Perancangan Sistem Logika Fuzzy Tipe-2 dalam Pemodelan Perilaku Penggunaan m-Learning Berdasarkan Persepsi Kemudahan dan Kegunaan Penggunaan: Studi Kasus Teknik Fisika ITS

Ramadhani, Rani Rizki (2021) Perancangan Sistem Logika Fuzzy Tipe-2 dalam Pemodelan Perilaku Penggunaan m-Learning Berdasarkan Persepsi Kemudahan dan Kegunaan Penggunaan: Studi Kasus Teknik Fisika ITS. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Hasil sistem pendidikan di Indonesia mendapatkan penilaian di bawah rata-rata dunia dari OECD PISA pada tahun 2020. Sistem pembelajaran dengan menggunakan mobile learning hadir untuk membantu meningkatkan sistem pendidikan di Indonesia. Harapannya dengan sistem ini, penilaian OECD PISA dapat meningkat. Namun realitanya penerimaan mahasiswa terhadap sistem mobile learning cukup kompleks. Pada penelitian ini dilakukan sebuah riset mengenai perilaku penggunaan mobile learning oleh mahasiswa Departemen Teknik Fisika Institut Teknologi Sepuluh Nopember (ITS) melalui data survey kuesioner. Metode sistem logika fuzzy tipe 2 interval digunakan dalam penelitian ini. Perancangan sistem logika fuzzy tipe-2 dirancang dengan 8 skenario, dengan variasi rancangan jenis fungsi keanggotaan dan operator AND dan OR. Desain model sistem logika fuzzy interval tipe-2 dibagi menjadi dua sistem. Sistem pertama memiliki tiga subsistem dengan input berupa persepsi kemudahan, persepsi kegunaan, dan niat perilaku. Sistem kedua terdiri dari 1 subsistem dengan input yaitu output dari subsistem pada sistem pertama dan 1 output yaitu perilaku penggunaan. Kemudian didapatkan model terbaik output perilaku penggunaan dengan nilai MAPE sebesar 5,806% yang dikategorikan sangat akurat menggunakan fungsi keanggotaan gaussian dengan lima variabel linguistik. Berdasakan pengujian hipotesis didapatkan bahwa variabel persepsi kemudahan dan kegunaan berpengaruh terhadap perilaku penggunaan mobile learning dengan nilai korelasi linear berganda 0,944 dikategorikan sangat kuat dan koefisien determinasi 88,8% perilaku penggunaan m-Learning dipengaruhi oleh variabel persepsi kemudahan dan persepsi kegunaan.

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The education system results in Indonesia are rated below the world average from the OECD PISA in 2020. The learning system using mobile learning is here to help improve the education system in Indonesia. It is hoped that with this system, the OECD PISA assessment can improve. However, in reality, student acceptance of the mobile learning system is quite complex. In this study, Students behaviour researched using mobile learning by students of the Department of Physics Engineering, Sepuluh Nopember Institute of Technology (ITS) through questionnaire survey data. The Fuzzy Logic System method of type 2 interval is used in this study. The interval type-2 fuzzy logic system design is designed with eight scenarios, with variations in the design of membership functions and AND and OR operators. The model design of the type-2 interval fuzzy logic system is divided into two systems. The first system has three subsystems with inputs in perceived convenience, perceived usefulness, and behavioral intentions. The second system consists of 1 subsystem with input, namely the subsystem's output in the first system, and one output, namely usage behavior. Then, the Gaussian membership function with five linguistic variables obtained the best model of usage behavior output with a MAPE value of 5.806%, categorized as very good. It can be concluded that Engineering Physics ITS students in class 2017-2020 agree on mobile learning in the learning system. Based on hypothesis testing, the results obtained that the perceived ease and usefulness variables affect the behaviour of using mobile learning with multiple linear correlation values of 0.944, which categorized as very strong, and the coefficient of determination of the use of m-Learning is 88.8% usage behaviour influenced by perceived ease and perceived usefulness variables.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: fuzzy logic, mobile learning, education, logika fuzzy, pendidikan
Subjects: L Education > L Education (General)
L Education > LA History of education
L Education > LB Theory and practice of education
L Education > LC Special aspects of education
Q Science > QA Mathematics > QA275 Theory of errors. Least squares. Including statistical inference. Error analysis (Mathematics)
Q Science > QA Mathematics > QA278.2 Regression Analysis. Logistic regression
Q Science > QA Mathematics > QA336 Artificial Intelligence
Q Science > QA Mathematics > QA9.64 Fuzzy logic
Q Science > QA Mathematics > QA278 Cluster Analysis. Multivariate analysis. Correspondence analysis (Statistics)
Divisions: Faculty of Industrial Technology and Systems Engineering (INDSYS) > Physics Engineering > 30201-(S1) Undergraduate Thesis
Depositing User: Rani Rizki Ramadhani
Date Deposited: 19 Aug 2021 03:17
Last Modified: 19 Aug 2021 03:17
URI: http://repository.its.ac.id/id/eprint/87217

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