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All Journal Jurnal Akuntansi dan Pajak Techno Nusa Mandiri : Journal of Computing and Information Technology
Zakila Cahya Ronika
UPN Veteran Jakarta
Computer Science & IT Economics, Econometrics & Finance Social Sciences

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PERENCANAAN PAJAK DENGAN MEMANFAATKAN PP NO. 23 TAHUN 2018 (Studi Kasus Pada PT X di Jawa Timur) Zakila Cahya Ronika; Dini Fatihatul Hidayah; Ari Febriansyah; Sultan Maulana Yunus; Ihsan Saifuljihad; Ni Putu Eka Widiastuti
Jurnal Akuntansi dan Pajak Vol 24, No 2 (2024): JAP : Vol. 24, No. 2, Agustus 2023 - Januari 2024
Publisher : ITB AAS INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/jap.v24i2.9520

Abstract

Tax management, especially in companies, has a huge impact. Some taxes have facilities that can be utilized by taxpayers with certain conditions, one of which is PP No. 23/2018. Thus, the purpose of this research is to find out the tax planning scheme in order to utilize the PP No. 23/2018 facility. This research is a literature study study derived from journals and supported by interviews as primary research data with the object of research is a company engaged in trading and goods in East Java. It was found that there was tax evasion in the implementation of tax planning by the company in the form of a scheme that made employee salaries below PKP and fictitious directors' salaries because this affected the company's gross turnover. This is done by the company to take advantage of the facilities from PP No. 23/2018 in the form of 0.5% final income tax. Although this scheme is considered illegal (tax evasion), it is carried out as an effort by the company to provide welfare in the form of not deducting employee salaries due to the fulfillment of tax obligations and to reduce the corporate tax rate.
COMPARISON OF KNN, NAIVE BAYES, DECISION TREE, ENSEMBLE, REGRESSION METHODS FOR INCOME PREDICTION Eri Mardiani; Nur Rahmansyah; Andy Setiawan; Zakila Cahya Ronika; Dini Fatihatul Hidayah; Atira Syakira
Jurnal Techno Nusa Mandiri Vol 20 No 2 (2023): Techno Nusa Mandiri : Journal of Computing and Information Technology Period of
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v20i2.4613

Abstract

Using the income classification dataset, we performed data analysis with the help of data mining to gather interesting information from the available data. Currently, data processing can be done using many tools. One of the tools that we use for data processing is the orange application. By using the dataset we looked at the welfare level ranging from marital status, school, gender, and from all fields related to income ranging from sales, to daily life to find out the income earned by employees or workers from several countries such as the United States, Cambodia, United Kingdom, Puerto-Rico, Canada, Germany, Outer US (Guam-USVI-etc). The purpose of this analysis is to determine the hourly income in one week that can affect the income classification. The classification technique uses various classification models, namely the K-Nearest Neighbor (KNN) algorithm model, Naïve Bayes, Decision Tree, Esemble Method and Linear Regression algorithm. The results of the analysis based on the test results of various algorithm models can be concluded that the best algorithm model for measuring workers' income is to use the Naive Bayes Decision. Analysis of variables based on Hours-per-Week and Capital-Gain affects Income Classification which determines whether the income earned is more than 50 thousand/50 K and the analysis results in a prediction of a person's income level.
Co-Authors Andy Setiawan Ari Febriansyah Atira Syakira Dini Fatihatul Hidayah Eri Mardiani Ihsan Saifuljihad Ni Putu Eka Widiastuti Nur Rahmansyah Sultan Maulana Yunus