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All Journal Faktor Exacta Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Decky Ryansyah
Universitas Budi Luhur
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Penerapan Algoritma K-Nearest Neighbors (KNN) dalam Menentukan Jenis KB Menggunakan Google Colab Abdul Hadi; Decky Ryansyah; Goenawan Radzi Brotosaputro
Faktor Exacta Vol 18, No 2 (2025)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v18i2.28604

Abstract

This study aims to apply the K-Nearest Neighbors (KNN) algorithm to determine the appropriate contraceptive method (KB) based on demographic data and user characteristics. The main problem faced is the lack of an effective decision support system to assist potential KB users in selecting the most suitable contraceptive method according to individual conditions. Additionally, the selection of contraceptive methods is often done manually by healthcare professionals without the support of predictive technology that could enhance recommendation accuracy. This research was conducted using Google Collab as a data processing platform, utilizing Python libraries such as Pandas, NumPy, and Scikit-learn. The dataset used includes information about KB users, including age, number of children, health history, and personal preferences. The data was pre processed to handle missing values and normalized to suit the analysis. The KNN model was tested with variations of the k value to find the optimal parameter that yields the highest accuracy. The results showed that the KNN algorithm was able to recommend contraceptive methods with an accuracy of 76% at k = 5. The main finding of this study is that the KNN model can be used as a decision support tool to determine the most appropriate contraceptive method for individuals. This research is expected to support efforts to improve reproductive health services through the utilization of machine learning technology.
Analisis Strategi Penerimaan Peserta Didik Baru Menggunakan K-Means Clustering dengan Optimasi Elbow Abdul Hadi; Decky Ryansyah; Goenawan Brotosaputro
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 02 (2026): Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Publisher : Program Studi Teknik Informatika, FTIK, Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/jrami.v7i02.1110

Abstract

New Student Admission is an annual routine activity at schools, including SMP Citra Negara Depok. Every year, the school receives a large amount of registration data, but it has not been optimally utilized to determine the new student admission strategy for the following year, resulting in a decrease in the number of applicants. This study aims to cluster effective new student admission strategies using the K-Means clustering method optimized with the Elbow method and min-max normalization. The dataset used is derived from the registration data for the 2021–2022 and 2022–2023 academic years, with attributes including school name, number of classes, number of registrants, and difference. The Elbow Method results indicate that the optimal number of clusters is three. The K-Means process stops at the 7th iteration out of a maximum of 10 iterations. The clustering results in three strategies: a 55% discount during the October–December period, a brochure distribution strategy, and a 50% discount during the January–June period. The research findings indicate that the K-Means method optimized with the Elbow Method can help determine a more effective new student admission strategy for the school.
Co-Authors Abdul Hadi Abdul Hadi Goenawan Brotosaputro Goenawan Radzi Brotosaputro