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All Journal ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA Jurnal Penelitian Sistem Informasi Indonesian Journal of Education And Computer Science Jurnal Penelitian Teknologi Informasi dan Sains Modem : Jurnal Informatika dan Sains Teknologi Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi
Marto Sihombing
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Computer Science & IT Decision Sciences, Operations Research & Management Education

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Journal : Modem : Jurnal Informatika dan Sains Teknologi

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Pemodelan K-Nearest Neighbor Untuk Identifikasi Pola Kepuasan Mahasiswa Terhadap Pelayanan Kampus (Studi Kasus : STMIK Kaputama) Muhammad Rizky R Ritonga; Marto Sihombing; Selfira Selfira
Modem : Jurnal Informatika dan Sains Teknologi. Vol. 2 No. 4 (2024): Oktober : Modem : Jurnal Informatika dan Sains Teknologi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/modem.v2i4.238

Abstract

This research focuses on using the K-Nearest Neighbor (KNN) algorithm to model student satisfaction with campus services. The study finds that the quality of the dataset strongly influences the accuracy of the KNN classification results. Factors such as data cleanliness, balanced class distribution, and sufficient training data volume are highlighted as crucial for a successful model. The research also emphasizes the significance of proper feature selection in enhancing classification performance, suggesting that irrelevant features can introduce noise and decrease model accuracy. The model was evaluated using a dataset of 1032 data points and K=5, achieving an accuracy of 93.72%. While the model performed well for certain classes such as "Very Good" and "None", challenges were encountered in classifying the "Fair" and "Deficient" classes. The study concludes that KNN is effective in identifying student satisfaction patterns but highlights the need for improvements in accurately classifying these challenging classes. Ultimately, the research underscores the importance of data quality and feature selection in enhancing the performance of classification models for student satisfaction analysis.
Diagnosa Penyakit Tuber Culosis (TBC) menggunakan Metode Case Based Reasoning (CBR) : (Studi Kasus : RSUD Dr.R.M. Djoelham) Muhammad Reza Habibi; Rusmin Saragih; Marto Sihombing
Modem : Jurnal Informatika dan Sains Teknologi. Vol. 2 No. 4 (2024): Oktober : Modem : Jurnal Informatika dan Sains Teknologi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/modem.v2i4.212

Abstract

Tuberculosis (TB) is one of the infectious diseases caused by Mycobacterium tuberculosis bacteria infection in the human lungs. Tuberculosis is a disease that can be transmitted from people with TB through coughing, sneezing, talking, laughing or singing. Lack of public knowledge about TB and lack of funds for health checks make many people late to be treated. Expert systems are technologies developed based on programs, in accordance with human methods and mindsets. This aims to help people who want to check their health, but are hampered by costs, besides saving time if the examination place is far from the residential environment of the community concerned. Expert systems require a method that can help solve existing problems. In this study, the method used is the Case-Based Reasoning (CBR) method, because the main function of this method is to diagnose the disease. The calculation process of the Case-Based Reasoning (CBR) method which looks for the similarity value or proximity of old cases to new cases of a patient.
Co-Authors Ambarita, Indah Danianty Miranda Br. Bangun Desiska Natalia Br. Purba Hari sabana Magdalena Simanjuntak Muhammad Reza Habibi Muhammad Rizky R Ritonga Nanda Wahyudi Nasution Novriyenni Novriyenni Pakpahan, Victor Maruli Pardede, Akim Manaor Hara Rahmadani RR. Ella Evrita Hestiandari Rusmin Saragih, Rusmin Selfira Selfira William Jhonatan