1. Home
  2. JUITA Vol. 11 No. 1, May 2023
  3. Anggraeni

Comparison of Classification Methods on Twitter Sentiment Analysis of PDAM Tugu Tirta Kota Malang

Anisa Dewi Anggraeni, Muhammad Farhansyah, Muhammad Risky Pratama Hermawan, Galih Wasis Wicaksono, Christian Sri Kusuma Aditya

Abstract


The Regional Drinking Water Company (PDAM) Tugu Tirta is a public service company in Malang's drinking water distribution field. The company uses a customer complaint feature that is provided on the website. However, only a few people know about it and use it. From this problem, the researcher uses social media data, namely Twitter, to explore data sources and collect feedback tweets from the customer. However, analyzing the sentiment of the 1000 data used is elusive. The tweets contain unstructured text, so the researcher applies the labeling from the dataset, preprocesses the text, and then extracts the tweets by applying the classification methods by comparing Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), Logistic Regression (LR), Short-Term Long-Term Memory (LSTM), and Indonesian BERT to achieve highly accurate results. The tests with six methods show that Logistic Regression and Indonesian BERT are the best methods, with an accuracy of 85%. In this study, we obtained an effective algorithm to classify a comment as positive, negative, or neutral related to the Tugu Tirta Regional Drinking Water Company (PDAM).

Keywords


Sentiment Analysis; Comparison of Methods; PDAM Tugu Tirta

References


Bioinformatics), 2012, vol. 7649 LNCS, no. PART 1, pp. 508–524. doi: 10.1007/978-3-642-35176-1_32.

[20] D. Duei Putri, G. Forda Nama, W. Eko Sulistiono Jurusan Teknik Elektro Universitas Lampung, B. Lampung Jl Sumantri Brojonegoro No, and B. Lampung, ‘Analisis Sentimen Kinerja Dewan Perwakilan Rakyat (DPR) Pada Twitter Menggunakan Metode Naive Bayes Classifier’, 2022.

[21] D. Rizki Aditya, E. Supriyati, and T. Listyorini, ‘ANALISIS SENTIMEN PENGGUNA TWITTER TERHADAP ROKOK ELEKTRIK (VAPE) DI INDONESIA MENGGUNAKAN METODE NAÏVE BAYES’.

[22] N. Sucahyo et al., ‘SWADHARMA (JRIS) ANALISIS SENTIMEN MASYARAKAT TERHADAP UU CIPTA KERJA PADA MEDIA SOSIAL TWITTER’.

[23] A. Kustanto, ‘DINAMIKA PERTUMBUHAN PENDUDUK DAN KUALITAS AIR DI INDONESIA’, JIEP, vol. 20, no. 1, 2020.

[24] D. N. Fitriana and Y. Sibaroni, ‘ARJUNA) Managed by Ministry of Research, Technology, and Higher Education’, Accredited by National Journal Accreditation, vol. 4, no. 2, pp. 846–853, 2020.

[25] O. F. Prihono and P. K. Sari, ‘Comparison Analysis Of Social Influence Marketing For Mobile Payment Using Support Vector Machine’, Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, pp. 367–374, Oct. 2019, doi: 10.22219/kinetik.v4i4.921.

[26] F. Alzami, E. D. Udayanti, D. P. Prabowo, and R. A. Megantara, ‘Document Preprocessing with TF-IDF to Improve the Polarity Classification Performance of Unstructured Sentiment Analysis’, Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, pp. 235–242, Aug. 2020, doi: 10.22219/kinetik.v5i3.1066.

[27] S. Cahyaningtyas, D. Hatta Fudholi, and A. Fathan Hidayatullah, ‘Deep Learning for Aspect-Based Sentiment Analysis on Indonesian Hotels Reviews’, Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Aug. 2021, doi: 10.22219/kinetik.v6i3.1300.

[28] M. A. Suprayogi and R. A. Supono, “Klasifikasi Helpdesk Menggunakan Metode K-Nearest Neighbor dan TF-ABS,” Techno.Com, vol. 20, no. 4, pp. 508–517, 2021, doi: 10.33633/tc.v20i4.5094.


Full Text: PDF

DOI: 10.30595/juita.v11i1.15485

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN: 2579-8901