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Journal : multica science and technology

Analysis of Measuring Student Satisfaction with Teacher Performance Assessment Using the Naive Bayes Model Irma Yurni; Arief Rahman; Zahratul Fitri
Multica Science and Technology Vol 4 No 1 (2024): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v4i1.852

Abstract

Naive Bayes model analysis to evaluate teacher quality based on the grades given by students with the variables of communication, mastery of material, teacher involvement, and teaching methods have an important role in determining student satisfaction assessments. The analysis results from the naive Bayes model show that student satisfaction tends to be higher for teachers who have good communication skills, strong mastery of the material, active involvement in the teaching and learning process, and the use of effective and innovative teaching methods. Therefore, to improve the quality of education, it is necessary to increase teacher competence in these four variables. In addition, the application of Naive Bayes model analysis can be an effective tool for identifying students who need improvement and development in analyzing student satisfaction. Naïve Bayes model analysis is used to predict the probability of student satisfaction based on the attributes involved. The results of the research show that analyzing and classifying student satisfaction assessments with good accuracy using the Naive Bayes model makes it easy to estimate the probability of satisfaction based on the attributes given. The results of research using the naive Bayes model with a probability of yes 0.0576 with a likelihood of yes and no getting a value of 0.6 while the probability of no is 0.0384 with a value of 0.4. for Normalization results 1 and Probability Value YES > Probability Value NO, Then Student Satisfaction with Teacher Performance is Satisfied with Teacher Performance.
Analysis of Measuring Student Satisfaction with Teacher Performance Assessment Using the Naive Bayes Model Irma Yurni; Arief Rahman; Zahratul Fitri
Multica Science and Technology (ACCREDITED-SINTA 5) Vol. 4 No. 1 (2024): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v4i1.852

Abstract

Naive Bayes model analysis to evaluate teacher quality based on the grades given by students with the variables of communication, mastery of material, teacher involvement, and teaching methods have an important role in determining student satisfaction assessments. The analysis results from the naive Bayes model show that student satisfaction tends to be higher for teachers who have good communication skills, strong mastery of the material, active involvement in the teaching and learning process, and the use of effective and innovative teaching methods. Therefore, to improve the quality of education, it is necessary to increase teacher competence in these four variables. In addition, the application of Naive Bayes model analysis can be an effective tool for identifying students who need improvement and development in analyzing student satisfaction. Naïve Bayes model analysis is used to predict the probability of student satisfaction based on the attributes involved. The results of the research show that analyzing and classifying student satisfaction assessments with good accuracy using the Naive Bayes model makes it easy to estimate the probability of satisfaction based on the attributes given. The results of research using the naive Bayes model with a probability of yes 0.0576 with a likelihood of yes and no getting a value of 0.6 while the probability of no is 0.0384 with a value of 0.4. for Normalization results 1 and Probability Value YES > Probability Value NO, Then Student Satisfaction with Teacher Performance is Satisfied with Teacher Performance.
Co-Authors . Budiaman Adrian Indarti Agus Khobir Permana Ahmad Fadhli Busthomi Ahmad Heru Romadhon Akbar, Moch Malik Ali Muhdor Alwi Fahruzy Nasution Amrulloh, Rifqi Amy Shientiarizki Anindhita Satria Mahendra Ansari Harahap, Dedy Anugrah Dwi Putra Arrum Normasari Aulia Helmina Putri Aulia Ramadanisti Ayu Praditias Sumono Putri Ayu Purwarianti Budi Winarni Christin Natalia P.N Christina Indrawati Cut Nina Latisa Maura Damai Syah Halawa Desi kusrini kusrini Dewi Puspitorini Husodo Diman Ade Mulada Diman Ade Mulada Diman Ade Mulada, Diman Ade Ella Suzanna Elmi Nurvianti Epi Supriyani Siregar Erny Setiawan Erwan Efendi Fatin Nadifa Tarigan Febriyanti Darnis Ferry Herdianto Fitriyah Kusuma Devi Geger Suhartono Gunawan Wibisono Guntur Adham Syahputra Hendry Setyawans Sutedjo Heny Fitriani Humairo Aziza Husnawati Yahya I Putu Diatmika Ika Amalia Imam Syahrobani Irma Yurni Jayen, Fredy Jemmy Najoan Jihan Pratiwi Ketut Ayusia Madewi Khairul Fadly Kurnia Nur Widyasari Lisa Ginayatri Mahsina Mahsina Maria G.M. Polii Maria Haryanti Butarbutar Maryanne Pattynama Miftahus Sholihin Muchlis Saputra Muhammad Aqil Al Munawar Muhammad Badrun Siregar Muhammad Ghaffar Shidqi Muhammad Habibie Al-Hamzah Muhammad Syapari Neneng Wahyuni Neni Nuraini Neni Nurhayati Nofridayati Nofridayati Pelita Ramadhaniah Priyo Susilo R Dewi Mutia Farida Ragiel Pangestu Rahayu Sri Utami Rahmat Novrianda Dasmen Rahmawati Rahmawati Reni Rohidin Rohidin Ronald Ignatius Zai Sahdin Hasibuan Sayed Fachrurrazi Sherliyanah Shinta Andriyani Shinta Andriyani Sri Djuniati Sri Ngapiyatun Srilasmawanti Srilasmawanti Sritomo Wignjosoebroto Sumarno Sumarno Sunanto Supanca Ade Putri Syafi’I Tina Lestari Tuti Tursilah Wahyu Nur Chalamsah Setiawan Warto Utomo Wartomo Wiwiek Wahyuningsih Wiwiek Wahyuningsih Yulian Ansori Yusmar, Arief Maulana Zahratul Fitri