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All Journal Journal of Information Technology and its Utilization
Hafizh Adam Muslim
Directorate General of Customs and Excise
Computer Science & IT Engineering

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NAIVE BAYES ALGORITHM IN HS CODE CLASSIFICATION FOR OPTIMIZING CUSTOMS REVENUE AND MITIGATION OF POTENTIAL RESTITUTION Hafizh Adam Muslim
Journal of Information Technology and Its Utilization Vol 5 No 1 (2022)
Publisher : Sekolah Tinggi Multi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56873/jitu.5.1.4740

Abstract

The Directorate General of Customs and Excise, as a government revenue collector, must maximise import duty receipts each year. One common issue is the return of unpaid import duty and/or administrative punishments in the form of fines based on the objection judgement document. The Tax Court could help you minimise your gross receipts at the Customs Office. Data mining techniques are intended to provide valuable information regarding the HS Code classification technique, which can assist customs agents in determining duties and/or customs values. This study makes use of data from the Notification of Import of Goods at Customs Regional Office XYZ from 2018 to 2020. The Cross-industry Standard Process for Data Mining (CRISP-DM) model is used in this study, and the Naive Bayes Algorithm in Rapidminer 9.10 is used for data classification. According to the model, the calculation accuracy is 99.97 percent, the classification error value is 0.03 percent, and the Kappa coefficient is 0.999..
Time Series Analysis for Customs Revenue Prediction using Arima Model in Python Hafizh Adam Muslim
Journal of Information Technology and Its Utilization Vol 5 No 2 (2022): December 2022
Publisher : Sekolah Tinggi Multi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56873/jitu.5.2.4927

Abstract

The Directorate General of Customs and Excise (DJBC) serves as a revenue collector in the field of customs and excise. This revenue plays an essential role in supporting infrastructure development. Predictions are needed to plan a good State Revenue and Expenditure Budget (APBN). Predictions serve as a tool for revenue optimization and control. However, forecasting is problematic because unpredictable external factors also influence these receipts. A logical and accountable approach is needed to predict acceptance to overcome this problem. The prediction method used is Autoregressive Integrated Moving Average (ARIMA). According to the computations, the Root Mean Square Percentage Error (RMSPE) value is less than 10%, indicating that the ARIMA model estimation is excellent
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