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Alwis Nazir
Universitas Islam Negeri Sultan Syarif Kasim, Pekanbaru
Computer Science & IT Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering

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Klasterisasi Peserta BPJS Berdasarkan Rekam Medis Menggunakan Algoritma K-Means Ana Fitri Khairani; Alwis Nazir; Teddie Darmizal; Yelfi Vitriani; Yusra Yusra
Journal of Computer System and Informatics (JoSYC) Vol 4 No 3 (2023): May 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i3.3442

Abstract

The history of the patient's medical examination with the BPJS (Social Security Administration Agency) at the Dumai City Hospital will be stored in the form of a medical record. The medical record is a file that contains patient identification information whether it contains the results of control, treatment and other services. The purpose of this research is to. to assist the Dumai Hospital in grouping BPJS participants based on medical records to find out the disease, gender and BPJS group that is most dominant in Dumai City BPJS participants. The results of grouping the disease will then be processed using data mining techniques. In this study, the focus was on BPJS PBI participants inpatient medical record data from January to December 2022, which were then processed using the K-Means Clustering algorithm with 3 clusters. Cluster 0 is dominated by disease types with code O342 (Maternal care due to uterine scar from previous surgery), female sex and age range 21-40 years. Cluster 1 is dominated by types of disease with code E119 (Diabetes mellitus without complications), female sex and age range 41-60 years and cluster 2 is dominated by disease types with code J180 (Bronchopneumonia, unspecified organism), female sex and age range 41-60 years.
Perbandingan Jarak Metrik pada Klasifikasi Jamur Beracun Menggunakan Algoritma K-Nearest Neighbor (K-NN) Andre Suarisman; Alwis Nazir; Fadhilah Syafria; Liza Afriyanti
Journal of Computer System and Informatics (JoSYC) Vol 5 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i1.4511

Abstract

Mushrooms are organisms from the kingdom fungi that have a fleshy body structure and can be consumed, but there are some species of mushrooms that are not safe to eat and have specific characteristics, so distinguishing between edible and poisonous mushrooms can be tricky due to the almost identical appearance of various mushroom species. Errors in identifying edible mushrooms can impact the health of consumers who consume the mushrooms. Evaluating the performance of various methods on a dataset is a key step in determining the most suitable classification method. This research is about how to measure the performance of classification methods on toxic mushroom datasets using the K-Nearest Neighbor algorithm with several metrics such as euclidean, manhattan and minkowski, which is a method for classifying new data based on proximity to existing training data. The results obtained in this study with several distance metrics can be concluded that the accuracy value of the manhattan metric is better than the euclidean and minkowski metrics. Because the manhattan metric gets the highest accuracy result of 99% with K = 100 and the lowest 82% with K = 3000, while the euclidean metric gets accuracy results with a value of 98% with K = 100 and 72% with K = 3000, and the minkowski metric gets accuracy results with a value of 96% at K = 100 and 64% at K = 3000.
Penerapan Metode Clustering Dengan K-Means Untuk Memetakan Potensi Tanaman Padi di Sumatera Irma Sanela; Alwis Nazir; Fadhilah Syafria; Elin Haerani; Lola Oktavia
Journal of Computer System and Informatics (JoSYC) Vol 5 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i1.4523

Abstract

Rice plants are the primary source of rice, the staple food for the majority of the Indonesian population. Despite the presence of other food alternatives, rice remains irreplaceable for those accustomed to consuming rice. According to data from the Food and Agriculture Organization of the United Nations (FAO) in 2018, Indonesia is the third-largest rice producer in the world, with a total production of 59.2 million tons. However, urban and agricultural spatial planning is not yet fully integrated, resulting in often conflicting decisions in land use planning for agriculture and urban development. To meet the rice demand in Sumatra, efforts are needed to increase rice production in each province. Therefore, this research aims to map the potential for rice cultivation in Sumatra based on production and harvest results from 1993 to 2020. The method used in this study is K-Means, which allows the grouping of rice potential areas into three categories: high, medium, and low. The research results produced three clusters, evaluated using the Davies Bouldin Index (DBI) with a value of 0.3943. The clustering results indicate that Cluster 0 contains 92 areas with a high success rate, Cluster 2 comprises 84 areas with a medium success rate, and Cluster 1 consists of 48 areas with a low success rate. The category of low success rate is found in Cluster 1 with 48 areas. Cluster 0 includes Aceh, North Sumatra, West Sumatra, South Sumatra, and Lampung within certain time periods. Cluster 1 encompasses other areas with different characteristics. Cluster 2 includes the provinces of Riau, Jambi, and Bengkulu.
Co-Authors Ana Fitri Khairani Andre Suarisman Elin Haerani Fadhilah Syafria Irma Sanela Liza Afriyanti Lola Oktavia Teddie Darmizal Yelfi Vitriani Yusra Yusra