Prosiding Seminar Nasional Ilmu Komputer Universitas Semarang 2022
Clustering Performa Siswa Menggunakan Algoritma K Means dan K-Medoid
Dublin Core
Title
Prosiding Seminar Nasional Ilmu Komputer Universitas Semarang 2022
Clustering Performa Siswa Menggunakan Algoritma K Means dan K-Medoid
Clustering Performa Siswa Menggunakan Algoritma K Means dan K-Medoid
Subject
Clustering, Performa Siswa, Algoritma K-Medoids, Algoritma K-Means.
Clustering, performance of high school student, K-Medoids,algorithm, K Means algorithm.
Clustering, performance of high school student, K-Medoids,algorithm, K Means algorithm.
Description
Faktor terpenting dalam lingkungan belajar adalah penilaian yang dianggap sebagai hasil keluaran proses pembelajaran karena dari penilaian juga dapat membantu mengidentifikasi kemajuan dan performa dari masing-masing siswa. Penelitian ini tujuannya adalah untuk mengetahui jumlah atau banyaknya siswa dari setiap cluster dan analisis performanya serta membandingkan algoritma mana yang paling bagus untuk diterapkan saat clustering performa siswa. Dataset dikumpulkan dari situs web kaggle yang berisi performa siswa SMA di Amerika Serikat tahun 2020. Untuk metode yang dipakai dalam pengolahan data menggunakan algoritma clustering K-Means dan K-Medoids dengan tools rapidminer, sedangkan untuk menghitung nilai accuracy, precission, dan recall peneliti memakai rumus confusion matrix. Dari penelitian ini dijelaskan bahwa hanya terdapat ΒΌ siswa dari keseluruhan untuk cluster dengan performa siswa yang bagus dan hasil perbandingan dari kedua algoritma adalah algoritma K-Medoids lebih baik karena nilai tingkat akurasi dan presisinya lebih tinggi apabila dibandingkan dengan algoritma K-Means.
The most important factor in the learning environment is the assessment which is considered as the outcome of the learning process because the assessment can also help identify the progress and performance of each student. This study aims to determine the number or number of students from each cluster and analyze their performance and compare which algorithm is the best to apply when clustering student performance. The dataset was collected from the kaggle website containing the performance of high school students in the United States in 2020. For the method used in data processing using the K-Means and K-Medoids clustering algorithm with rapidminer tools, while to calculate the accuracy, precision, and recall values, the researchers used confusion matrix formula. From this study, it is explained that there are only students from the whole cluster with good student performance and the comparison result of the two algorithms is that the K-Medoids algorithm is better because the value of the accuracy and precision level is higher when compared to the K-Means algorithm.
The most important factor in the learning environment is the assessment which is considered as the outcome of the learning process because the assessment can also help identify the progress and performance of each student. This study aims to determine the number or number of students from each cluster and analyze their performance and compare which algorithm is the best to apply when clustering student performance. The dataset was collected from the kaggle website containing the performance of high school students in the United States in 2020. For the method used in data processing using the K-Means and K-Medoids clustering algorithm with rapidminer tools, while to calculate the accuracy, precision, and recall values, the researchers used confusion matrix formula. From this study, it is explained that there are only students from the whole cluster with good student performance and the comparison result of the two algorithms is that the K-Medoids algorithm is better because the value of the accuracy and precision level is higher when compared to the K-Means algorithm.
Creator
Erika Noor Dianti, Sayidah Rohmatul Hidayah
Publisher
Universitas Semarang
Date
19 Oktober 2022
Contributor
Sri Wahyuni
Rights
ISSN: 2614-1205
Format
PDF
Language
Indonesian
Type
Text
Coverage
Prosiding Seminar Nasional Ilmu Komputer Universitas Semarang 2022
Files
Citation
Erika Noor Dianti, Sayidah Rohmatul Hidayah, “Prosiding Seminar Nasional Ilmu Komputer Universitas Semarang 2022
Clustering Performa Siswa Menggunakan Algoritma K Means dan K-Medoid,” Repository Horizon University Indonesia, accessed November 21, 2024, https://repository.horizon.ac.id/items/show/3503.
Clustering Performa Siswa Menggunakan Algoritma K Means dan K-Medoid,” Repository Horizon University Indonesia, accessed November 21, 2024, https://repository.horizon.ac.id/items/show/3503.