Perancangan Sistem Logika Fuzzy Tipe-2 Interval Dalam Penentuan Perilaku Penggunaan Mobile Learning Dengan Variabel Eksternal Kesiapan Rekan Mahasiswa Dan Kesiapan Dosen: Studi Kasus Di Teknik Fisika ITS

Hermantara, Robert Ciputra (2021) Perancangan Sistem Logika Fuzzy Tipe-2 Interval Dalam Penentuan Perilaku Penggunaan Mobile Learning Dengan Variabel Eksternal Kesiapan Rekan Mahasiswa Dan Kesiapan Dosen: Studi Kasus Di Teknik Fisika ITS. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Pemanfaatan perangkat seluler dalam dunia pendidikan diterapkan oleh Institut Teknologi Sepuluh Nopember (ITS) dengan MyITS Classroom sebagai media pembelajaran. Penggunaan mobile learning MyITS Classroom memiliki beberapa kendala seperti kesulitan akses internet, perangkat seluler kurang memadai, hingga pengaruh lingkungan penggunanya. Berdasarkan model Technology Acceptance Model (TAM) dan Theory of Planned Behavior (TPB), terdapat beberapa variabel eksternal yang mempengaruhi perilaku penggunaan mobile learning, yaitu kesiapan rekan mahasiswa dan kesiapan dosen. Penelitian dilakukan dengan metode survei kuesioner dan hasilnya dianalisis menggunakan logika fuzzy tipe-2 interval Mamdani untuk penentuan perilaku penggunaan mobile learning MyITS Classroom di lingkungan Teknik Fisika ITS karena dapat menyelesaikan permasalahan yang tidak pasti atau ambigu terhadap hasil survei kuesioner. Data yang digunakan sebanyak 489 data responden mahasiswa strata-1 Teknik Fisika ITS. Perancangan model logika fuzzy tipe-2 Interval membandingkan nilai Mean Absolute Percentage Error (MAPE) dari 8 skenario fungsi keanggotaan yang berbeda. Hasil penelitian menunjukkan mahasiswa Teknik Fisika ITS setuju terhadap penggunaan MyITS Classroom dan model logika fuzzy tipe-2 interval yang terdiri dari fungsi keanggotaan segitiga pada input dan segitiga-trapesium pada output dengan 5 variabel linguistik yang memiliki nilai MAPE sebesar 4.606% yang berarti model perancangan memiliki akurasi sangat baik.
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The use of mobile devices in the world of education is implemented by the Sepuluh Nopember Institute of Technology (ITS) with MyITS Classroom as a learning medium. The use of MyITS Classroom mobile learning has several obstacles such as difficulty in accessing the internet, inadequate mobile devices, to the influence of the user's environment. Based on the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB), there are several external variables that influence the behavior of using mobile learning, namely the readiness of fellow students and the readiness of lecturers. The research was conducted using a questionnaire survey method and the results were analyzed using Mamdani type-2 interval fuzzy logic to determine the behavior of using MyITS Classroom mobile learning in the ITS Physics Engineering environment because it can solve uncertain or ambiguous problems with the results of the questionnaire survey. The data used as many as 489 data from ITS Physics Engineering undergraduate student respondents. The design of the Interval type-2 fuzzy logic model compares the Mean Absolute Percentage Error (MAPE) values from 8 different membership function scenarios. The results showed that ITS Physics Engineering students agreed with the use of MyITS Classroom and a type-2 interval fuzzy logic model which consisted of a triangular membership function at the input and a triangle-trapezoid at the output with 5 linguistic variables that had a MAPE value of 4.606%, which means the design model has very good accuracy.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Fuzzy Tipe-2 Interval Mamdani, Mobile Learning, MAPE, Fuzzy Type-2 Interval Mamdani
Subjects: L Education > L Education (General)
L Education > LB Theory and practice of education
L Education > LB Theory and practice of education > LB1044.87 Internet in education (e-learning). Virtual reality in 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 > QA39.3 Fuzzy mathematics
Q Science > QA Mathematics > QA9.64 Fuzzy logic
Q Science > QA Mathematics > QA248_Fuzzy Sets
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: Robert Ciputra Hermantara
Date Deposited: 16 Aug 2021 09:59
Last Modified: 16 Aug 2021 09:59
URI: http://repository.its.ac.id/id/eprint/87283

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