Carles Juliandy Chandra Chandra De Rosal Ignatius Moses Setiadi Didik Aryanto Didik Aryanto Donna Natalia edi edi Edi Wijaya Edi Wijaya Eferhire Valentine Ugbotu Etika Kartikadarma Feriani Astuti Tarigan Feriani Astuti Tarigan Feriani, Feriani Astuti Tarigan Haria Wirawan Hendri Hendri Hendri Hendri Hoki, Leony Hose, Fernando Iqbal Giffari Ritonga Jackri Hendrik Jenime Darwin Jerico Setiawan Johanes Terang Kita Perangin Angin Jonathan Kevin Fernando Juan Charles Kelvin Leonardi Kohsasih Lase, Wisriani Liecero, Marcus Md Kamruzzaman Sarker Michael Angelo Nicholas Tio Nurhayati Harahap Octara Pribadi Panjaitan, Tabitha Martha Agustine Perangin Angin, Johanes Poppy Amalia Putri, Jihan Hidayah Randy Wilson Rizkita, Ari Robin Robin Robin Robin, Robin Robin Zheng Satriya Miharja Satriya Miharja Siahaan, Agus Maringan Simanjuntak, Martina Sances Sinaga, Asra Gretya Siregar, Tarq Hilmar Suyud Widiono Suyud Widiono Tabitha Chukwudi Aghaunor Vic Devlin Yehezkiel E.A Parapak Yessica Yamin Yuliana Yuliana