ANALYSIS OF GOOGLE USER SENTIMENT TOWARDS UNIVERSITAS PEMBANGUNAN PANCA BUDI BASED ON REVIEWS GOOGLEUSING THE NAÏVE BAYES ALGORITHM
Main Article Content
M Imam Santoso
Rian Farta Wijaya
Zulham Sitorus
Muhammad Iqbal
Leni Marlina
This thesis examines user sentiment towards Panca Budi Development University by utilizing Google reviews as the main data and using the Naïve Bayes algorithm for sentiment analysis. This research aims to understand the public's perception of the university through reviewing reviews available on the Google platform. The data used consists of user reviews collected from Google Reviews. The analysis process begins with data pre-processing, including text cleaning and tokenization, followed by the development of a Naïve Bayes model for classification of review sentiment into positive, negative, or neutral categories. The results of this analysis provide insight into the strengths and weaknesses of Panca Budi Development University from a user perspective, as well as identifying areas that require improvement. It is hoped that these findings can become a basis for the university to improve the quality of its services and reputation in the eyes of the public. This research also highlights the effectiveness of the Naïve Bayes algorithm in sentiment analysis, and contributes to further studies on sentiment analysis in the education sector
Annizar, Anas Ma'ruf, and Miftah Arifin. "Perbedaan Prestasi Belajar Mahasiswa Ditinjau dari Jalur Seleksi Masuk Perguruan Tinggi." SAP (Susunan Artikel Pendidikan) 5.3 (2021).
Amin, Nur Fadilah, Sabaruddin Garancang, and Kamaluddin Abunawas. "Konsep Umum Populasi dan Sampel dalam Penelitian." PILAR 14.1 (2023): 15-31. Abriyanto, Arif, and Natalia Damastuti. "SEGMENTASI MAHASISWA DENGAN ‘UNSUPERVISED’ALGORITMA GUNA MEMBANGUN STRATEGI MARKETING PENERIMAAN MAHASISWA." Insand Comtech: Information Science and Computer Technology Journal 4.2 (2019)
Ajimotokan, Habeeb Adewale. Research Techniques: Qualitative, Quantitative and Mixed Methods Approaches for Engineers. Springer Nature, 2022. Mueller, Jennifer J., et al. Understanding research in early childhood education: Quantitative and qualitative methods. Taylor & Francis, 2024.
Arsyad, Aisyah Tiar, and Hanny Nurlatifah. "Penerapan k-means clustering dalam menentukan Strategi promosi Universitas Al Azhar Indonesia." (2022).
Budiman, Ramdani. "Penerapan Data Mining Untuk Menentukan Lokasi Promosi Penerimaan Mahasiswa Baru Pada Universitas Banten Jaya (Metode K-Means Clustering)." ProTekInfo (Pengembangan Riset dan Observasi Teknik Informatika) 6 (2019): 6-14.
Burk, Scott, and Gary D. Miner. It's All Analytics!: The Foundations of Al, Big Data and Data Science Landscape for Professionals in Healthcare, Business, and Government. CRC Press, 2020.
Bellanov, Agrienta. "K-Means Clustering Analysis Untuk Menentukan Strategi Promosi Kampus." Jurnal Teknik Industri: Jurnal Hasil Penelitian dan Karya Ilmiah dalam Bidang Teknik Industri 9.1 (2023): 259-268. Khusnuliawati, Hardika, and Dhian Riskiana Putri. "Identifikasi Segmen Pasar Mahasiswa Perguruan Tinggi Menggunakan Analisis Klaster Berdasarkan Variabel Psikografis." Risenologi 6.1b (2021): 44-49.
Boiy, E., & Moens, M. F. (2009). A Machine Learning Approach to Sentiment Analysis in Multilingual Web Texts. Information Retrieval, 12(5), 526-558.
Muhima, Rani Rotul, et al. Kupas Tuntas Algoritma Clustering: Konsep, Perhitungan Manual, dan Program. Penerbit Andi, 2022.
Kim, S. M., & Hovy, E. (2004). Determining the Sentiment of Opinions. Proceedings of the 20th International Conference on Computational Linguistics (COLING), 1367-1373.
Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
Sun, Zhaohao. "Data, Analytics, and Intelligence." Journal of Computer Science Research 5.4 (2023): 43-57.
Witten, I. H., Frank, E., & Hall, M. A. (2011). Data Mining: Practical Machine Learning Tools and Techniques. Elsevier.
