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All Journal Jurnal Informatika dan Rekayasa Elektronik Systematics
Garno
Universitas Singaperbangsa Karawang
Computer Science & IT Library & Information Science

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ANALISIS CLUSTER FAKTOR PENUNJANG PENDIDIKAN MENGGUNAKAN ALGORITMA K-MEANS (STUDI KASUS: KABUPATEN KARAWANG) Abdussalam Amrullah; Intam Purnamasari; Betha Nurina Sari; Garno; Apriade Voutama
Jurnal Informatika dan Rekayasa Elektronik Vol. 5 No. 2 (2022): JIRE Nopember 2022
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Education is one of the means to create and improve the quality of better human resources. This is expected to improve human welfare. Based on data from the Ministry of Education and Culture, there are 4 sub-districts in Karawang Regency that do not have state high schools. This can result in difficulties for students who have financial deficiencies which can ultimately lead to dropping out of school. Then apart from that distance can also be an obstacle. The purpose of this study is to apply the clustering method for the distribution of educational supporting factors in Karawang District so that later this research can improve the quality of education evenly in Karawang District, not only concentrated in certain areas by paying attention to educational supporting factors. The algorithm used in this research is K-Means. The elbow method used in determining the optimal cluster supported by the silhouette method resulted in the best number of clusters being 2 clusters. The results of the clustering evaluation resulted in Davies-Bouldin Index (DBI) value of 0.408 and Silhouette Coefficient value of 0.747 (strong structure). Cluster 1 consists of 7 sub-districts and Cluster 2 consists of 23 sub-districts. Based on the results of clustering analysis, Cluster 1 has the average number of attributes of schools, teachers, classes, laboratories, libraries at all levels of education (SD, SMP, SMA, SMK, and SLB) with state status and the population shows higher results if compared with the average number of each attribute in Cluster 2. So it can be concluded that Cluster 1 is a high category and Cluster 2 is a low category.
Prediction of Rice Field Planted Area with CRISP-DM Using Classification and Regression Tree (Cart) Algorithms : Prediksi Luas Tanam Sawah dengan CRISP-DM Menggunakan Algoritma Classification and Regression Tree (Cart) Elfina Novalia; Apriade Voutama; Garno
SYSTEMATICS Vol 5 No 1 (2023): April 2023
Publisher : Universitas Singaperbangsa Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35706/sys.v5i1.8755

Abstract

Every year the area of paddy fields in Karawang Regency has increased and decreased due to land conversion. Climate change also causes changes in the amount of rain and rain patterns that cause shifts in the beginning of the season and the planting period. If the decrease in planting area will be affected, then the price of rice will increase and farmers will maintain the area and not convert their rice fields to function, therefore a study was conducted to predict the rice planting area in order to know the description of the area of rice planted in Karawang Regency will increase. , decreased or stabilized. So the search for information on the data on the area of rice planting in Karawang Regency was carried out. A total of 180 data were processed using data mining techniques so that they could mine information from the data. Data mining is a technique of extracting or new discoveries from large data and then extracting the data into information that can later be used. Experiments were carried out using the CART algorithm and cross validation using the Weka tools. The results of the evaluation carried out can be concluded that the CART algorithm using different K values provides different evaluation results. The performance of the algorithm is seen from the accuracy, precision, recall and F-Measurement, thus providing different performance values for each result. The value of k=8 has the highest accuracy value, which is 90% with precision 0.918%, recall 0.906% and F-measure 0.949%.
Co-Authors Abdussalam Amrullah Apriade Voutama Intam Purnamasari Novalia, Elfina