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All Journal Journal of Informatics and Data Science (J-IDS) Journal of Mathematics, Computation and Statistics (JMATHCOS)
Tarigan, Dewan Dinata
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Mathematics

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Sentiment Analysis of Twitter Users Regarding Taxation Topics in Indonesia Utilizing Multinomial Naive Bayes Tarigan, Dewan Dinata; Al Idrus, Said Iskandar
Journal of Informatics and Data Science Vol 3, No 1 (2024): JUNE 2024
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/j-ids.v3i1.52465

Abstract

The country's income is heavily dependent on taxes, which contribute to improved public well-being. Public confidence in tax authorities plays a key role in increasing tax receipts. Therefore, it is important to measure this level of confidence. One of the methods used is sentimental analysis, which helps to understand public views on regulations, services, performance, and tax policies. One of the purposes of this study is to measure the sentiment of Twitter users towards taxation in Indonesia. Sentiment analysis involves data collection processes, initial data processing, separation of datasets, feature extraction, classification, and evaluation. The classification model used is Multinomial Naive Bayes with a comparison of 80% training data and 20% test data. The results show that 89.65% of tweets about taxation in Indonesia have negative sentiment. The model evaluation was carried out on two test scenarios, namely initial data and randomly under-sampleed data. Classification on initial data achieved accuracy of 89.97%, precision of 46.68%, and sensitivity of 33.61%. Whereas on undersampling data results, accuration reached 53.28%, accurateness of 52.66%, and sensibility of 52.52%. Analysis showed significant differences between the two scenarios in which undersammpling techniques resulted in a more balanced distribution of data. Despite this, the model still faces difficulties in classifying positive and neutral data due to the dominance of negative sentiment.
Penggunaan Analisis Biplot dalam Pengelompokan Kabupaten/Kota di Provinsi Sulawesi Selatan Berdasarkan Indikator Kesejahteraan Masyarakat Rangkuti, Yulita Molliq; Landong, Ahmad; Tarigan, Dewan Dinata
Journal of Mathematics, Computations and Statistics Vol. 6 No. 2 (2023): Volume 06 Nomor 02 (Oktober 2023)
Publisher : Jurusan Matematika FMIPA UNM

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Abstract

Fixed free beam is one of the components that are most often used as raw materials for furnitureand building construction. The use of wooden beams is inseparable from several important issues that mustbe taken into account, including stiffness, strength, stability, and flexibility. The problem of free vibrationthat occurs in wooden beams must also be taken into account. All systems that have mass and elasticity canexperience free vibrations or vibrations that occur without external stimulation. This free vibration occurswhen the system on the object oscillates due to the work of the forces that exist in the system itself (inherent).The free vibration of the wooden beams must be overcome by damping, so that it does not seriously affectthe structure of the strength, stability, and flexibility of the wooden beams. Damping can be done by lookingfor free vibration points or vibration mode shapes on fixed free beams. One approach that can be used tofind the mode shapes is a numerical approach using the Reyleigh-Ritz method. The methodology used isthe development of a python script to find free vibration modes on the beam. In this article, the beam with a length of 0.5 meters and a thickness of 2.25 × 10^−4 meters is selected as a simulation model.
Sentiment Analysis of Twitter Users Regarding Taxation Topics in Indonesia Utilizing Multinomial Naive Bayes Tarigan, Dewan Dinata; Al Idrus, Said Iskandar
Journal of Informatics and Data Science Vol. 3 No. 1 (2024): JUNE 2024
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/j-ids.v3i1.52465

Abstract

The country's income is heavily dependent on taxes, which contribute to improved public well-being. Public confidence in tax authorities plays a key role in increasing tax receipts. Therefore, it is important to measure this level of confidence. One of the methods used is sentimental analysis, which helps to understand public views on regulations, services, performance, and tax policies. One of the purposes of this study is to measure the sentiment of Twitter users towards taxation in Indonesia. Sentiment analysis involves data collection processes, initial data processing, separation of datasets, feature extraction, classification, and evaluation. The classification model used is Multinomial Naive Bayes with a comparison of 80% training data and 20% test data. The results show that 89.65% of tweets about taxation in Indonesia have negative sentiment. The model evaluation was carried out on two test scenarios, namely initial data and randomly under-sampleed data. Classification on initial data achieved accuracy of 89.97%, precision of 46.68%, and sensitivity of 33.61%. Whereas on undersampling data results, accuration reached 53.28%, accurateness of 52.66%, and sensibility of 52.52%. Analysis showed significant differences between the two scenarios in which undersammpling techniques resulted in a more balanced distribution of data. Despite this, the model still faces difficulties in classifying positive and neutral data due to the dominance of negative sentiment.
Co-Authors Ahmad Landong Said . Iskandar Yulita Molliq Rangkuti