Aplikasi Jaringan Syaraf Tiruan Kohonen Self Organizing Map (K-SOM) Untuk Evaluasi Mutu Sekolah

Kapita, Syarifuddin N. (2015) Aplikasi Jaringan Syaraf Tiruan Kohonen Self Organizing Map (K-SOM) Untuk Evaluasi Mutu Sekolah. Undergraduate thesis, Institut Teknology Sepuluh Nopember.

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Abstract

Penjaminan mutu pendidikan merupakan upaya dalam peningkatan mutu pendidikan dan pengembangan sumber daya manusia. Peningkatan mutu pendidikan telah diupayakan dari berbagai kalangan pendidikan, masyarakat umum, terutama pemerintah. Banyaknya keragaman potensi dan sumber daya daerah dapat menyebabkan hasil mutu sekolah sangat bervariasi. Oleh karena itu, upaya untuk menyeragamkan mutu pendidikan harus menjadi pusat perhatian dalam menjaga mutu pendidikan secara nasional. Langkah yang dilakukan pemerintah untuk menjamin mutu pendidikan secara menyeluruh yaitu membuat Standar Nasional Pendidikan (SNP) yang memuat delapan indikator. Kedelapan indikator ini diacu oleh Evaluasi Diri Sekolah (EDS) maupun akreditasi sekolah. Tujuan dari penelitin ini adalah untuk mengevaluasi mutu sekolah pada jenjang SD, SMP dan SMA. Jaringan Syaraf Tiruan (JST) merupakan sistem pemrosesan informasi yang diilustrasikan seperti otak manusia. JST telah banyak digunakan dalam beberapa aplikasi, salah satunya yaitu Clustering atau pengelompokan. JST yang dapat digunakan dalam pengelompokan adalah Kohonen Self Organizing Map (KSOM). Pada penelitian ini, peneliti menggunakan metode Kohonen Self Organizing (K-SOM) untuk mengevaluasi mutu sekolah berdasarkan data EDS di Provinsi Maluku Utara. Kelompok terbaik dihasilkan dengan validasi Davies- Bouldin Index (DBI). Hasil evaluasi mutu sekolah dengan algoritma Kohonen-SOM, menunjukkan bahwa DBI yang diperoleh pada jenjang SD yaitu dengan learning rate , DBI yang diperoleh pada jenjang SMP 1.3970 dengan Learning rate dan DBI yang diperoleh pada jenjang SMA 1.5829 dengan Learning rate ========================================================================================================= The ensuring for quality of education is effort in increasing the quality of education and development of human resources. The increasing for quality of education has been attempted by some civitas academic, public society, especially the government. The number of various potential and regional resources can cause result of quality in school to be very varied. Therefore, the efforts to uniform the quality of education must be point of interest to protect the quality of education nationally. Some steps implemented by the government to ensure the quality of education nationally is Education National Standard containing eight indicators. These eight indicators refer to School Self Evaluation and accreditation of school. The purpose of this research is to cluster the quality of education at the level of Elementary School, Junior High School and Senior High School that refer to School Self Evaluation. The Artificial Neural Network is information processing system that illustrated as human‟s brain. The Artificial Neural Network has been used in some applications, one of them is Clustering. The Artificial Neural Network that can be used in Clustering is Kohonen Self Organizing Map (K-SOM). In this research, the researcher uses Kohonen Self Organizing Map (K-SOM) method to group the quality of education based on data School Self Evaluation in North Maluku Province. The best clustering results are obtained by using the validation of Davies-Bouldin Index (DBI) and compared with the Accreditation of school. vi Results of school quality evaluation using Kohonen-SOM algorithm, shows that DBI obtained at the elementary school is 1.6948 with learning rate 0.2, DBI obtained at the junior high school is 1.3970 with Learning rate 0.9 and DBI obtained at the Senior High School is 1.5829 with learning rate 0.8.

Item Type: Thesis (Undergraduate)
Additional Information: RTMa 006.32 Kap a
Uncontrolled Keywords: Artificial Neural Network, Kohonen Self Organizing Map (K-SOM), Education National Standard, School Self Evaluation
Subjects: Q Science > QA Mathematics > QA76.87 Neural networks (Computer Science)
Divisions: Faculty of Mathematics and Science > Mathematics > 44101-(S2) Master Thesis
Depositing User: Mr. Tondo Indra Nyata
Date Deposited: 02 Dec 2019 03:08
Last Modified: 02 Dec 2019 03:08
URI: https://repository.its.ac.id/id/eprint/72136

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