DATA MINING SELEKSI SISWA BERPRESTASI UNTUK MENENTUKAN  KELAS UNGGULAN MENGGUNAKAN METODE K-MEANS CLUSTERING (Studi Kasus di MTS Darul Fikri )

Primanda, Reyhan Putra; Alwi, Aslan; Mustikasari, Dyah

doi:10.24269/jkt.v5i1.686

Cited by 14 publications

(18 citation statements)

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“…Metode data mining yang digunakan dalam penelitian ini adalah metode clustering dengan algoritma K-Means. Metode K-Means clustering bertujuan untuk membagi data menjadi beberapa kelompok [10] yang mendukung pengambilan keputusan dalam mengatasi masalah pengolahan data menjadi strategi sumber informasi [11]. Dengan demikian, hasil penelitian diharapkan dapat memberikan informasi strategis dan pengetahuan yang berkualitas, sehingga dapat mendukung pengambilan keputusan bagi peserta didik ketika memilih konsentrasi keahlian dan memudahkan pihak sekolah untuk mengarahkan dan menentukan konsentrasi keahlian peserta didik yang tepat.…”

Section: Pendahuluanunclassified

“…Clustering bersifat homogen. Dengan kata lain, kualifikasi setiap bagian yang memiliki kesamaan dari kumpulan data yang berbeda dan harus menyimpan data yang tidak sama dengan data lainnya [11].…”

Section: Klasterisasiunclassified

“…Algoritma K-Means adalah metode clustering yang menentukan berapa banyak cluster yang harus dibagi menjadi satu atau lebih cluster berdasarkan analisis kesamaan data dan karakteristiknya [11]. Algoritma K-Means juga memberikan kemudahan dalam pengelompokan, karena menentukan jumlah cluster yang ditentukan sebelum melakukan analisis [20].…”

Section: Algoritma K-meansunclassified

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Klasterisasi Konsentrasi Keahlian Siswa SMK Berdasarkan Kurikulum Merdeka

Sukmayadi¹,

Firdaus²,

Juliane³

2022

mib

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The process of determining the concentration of expertise carried out at the YPC Tasikmalaya Vocational School has shortcomings such as making decisions based on the wishes of students without paying attention to academic grades at the previous level of education. So that there are some students who feel it is not right in choosing the concentration of expertise, resulting in a lack of competence possessed by students with the concentration of expertise selected. The choice of concentration of expertise is the right of every student, but if it is wrong it can cause a decrease in learning motivation and low learning achievement. This problem can be solved by using clustering method with K-Means algorithm. This study aims to classify students' interests in choosing a concentration of expertise at YPC Tasikmalaya Vocational School based on the Merdeka Curriculum. The results showed that the grouping of students' interests in choosing the concentration of expertise was formed into 4 clusters. The cluster with the most members is cluster 0, namely students who have an average score of 79 Mathematics, then Indonesian and English 83. Furthermore, the cluster with the least number of members is cluster 2, namely students who have an average score of 78 Mathematics and English, then Indonesian 79.

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Section: Pendahuluanunclassified

Section: Klasterisasiunclassified

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Klasterisasi Konsentrasi Keahlian Siswa SMK Berdasarkan Kurikulum Merdeka

Sukmayadi¹,

Firdaus²,

Juliane³

2022

mib

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“…The superior class curriculum structure is no different from the regular class, which uses the applicable curriculum structure (2013 Curriculum) at the SMP/MTs level. Only the curriculum structure of the only course has additions (advantages), both in terms of quantitative (comparative advantage) and qualitative (competitive advantage) (Primanda, Alwi, & Mustikasari, 2021).…”

Section: Excellent Class Curriculum At Mts N 2 Cirebonmentioning

confidence: 99%

Leading Class Program In Increasing Learning Achievement At MTS 2 Cirebon

Munjiat¹

2019

JAT

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This research examines the innovations carried out by educational institutions to increase public interest in Madrasahs. The form of this innovation is by organizing the Superior class. This research was conducted in the superior class at MTs N 2 Cirebon to analyze the management of the particular class program in improving student achievement. This researcher uses a qualitative approach with data collection procedures using interviews, observation, and documentation. Based on the process of data collection and data analysis, researchers produced superior class program management in improving student achievement at MTs N 2 Cirebon, namely planning covering concepts, input processes, infrastructure, and data sources; the organization includes organizational structure: implementation includes recruitment of teaching staff, learning process, and curriculum used; evaluation includes evaluation of learning, student achievement, and output. The management of this particular class program has implications for the achievement of madrasa principals, reputation, student achievements in academic and non-academic fields, and increasing public interest.

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“…To achieve these objectives through the implementation of superior class programs effectively and efficiently (Hakim, 2021). Data science-based computing can be used to classify outstanding students so as to make it easier for schools to make objective decisions (Primanda et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Superior Class to Improve Student Achievement Using the K-Means Algorithm

Syahputra¹,

Hutagalung²

2022

SinkrOn

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The accumulation of new student data every year makes searching and processing data difficult, including selecting superior class students according to their talents and abilities. Therefore, the application of the K-Means Clustering data mining method is carried out to support decisions in grouping superior classes. The report card values for each class were used as parameters with a data sample of 80 students and 3 clusters were taken which then resulted in the selection and distribution of superior classes. The purpose of the study was to classify students in the superior class so that they could improve student achievement at SMK Raksana 2 Medan. Results Based on the calculation of the variable distance at the initial centroid with a sample of 80 students and the third iteration, the WCV value is 360.9745 and the BCV value is 7.3575 with a ratio value of 0.0203. Each cluster, namely: Cluster 1 has 43 students including the superior class category. Cluster 2 has 18 students and Cluster 3 has 19 students. Clusters 2 and 3 are included in the regular class category with a total of 37 students. The web-based K-Means application can provide information and solutions needed by schools to classify and determine superior classes so that they can improve student achievement in schools. These results can be used by the school to analyze student achievement and can assist teachers in forming superior classes so as to motivate students to study harder.

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DATA MINING SELEKSI SISWA BERPRESTASI UNTUK MENENTUKAN KELAS UNGGULAN MENGGUNAKAN METODE K-MEANS CLUSTERING (Studi Kasus di MTS Darul Fikri )

Cited by 14 publications

References 0 publications

Klasterisasi Konsentrasi Keahlian Siswa SMK Berdasarkan Kurikulum Merdeka

Klasterisasi Konsentrasi Keahlian Siswa SMK Berdasarkan Kurikulum Merdeka

Leading Class Program In Increasing Learning Achievement At MTS 2 Cirebon

Superior Class to Improve Student Achievement Using the K-Means Algorithm

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