Abstract. Sentiment Analysis Using Naive Bayes Classifier Against Restaurant Reviews in Singapore. Various restaurant options bring up a problem for diners to pick a restaurant to dine in. Thus, visitors usually perceive the restaurant's recommendation or rating in advance to know other diners' opinions about the restaurant. Previous restaurant diners' comments can be presented in sentiment analysis to determine their satisfaction. This research investigates the Naïve Bayes Classifier algorithm's performance in classifying visitors' sentiment based on restaurant diner comments. We will group visitors' comments into two types of sentiment: positive (satisfied) and negative (unsatisfied). The results of the data classification test are analyzed to determine its accuracy. The grouping of visitor satisfaction reviews using the naïve bayes algorithm provides an accuracy score of 73%. Besides, we visualize the research classification results in the browser-based R Shiny web application through word cloud and diagrams.Keywords:restaurant review, sentiment analysis, Naïve Bayes ClassifierAbstrak. Variasi pilihan restoran yang tidak sedikit menjadi salah satu masalah bagi pengunjung ketika ingin memilih restoran. Sehingga, pengunjung biasanya melihat rekomendasi atau penilaian pengunjung lain terhadap restoran tersebut terlebih dahulu untuk mengetahui penilaian pengunjung lain terhadap restoran tersebut. Penilaian atau review pengunjung dapat disajikan dalam analisis sentimen berdasarkan komentar para pengunjung restoran sebelumnya untuk melihat kepuasan pengunjung terhadap restoran tersebut. Penelitian ini dilakukan untuk mengetahui performa algoritma Naïve Bayes Classifier dalam melakukan klasifikasi sentimen berdasarkan komentar pengunjung restoran. Penelitian dilakukan dengan mengklasifikasikan data komentar pengunjung restoran menjadi dua kategori sentimen, yaitu: positif (satisfied) dan negatif (unsatisfied). Hasil pengujian pengklasifikasian data kemudian dianalisis akurasinya. Hasil pengelompokan review kepuasan pengunjung menggunakan algoritma naïve bayes memberikan nilai akurasi sebesar 73%. Visualisasi hasil klasifikasi dari analisis kemudian ditampilkan pada aplikasi berbasis web yaitu R Shiny berupa wordcloud dan diagram. Kata Kunci: penilaian restoran, analisis sentimen, Naïve Bayes Classifier
This research was conducted to find the groups of elementary schools in the Special Capital Region of Jakarta, also known as DKI Jakarta. Elementary school data were selected because it is the first stage of formal education in Indonesia. This research used K-means clustering with the elbow method to determine optimal cluster numbers. The optimal cluster number is three with Cluster 2 having the most members, followed by Cluster 1 and Cluster 0. The data distribution of Cluster 2 shows that the second-most student body and public schools located in East and West Jakarta have an adequate student-to-teacher ratio based on Article 17 of Government Regulation 74, 2008.
This research uses a five-construct theoretical model as the basis for finding out what factors can satisfy customers in the online leathercraft industry. The questionnaire is based on this model, which is distributed through online channels and social media. There are 691 samples obtained and analyzed by looking at the demographic data and then correlation and regression analysis. The results obtained are that all hypotheses are accepted with factors that can satisfy customers: online shopping experience; external incentives; customer service; and security/privacy; and personal characteristics. For personal characteristics that can be used as factors, namely monthly salary and occupation. These five factors are recommended to be implemented by the online leathercraft industry to satisfy their customers.
This research focuses on the Information Technology Infrastructure Library V3 (ITIL V3) and its Service Strategy stage, specifically on the IT Service Portfolio. The main aim is to establish the organization's strategic plans for information technology and systems through four key processes: strategy management for IT services, demand management, financial management, and prioritization of IT service proposals. The methods used involve identifying service users and their demands, determining expected costs and benefits of service development, and analyzing previous stages to prioritize IT services for the next three years. The result is a list of recommended IT services in the IT Service Portfolio that can assist the company's development. The implication of this research is that it provides guidance for organizations to prioritize their IT services and ensure they align with their strategic goals.
Krebs Tourism Village is a tourism destination that specifically produces the creative industries of Batik Kayu crafts. Small Micro Medium-Sized Enterprises of Batik Kayu craft in Krebet have developed rapidly and become a specific icon of Krebet Tourism Village. The unique styles of Batik upon wooden media have attracted both domestic and international customers. This research purpose to formulate the efficient strategies that can be applied to extend the growth of Batik Kayu craft in Krebs. This paper adopts the method of SWOT analysis to obtain a comprehensive evaluation of this SME Industries. Field observations and direct interviews have carried out with numerous Batik Kayu industrialists. This research only addresses the process of new Batik Kayu product variations development, information, and communication technology-based marketing network utilization, as well as the shortened supply processes from producers to consumers. The proposed strategies are new strategies that have never been implemented before in our partner region. Our contributions were acknowledged to increase sales capacity, extend the markets, and diminish the distribution layers to increase profits.
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