Identifikasi Karakter Siswa Menggunakan Metode K-Means (Studi Kasus Sdn 156 Pekanbaru)

Authors

  • Kasini Kasini Prodi Teknik Informatika, Fakultas Sains dan Teknologi, Universitas Pahlawan Tuanku Tambusai

Abstract

Good character education can have a characteristic impact on students. each student has a different character. Various ways done by the school in character education based on kemendiknas, including State Elementary School 156 Pekanbaru. Problems that arise in the field is there is no method that can determine the character of the students so that the school's special teachers can not understand precisely the characters in the students. The lack of understanding of the character of the students makes the vision of the school mission has not been seen so that character education in SDN 156 Pekanbaru has not been right target. Therefore, it needs to be done grouping student character in SDN 156 Pekanbaru with the aim of school know character owned by students in school. The K-Means algorithm is used to classify the character of the students with the number of clusters as much as 2 using the six attributes of characters studied: Honest, disciplined, confident, caring, creative and responsible with 130 student data. The results of K-Means manual calculation with sample data 10 data from 130 data that is weak character (C1) amounted to 1 student and weak character of 9 students, this result is same with calculation executed by RapidMiner application. Test results with 130 data using RapidMiner resulted in the number of students with weak character 26 students with the average centroid (0.665) with caring and creative characters. While students who have strong character 104 students with average value of centroid (0.900) with honest character, discipline, confidence, and responsibility. The result of character grouping based on class cluster position in RapidMiner is grade 3 which has weak character (C1) 8 students from 35 students, grade 4 is 8 out of 24 students, 5th grade is 1 of 17 students and grade 6 is 9 of 46 students. While clusters with strong characters (C2) class 3 amounted to 27 students, grade 4 amounted to 24 students, class 5 amounted to 16 students, and grade 6 amounted to 37 students. From the results of this study is expected Strong characters can be developed by school continue to perform habits which involves the students so that the characters in the students can be seen while for the caring and creative characters so as not to be weak then the school always provide guidance to the students and give examples of good habits and activities that can be followed by students in school .

References

Rohmawati N.W., Defiyanti S. dan Jajuli M. (2015), “Implementasi Algoritma K-Means Dalam Pengklasteran Mahasiswa Pelamar Beasiswaâ€, Jurnal Ilmiah Teknologi Informasi Terapan, Vol.1, No.2, Hal.62-68.

Metisen B.M., dan Sari H.L., (2015), “Analisis Clustering Menggunakan Metode K-means Dalam Pengelompokkan Penjualan Produk Pada Swalayan Fadhilaâ€,Jurnal media infotama, Vol. 11, No.2, Hal. 111-118.

Agustin E.F.M., Fitria A., dan Hanifah S.A., (2015), “Implementasi Algoritma K-Means Untuk Menentukan Kelompok pengayaan Materi Mata Pelajaran Ujian Nasional (Studi Kasus: Smp Negeri 101 Jakarta) “, Jurnal Teknik Informatika, Vol. 8 No.1, Hal. 73-78.

Nasari F., dan Darma S. (2015), “Penerapan k-means clustering Pada Data Penerimaan Mahasiswa Baru (Studi Kasus : Universitas Potensi Utama)â€, Seminar Nasional Teknologi Informasi dan Multimedia, STMIK AMIK Yogyakarta, Hal.73-78.

Siska S.T. (2016), “Analisa Dan Penerapan Data Mining Untuk Menentukan Kubikasi Air Terjual Berdasarkan Pengelompokan Pelanggan Menggunakan Algoritma K-Means Clusteringâ€,Jurnal Teknologi Informasi dan Pendidikan, Vol. 9 No.1 Hal.86-93.

Nuraeni (2014), “Pendidikan Karakter Pada Anak Usia Diniâ€,Jurnal Paedagogy, Vol. 1 No.2

Lestari P., dan Sukanti (2016), “Membangun Karakter Siswa Melalui Kegiatan Intrakurikuler Ekstrakurikuler, Dan Hidden curriculum (di SD budi Mulia Dua Pandeansari Yogyakarta)â€,Jurnal Penelitian, Vol. 10 No.1 Hal.71-96

Prasetyo E (2014), Data Mining Mengolah Data Menjadi Informasi Menggunakan Matlab, Andi, Jakarta, Hal.189

Ong, Oscar. (2013). “Implementasi Algoritma K-Means Clustering Untuk Menentukan Strategi Marketing President University.â€,Jurnal Ilmiah Teknik Industri,Vol.12.No 1.Hal.10-20

Juni K.A., Indrawan G.dan Rasben G.D (2016).†Data Mining Rekomendasi Calon Mahasiswa Berprestasidi Stmik Denpasar Menggunakan Metode Technique For Others Reference By Similarity To Ideal Solutionâ€, Jurnal Sains Dan Teknologi,Vol.5 No.2 Hal.746-760

Purnawansyah., dan Haviluddin (2016),†K-Means Clustering Implementation in NetworkTraffic Activities “, IEEE

Alsayat A., dan El-Sayed H. (2016),†Social Media Analysis using Optimized K-MeansClusteringâ€, Sera 2016, Baltimore USA,IEEE.

Haryati S., Sudarsono A. dan Suryana E (2015), “Implementasi Data Mining Untuk Memprediksi Masa Studi Mahasiswa Menggunakan Algoritma C4.5†Jurnal Media Infotama, Vol 11 No.2 Hal 130-138.

Downloads

Published

2018-03-20

How to Cite

Kasini, K. (2018). Identifikasi Karakter Siswa Menggunakan Metode K-Means (Studi Kasus Sdn 156 Pekanbaru). Jurnal Inovasi Teknik Informatika, 1(1), 29–38. Retrieved from http://journal.universitaspahlawan.ac.id/index.php/jiti/article/view/22

Issue

Section

Articles