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All Journal Proceeding of the Electrical Engineering Computer Science and Informatics JURNAL MEDIA INFORMATIKA BUDIDARMA Information Management For Educators And Professionals (IMBI) Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Ilmiah Matrik METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Sebatik JUTIM (Jurnal Teknik Informatika Musirawas) Jurnal Nasional Komputasi dan Teknologi Informasi JURIKOM (Jurnal Riset Komputer) METIK JURNAL Building of Informatics, Technology and Science Jurnal Manajemen Informatika dan Sistem Informasi Journal of Information Systems and Informatics Just TI (Jurnal Sains Terapan Teknologi Informasi) Brahmana : Jurnal Penerapan Kecerdasan Buatan Tematik : Jurnal Teknologi Informasi Komunikasi TEPIAN Buletin Poltanesa Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat Informatika Wicida Jurnal Riset Inossa : Media Hasil Riset Pemerintahan, Ekonomi dan Sumber Daya Alam eProceedings of Engineering J-Icon : Jurnal Komputer dan Informatika Journal of Informatics, Electrical and Electronics Engineering Management of Information System Journal Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Sasambo: Jurnal Abdimas (Journal of Community Service)
Pitrasacha Adytia
STMIK Widya Cipta Dharma, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Neuroscience Social Sciences Transportation Other

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Classification of Diabetes Diseases Based on Medical Features Using Optimized Support Vector Machine Ita Arfyanti; Amelia Yusnita; Pitrasacha Adytia
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.v7i3.8880

Abstract

Diabetes mellitus is a chronic disease caused by impaired glucose metabolism and has become a global health threat with a steadily increasing prevalence each year. According to WHO and IDF, the number of people living with diabetes is projected to reach 783 million by 2045. This condition demands the development of an accurate and efficient early detection system to support medical decision-making. This study aims to develop an optimized Support Vector Machine (SVM)-based classification model to enhance the accuracy and interpretability of diabetes prediction. The dataset used is the Pima Indians Diabetes Dataset, which consists of eight medical features such as glucose level, blood pressure, and body mass index (BMI). The research stages include data preprocessing, class balancing using the Synthetic Minority Over-sampling Technique (SMOTE), parameter optimization with GridSearchCV, and interpretability analysis through SHapley Additive exPlanations (SHAP). The results show that the optimized SVM model with the Radial Basis Function (RBF) kernel achieved an accuracy of 82%, with a significant improvement in the diabetes class recall value from 0.564 to 0.83 after optimization. The Area Under Curve (AUC) value of 0.871 indicates the model’s effectiveness in distinguishing between positive and negative classes. The SHAP analysis reveals that Glucose, Age, BMI, and Diabetes Pedigree Function are the most influential features in prediction. These findings emphasize that the combination of normalization, balancing, hyperparameter optimization, and interpretability produces a reliable and transparent SVM model. This model has strong potential for implementation in Clinical Decision Support Systems (CDSS) for accurate and explainable early diabetes detection.
HYBRID ARTIFICIAL NEURAL NETWORK DAN RULE-BASED UNTUK EARLY WARNING SYSTEM STATUS GIZI BALITA BERBASIS DATA ANTROPOMETRI Muhammad Firza Fernanda; Wahyuni Wahyuni; Pitrasacha Adytia
JUTIM (Jurnal Teknik Informatika Musirawas) Vol. 11 No. 2 (2026): JUTIM (Jurnal Teknik Informatika Musirawas) Juni
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jutim.v11i2.3017

Abstract

Nutritional status of toddlers is a critical indicator of early childhood growth and health; however, manual identification methods remain prone to inconsistency and delayed decision-making, particularly when applied to large-scale data. The absence of a Hybrid system integrating Machine Learning and Rule-based approaches for an Early Warning System (EWS) of toddler nutritional status represents a research gap that needs to be addressed. This study implements an Artificial Neural Network (ANN) using the Multilayer Perceptron (MLPClassifier) algorithm on a synthetic dataset generated from the WHO child growth Z-score formula, with class distributions of Normal (54.57%), Tall (17.69%), Severely Stunted (16.54%), and Stunted (11.20%). Stepwise experiments were conducted on hidden layer configurations, activation functions, and solvers to determine the optimal model. The best configuration was achieved using hidden layers (64,32), ReLU activation, and LBFGS solver, yielding an accuracy of 0.9954 and a Macro F1-Score of 0.9935. Validation through 5-fold cross-validation produced a mean accuracy of 0.9955 with a standard deviation of 0.0005, confirming model stability and absence of overfitting. The model was integrated into a Rule-based EWS to provide early risk-level classification of toddler growth status. This Hybrid ANN and Rule-based combination proves effective as a decision support system in child healthcare.
Co-Authors Adha Trisna Lidya Ahmad Fahrijal Pukeng Alvin Hernanda Ekaputra Amelia Yusnita Amelia Yusnita Andriawan Arriyanti, Eka Asmaramany, Dimas Astin, Siti Namira Rizqi Aulia Abdul Rahman Bustomi , Tommy David Hendrawan Dhali Saputra Didi Kuswandi Ekawati Yulsilviana Ekawati, Hanifah Eko Jheremy Oktavianus Fariz Mirza Ahmad Hanifah Ekawati Hanifah Ekawati Harianto, Kusno Harpad, Bartolomius Heny Pratiwi I Nengah Riandika Ika Nurmila Avinda Ita Arfyanti Kasim, Fadly Kelik Sussolaikah Kusnandar Lisda Aulia Rahmi Maulana Bakti Maulana Umar Muhammad Ahsanu Qaulan Muhammad Fachri Sanjaya Muhammad Fahmi Muhammad Firza Fernanda Muhammad Hudzaifiy Nur Muhammad Ibnu Saad Saad Muhammad Ibnu Sa’ad Muhammad Ibnu Sa’ad Nurhuda, Asep Nursobah Nursobah, Nursobah Pahrudin, Pajar Pratama, Jerio Putra Rahmat Hidayat Reza Andrea Rizky Zakariyya Rasyad SA'AD, MUHAMMAD IBNU Salmon Salmon Salmon Sugih Niansyah Supriady Supriady Sussolaikah, Kelik Tommy Bustomi Ujang Sumarwan Wahyuni - Wahyuni wahyuni wahyuni William Carey Kornelius Yudha Satria Yuli Adam Prasetyo Yunita Yunita Yunita Yunita