IMPLEMENTATION OF TEXT PROCESSING FOR SENTIMENT ANALYSIS OF TAX PAYMENT INTEREST AFTER THE "RUBICON" PHENOMENON

Ridian Gusdiana; Iqbal  Alfian; Christina  Juliane

doi:10.52436/1.jutif.2023.4.5.1014

Ridian Gusdiana Master of Information Systems, STMIK LIKMI Bandung, Indonesia
Iqbal Alfian Master of Information Systems, STMIK LIKMI Bandung, Indonesia
Christina Juliane Master of Information Systems, STMIK LIKMI Bandung, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2023.4.5.1014

Keywords: K-Nearest Neighbor, Naïve Bayes, Sentiment Analisys, Support Vector Machine, Tax

Abstract

In February 2023, an incident occurred involving the child of an official from the Indonesian Directorate General of Taxes who committed violence against a member of the GP Ansor organization. The news spread widely and brought a new issue, namely suspicious reporting of the official's wealth with an amount of up to 56 billion Indonesian Rupiahs. In order to determine public sentiment towards the "RUBICON" case, which was receiving attention, sentiment analysis of tax payment interest was conducted using text mining techniques. Data processing was done using the R language and RStudio application, taking a dataset of 23,785 tweets from the public about paying taxes on Twitter. Next, text cleaning was done to remove numbers, symbols, and URLs, as well as text processing using stemming, tokenizing, stopword removal, and TF-IDF methods. The TF-IDF method shows that the words "rafael" and "case" are the top keywords. This study used a supervised model by comparing SVM, KNN, and Naive Bayes algorithms, and evaluation was done using a confusion matrix with accuracy results in descending order of 0.8922, 0.8049, and 0.7369. The conclusion of this study is that the SVM algorithm successfully classified sentiment with the highest level of accuracy and obtained the highest negative sentiment of 5,616 sentences.

Downloads

Download data is not yet available.

References

A. Deolika, K. Kusrini, and E. T. Luthfi, “Analisis Pembobotan Kata Pada Klasifikasi Text Mining,” J. Teknol. Inf., vol. 3, no. 2, p. 179, 2019, doi: 10.36294/jurti.v3i2.1077.

H. Irsyad and M. R. Pribadi, “Implementasi Text Mining Dalam Pengelompokan Data Tweet Pertanian Indonesia Dengan K-Means,” KURAWAL J. Teknol. Inf. dan Ind., vol. 3, no. 2, pp. 164–172, 2020, [Online]. Available: https://t.co/FXtzMcbdHp

D. A. Agustina, S. Subanti, and E. Zukhronah, “Implementasi Text Mining Pada Analisis Sentimen Pengguna Twitter Terhadap Marketplace di Indonesia Menggunakan Algoritma Support Vector Machine,” Indones. J. Appl. Stat., vol. 3, no. 2, p. 109, 2021, doi: 10.13057/ijas.v3i2.44337.

T. Arimurti et al., “Pengaruh Leverage , Return on Asset ( Roa ) Dan Intensitas Modal Terhadap Penghindaran Pajak Dengan,” J. KRISNA Kumpul. Ris. Akunt., vol. 13, no. 2, 2022.

S. Sanjaya and R. P. Sipahutar, “Pengaruh Current Ratio, Debt to Asset Ratio dan Total Asset Turnover terhadap Return on Asset pada Perusahaan Otomotif dan Komponennya yang Terdaftar di Bursa Efek Indonesia,” J. Ris. Akunt. dan Bisnis, vol. 19, no. 2, pp. 136–150, 2019, doi: 10.30596/jrab.v19i2.4599.

I. M. D. Ardiada, M. Sudarma, and D. Giriantari, “Text Mining pada Sosial Media untuk Mendeteksi Emosi Pengguna Menggunakan Metode Support Vector Machine dan K-Nearest Neighbour,” Maj. Ilm. Teknol. Elektro, vol. 18, no. 1, p. 55, May 2019, doi: 10.24843/mite.2019.v18i01.p08.

A. S. Sri Widaningsih, “Klasifikasi Jurnal Ilmu Komputer Berdasarkan Pembagian,” Semin. Nas. Teknol. Inf. dan Komun. 2018 (SENTIKA 2018), vol. 2018, no. Sentika, pp. 320–328, 2018.

F. Sodik and I. Kharisudin, “Analisis Sentimen dengan SVM , NAIVE BAYES dan KNN untuk Studi Tanggapan Masyarakat Indonesia Terhadap Pandemi Covid-19 pada Media Sosial Twitter,” vol. 4, pp. 628–634, 2021.

N. Tri Romadloni, I. Santoso, S. Budilaksono, and M. Ilmu Komputer STMIK Nusa Mandiri Jakarta, “Perbandingan Metode Naive Bayes, Knn Dan Decision Tree Terhadap Analisis Sentimen Transportasi Krl Commuter Line,” J. IKRA-ITH Inform., vol. 3, no. 2, 2019.

I. Olive, D. Putra, K. R. Prilianti, P. Lucky, and T. Irawan, “Implementasi Text Mining untuk Analisis Layanan Transportasi Online dengan Analisis Faktor,” J. SimanteC, vol. 8, no. 2, pp. 1–9, 2020.

I. Oktanisa and A. A. Supianto, “Perbandingan Teknik Klasifikasi Dalam Data Mining Untuk Bank a Comparison of Classification Techniques in Data Mining for,” Teknol. Inf. dan Ilmu Komput., vol. 5, no. 5, pp. 567–576, 2018, doi: 10.25126/jtiik20185958.

M. A. Ayu, E. Irawan, and T. Mantoro, “Text mining approaches for analyzing an Indonesian tafseer and translation of the Holy Quran,” Indones. J. Electr. Eng. Comput. Sci., vol. 25, no. 3, 2022, doi: 10.11591/ijeecs.v25.i3.pp1469-1480.

R. A. Wildan, R. A. Rajagede, and R. Rahmadi, “Analisis Sentimen Politik Berdasarkan Big Data dari Media Sosial Youtube : Sebuah Tinjauan Literatur,” Automata, vol. 2, 2021.

M. H. Asnawi, I. Firmansyah, R. Novian, and R. S. Pontoh, “Perbandingan Algoritma Naïve Bayes , K-NN , dan SVM dalam Pengklasifikasian Sentimen Media Sosial,” 2021.

R. Pratiwi, M. N. Hayati, and S. Prangga, “Perbandingan Klasifikasi Algoritma C5.0 Dengan Classification and Regression Tree (Studi Kasus : Data Sosial Kepala Keluarga Masyarakat Desa Teluk Baru Kecamatan Muara Ancalong Tahun 2019),” BAREKENG J. Ilmu Mat. dan Terap., vol. 14, no. 2, pp. 273–284, 2020, doi: 10.30598/barekengvol14iss2pp273-284.