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All Journal Journal of Computer Networks, Architecture and High Performance Computing Journal of Computer Science and Informatics Engineering
Asti, Dini
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Computer Science & IT Education

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Penerapan Algoritma K-Means Untuk Mengetahui Tingkat Kepatuhan Wajib Pajak Kendaraan Bermotor Pada UPT Samsat Medan Selatan Asti, Dini; Hasibuan, Muhammad Siddik; Siregar, Putri Aprilia
Journal of Computer Science and Informatics Engineering Vol 2 No 4 (2023): Oktober
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosie.v2i4.711

Abstract

Kepatuhan Wajib Pajak adalah tindakan menunjukkan patuh dan tertib terhadap kewajiban perpajakan dengan melakukan pembayaran pajak dan melaporkan pajak secara berkala oleh Wajib Pajak yang bersangkutan sesuai ketentuan perpajakan yang berlaku. Kelompok kepatuhan pajak kendaraan bermotor di samsat Medan Selatan dibagi menjadi banyak tingkatan dari rendah ke tinggi. Besaran iuran pajak kendaraan bermotor tergantung dari perhitungan dan pembayaran pajak terutang atas penghasilan yang diperoleh wajib pajak dan Pembayaran tunggakan pajak sebelum jatuh tempo. Tujuan penelitian ini adalah untuk mengetahui tingkat kepatuhan pajak kendaraan bermotor di sammsat Medan Selatan tahun 2023. Algoritma K-Means, yang digunakan melalui metode clustering, menggunakan tahapan KDD, yang mencakup 146 data, yang berasal dari data pembayaran pajak kendaraan bermotor di Samsat Medan Selatan pada tahun 2023. Nilai determinasi cluster sebesar 0,294 dihasilkan oleh hasil pengujian RapidMiner yang menggunakan perhitungan indeks Davies-Bouldin. Dalam cluster 0 ada sepuluh wajib pajak dengan kepatuhan tingkat sangat rendah, di cluster 1 ada 56 wajib pajak dengan kepatuhan tingkat sedang, di cluster 2 ada 19 wajib pajak dengan kepatuhan tingkat rendah; dan di cluster 3, ada 61 wajib pajak dengan kepatuhan tingkat tinggi.
Analysis of Vina Film Sentiment on Social Media X Using The Naïve Bayes Method Asti, Dini; Putri, Raissa Amanda
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4341

Abstract

The increasingly rapid development of technology and information, one of which is the internet. Where users can share opinions and discuss various topics or problems around them, namely social media One of the news items that frequently appears as a trending topic on X is the Vina film controversy. However, with the large amount of review data available, it will be difficult to process manually. Therefore, sentiment analysis is needed to see whether people's tendencies toward the Vina film case are positive or negative. The stages carried out were data collection taken via web scrapping with an initial amount of data of 833 and processed through the preprocessing stage, including cleaning, case folding, normalization, stopword removal, tokenization, and stemming, the data became 830. The application of the Naïve Bayes algorithm in this research uses the probability method to classify and predict 664 training data and 166 test data, with the help of the Python library. The accuracy calculation results show quite good performance with TF-IDF weighting producing an accuracy of 78%, precision of 80%, and recall of 90%, f1-score of 84%. Analysis from this research shows that the dominance of negative sentiment is 517 while positive sentiment is 313. The amount and quality of training data play an important role in system quality, where high data quality provides better accuracy in predicting sentiment classes.
Analysis of Vina Film Sentiment on Social Media X Using The Naïve Bayes Method Asti, Dini; Putri, Raissa Amanda
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4341

Abstract

The increasingly rapid development of technology and information, one of which is the internet. Where users can share opinions and discuss various topics or problems around them, namely social media One of the news items that frequently appears as a trending topic on X is the Vina film controversy. However, with the large amount of review data available, it will be difficult to process manually. Therefore, sentiment analysis is needed to see whether people's tendencies toward the Vina film case are positive or negative. The stages carried out were data collection taken via web scrapping with an initial amount of data of 833 and processed through the preprocessing stage, including cleaning, case folding, normalization, stopword removal, tokenization, and stemming, the data became 830. The application of the Naïve Bayes algorithm in this research uses the probability method to classify and predict 664 training data and 166 test data, with the help of the Python library. The accuracy calculation results show quite good performance with TF-IDF weighting producing an accuracy of 78%, precision of 80%, and recall of 90%, f1-score of 84%. Analysis from this research shows that the dominance of negative sentiment is 517 while positive sentiment is 313. The amount and quality of training data play an important role in system quality, where high data quality provides better accuracy in predicting sentiment classes.
Co-Authors Muhammad Siddik Hasibuan Raissa Amanda Putri Siregar, Putri Aprilia