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All Journal TECHNOVATE Galaksi
Wayan Gede Suka Parwita
Politeknik Negeri Bali, Denpasar, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering

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Descriptive Analytics Sales Data Visualization at Kebab Made Using Google Data Studio Ni Komang Indah Candrika Riani; Komang Arya Ganda Wiguna; Wayan Gede Suka Parwita; Luh Putu Rara Ayu Ratnaningrum
TECHNOVATE: Journal of Information Technology and Strategic Innovation Management Vol. 1 No. 3 (2024): July 2024
Publisher : PT.KARYA GEMAH RIPAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52432/technovate.1.3.2024.141-147

Abstract

Kebab Made is a company engaged in the sale of food. It applies an information system in recording and managing data that produces operational information, including sales reports were presented in tabular form. The report cannot display the information about sales growth. The difference in product sales comparison is a weakness of the report results. Thus, another application is needed to reprocess tabulated data into information in the form of graphs. In this study, a system was built using website-based google data studio tools to assist companies in processing tabulated data into a graphs that do not require a long time. The research method used in this research is Kimball's Nine Steps method and conducted through the stages of functional requirements analysis, data warehouse design, Extract Transform Load (ETL) process, system implementation, and system testing. The system was tested using User Acceptance Test (UAT), and showed that the system had run according to the needs. The result of this research is a dashboard that displays information in graphical form to assist in decision making. This visualization allows companies to easily see and analyze sales developments, so they can make more precise and quick decisions.
Sentiment Analysis of YouTube Comments on the Closure of TikTok Shop Using Naïve Bayes and Decision Tree Method Comparison Putu Pebri Armaeni; I Komang Arya Ganda Wiguna; Wayan Gede Suka Parwita
Jurnal Galaksi Vol. 1 No. 2 (2024): Galaksi - August 2024
Publisher : Yayasan Sraddha Panca Widya Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70103/galaksi.v1i2.15

Abstract

As technology advances, YouTube has become a social media platform that allows users to watch, broadcast, and share videos. One of the videos that has garnered a lot of comments from the public is about the closure of TikTok Shop. This research uses two methods: Decision Tree and Naïve Bayes. The aim of this study is to compare the Naïve Bayes and Decision Tree methods in analyzing public sentiment regarding the closure of TikTok Shop. The test results for both methods are not significantly different. Each method is divided into three research scenarios. In Scenario 1, with an 80:20 data split, the Decision Tree method achieved an accuracy of 74.71%, a precision of 57%, a recall of 57%, and an F1-score of 57%, while Naïve Bayes had an accuracy of 73.96%, a precision of 58%, a recall of 34%, and an F1-score of 29%. In Scenario 2, with a 70:30 data split, the Decision Tree method achieved an accuracy of 73.27%, while Naïve Bayes achieved an accuracy of 73.99%. In Scenario 3, with a 60:40 data split, the Decision Tree method achieved an accuracy of 71.78%, while Naïve Bayes achieved an accuracy of 74.02%. The evaluation results indicate that the Decision Tree method using an 80:20 data split has superior accuracy compared to the Naïve Bayes method.
Co-Authors I Komang Arya Ganda Wiguna Komang Arya Ganda Wiguna Luh Putu Rara Ayu Ratnaningrum Ni Komang Indah Candrika Riani Putu Pebri Armaeni