Implementasi Metode K-Medoids Clustering Untuk Mengetahui Pola Pemilihan Program Studi Mahasiwa Baru Tahun 2018 Di Universitas Kanjuruhan Malang

Wira, Bagus; Budianto, Alexius Endy; Wiguna, Anggri Sartika

doi:10.21067/jtst.v1i3.3046

Cited by 26 publications

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“…To solve the problem in this research, data mining techniques with the k-medoids algorithm are used as follows: [15], [16], [17], [18], [19], [20]:…”

Section: Resultsmentioning

confidence: 99%

A Oil Palm Harvest Grouping Using K-Medoids Algorithm

Angraini

Safii

Anggraini

2021

ijobas

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Oil palm (Elaies Guinnnsiss Jacq) is one of the important industrial crops producing cooking oil, industrial oil, and fuel. Indonesia is the largest palm oil producer in the world. The rest of the processing of oil palm fruit is called janjang. Janjang also serves to be used as compost. The data that is processed in this research is the harvest data at PT. Surya Intisariraya Mandau. Data mining is the process of looking for patterns or information in selected data using certain techniques or methods. The processing steps are grouped using the K-Medoids method and then the data will be processed using RapidMiner tools. Where this grouping is done to minimize the amount of similarity of data and appropriate so that it becomes more valid data. This study aims to simplify the grouping of harvest data based on high, medium and low clusters.

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“…To solve the problem in this research, data mining techniques with the k-medoids algorithm are used as follows: [15], [16], [17], [18], [19], [20]:…”

Section: Resultsmentioning

confidence: 99%

A Oil Palm Harvest Grouping Using K-Medoids Algorithm

Angraini

Safii

Anggraini

2021

ijobas

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“…Hasil penelitian menyatakan K-Medoids dapat diterapkan dengan hasil kluster tinggi (C1) terdiri dari 3 negara (Jepang, Thailand dan Amerika Serikat) dan kluster rendah (C2) terdiri dari 6 negara (Korea Selatan, Taiwan, China, Singapura, Malaysia dan Singapura) [16]. Selanjutnya penelitian (Wira et al, 2019) tentang clustering untuk mengetahui pola pemilihan program studi mahasiwa baru tahun 2018 di universitas Kanjuruhan Malang. Tujuan dari penelitian adalah untuk mengetahui pola pemilihan program studi mahasiwa baru dengan menggunakan metode K-Medoids.…”

Section: Pendahuluanunclassified

Utilization of K-Medoids Algorithm for Klustering of Oil Palm Sprouts

Nuraini

Gunawan

Saputra

2022

JOMLAI: Journal of Machine Learning and Artificial Intelligence

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Palm oil is still a prima donna commodity in the plantation sector and as a major foreign exchange earner to date. Research and development of this commodity is very important to maintain Indonesia's position as the largest palm oil producing country in the world. The purpose of this study was to analyze what internal and external factors are the strengths, weaknesses, opportunities and threats for marketing oil palm sprouts in PPKS Marihat. To analyze what are the priority strategies to be implemented for the marketing of sprouts at PPKS Marihat. The research method used is the K-Medoids clustering algorithm by selecting the sprout data in order to determine the best quality of sprouts. Based on the results of research using the K-Medoids algorithm with manual calculations and testing, the same results were obtained, namely cluster 1 with very good sprouts category had 7 members, cluster 2 with good sprouts category had 12 members and cluster 3 with poor sprouts category had 7 members. . Testing data on Rapid Miner using the K-Medoids algorithm can display 3 classes with an accuracy percentage of 100%. So it can be concluded that the K-Medoids algorithm can be used for clustering oil palm sprouts at PPKS Marihat.

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“…Algoritma K-Medoids digunakan untuk menangani kekurangan dari algoritma k-means atas kesensitifannya terhadap outlier. Keberadaan outlier pada k-means dapat mendistorsi nilai rata-rata cluster karena objek tersebut memiliki karakteristik sangat jauh terhadap sebagian besar data lainnya (Wira et al, 2019). Dengan analisis ini peneliti dapat mengetahui pengelompokkan kabupaten/kota yang memiliki karakteristik sama berdasarkan indikator kemiskinan.…”

Section: Pendahuluanunclassified

Analisis Clustering K-Medoids Berdasarkan Indikator Kemiskinan di Jawa Timur Tahun 2020

Alfiah

Almadayani

Farizi

et al. 2021

JIS

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Keberadaan pandemi COVID-19 di Indonesia, mengakibatkan kemiskinan di Indonesia semakin tinggi terutama di Jawa Timur yang menjadi satu diantara provinsi lain dengan kasus COVID-19 tinggi di Indonesia. Tujuan penelitian ini yaitu mengetahui pengelompokan kabupaten/kota di Jawa Timur yang mempunyai kesamaan karakteristik berdasarkan indikator kemiskinan tahun 2020. Penelitian ini menggunakan data yang didapatkan dari Badan Pusat Statistik. Metode yang digunakan ialah metode k-medoids clustering yang merupakan metode partisi clustering guna pengelompokan n objek ke dalam k cluster. Berdasarkan hasil penelitian, diperoleh pengelompokan karakteristik masing-masing cluster yang dibentuk berdasarkan nilai indikator kemiskinan di Jawa Timur tahun 2020 sebanyak 2 cluster. Dimana 30 kabupaten/kota pada cluster 1 dan dan 8 kabupaten/kota pada cluster 2. Cluster 1 memiliki karakteristik Persentase Rumah Tangga yang Mempunyai Sanitasi Layak, Angka Harapan Hidup, dan Persentase Angka Melek Huruf Umur 15-55 Th tinggi. Sedangkan cluster 2 memiliki karakteristik Persentase Rumah Tangga Miskin Penerima Raskin, Persentase Penduduk Miskin, dan Persentase Pengeluaran Perkapita untuk Makanan dengan Status Miskin tinggi. Kata kunci: Clustering; Jawa Timur; K-medoids; kemiskinan K-Medoids Clustering Analysis Based on Poverty Indicators in East Java in 2020 ABSTRACT The existence of the pandemic COVID-19 in Indonesia has resulted in higher poverty in Indonesia, especially in East Java, which is one of the other provinces with high cases in Indonesia. The purpose of this study is to find out the grouping of regencies/cities in East Java that have similar characteristics based on the poverty indicators in 2020. This study uses data obtained from the Badan Pusat Statistik. The method used is k-medoids clustering method which is a clustering partition method for grouping n objects into k clusters. Based on the results of the study, it was found that the grouping of the characteristics of each cluster formed based on the value of the poverty indicator in East Java in 2020 was 2 clusters. Where 30 regencies/cities in cluster 1 and and 8 regencies/cities in cluster 2. Cluster 1 has the characteristics of the percentage of households that have proper sanitation, life expectancy, and a high percentage of literacy rates aged 15-55 years. While cluster 2 has the characteristics of the percentage of poor households receiving Raskin, the percentage of poor people, and the percentage of per capita expenditure on food with high poor status. Keywords: Clustering; East Java; K-Medoids; poverty

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Implementasi Metode K-Medoids Clustering Untuk Mengetahui Pola Pemilihan Program Studi Mahasiwa Baru Tahun 2018 Di Universitas Kanjuruhan Malang

Cited by 26 publications

References 0 publications

A Oil Palm Harvest Grouping Using K-Medoids Algorithm

A Oil Palm Harvest Grouping Using K-Medoids Algorithm

Utilization of K-Medoids Algorithm for Klustering of Oil Palm Sprouts

Analisis Clustering K-Medoids Berdasarkan Indikator Kemiskinan di Jawa Timur Tahun 2020

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