Utilization Of Data Mining To Classify The Locations Of New Street Food Businesses Attracted And Potentially Big Profits In The City Of Surakarta


  • Ismail Setiawan AMIK Harapan Bangsa Surakarta
  • Eko Purbiyanto AMIK Harapan Bangsa Surakarta




classification, C4.5, business location, big profit, Car Free Day


This study uses C4.5 algorithm to classify potentially large-profit businesses in the city of Surakarta. The data used are street vendors who are divided into 4 types of merchandise namely snacks, snacks, heavy foods and drinks. The locations that are targeted for classification are Car Free Day. The division of the Car Free Day zone was carried out to find out which areas had more influence on certain foods to get big profits. Car free day zone is divided into 4 parts, namely purwosari area - rumah makan diamond, rumah makan diamond - toko buku gramedia, toko buku gramedia - ngarsopuro and the last one is ngarsopuro – bundaran gladag. Based on the results of the study, the most profitable area to sell is the toko buku gramedia - ngarsopuro. Besides this research also classifies based on the ability of the production of raw materials, namely medium and large. The best business category that requires this type of medium-sized raw material is selling at the toko buku Gramedia - Ngarsopuro area, while for the best raw material the best area is the same among Purwosari - Rumah makan diamonds, Gramedia toko buku - Ngarsopuro and Ngarsopuro – bundaran gladak


Download data is not yet available.


Akter, S., & Wamba, S. F. (2016). Big data analytics in E-commerce: a systematic review and agenda for future research. Electronic Markets, 26(2), 173–194. https://doi.org/10.1007/s12525-016-0219-0

Andriani, A. (2012). Penerapan Algoritma C4.5 Pada Program Klasifikasi Mahasiswa Dropout. Seminar Nasional Matematika, 139–147. Retrieved from http://demo.pohonkeputusan.com/files/PENERAPAN ALGORITMA C4.5 PADA PROGRAM KLASIFIKASI MAHASISWA DROPOUT.pdf?i=1

Azevedo, A., & Santos, M. F. (2008). KDD, SEMMA and CRISP-DM: a parallel https://journal.yrpipku.com/index.php/jaets/workflow/index/45/4#overview. International Association for Development of the Information Society, (January), 182–185. https://doi.org/ISBN: 978-972-8924-63-8

Fakhrurrifqi, M., & Wardoyo, R. (2013). Perbandingan Algoritma Nearest Neighbour, C4. 5 dan LVQ untuk Klasifikasi Kemampuan Mahasiswa. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 7(2), 145–154.

Holmes, D. E., & Jain, L. C. (2012). Data Mining : Foundation and Intelligent Paradigms Volume 2 : Statistical, Bayesian, Time Series and other Theoretical Aspects. Berlin: Springer. https://doi.org/10.1007/978-3-642-23242-8

Nugroho, Y. S. (2014). Penerapan Algoritma C4.5 Untuk Klasifikasi Predikat Kelulusan Mahasiswa Fakultas Komunikasi Dan Informatika Universitas Muhammadiyah Surakarta. Prosiding Seminar Nasional Aplikasi Sains & Teknologi (SNAST) 2014, (November), 1–6. https://doi.org/10.13140/RG.2.1.2734.8247

Riwayati, et al. (2014). Prosiding Seminar Nasional Aplikasi Sains & Teknologi (SNAST) 2014 Yogyakarta, 15 November 2014 ISSN: 1979-911X. Snast, 3(November), 211–216. https://doi.org/1979-911X

Santoso, teguh budi. (2011). ANALISA DAN PENERAPAN METODE C4.5 UNTUK PREDIKSI LOYALITAS PELANGGAN. Jurnal Ilmiah Fakultas Teknik LIMIT’S, 10(1). https://doi.org/10.1080/01402390.2011.569130

Sunjana. (2010). Seminar Nasional Aplikasi Teknologi Informasi (SNATI). Seminar Nasional Aplikasi Teknologi Informasi (SNATI), 2010(Snati), 24–29. Retrieved from http://journal.uii.ac.id/Snati/article/view/1857

Wu, X., & Kumar, V. (2009). the top ten algorithms in data mining. (V. Kumar, Ed.). london: crc press.




How to Cite

Setiawan, I., & Purbiyanto, E. (2020). Utilization Of Data Mining To Classify The Locations Of New Street Food Businesses Attracted And Potentially Big Profits In The City Of Surakarta. Journal of Applied Engineering and Technological Science (JAETS), 1(2), 162–168. https://doi.org/10.37385/jaets.v1i2.45