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All Journal The Indonesian Journal of Computer Science
Santika, Aditya Purwa
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Akurasi Metode Mesin Pembelajaran dalam Analisis Variabel Penting Faktor Risiko Sindrom Down Palit, Oscar Oleta; Dhenanta, Rafi Prayoga; Susanto, Agnes Indarwati; Syawly, Adzky Matla; Ivansyah, Atthar Luqman; Santika, Aditya Purwa; Arifyanto, Mochamad Ikbal; Muttaqien, Fahdzi
The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i5.4354

Abstract

This study aims to identify risk factors for Down syndrome using machine learning methods. Data were obtained from an epidemiological case-control study conducted at Special Needs Schools in the cities and regencies of Tangerang. Methods used include Random Forest, K-Nearest Neighbors, Support Vector Machine (SVM), Naive Bayes, K-Means, Artificial Neural Network (ANN), and Multi-Layer Perceptron (MLP). The results indicate that maternal age, paternal age, and the time interval of parents' work before the child's birth are the most influential factors in the incidence of Down syndrome. The SVM method achieved the highest accuracy of 76% with data categorized into two groups and using important variables. In addition to SVM, Naive Bayes and Random Forest methods also demonstrated good performance for analyzing epidemiological data with case-control types.
Polinomial Jacobi dan T-Design untuk Kode Linear Susanto, Agnes Indarwati; Santika, Aditya Purwa
The Indonesian Journal of Computer Science Vol. 14 No. 3 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i3.4614

Abstract

This thesis explores the use of Jacobi polynomials and t-design properties in linear codes. The primary goal of the research is to develop a Python and SageMath program to compute the Jacobi polynomial for linear codes with multiple reference vectors. The methodology involves analyzing self-dual codes over various f ields to derive Jacobi polynomials under specific conditions. The results indicate that the analyzed codes do not satisfy the t-design criteria, as different random reference vectors yield varying Jacobi polynomials. The study offers insights into the relationship between linear codes and Jacobi polynomials, with suggestions for further exploration of more complex codes to meet the t-design criteria.
Co-Authors Arifyanto, Mochamad Ikbal Atthar Luqman Ivansyah Dhenanta, Rafi Prayoga Muttaqien, Fahdzi Palit, Oscar Oleta Susanto, Agnes Indarwati Syawly, Adzky Matla