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All Journal Jurnal Informatika dan Rekayasa Komputer Computer & Science Industrial Engineering Journal
saputra, sahril
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Computer Science & IT

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ANALISIS KELAYAKAN PENERIMA BANTUAN COVID-19 MENGGUNAKAN METODE K–MEANS PADA KECAMATAN SAGULUNG KOTA BATAM saputra, sahril; Saragih, Saut Pintubipar
Computer Science and Industrial Engineering Vol 5 No 1 (2021): Comasie
Publisher : LPPM Universitas Putera Batam

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Abstract

During the pandemic, economic difficulties are part of the problems that occur to the residents of Tembesi Village, Sagulung District. Cash Social Assistance, Non-Cash Food Assistance, and Family Hope Program are several types of assistance that have been received by residents of Tembesi Village, but in its distribution, there are still many beneficiaries who do not meet the criteria based on the rules given by the government. K-Means Clustering Analysis method was chosen to solve this problem. This study aims to find beneficiaries who really deserve assistance of Cash.Social.Assistance, Non-Cash.Food.Assistance, and Family.Hope Program with the following criteria: poor people, not working, and not receiving more than one aid. This study uses 92 recipient data as research samples to be processed and the results obtained from this research are the criteria for not working with a value of C1 0.250 and C2 0.969 which have the highest feasibility level compared to the other two criteria.
Analisis Prediksi Kelulusan Mahasiswa Universitas Dinamika Bangsa Menggunakan Metode Naïve Bayes Hakim, Muhammad Furqan; Saputra, Sahril
Jurnal Informatika Dan Rekayasa Komputer(JAKAKOM) Vol 5 No 1 (2025): JAKAKOM Vol 5 No 1 APRIL 2025
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jakakom.2025.5.1.1999

Abstract

In order to facilitate the learning process, Universitas Dinamika Bangsa (UNAMA) has a database. Every year, the alumni data gets larger, and the database can be used. Utilising alumni data involves classifying and analysing long-term study periods of Universitas Dinamika Bangsa (UNAMA) students using the naïve bayes method.The results of the naïve bayes classification in the student of information system with the highest accuracy are obtained by using the Use Training Set, which consists of 161 correctly classified instances and 39 incorrectly classified instances, with an accuracy percentage of 85% for correctly classified instances and 19.5% for incorrectly classified instances. The results of attribute selection using the Classifier Attribute Evaluation algorithm (ClassifierAttributeEval) indicate that IPK is the attribute that has the greatest influence on kelulusan speed. Akurasi in the model is calculated using a confusion matrix, and at the beginning of the data-data mahasiswa, there is a lot of noise, which is revealed through the data cleaning process. It is the process of reducing noise in data using Microsoft Excel, which the author typically uses to analyse data From of information System students. Overall accuracy is 77.5%, which is a very good accuracy when analysing training data.
Analisis Perbandingan Algoritma K-Means Dan K-Medoids Dalam Mengukur Tingkat Kepuasan Mahasiswa Terhadap Pelayanan Akademik Saputra, Sahril; Kurniabudi; Jasmir
Jurnal Informatika Dan Rekayasa Komputer(JAKAKOM) Vol 5 No 2 (2025): JAKAKOM Vol 5 No 2 SEPTEMBER 2025
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jakakom.2025.5.2.2292

Abstract

This study aims to analyze the comparison of K-Means and K-Medoids algorithms in measuring the level of student satisfaction with academic services at the Islamic Institute of Mamba'ul Ulum Jambi. Student satisfaction data were collected through questionnaires and analyzed using both algorithms with the help of RapidMiner tools. Clustering results were evaluated using the Davies Bouldin Index (DBI) to determine the most optimal algorithm. The results showed that most students at the Islamic Institute of Mamba'ul Ulum Jambi were very satisfied with the academic services provided. Clustering with K-Means and K-Medoids successfully grouped students into three clusters: "Very Satisfied", "Satisfied", and "Unsatisfied". The K-Means algorithm produced clusters with 450 members ("Very Satisfied"), 351 members ("Satisfied"), and 218 members ("Unsatisfied"). Meanwhile, K-Medoids produced clusters with 638 members ("Very Satisfied"), 270 members ("Satisfied"), and 111 members ("Unsatisfied"). Based on the DBI value, the K-Medoids algorithm (0.222) showed better performance than K-Means (0.396) in clustering student satisfaction data. This study has important implications for the Islamic Institute of Mamba'ul Ulum Jambi in evaluating and improving academic services
Co-Authors Hakim, Muhammad Furqan Jasmir Kurniabudi Saragih, Saut Pintubipar