Syafitri, Woelan Aziz (2020) PENERAPAN ALGORITMA K-MEANS CLUSTERING PADA PAJAK REKLAME DI KANTOR BADAN PENDAPATAN DAERAH KOTA SEMARANG. Undergraduate thesis, Universitas Stikubank.
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Abstract
Pajak reklame merupakan aset penting untuk pemasukan dan pembangunan di Kota Semarang yang dikelola Badan Pendapatan Daerah (BAPENDA). Kawasan pajak reklame berdasarkan Walikota Semarang Nomor 973/90 tanggal 8 Maret 2012 dibagi menjadi enam kawasan, yaitu kawasan khusus, kawasan sentral bisnis, kawasan bisnis, kelas jalan A, kelas jalan B, dan kelas jalan C. Jenis reklame dibagi menjadi tiga jenis diantaranya billboard, baleho, dan umbul- umbul. Data mining adalah proses ekstraksi dan identifikasi informasi yang bermanfaat dan pengetahuan yang terakit dari database besar yang menggunakan teknik matematika, statistik, kecerdasan buatan dan machine learning. Penerapan data mining dengan Algoritma K-Means Clustering pada pajak reklame di kantor Badan Pendapatan Daerah Kota Semarang menggunakan data yang diolah berdasarkan kawasan reklame, jenis reklame, total reklame dan letak kecamatan reklame. Hasil penelitian dibagi menjadi ke dalam tiga cluster yaitu cluster sangat diminati oleh wajib pajak, cluster cukup diminati oleh wajib pajak, dan cluster kurang diminati oleh wajib pajak. Hasil penelitian menunjukkan dari 63 jenis data reklame terdapat 19 data reklame yang memasuki kawasan kurang diminati masyarakat , 35 data reklame termasuk kawasan cukup diminati masyarakat, dan 9 data reklame termasuk kawasan sangat diminati masyarakat. Masing-masing kecamatan dapat memiliki lebih dari satu cluster, namun terdapat kecamatan yang hanya masuk ke dalam cluster yang kurang diminati, yaitu Kecamatan Candisari, Kecamatan Mijen, Kecamatan Gayamsari, dan Kecamatan Gunungpati. Kecamatan yang kurang diminati oleh wajib pajak memerlukan pembangunan dari pemerintah agar meningkatkan pemasukan Kota Semarang dari pajak reklame. Billoard tax is an important asset for income and development in the city of semarang managed by the regional revenue agency ( BAPENDA). The advertisement tax area based on Semarang Mayor No. 973/90 date March 8, 2012 is divided into six regions, namely special areas, central business districts, Class A roads, clas B roads and class C. The type of billboards are divided into three types including billoards, baleho and banners. Data mining is the procsess of extracing and identifying useful information and knowledge that is assembled from large databases that use mathematical, statistical, artifical intelegence and machine learning tachniques. The aplication of data mining with K-meas clustering algorithm on the advertisement tax at the Semarang city regional revenue agency office use data that is processed base on the adverisement are, type of advertisement and sub-district advertisement location. The result of the study were quite in demand by taxpayers, and cluster were less in demand by taxpayers. The results showed that of the 63 types of adversitement data, there were 19 adversitement data that entered areas of less interest to the public, 35 adversitement data included areas of considerable public interest and 9 advertisement data including areas of high public interest. Each sub district can have more than one cluster, but there are sub discrict that onliy enter into cluster that are less desirable, namely candisari district, mijen district, gayamsari district and gunungpati district. Ssubdisdticts that are less desirable by taxpayers require development from the govermnent in order to increase semarang city revenue from advertesiment tax.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Additional Information: | NIM.16.01.53.0023 SKR.I.05.01.1925 |
| Uncontrolled Keywords: | K-Means Clustering, Data Mining, Reklame K-Means Clustering, Data Mining, Billoard |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Faculty / Institution: | Fakultas Teknologi Informasi > Program Studi Teknik Informatika |
| Depositing User: | H Hayati |
| Date Deposited: | 15 Sep 2020 02:39 |
| Last Modified: | 15 Sep 2020 02:39 |
| URI: | https://eprints.unisbank.ac.id/id/eprint/7105 |
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