Pemodelan Guru Cemerlang KPM Menggunakan Perlombongan Data
Excellence Teacher Modelling using Data Mining Techniques
DOI:
https://doi.org/10.37134/jictie.vol4.3.2017Keywords:
guru cemerlang, pepohon keputusan, J48, hutan rawakAbstract
Guru merupakan panggilan kepada seorang pendidik yang mengajar di sekolah. Kecemerlangan seseorang pelajar biasanya berkait rapat dengan kecemerlangan guru yang mengajar. Kewujudan guru cemerlang amat diperlukan di semua sekolah dan masalah yang biasa dihadapi oleh pihak pentadbiran sekolah ialah menjadikan seseorang guru itu guru yang cemerlang. Oleh itu tujuan kajian ini dilakukan adalah untuk mendapatkan model guru cemerlang yang terbaik menggunakan dua algoritma dalam teknik pepohon keputusan. Algoritma yang digunakan ialah C4.5 (J48) dan Hutan Rawak (RF). Kajian ini menggunakan data guru cemerlang di sekolah. Atribut yang digunakan merupakan 4 faktor dengan 26 kriteria pemilihan guru cemerlang sebagai input serta 1 output. Set data diambil daripada Sistem Pengurusan Latihan Kementerian Pelajaran Malaysia (SPL KPM) yang ditadbir oleh Sektor Latihan ICT, BPG. Keputusan kajian menunjukkan bahawa Model 2 algoritma J48 memperolehi keputusan yang lebih baik iaitu ketepatan sebanyak 98.86% dengan min kuasa dua ralat (RMSE) hanya 0.1061 berbanding dengan algoritma Hutan Rawak (RF).
Teacher is an educator who is teaching at the school. Student’s performances are usually associated with excellence teacher. The existence of excellent teachers is needed in all schools and the common problems faced by the school administration is making an excellent teacher's teacher. Therefore, the purpose of this study is to get the excellence teachers using two algorithms from decision tree technique. The algorithm used is a C4.5 (J48) and Random Forests (RF). This study uses the data of excellence teachers in schools. The attributes used are 4 factors with 26 criteria for selection of outstanding teachers as input and one output. The data set used were taken from the Training Management System Ministry of Education (MOE SPL) which is administered by the ICT Training Sector, BPG. The results show that the Model 2 algorithm J48 was better that an accuracy of 98.86% with a mean square error (RMSE) is only 0.1061 compared with algorithm Random Forest (RF).
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