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All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING
M. Afdal
Universitas Islam Negeri Sultan Syarif Kasim
Computer Science & IT Engineering

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A Comparative Study of Student Satisfaction Levels on Online Learning Using K-NN and Naïve Bayes Hilda Mutiara Nasution; Mustakim Mustakim; Inggih Permana; M. Afdal
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 7 No. 1 (2023): Issues July 2023
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v7i1.9581

Abstract

The outbreak of the Covid-19 pandemic in Indonesia led to restrictions on human social activities to minimize transmission. Teaching-learning is also affected when students must stay home and follow distance learning based on Government Regulation Number 21 of 2020, the Large-Scale Social Restrictions (PSBB) policy, issued on March 31, 2020. This has led to the emergence of learning support applications such as Zoom, Google Classroom, Google Meet, E-Learning, and many more. However, this new learning culture requires adaptation for effective implementation. During the adaptation process, researchers want to measure the level of student satisfaction and find out the best algorithm for classifying the level of student satisfaction. This measurement uses two data mining algorithms, K-Nearest Neighbour (K-NN) and Naïve Bayes, and the Islamic State University of Sultan Syarif Kasim Riau students as the research object. Different algorithms have varying strengths and weaknesses in handling specific data types and classification tasks. By comparing both algorithms, we can assess their generalization capabilities. A model that performs well on training data but fails to generalize to unseen data may not be as effective as a more robust algorithm that exhibits better generalization performance. K-NN classification with a value of k = 3 gets good results. Based on the study results, the conclusion is that K-NN is more optimal in classifying student satisfaction levels than Naïve Bayes with an accuracy ratio of 85% : 80%, precision of 85% : 84%, and recall of 99% : 93%.
Model for Estimating Waste Generation in Pekanbaru Using Backpropagation Algorithm Farahdina Risky Ramadani; Inggih Permana; M. Afdal; Siti Monalisa
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 7 No. 1 (2023): Issues July 2023
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v7i1.9767

Abstract

Waste generation in Pekanbaru City cannot be managed optimally. Based on 2020 data, less than 50% of the waste that reaches the Final Disposal Site (TPA) reaches. To overcome this problem, this study aims to create an estimation model that can estimate the amount of waste generated each year. So that it can help the authorities to implement various policies to control waste generation. The estimation model is created using the backpropagation algorithm. The attributes used are those related to population and waste generation. Based on the results of experiments conducted using RapidMiner, the best network architecture model is the 6-6-1 model, namely six nodes in the input layer, six nodes in the hidden layer, and one node in the output layer. The six nodes in the input layer refer to the number of attributes used. The activation function used is binary sigmoid. The RMSE value generated from the best model is very low, namely 0.0181. So it can be concluded that this model can be used to estimate the generation of solid waste in Pekanbaru City
Co-Authors Farahdina Risky Ramadani Hilda Mutiara Nasution Inggih Permana Mustakim Mustakim Siti Monalisa