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All Journal PERFORMA : Media Ilmiah Teknik Industri Indonesian Journal of Electrical Engineering and Computer Science
Dewi Wisnu Wardani
Universitas Sebelas Maret
Engineering Industrial & Manufacturing Engineering

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Decision Support System to Select Elective Courses Using Hybrid AHP-Promethee Method Haryono Setiadi; Noor Azizah Mosaik Suni; Dewi Wisnu Wardani
Performa: Media Ilmiah Teknik Industri Vol 21, No 1 (2022): Performa: Media Ilmiah Teknik Industri
Publisher : Industrial Engineering Study Program, Faculty of Engineering, Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/performa.21.1.60307

Abstract

The selection of elective courses is often confusing for students as indicated by the initial survey of class 2015 -2018 students in the Informatics Department of Sebelas Maret University which showed that 88,9% needed a system to assist in selecting elective courses. This study was conducted to accommodate students’ requirements by designing the decision support system to recommend elective courses by combining AHP and PROMETHEE methods. AHP was used to weight the criteria, after which they were then ranked using the PROMETHEE such that the elective courses were sorted partially using PROMETHEE I as scenario 1 and completely through PROMETHEE II as scenario 2. The accuracy test showed that scenarios 1 and 2 were 67.4% and 60.4%, respectively, accurate.
Fast Naïve Bayes classifiers for COVID-19 news in social networks Hasan Dwi Cahyono; Atara Mahadewa; Ardhi Wijayanto; Dewi Wisnu Wardani; Haryono Setiadi
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1033-1041

Abstract

The growth of fake news has emerged as a substantial societal concern, particularly in the context of the COVID-19 pandemic. Fake news can lead to unwarranted panic, misinformed decisions, and a general state of confusion among the public. Existing methods to detect and filter out fake news have accuracy, speed, and data distribution limitations. This study explores a fast and reliable approach based on Naïve Bayes algorithms for fake news detection on COVID-19 news in social networks. The study used a dataset of 10,700 tweets and applied text pre-processing, term-weighting, document frequency thresholding (DFT), and synthetic minority oversampling techniques (SMOTE) to prepare the data for classification. The study assessed the performance and runtime of four models: gradient boosting (GDBT), decision tree (DT), multinomial Naïve Bayes (MNB), and complement Naïve Bayes (CNB). The testing results showed that the CNB model reaches the highest accuracy, precision, recall, and F1-score of approximately 92% each, with the shortest runtime of 0.55 seconds. This study highlights the potential of the CNB model as an effective tool for detecting online fake news about COVID-19, given its superior performance and rapid processing time.
Co-Authors Ardhi Wijayanto Atara Mahadewa Hasan Dwi Cahyono Noor Azizah Mosaik Suni Setiadi, Haryono