Penerapan Metode Silhouette Coefficient untuk Evaluasi Clustering Obat

Paembonan, Solmin; Abduh, Hisma

doi:10.51557/pt_jiit.v6i2.659

Cited by 26 publications

(25 citation statements)

References 2 publications

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“…Silhouette Coefficient is used to calculate the accuracy of data grouping. The formula used in the calculation of the Silhouette Coefficient is defined in (8) [22].…”

Section: B Methodsmentioning

confidence: 99%

Assessment decisions of independent learning activities using SMART–FCM method

Saputra

Aslamiyah

Chusyairi³

2023

J.INFOTEL

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Sekolah Tinggi Ilmu Komputer (STIKOM) PGRI Banyuwangi implemented the Independent Learning - Independent Campus (MBKM) activity for two semesters. The results of student assessments for MBKM activities for one semester are influenced by the results of daily and weekly logbook monitoring, monitoring and evaluation assessments, and assessments of supervisors, examiners, and work partners. Assessments that are less objective cause many students to get good grades even though the implementation of MBKM activities is not well. The Simple Multi-Attribute Rating Technique (SMART) method is used to produce student eligibility group data and a more objective assessment. The results of the SMART calculations are combined with the Fuzzy C-Means (FCM) algorithm so that the results of grouping student data are more appropriate based on the similarities and characteristics of the members. Silhouette Coefficient is used to compare the grouping results. The results obtained that the use of SMART-FCM is better than the SMART results because it has a Silhouette Coefficient value close to 1 of 0.31187.

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“…Silhouette Coefficient is used to calculate the accuracy of data grouping. The formula used in the calculation of the Silhouette Coefficient is defined in (8) [22].…”

Section: B Methodsmentioning

confidence: 99%

Assessment decisions of independent learning activities using SMART–FCM method

Saputra

Aslamiyah

Chusyairi³

2023

J.INFOTEL

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“…Negative values, on the other hand, reflect the possibility of erroneous assignment of data points to clusters. The silhouette index has become widely recognised and valued in the field primarily because of its intuitive interpretation and its effectiveness in accommodating diverse cluster shapes and densities [32][33][34][35]. The silhouette index can be derived by utilising Equation (3).…”

Section: Silhouette Indexmentioning

confidence: 99%

Regional Clustering Based on Types of Non-Communicable Diseases Using k-Means Algorithm

Munandar,

Yunizar Yusuf Pratama

2024

matrik

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Noncommunicable diseases (NCDs) have become a global threat to public health, necessitating a comprehensive understanding of their geographic and epidemiological distribution in order to devise appropriate interventions. The objective of this study is to clustering areas of Banten Province based on NCDS profiles using the unsupervised learning technique. The method used in this study is the k-means algorithm for grouping types of non-communicable diseases based on region. The processing and normalisation of NCDS prevalence data from various health sources preceded cluster analysis using the k-means clustering algorithm. This research is categorised into two scenarios: the first involves the clustering of data obtained from outlier analysis, while the second scenario excludes any outliers. The objective is to observe disparities in regional clustering outcomes by categorising non-communicable diseases according to these two scenarios. The silhouette index is used to determine the validity of cluster results. These findings are analysed in depth to determine the geographic and socioeconomic patterns associated with each cluster's NCDS profile. Based on the mean silhouette index value of 0.812, the results indicate that the sum of k = 2 in the k-means algorithm is the optimal cluster result in this case. Five non-communicable diseases, namely diabetes, hypertension, obesity, stroke, and cataracts, necessitate significant focus in the first cluster (C1), where 202 regions were grouped. Six regions belong to the second cluster (C2), which includes areas that are not only susceptible to the five non-communicable diseases in cluster C1 but also to breast cancer, cervical cancer, heart disease, chronic obstructive pulmonary disease (COPD), and congenital deafness.

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“…Silhouette Coefficient adalah metode untuk menentukan berhasil atau tidaknya pengelompokan [11] [12]. Tabel yang dibuat oleh Kaufman dan Rousseeuw digunakan sebagai evaluasi nilai koefisien silhouette ditunjukkan pada Tabel 1 [13][14].…”

Section: Silhouette Coefficientunclassified

Penerapan Fuzzy C-Means Untuk Pengelompokkan Tingkat Kualitas Pendidikan Di Jawa Timur

Ulinnuha,

Novitasari

2023

Simetris J. Teknik Mesin, Elektro dan Ilmu Komput.

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Salah satu faktor yang menentukan apakah perkembangan manusia itu positif atau negatif adalah pendidikan. Khususnya Provinsi Jawa Timur yang memiliki angka kesenjangan mutu pendidikan antar daerah dan Lembaga. Penelitian ini bertujuan untuk untuk mengelompokkan kualitas pendidikan pada kota/kabupaten di Jawa Timur berdasarkan sejumlah parameter yaitu, angka partisipasi sekolah kasar, angka partisipasi murni, rata-rata lama sekolah, angka melek huruf. Metode clustering pada penelitian ini menggunakan algoritma Fuzzy C-Means. Didapatkan cluster terbaik 0.65 dengan 2 cluster. Berdasarkan kualitas pendidikan, temuan mengungkapkan bahwa Jawa Timur terbagi menjadi dua cluster kota/kabupaten: cluster 1 (dengan tingkat kualitas pendidikan tinggi) dengan 14 anggota, dan cluster 2 (dengan tingkat pendidikan rendah) dengan 24 anggota.

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Penerapan Metode Silhouette Coefficient untuk Evaluasi Clustering Obat

Cited by 26 publications

References 2 publications

Assessment decisions of independent learning activities using SMART–FCM method

Assessment decisions of independent learning activities using SMART–FCM method

Regional Clustering Based on Types of Non-Communicable Diseases Using k-Means Algorithm

Penerapan Fuzzy C-Means Untuk Pengelompokkan Tingkat Kualitas Pendidikan Di Jawa Timur

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