Analisis Sentimem Pengguna Twitter Terhadap Program Kartu Prakerja Menggunakan Metode Naïve Bayes Classifier (NBC)

Novianti, Ela Wahyu (2021) Analisis Sentimem Pengguna Twitter Terhadap Program Kartu Prakerja Menggunakan Metode Naïve Bayes Classifier (NBC). Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

	Text 10611710000105-Undergraduate_Thesis.pdf - Accepted Version Restricted to Repository staff only until 1 October 2023. Download (911kB) | Request a copy
	Text 10611710000105-Undergraduate_Thesis.pdf Restricted to Repository staff only Download (911kB) | Request a copy
	Text 10611710000105-Undergraduate_Thesis.pdf Restricted to Repository staff only Download (911kB) | Request a copy
	Text 10611710000105-Undergraduate_Thesis.pdf Restricted to Repository staff only Download (911kB) | Request a copy

Abstract

Indonesia mengkonfirmasi virus corona penyebab COVID-19 masuk pertama kali pada awal Maret 2020. Sejak itu seluruh sektor terdampak dari pandemi COVID-19 tak hanya kesehatan, sektor ekonomi juga menagalami dampak serius akibat pandemi ini. Pemerintah melakukan berbagai upaya penanggulangan salah satunya adalah dengan melakukan Pembatasann Aktivitas Berskala Besar (PSBB). Kebijakan PSBB berpengaruh pada aktivitas bisnis yang berimbas pada perekonomian sehingga berdampak pada situasi ketenagakerjaan di Indonesia. Dalam mengatasi masalah ketenagakerjaan pemerintah membuat kebijkan program Kartu Prakerja. Masalahnya muncul persepsi bahwa ditengah pandemi COVID-19 ini, logika Kartu Prakerja tidak tepat digunakan sebab tak ada jaminan bahwa pekerja yang telah dilatih mendapatkan pekerjaan baru, apalagi ditengah kondisi ekonomi yang sedang terpuruk. Akibatnya timbul pro dan kontra dari masyarakat terkait Kartu Prakerja yang sempat menjadi trending topic di Twitter. Hasil analisis sentimen program kartu prakerja kebanyakan bersifat negatif. Sentimen negatif disini menunjukkan kritik masyarakat mengenai kesulitan saat proses pendaftaran. Sentimen positif menunjukkan bahwa banyak yang mendapatkan manfaat dengan adanya program kartu prakerja. Hasil klasifikasi menggunakan metode naïve bayes classifier didapatkan nilai nilai G-mean sebesar 80,1% dan nilai AUC sebesar 81,2%. Sedangkana pada data testing nilai G-mean sebesar 69,2% dan nilai AUC sebesar 73,4%.
====================================================================================================
Indonesia confirmed that the corona virus that caused COVID-19 first entered in early March 2020. Since then all sectors affected by the COVID-19 pandemic are not only health, the economic sector has also experienced serious impacts due to this pandemic. The government has taken various countermeasures, one of which is by limiting large-scale activities (PSBB). The PSBB policy affects business activities that have an impact on the economy so that it has an impact on the labor situation in Indonesia. In dealing with labor problems, the government made a policy for the Pre-Employment Card program. The problem arises the perception that in the midst of the COVID-19 pandemic, the logic of the Pre-Employment Card is not appropriate because there is no guarantee that workers who have been trained will get a new job, especially in the midst of a slumping economic condition. As a result, there were pros and cons from the community regarding the Pre-Employment Card, which had become a trending topic on Twitter. The results of the sentiment analysis of the pre-employment card program tend to be negative. The negative sentiment here shows public criticism of the difficulties during the registration process. Positive sentiment shows that many have benefited from the pre-employment card program. The results of the classification using the nave Bayes classifier method obtained the G-mean value of 80,1% and the AUC value of 81,2% Meanwhile,in the testing data, the G-mean value is 69,8% and the AUC value is 73,4%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Analisis Sentimen, Kartu Prakerja, Naïve Bayes Classifier, Naïve Bayes Classifier, Pre-Employment Cards, Sentiment Analysis
Subjects:	Q Science > Q Science (General) > Q325.5 Machine learning. Support vector machines. T Technology > T Technology (General) > T385 Visualization--Technique
Divisions:	Faculty of Vocational > 49501-Business Statistics
Depositing User:	Ela Wahyu Novianti
Date Deposited:	20 Aug 2021 01:35
Last Modified:	20 Aug 2021 01:35
URI:	http://repository.its.ac.id/id/eprint/86944

Actions (login required)

View Item