Sistem Pembagian Kelas Kuliah Mahasiswa Dengan Metode K-Means Dan K-Nearest Neighbors Untuk Meningkatkan Kualitas Pembelajaran

Pradnyana, Gede Aditra; Permana, Agus Aan Jiwa

doi:10.12962/j24068535.v16i1.a696

Cited by 11 publications

(8 citation statements)

References 4 publications

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“…Nilai SI semakin baik jika mendekati 1 (Pramesti et al, 2017). Langkah-langkah dalam menghitung SI adalah (Pradnyana & Permana, 2018): a. Menghitung jarak rata-rata dari suatu objek ke-I dengan semua objek yang lain dalam satu cluster.…”

Section: Silhouette Indexunclassified

Implementasi Metode K-Means Clustering Dalam Pengelompokan Penyebaran Covid-19 Di Surabaya

Asegaf¹,

Arfianti²,

Hamdani³

2022

Preprint

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COVID-19 is an infection or spread of the CORONA virus. The spread of the Corona Virus in Indonesia itself includes a fairly fast spread due to the way it is spread which is quite easy. The impact of the COVID-19 pandemic can still be felt today. The spread of COVID-19 that is evenly distributed in various provinces in Indonesia makes it difficult to handle and overcome it, therefore a grouping based on regions in Indonesia is needed. This grouping will produce a focal point for the spread of COVID-19 in various regions. This study uses the K-Means Clustering method to group data on the spread of COVID-19. This study tested the number of clusters using the Silhouette Index method to find out the optimal number of clusters of 2,3, 4, and 5 clusters. The results of the trial of the number of clusters in grouping the data on the spread of COVID-19 in each kelurahan in Surabaya using the K-Means Clustering method resulted in a good structure in the 3, 4, and 5 cluster trials, while the 2 cluster trial resulted in a strong structure with Silhouette. The index is 0.8021.

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Section: Silhouette Indexunclassified

Implementasi Metode K-Means Clustering Dalam Pengelompokan Penyebaran Covid-19 Di Surabaya

Asegaf¹,

Arfianti²,

Hamdani³

2022

Preprint

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“…Lalu dihitung antar jarak data dengan tiap titik utama pada cluster. Penjelasan cara kerja algoritma K-mean seperti dijelaskan dalm penelitian (Pradnyana & Permana, 2018) sebagai berikut: a) Tentukan jumlah cluster (k), tetapkan pada pusat cluster sembarang. b) Hitung jarak antara setiap data ke dalam cluster dengan jarak yang paling pendek dengan menggunakan persamaan ukuran jarak Euclidean distance dengan persamaan:…”

Section: Pre-processingunclassified

Penerapan Metode K-Means Clustering pada Data Tingkat Pengangguran Terbuka Tahun 2016-2018 dan 2019-2021

et al. 2022

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Masalah pengangguran berdampak pada kemiskinan, kriminalitas, dan ketimpangan taraf hidup. Pemerintah harus mengantisipasi dampak tersebut melalui berbagai kebijakan pemerintah. Pengetahuan memainkan peran penting dalam mendukung pengambilan keputusan dan perumusan kebijakan pemerintah terkait dengan pengangguran. Beberapa peneliti telah menggali data untuk mendapatkan pengetahuan baru dari data Tingkat Pengangguran Terbuka (TPT) Indonesia. Data yang tersedia perlu terus digali untuk mendapatkan pengetahuan baru. Penelitian ini bertujuan untuk menambang data TPT Indonesia dari tahun 2016 hingga tahun 2021. Secara lebih spesifik penelitian ini melihat perubahan klaster data TPT tahun 2016-2018 dibandingkan dengan klaster data TPT tahun 2019-2021. Metode data mining adalah analisis clustering menggunakan algoritma k-means. Hasil penelitian; berdasarkan analisis k-means clustering, hanya Provinsi Riau yang naik ke cluster 1 (TPT rendah), dan hanya provinsi Sumatera Barat yang turun ke cluster 2 (TPT Tinggi).

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“…Perhitungan dengan K-Means clustering adalah proses yang dilakukan secara berulang-ulang [12]. Clustering dengan algoritma K-Means melakukan pengelompokan terhadap data nilai rapor siswa dengan data sampel sebanyak 23 siswa yang menghasilkan 3 cluster tingkat kompetensi siswa untuk pemilihan peserta Olimpiade Sains Nasional [13].…”

Section: Pendahuluanunclassified

Klasterisasi Penempatan Siswa yang Optimal untuk Meningkatkan Nilai Rata-Rata Kelas Menggunakan K-Means

Elda

Defit

Yunus

et al. 2021

jidt

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The implementation of learning by teachers can measure the quality of schools and students. Schools with diverse student backgrounds need to take strategic steps in managing learning to get optimal learning outcomes. Good learning designs and techniques can motivate students' interest in learning. The teacher's role is very important in managing learning to create an effective teaching and learning process. Data Mining or also known as Knowledge Discovery in Database (KDD) is the process of extracting knowledge from large data to find new patterns to get new knowledge and information. Data Mining technology is used to explore existing knowledge in the database. One of the methods used in data mining is clustering with the K-Means algorithm. This study aims to conduct student clustering to obtain a balanced class composition in order to improve the quality and student learning outcomes as seen in the increasing in the class average score. The data processed in this study came from the main school data as many as 90 students of the XI class of Computer Network Engineering Skills Competency at SMKN Negeri 2 Padang Panjang in the 2020/2021 school year. The variables used in data processing are student scores, parents' income and the distance from where students live to school. The student clustering calculation using K-Means succeeded in grouping 90 students into 3 clusters where cluster 1 totaled 47 students, cluster 2 totaled 10 students and cluster 3 totaled 33 students. Each member of the cluster will be divided evenly into 3 groups studying to get a balanced class composition. This research can be used as a basis for decision making by schools in clustering student placements to improve learning outcomes. By the increasing in the grade point average, the school average score will also increased.

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Sistem Pembagian Kelas Kuliah Mahasiswa Dengan Metode K-Means Dan K-Nearest Neighbors Untuk Meningkatkan Kualitas Pembelajaran

Abstract: ABSTRAK

Cited by 11 publications

References 4 publications

Implementasi Metode K-Means Clustering Dalam Pengelompokan Penyebaran Covid-19 Di Surabaya

Implementasi Metode K-Means Clustering Dalam Pengelompokan Penyebaran Covid-19 Di Surabaya

Penerapan Metode K-Means Clustering pada Data Tingkat Pengangguran Terbuka Tahun 2016-2018 dan 2019-2021

Klasterisasi Penempatan Siswa yang Optimal untuk Meningkatkan Nilai Rata-Rata Kelas Menggunakan K-Means

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