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All Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Julius Chrisostomus Aponno
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Automotive Engineering Computer Science & IT Control & Systems Engineering

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Penerapan Algoritma Sentimen Analysis dan Naïve Bayes terhadap opini pengunjung di tempat wisata pantai Pintu Kota, Kota Ambon Julius Chrisostomus Aponno
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 9 No 4 (2022): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v9i4.2697

Abstract

In the current development of information technology, sentiment analysis is often used as a tool to seek public opinion about something. Naive Bayes analysis is a method of classifying data based on simple probabilities and is designed to be used with the assumption that there is no interdependence between one class and another. The purpose of this study was to determine public opinion about visitor satisfaction at Pintu Kota Ambon by testing positive and negative recall values, as well as positive and negative precision values ​​and accuracy values ​​using sentiment analysis and naive Bayes analysis. All data are processed using RapidMiner tools. In this survey, we obtained data from the results of the search for comments from visitors to the Pintu Kota Ambon attraction on the Google Maps website. The results obtained from this study indicate that the tracking of sentiment analysis and Naive Bayes analysis of the opinions of 113 visitors showed a negative recall value of 97.62% and a negative recall value of 83.33%.The negative accuracy value is 85.42%, while the positive accuracy value is 97.22% From these results obtained an accuracy value of 90.65%.From the results of the analysis, tourist destinations at Ambon City Gate are considered good because they get a high accurate value of 90.65%. From this research, it can be concluded that it is necessary to develop an information system to track visitor satisfaction in other tourist destinations.
Visualization of Lecturer Teaching Evaluation Data Using K-Means Clustering and Tableau Methods Golda Tomasila; Marchello Gefan Salenussa; Maryo Indra Manjaruni; Ravensca Matatula; Paul Rio Pelupessy; Julius Chrisostomus Aponno
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1920

Abstract

In the process, the results of monitoring and evaluating lecturers in each semester are usually only presented in the form of tables and descriptive explanations, but have not yet visualized the data for further analysis. The purpose of this study is to visualize the results of lecturer teaching evaluation using the K-Means Clustering and Tableau algorithms, and is expected to help the faculty and university monitor and evaluate lecturers in each semester in a more objective and informative manner. The results of the study found that the k-means clustering algorithm succeeded in finding the pattern of student clustering on the evaluation of lecturer teaching and based on the visualization of the results of k-means with a tableau it was found that most students gave a positive response to lecturer teaching and only a small number of students gave a poor assessment of lecturer teaching by emphasizing on improving the teaching process, namely consistently carrying out RPS, punctuality and so on
Co-Authors Golda Tomasila Marchello Gefan Salenussa Maryo Indra Manjaruni Paul Rio Pelupessy Ravensca Matatula