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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JUTIM (Jurnal Teknik Informatika Musirawas)
Wibowo, Yudha
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Computer Science & IT Control & Systems Engineering Engineering Other

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Metaheuristic-Optimized SVM for Stunting Risk Detection in Pregnancy Wibowo, Yudha; Agung Mulyo Widodo; Gerry Firmansyah; Budi Tjahjono
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14710

Abstract

Stunting is a chronic growth disorder that originates during pregnancy, making early risk detection crucial for effective prevention and long-term child development. This study introduces a stunting risk prediction model based on urine testing, employing a Support Vector Machine (SVM) algorithm enhanced through metaheuristic optimization. Three metaheuristic algorithms—Grey Wolf Optimizer (GWO), Simulated Annealing (SA), and Firefly Algorithm (FA)—were utilized to fine-tune the SVM hyperparameters (C and gamma). Clinical urine samples collected from pregnant women served as the dataset for model training and validation. The results indicate that the SVM model optimized using GWO achieved the highest prediction accuracy at 94.15%, outperforming both the default SVM (88.46%) and the models optimized using SA (94.12%) and FA (85.71%). Additionally, significant improvements were observed in precision, recall, and F1-score metrics, affirming the effectiveness of metaheuristic tuning in enhancing classification performance. These findings highlight the potential of integrating metaheuristic algorithms with SVM for robust medical prediction tasks, especially in the early detection of stunting risks. The proposed model offers a promising and non-invasive diagnostic approach that can be implemented in prenatal care settings, enabling timely interventions to mitigate stunting and improve maternal and child health outcomes.
DEVELOPMENT OF AN ANDROID-BASED LAUNDRY APPLICATION SYSTEM WITH SELF-SERVICE FEATURES Lelitasari, Anis; Ilyasa, Reza; Vetian, Rifky Akbar; Satria, Rangga Gading; Kurniawan, Eko; Wibowo, Yudha; Suhendry, Muhammad Roffi; Adhisthanaya, Wayan , Deva
JUTIM (Jurnal Teknik Informatika Musirawas) Vol 10 No 2 (2025): JUTIM (JURNAL TEKNIK INFORMATIKA MUSIRAWAS) DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jutim.v10i2.2788

Abstract

The increasing societal mobility and consumer demand for independent solutions have amplified the need for efficient and convenient laundry services. Traditional laundry operations often struggle with common issues such as long waiting times, potential order inaccuracies, and limited transparency. The primary objective of this research was to develop an Android-based laundry application system featuring robust self-service capabilities. This application empowers customers to seamlessly perform online ordering, secure digital payments, and real-time laundry status tracking, effectively minimizing the need for direct staff intervention. This innovative approach is designed to significantly reduce customer wait periods and enhance the precision of order data. The system was developed using the systematic Waterfall Model. The process involved in-depth requirements analysis, detailed system design, program code implementation, and thorough testing to ensure optimal and bug-free operation. The culmination of this research is a functional prototype of the Android-based application, featuring an intuitive interface and comprehensive self-service functions. This application is expected to both enhance the operational efficiency for laundry providers and deliver a more practical, modern, and independent experience for users managing their laundry needs.
Co-Authors Adhisthanaya, Wayan , Deva Budi Tjahjono Eko Kurniawan Gerry Firmansyah Ilyasa, Reza Lelitasari, Anis Satria, Rangga Gading Suhendry, Muhammad Roffi Vetian, Rifky Akbar Widodo, Agung Mulyo