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All Journal Teknika TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Informatika Jurnal Ilmiah KOMPUTASI Jurnal Nasional Komputasi dan Teknologi Informasi IJISCS (International Journal Of Information System and Computer Science) JSR : Jaringan Sistem Informasi Robotik Jurnal Teknik Informatika (JUTIF) IndraTech TIERS Information Technology Journal SIENNA Seminar Nasional Teknologi dan Multidisiplin Ilmu IJISCS (International Journal of Information System and Computer Science)
Sriyanto, Sriyanto
Department Of Informatics Engineering, Institute Of Informatics And Business Darmajaya Lampung
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Library & Information Science Neuroscience Social Sciences Transportation Other

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NEURAL NETWORK OPTIMIZATION WITH GENETIC ALGORITHM FOR HEART DISEASE PREDICTION M Agus Badruzaman Al Khoir; Sriyanto Sriyanto
IJISCS (International Journal of Information System and Computer Science) Vol 6, No 2 (2022): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v6i2.1235

Abstract

Coronary Heart Disease (CHD) is a contributor to the number 1 cause of death in the world besides cardiovascular disease. The tendency of Indonesian people who do not know and ignore coronary heart disease is a factor that causes Indonesia to be high a contributor to deaths caused by coronary heart disease. This research is expected to produce new predictions of heart disease using genetic optimization of neural networks with better prediction results and can obtain algorithms with new percentage values in predicting coronary heart disease. Genetic optimization of the neural network is used because the algorithm follows the human nervous system which has the characteristics of parallel processing, processing elements in large quantities, and fault tolerance. The results of the research carried out are the accuracy obtained by 82.18% and increased to 83.50% after using genetic algorithm optimization, from these results it can be concluded that the neural network algorithm can be better if it is supported by genetic algorithm optimization
Enhancing Chronic Kidney Disease Classification Using Decision Tree And Bootstrap Aggregating: Uci Dataset Study With Improved Accuracy And Auc-Roc Zuriati, Zuriati; Meilantika, Dian; Arpan, Atika; Permata, Rizka; Sriyanto, Sriyanto; Mas'ud, Mohd. Zaki
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.2025.6.5.5271

Abstract

Chronic Kidney Disease (CKD) is a progressive medical disorder that requires timely and precise identification to avoid permanent impairment of kidney function. However, Decision Tree models, although widely used in clinical applications due to their transparency, ease of implementation, and ability to handle both categorical and numerical data, are prone to overfitting and instability when applied to small or imbalanced datasets. The purpose of this study is to optimize CKD classification by integrating Bootstrap Aggregating (Bagging) with Decision Tree to enhance accuracy and robustness. The methodology involves testing two model variants a standalone Decision Tree and a Bagging-supported Decision Tree using 10-fold cross-validation and evaluating performance with accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC-ROC). Findings reveal that Bagging enhances model accuracy from 0.980 to 0.987, raises precision from 0.976 to 1.000, and improves recall from 0.954 to 0.954, and increases F1-score from 0.965 to 0.976. These results demonstrate that Bagging significantly improves the reliability and generalizability of Decision Tree classifiers, making them more effective for CKD prediction.
Comparative Analysis Of Machine Learning Algorithms For Dengue Fever Prediction Based On Clinical And Laboratory Features Sriyanto, Sriyanto; Aziz, RZ Abdul; Rahayu, Dewi Agushinta; Zuriati, Zuriati; Abdollah, Mohd Faizal; Irianto, Irianto
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.2025.6.6.5309

Abstract

Dengue fever (DF) remains a global health problem requiring accurate early detection to prevent severe complications. This study applies machine learning (ML) algorithms to clinical and laboratory data for improving diagnostic accuracy. Six classifiers were compared: Decision Tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Naïve Bayes (NB), Neural Network (NN), and Support Vector Machine (SVM). The dataset consists of 1,003 patient records with nine feature columns, of which 989 were used after preprocessing. Class distribution was imbalanced, with 67.6% positive and 32.4% negative cases. Model performance was evaluated using 10-fold cross-validation based on accuracy, precision, recall, F1-score, confusion matrix, and ROC curve analysis. The results indicate that DT achieved the highest performance with 99.4% accuracy, 99.4% precision, 99.7% recall, and 99.6% F1-score, slightly outperforming NN. KNN, LR, and SVM produced comparable results, while NB showed substantially lower accuracy (44.3%) and limited discriminatory power. ROC analysis confirmed these findings, with DT, NN, SVM, and LR achieving AUC values between 0.992 and 0.999, whereas NB performed poorly. These findings highlight the strong potential of ML algorithms, particularly DT, to support medical decision systems, strengthen informatics-based decision support applications, and enhance the accuracy and speed of dengue diagnosis in clinical practice.
Hybrid Machine Learning Approach for Nutrient Deficiency Detection in Lettuce Zuriati, Zuriati; Widyawati, Dewi Kania; Arifin, Oki; Saputra, Kurniawan; Sriyanto, Sriyanto; Ahmad, Asmala
TIERS Information Technology Journal Vol. 6 No. 2 (2025)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v6i2.7143

Abstract

Early detection of nutrient deficiencies in lettuce is essential for precision agriculture. However, this task remains challenging due to limited data availability and class imbalance, which reduce model sensitivity toward minority classes and hinder generalization. This study introduces a hybrid machine learning approach integrating SMOTE, Optuna, and SVM to enhance the accuracy of nutrient deficiency classification using digital leaf image analysis. The dataset, obtained from Kaggle, includes four categories: Nitrogen Deficiency (-N), Phosphorus Deficiency (-P), Potassium Deficiency (-K), and Fully Nutritional (FN). Image features were extracted using MobileNetV2 pretrained on ImageNet and classified with a Support Vector Machine. Three scenarios were tested: (1) SVM before SMOTE, (2) SVM after SMOTE, and (3) Optuna-SVM after SMOTE, evaluated using accuracy, precision, recall, and f1-score. The hybrid model achieved the best performance with accuracy 0.929, precision 0.946, recall 0.835, and f1-score 0.869, outperforming the other scenarios. This hybrid framework effectively addressed class imbalance and improved classification margin stability through adaptive hyperparameter tuning using the Tree Structured Parzen Estimator within Optuna. The novelty of this study lies in combining MobileNetV2 based feature extraction with SMOTE and Optuna-SVM for small agricultural datasets. The proposed approach offers an efficient, accurate, and practical solution for automated nutrient deficiency diagnosis and contributes to the development of AI-driven smart agriculture systems.
Co-Authors A. Toding Abdollah, Mohd Faizal Adhi Tristiyanto Agiska Ria Supriyatna Ahmad, Asmala Ahmed Jamal Abdullah Al-Gburi Arifin, Oki Arpan, Atika Asep Eryana Asep Eryana Ayu Brahdika Putri Aziz, RZ. Abdul Chairani Chairani Dewi Agushinta R. Firmansyah Firmansyah Yunialfi Fitria . Hartono Hartono Herwanto, Riko Irianto Irianto Irma Agustina Juwariyem Juwariyem Khodijah Khodijah Kurniawan Saputra M Agus Badruzaman Al Khoir M. Abu Mas'ud, Mohd. Zaki meilantika, Dian Mohammed Yousif Zeain Muhamad Fahrurrozi Nursiyanto Nurul Qomariyah Oktaf Rina Onno W. Purbo Onno W. Purbo Pakarti Riswanto Permata, Rizka R. Syahputri Suhendro Suhendro Sutedi Sutedi Widyawati, Dewi Kania Y. Suhendro Z. Zakaria Zuriati, Zuriati