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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA
N P Dharshinni
Universitas Prima Indonesia, Medan
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Penerapan Metode K-Medoids Clustering Untuk Mengelompokkan Ketahanan Pangan N P Dharshinni; Ciok Fandi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4939

Abstract

Food is a basic need that must be fulfilled and easily accessible to the entire community. After the end of the pandemic period, it still caused several sectors to decline, including the agricultural sector, which resulted in crop yields also declining. The problem faced by several regions in Indonesia, one of which is the North Sumatra region, is that the availability of food products has decreased and increased unstably due to the lack of information about the grouping food security every year. This results in the food needs of the people in each region being unfulfilled. The purpose of this study is to group areas with the number of increases and decreases in food crop yields in North Sumatra using the K-Medoids algorithm. The K-Medoids algorithm includes a deflection algorithm that is quite efficient in carrying out the shaking of small datasets and the search for the most representative points and can overcome outliers. So that it can be used in the floundering of the influence of productivity and the level of food security. The results showed that the application of the K-Medoids algorithm resulted in a DBI (Davies Bouldin Index) value of 0.062 and a Silhouette Coefficient value of 0.8980, with the number of clusters as many as 3 clusters where Cluster_0 dominated by corn food crops experienced an increase in production by 5% and peanuts by 5%, Cluster _1 was dominated by a decrease in the number of soybean production yields by 38%, and Cluster_2 dominated by a decrease in green bean yield by 33%.
Application of Data Mining using Naive Bayes for Student Success Rates in Learning Bayu Angga Wijaya; Vijay Kumar; Berlian Fransisco Jhon Wau; Juliansyah Putra Tanjung; N P Dharshinni
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4639

Abstract

Education is a very important part of human life because through education quality human resources will be formed. Quality education can be read and measured by the achievement of various indicators. However, achieving these indicators is not easy, because learning success is influenced by several factors. One of the factors that can affect the success of learning is the learning system. To understand the level of student success in learning, a data mining processing technique is needed. The algorithm that will be used in this research is the naive Bayes algorithm. This study uses 601 datasets per year from Academic Year 2019/2020 to Academic Year 2021/2022, the data used are attendance score data, assignment scores, mid-exam scores, semester exam scores, and averages. The test is divided into 3, namely testing for the Academic Year 2019/2020 dataset, testing for the Academic Year 2020/2021 dataset, and testing for Academic Year 2021/2022 using the split validation operator. The test results using the Academic Year 2019/2020 – Academic Year 2020/2021 student score dataset have an accuracy value of 95.01% while the Academic Year 2021/2022 student score dataset has an accuracy value of 97.79%.
Co-Authors Bayu Angga Wijaya Berlian Fransisco Jhon Wau Ciok Fandi Juliansyah Putra Tanjung Vijay Kumar