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All Journal J-SAKTI (Jurnal Sains Komputer dan Informatika) J-SAKTI (Jurnal Sains Komputer dan Informatika)
Erina Undamayanti
Sekolah Tinggi Teknologi Wastukancana Purwakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Energy

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Analisis Sentimen Menggunakan Metode Naive Bayes Berbasis Particle Swarm Optimization Terhadap Pelaksanaan Program Merdeka Belajar Kampus Merdeka Erina Undamayanti; Teguh Iman Hermanto; Ismi Kaniawulan
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i2.502

Abstract

During the MBKM program running at several universities in Indonesia, several problems occurred, namely the implementation of the curriculum that did not have a reference, the disbursement of pocket money given was not on schedule, the policies of each partner were different, and the existence of the covid-19 pandemic. The way to find out public opinion or opinion about the MBKM program is to summarize public opinion on Twitter social media. This study aims to analyze the results of the classification of twitter users opinions on the MBKM program in Indonesia through sentiment analysis using the Naive Bayes method based on Particle Swarm Optimization. The research metodology carried out in this study was through the stages of data crawling, text preprocessing, feature extraction, classification, and evaluation. The data used in this study are 428 data. The results of the research in the form of sentiment analysis obtained are positive sentiments of 61.92%, it can be concluded that the MBKM program can be well received by the Twitter user community, especially students. Although there are some negative sentiments that appear around 38.08%. The results of this study can be used as a reference for the MBKM policy development team, especially the Kemendikbud POKJA team, because this program can provide benefits and experiences for students while the results of this research can be used as evaluation material for the team in the future to be even better
Analisis Sentimen Menggunakan Metode Naive Bayes Berbasis Particle Swarm Optimization Terhadap Pelaksanaan Program Merdeka Belajar Kampus Merdeka Erina Undamayanti; Teguh Iman Hermanto; Ismi Kaniawulan
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i2.502

Abstract

During the MBKM program running at several universities in Indonesia, several problems occurred, namely the implementation of the curriculum that did not have a reference, the disbursement of pocket money given was not on schedule, the policies of each partner were different, and the existence of the covid-19 pandemic. The way to find out public opinion or opinion about the MBKM program is to summarize public opinion on Twitter social media. This study aims to analyze the results of the classification of twitter users opinions on the MBKM program in Indonesia through sentiment analysis using the Naive Bayes method based on Particle Swarm Optimization. The research metodology carried out in this study was through the stages of data crawling, text preprocessing, feature extraction, classification, and evaluation. The data used in this study are 428 data. The results of the research in the form of sentiment analysis obtained are positive sentiments of 61.92%, it can be concluded that the MBKM program can be well received by the Twitter user community, especially students. Although there are some negative sentiments that appear around 38.08%. The results of this study can be used as a reference for the MBKM policy development team, especially the Kemendikbud POKJA team, because this program can provide benefits and experiences for students while the results of this research can be used as evaluation material for the team in the future to be even better
Co-Authors Ismi Kaniawulan, Ismi Muhammad Ali Akbar Teguh Iman Hermanto