Klasifikasi Jenis Kismis Menggunakan Teknik Data Mining

Authors

  • Recha Abriana Anggraini Fakultas Teknik dan Informatika; Universitas Bina Sarana Informatika
  • Ratningsih Fakultas Teknik dan Informatika; Universitas Bina Sarana Informatika
  • Yanti Apriyani Fakultas Teknik dan Informatika; Universitas Bina Sarana Informatika
  • Melisa Winda Pertiwi Fakultas Teknik dan Informatika; Universitas Bina Sarana Informatika
  • Mira Kusmira Fakultas Teknik dan Informatika; Universitas Bina Sarana Informatika
  • Saeful Bahri Fakultas Ilmu Komputer; Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.31599/ryvqk945

Keywords:

Classification, Data Mining, Raisins

Abstract

Raisins are one of the processed grape products that are often found in the market, raisins are one of the processed grape products by drying. The color and quality of raisins are usually determined by the type of grape and the drying process. To assess the quality of raisins, many methods can be used, one of which is the traditional method carried out by humans manually. However, since traditional methods are considered to tend to take a long time and errors often occur due to human error. Currently, machine vision systems can be used to assess the quality of raisins. In addition to assessing the quality of raisins, this method can also be used to identify and classify raisins. One way to classify raisins is to use data mining with classification algorithms. This research applies 5 data mining classification algorithms namely naïve bayes, decision tree, random forest, neural network and SVM. From the modeling results of the five algorithms, the neural network algorithm has the highest accuracy of 86.81%.

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Author Biography

  • Recha Abriana Anggraini, Fakultas Teknik dan Informatika; Universitas Bina Sarana Informatika

    Fakultas Teknik dan Informatika; Universitas Bina Sarana Informatika

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Published

2024-01-31

How to Cite

Klasifikasi Jenis Kismis Menggunakan Teknik Data Mining. (2024). Jurnal Kajian Ilmiah, 24(1), 45-56. https://doi.org/10.31599/ryvqk945