Analisis Sentimen Pengguna Terhadap Kinerja Sistem Transportasi Umum Jakarta Menggunakan Algoritma Naive Bayes
DOI:
https://doi.org/10.30865/klik.v4i6.1936Keywords:
Sentiment Analysis; Naïve Bayes Algorithm; Public Transportation; Transjakarta; Jaklingko Fast MinerAbstract
This study uses the Naive Bayes algorithm to analyze netizen sentiment regarding public transportation in Jakarta. In the past, Jakarta's public transportation system was dominated by private operators, including buses, angkot (minibuses), and taxis. However, various challenges arose, such as lack of coordination, inconsistent service quality, safety issues, and inadequate coverage. To improve the quality and availability of public transportation, local or national governments have intervened by taking over public transportation services or imposing stricter regulations on private operators. Significant investments have been made in developing public transportation modes such as TransJakarta (bus rapid transit), KRL Commuter Line (electric train), MRT (Mass Rapid Transit), Jaklingko, and other public transport services. This study aims to analyze the benefits of public transportation, which has largely been taken over by the government, to minimize existing shortcomings. The research focuses on analyzing the differing opinions spread across various social media platforms. Data was collected from social media platforms such as YouTube and X. A total of 987 data points were used in this study, with 612 positive and 375 negative data points. After conducting the research, an accuracy of 94.22% was achieved. The analysis revealed significant variations in sentiment among netizens regarding public transportation in Jakarta. Some groups of netizens have begun to feel positive effects from the current integration of public transportation, but there are still execution shortcomings. The analysis also identified key factors influencing differing opinions, such as user areas, the uneven distribution of drivers with good personal qualities, and the economic conditions of the community. Consequently, this study contributes to sentiment analysis and natural language processing by applying problem-solving procedures to classify netizen comments on various platforms. The results of this study indicate that the Naive Bayes algorithm can be used to classify netizen sentiment about public transportation in Jakarta with a high level of accuracy. These findings can be useful for the government and Jakarta residents in finding solutions to these issues. Thus, this study can serve as a basis for a more comprehensive understanding of the government's response to public transportation issues in Jakarta.
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