Validity Test of Self-Organizing Map (SOM) and K-Means Algorithm for Employee Grouping

Susilowati, Titik; Sugiarto, Dedy; Mardianto, Is

doi:10.29207/resti.v4i6.2492

Cited by 6 publications

(4 citation statements)

References 3 publications

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“…It computes the cluster density and how far the clusters are apart from each other [29]. A higher coefficient value indicates a denser cluster structure and is different from other clusters [18], [30]. Furthermore, the results of clustering were analyzed to understand the characteristics and changes in learning patterns.…”

Section: Clustering Methodsmentioning

confidence: 99%

Capturing Students’ Dynamic Learning Pattern Based on Activity Logs Using Hierarchical Clustering

Laksitowening

Prasetya

Suwawi

et al. 2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Students can have various characteristics and learning patterns. By understanding the characteristics and learning pattern of individual students, teachers can provide individualized learning strategies based on students' needs. Students' learning patterns may experience changes depending on their conditions during the learning process. If the learning pattern analysis is only run once, then the progress and changes in student learning patterns throughout the learning process cannot be recognized. On the other hand, periodical analysis is expected to describe the dynamics of student learning patterns from time to time. This research is intended for capturing students' dynamic learning pattern using Hierarchical Clustering. We clustered the learning patterns based on Learning Management Systems (LMS) activity logs. The activity log data were partitioned into several periodical datasets. The results of the periodic clustering indicated that students’ learning patterns varied from one another and changed from time to time. Most students experienced change in learning patterns throughout the semester. The analysis also indicated that learning pattern also has the potential to be improved and maintained.

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Section: Clustering Methodsmentioning

confidence: 99%

Capturing Students’ Dynamic Learning Pattern Based on Activity Logs Using Hierarchical Clustering

Laksitowening

Prasetya

Suwawi

et al. 2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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“…Validasi cluster digunakan untuk mengetahui ketepatan dan kualitas dari hasil analisis cluster [17]. Selain itu, jumlah cluster optimum dapat diperoleh melalui validasi cluster [18]. Setiap objek dapat membentuk cluster sebanyak 2 ≤ K ≤ (N − 1) dengan N merupakan jumlah objek dalam pengamatan [19].…”

Section: Silhouette Coefficientunclassified

Pengelompokan Provinsi di Indonesia Menggunakan Time Series Clustering pada Sektor Ekspor Nonmigas

Putri,

Satyahadewi,

Aprizkiyandari

2024

Jambura J. Math

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Indonesia's export activities are dominated by non-oil and gas exports consisting of four sectors, namely the processing industry, agriculture, mining, and others. The government must pay attention to non-oil and gas exports for each province because exports can play an essential role in a country's economic growth. This study was conducted to cluster provinces in Indonesia using time series clustering in the non-oil and gas export sector based on data patterns concerning Dynamic Time Warping (DTW) distance. The sectors used in this study are the manufacturing industry sector and the agricultural sector in 34 Indonesian provinces in the period 2017 - 2021. Time series clustering analysis uses the average linkage method with DTW distance and the selection of the optimum number of clusters using the silhouette coefficient method. The results of the analysis in the processing industry sector resulted in 3 optimum clusters, namely cluster 1 consisting of 1 province that has high processing industry exports, cluster 2 consisting of 8 provinces that have medium processing industry exports, and cluster 3 consisting of 25 provinces that have low processing industry exports. As for the agricultural sector, it produces 2 optimum clusters, namely cluster 1 consisting of 5 provinces that have high agricultural industry exports, and cluster 2 consisting of 29 provinces that have low agricultural industry exports. The clustering results in the processing industry sector and the agricultural sectors have a silhouette coefficient value of 0.778 and 0.798, so it is said to have a strong cluster structure.

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“…Sarana dan prasarana sekolah dapat terdiri dari data campuran sehingga, klasterisasi SMA dapat menggunakan metode SKM. Hasil klaster yang didapat harus divalidasi untuk melihat jumlah klaster yang terbaik [8]. Metode validasi klaster yang cocok untuk metode SKM yaitu metode Medoid Shadow Value (MSV) [9].…”

Section: A Pendahuluanunclassified

Penerapan Metode Simple K-Medoid (SKM) dalam Mengklasterkan SMA di Kabupaten Karanganyar Tahun 2023

10060119034,

Nur Azizah Komara Rifai

2024

BCSS

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Abstract. Cluster analysis is an analysis that classifies objects that have similar characteristics into the same group. One of the most common cluster analysis methods is the non-hierarchical method where the number of clusters is determined by the researcher. Some of the methods included in non-hierarchical methods are k-means and k-medoid. The k-means method is a clustering method used for numeric variable data, while the k-medoid method can be used for mixed variable data (numeric and categorical data). The k-medoid method used for mixed variable data is the Simple K-Medoid (SKM) method. The cluster results obtained in the SKM method must be validated using the Medoid Shadow Value (MSV) to see the best number of clusters. The data used in this study comes from the Sekolah Kita website and the Basic Education Data which is mixed data consisting of 14 numerical variables and 3 binary and categorical variables. Based on the results of the SKM method clustering and cluster validation using MSV, it was found that there were 2 clusters of high schools in Karanganyar District. Cluster 1 consists of 15 high schools from 18 high schools with more complete facilities and infrastructure than cluster 2 which consists of 3 high schools from 18 high schools. Abstrak. Analisis klaster adalah analisis yang mengklasifikasikan objek yang memiliki kesamaan karakteristik ke dalam kelompok yang sama. Salah satu metode analisis klaster yang paling umum adalah metode non hierarki di mana jumlah klaster ditentukan oleh peneliti. Beberapa metode yang termasuk ke dalam metode non hierarki adalah k-means dan k-medoid. Metode k-means adalah metode klaster yang digunakan untuk data bervariabel numerik, sedangkan metode k-medoid dapat digunakan untuk data bervariabel campuran (data numerik dan kategorik). Metode k-medoid yang digunakan untuk data bervariabel campuran adalah metode Simple K-Medoid (SKM). Hasil klaster yang didapat pada metode SKM harus divalidasi menggunakan Medoid Shadow Value (MSV) untuk melihat jumlah klaster yang terbaik. Data yang digunakan pada penelitian ini berasal dari website Sekolah Kita dan Data Pokok Pendidikan yang merupakan data campuran yakni terdiri dari 14 variabel numerik dan 3 variabel biner dan kategorik. Berdasarkan hasil dari pengelompokkan metode SKM dan validasi klaster menggunakan MSV didapatkan pengelompokkan SMA di Kabupaten Karanganyar sebanyak 2 klaster. Klaster 1 beranggotakan 15 SMA dari 18 SMA dengan memiliki sarana dan prasarana yang lebih lengkap dibandingkan klaster 2 yang beranggotakan 3 SMA dari 18 SMA.

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Validity Test of Self-Organizing Map (SOM) and K-Means Algorithm for Employee Grouping

Cited by 6 publications

References 3 publications

Capturing Students’ Dynamic Learning Pattern Based on Activity Logs Using Hierarchical Clustering

Capturing Students’ Dynamic Learning Pattern Based on Activity Logs Using Hierarchical Clustering

Pengelompokan Provinsi di Indonesia Menggunakan Time Series Clustering pada Sektor Ekspor Nonmigas

Penerapan Metode Simple K-Medoid (SKM) dalam Mengklasterkan SMA di Kabupaten Karanganyar Tahun 2023

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