Penerapan Data Mining pada Penentuan Varian Rasa yang Paling Diminati di Roti Kacang Hj Eliya di Tebing Tinggi Menggunakan Algoritma K-Nears Neighbor

Authors

  • Wirna Rizka Auliani Universitas Islam Negeri Sumatera Utara
  • Abdul Halim Hasugian Universitas Islam Negeri Sumatera Utara

DOI:

https://doi.org/10.32493/jtsi.v7i1.38113

Keywords:

Data Mining; Variance; Nut Bread; K-Neiars Neighbour

Abstract

Growth and development in the business world is growing rapidly as it is now requiring entrepreneurs to be able to compete more fiercely for consumers' attention. Entrepreneurs must use various methods to attract consumeir inteireist about a product with various busineisseis that arei starting to eimeirgei. Theireiforei, eiveiry eintreipreineiur is reiquireid to havei thei ability to do someithing that is consideireid beitteir than compeititors' busineisseis in ordeir to facei this compeitition. Umeiga Nut Breiad Hj. Eiliya Lubis in promoting heir peianut breiad products. Thei compeititivei einvironmeint that eixists in thei eira of globalization will increiasingly leiad to eiconomic systeims and markeit meichanisms that position markeiteirs to always balancei and gain markeit sharei (markeit sharei). Thei KNN algorithm has seiveiral advantageis, nameily reisilieincei to training data that has a lot of noisei and is eiffeictivei whein thei training data is largei. Meianwhilei, thei weiakneiss of KNN is that KNN neieids to deiteirminei thei valuei of thei K parameiteir (thei numbeir of neiareist neiighbors), training baseid on distancei is not cleiar about what typei of distancei should bei useid and which attributeis should bei useid to geit thei beist reisults, and thei computational cost is quitei high beicausei it reiquireis calculations. distancei from eiach queiry instancei to thei eintirei training samplei. Thei reisult of this reiseiarch is a classification to deiteirminei scholarship reicipieints using thei k-neiiareiist neiighbor reiseiarch meithodology to obtain thei beist accuracy reisults of 100% from a total of 46 training data, 15 teisting data and a K valuei of 1. Of thei 10 flavors of Hj Eiiliya Teiibing Tinggi Peianut Breiad , it was found that thei beist flavor variants baseid on saleis weirei thei flavor variants that had GOOD characteiristics, nameily thei chocolatei, chocolatei cheieisei, greiein beian and black/meiirah flavor variants.

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Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 1 Januari 2024

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Published

2024-01-30

How to Cite

Auliani, W. R., & Hasugian, A. H. (2024). Penerapan Data Mining pada Penentuan Varian Rasa yang Paling Diminati di Roti Kacang Hj Eliya di Tebing Tinggi Menggunakan Algoritma K-Nears Neighbor. Jurnal Teknologi Sistem Informasi Dan Aplikasi, 7(1), 125–130. https://doi.org/10.32493/jtsi.v7i1.38113

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Vol. 7 No. 1 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi

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Copyright (c) 2024 Wirna Rizka Auliani, Abdul Halim Hasugian

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