Penerapan Metode Clustering dengan Algoritma K-Means pada Pengelompokkan Data Calon Mahasiswa Baru di Universitas Muhammadiyah Yogyakarta (Studi Kasus: Fakultas Kedokteran dan Ilmu Kesehatan, dan Fakultas Ilmu Sosial dan Ilmu Politik)

Asroni, Asroni; Fitri, Hidayatul; Prasetyo, Eko

doi:10.18196/st.211211

Cited by 39 publications

(35 citation statements)

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“…Data Mining atau disebut juga Knowledge Discovery in Database (KDD) merupakan cara pengekstrasian pengetahuan dari data yang berukuran besar untuk menemukan pola-pola baru sehingga mendapatkan sebuah pengetahuan dan informasi yang baru [2] [3]. Teknologi Data Mining digunakan untuk menggali pengetahuan yang ada didalam database [4] [5].…”

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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Section: Pendahuluanunclassified

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

Elda

Defit

Yunus

et al. 2021

jidt

View full text Add to dashboard Cite

show abstract

“…ditemukan (untuk menemukan informasi dan pengetahuan yang menarik) dan yang terakhir Proses Pola (pattern evaluation) [3]. Teknik Data Mining dapat mengolah data yang berlimpah menjadi informasi yang penting biasanya disebut knowledge discovery database (KDD) [4]. KDD adalah metode yang digunakan untuk dapat memperoleh pengetahuan yang berasal dari database yang ada.…”

Section: Pendahuluanunclassified

Data Mining dalam Mengukur Tingkat Keaktifan Siswa dalam Mengikuti Proses Belajar pada SMP IT Andalas Cendekia

Triandini

Defit

Nurcahyo

2021

jidt

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The learning process is essentially to develop the activities and creativity of students through various interactions and learning experiences. The teacher is the most important factor in the process of improving the quality of education. In addition, student learning activeness is also an important basic element for the success of the learning process. The quality and activeness of students in learning at school has a lot of diversity which makes students have different levels of understanding, this needs to be a concern for the school, especially teachers as teachers and educators of students in schools. The purpose of this study is to measure the extent to which students' ability to undergo the learning process as well as a reference and evaluation material for the school in the success of educators when carrying out the teaching and learning process. In this study the data were sourced from the Integrated Islamic Junior High School Andalas Cendekia Dharmasraya which consisted of several variables, namely the presence of student data, Academic value (knowledge), Psychomotor value (skills), Affective value (spiritual and social). In grouping the data, the appropriate method in this study is the Clustering method with the K-Means Algorithm. The results of this study obtained 3 groupings of students, namely students who are very active, students who are active and students who are less active. This research is used as a guideline for teachers in the field of study in selecting students to participate in competitions and Olympics, and can be used as a benchmark for schools of the ability of educators in the teaching and learning process.

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“…Data mining adalah proses untuk menemukan pola atau informasi yang berharga dan terpendam dari sejumlah besar data dengan menerapkan teknik ataupun metode tertentu [14], [15]. Salah satu teknik yang termasuk dalam data mining adalah teknik clustering.…”

Section: Pendahuluanunclassified

Pengelompokkan Data Bencana Alam Berdasarkan Wilayah, Waktu, Jumlah Korban dan Kerusakan Fasilitas Dengan Algoritma K-Means

Murdiaty¹,

Angela²,

Sylvia³

2020

mib

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Indonesia has fertile soil, natural resources and abundant marine resources. However, Indonesia is also not immune to the risk of natural disasters which are a series of events that disturb and threaten life safety and cause material and non-material losses. Indonesia's strategic geological location causes Indonesia to be frequently hit by earthquakes, volcanic eruptions and other natural disasters. From the data collected, natural disasters that occurred in Indonesia consisted of several categories, namely earthquakes, volcanic eruptions, floods, landslides, tornados, and tsunamis. Many natural disasters in Indonesia have caused casualties, both fatalities and injuries, destroying the surrounding area and destroying infrastructure and causing property losses. The trend of increasing incidence of natural disasters needs to be further investigated to prevent the number of victims from increasing. This information can be obtained through a data mining approach given the large amount of data available. In relation to natural disaster data, clustering techniques in data mining are very useful for grouping natural disaster data based on the same characteristics so that the data can be adopted as a groundwork for predicting natural disaster events in the future. Thus, this research is supposed to group natural disaster data using clustering techniques using the k-means algorithm into several groups, in terms of natural disaster types, time of disaster, number of victims, and damage to various facilities as a result of natural disasters

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Penerapan Metode Clustering dengan Algoritma K-Means pada Pengelompokkan Data Calon Mahasiswa Baru di Universitas Muhammadiyah Yogyakarta (Studi Kasus: Fakultas Kedokteran dan Ilmu Kesehatan, dan Fakultas Ilmu Sosial dan Ilmu Politik)

Cited by 39 publications

References 0 publications

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

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

Data Mining dalam Mengukur Tingkat Keaktifan Siswa dalam Mengikuti Proses Belajar pada SMP IT Andalas Cendekia

Pengelompokkan Data Bencana Alam Berdasarkan Wilayah, Waktu, Jumlah Korban dan Kerusakan Fasilitas Dengan Algoritma K-Means

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