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Comparison of Naïve Bayes, CART, dan CART Adaboost Methods in Predicting Tire Product Sales

Perbandingan Metode Klasifikasi dalam Memprediksi Penjualan Produk Ban Terlaris

Jurnal Matematika, Statistika dan Komputasi · Vol. 20 No. 3 (2024) · pp. 596–605

DOI
10.20956/j.v20i3.33187
ISSN
2614-8811, 1858-1382
Published
2024-05-15
Publisher
Department of Mathematics, Hasanuddin University
Language
en
License
creativecommons.org/licenses/by/4.0

Abstract

English

Data mining is a term to describe the process of moving through large databases in search of certain previously unknown patterns. In finding certain patterns, you need a supporting technique, called machine learning. Machine learning involves learning hidden patterns in data and further using patterns to classify or predict an event related to a problem. One of the problems can be solved with machine learning such as predicting the sales rate of tire products. This can help companies predict tire products that are selling well in the market. In producing an accurate prediction model, it will be compared with decision tree classification methods of CART, CART + Discrete Adaboost, and Naive Bayes applied to tire sales data by PT. Mitra Mekar Mandiri. The results of the study based on successive model performance evaluations are model Naive Bayes < model CART < model CART+Discrete Adaboost. The Discrete Adaboost model with a data proportion of 90:10 is the best model for predicting tire sales. The accuracy, sensitivity and specificity values for the model were 79.17%; 89.47%; and 68.84%. The AUC value is 0.8 which indicates the model is good

Indonesian

Data mining merupakan istilah untuk mendeskripsikan proses perpindahan melalui database besar untuk mencari pola tertentu yang sebelumnya tidak diketahui. Dalam menemukan pola tertentu maka perlu teknik yang mendukung yang disebut machine learning. Machine learning melibatkan pembelajaran pola tersembunyi dalam data dan selanjutnya menggunakan pola untuk mengklasifikasikan atau memprediksi suatu peristiwa yang terkait dengan masalah. Penelitian bertujuan untuk membandingkan metode klasifikasi decision tree tipe CART, discrete adaboost, dan naive bayes yang diaplikasikan pada data penjualan ban oleh PT. Mitra Mekar Mandiri. Hasil penelitian berdasarkan evaluasi kinerja model berturut-turut adalah model discrete adaboost < model naive bayes < model CART. Model CART dengan proporsi data 80:20 adalah model terbaik dalam memprediksi penjualan ban terlaris. Nilai akurasi, sensitifitas dan spesifisitas untuk model tersebut masing-masing 77,5%; 73,5%; dan 81,3%. Nilai AUC diperoleh 0,774 yang mengindikasikan model cukup baik

Keywords

Collaboration network of the authors

8 authors · 12 co-authorship links · 5 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Dian Nuswantoro
  • University of Muhammadiyah Mataram
  • Universitas Gadjah Mada
  • Universitas San Pedro
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Sefri Imanuel FalloUniversitas San Pedro1
Jus PrasetyaUniversitas Gadjah Mada1
Zulhandi PutrawanUniversitas Dian Nuswantoro1
Dicky ZulhanUniversitas Dian Nuswantoro1
Fatma Ahardika NurfaizalUniversitas Dian Nuswantoro1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/jmsk/oai, first harvested 5 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Comparison of Naïve Bayes, CART, dan CART Adaboost Methods in Predicting Tire Product Sales</dc:title>
	<dc:title xml:lang="id">Perbandingan Metode Klasifikasi dalam Memprediksi Penjualan Produk Ban Terlaris</dc:title>
	<dc:creator xml:lang="en">aprihartha, moch anjas </dc:creator>
	<dc:creator xml:lang="en">Astutik, Fitri </dc:creator>
	<dc:creator xml:lang="en">Sulistianingsih, Nani </dc:creator>
	<dc:subject xml:lang="en">CART</dc:subject>
	<dc:subject xml:lang="en">discrete adaboost</dc:subject>
	<dc:subject xml:lang="en">naive bayes</dc:subject>
	<dc:description xml:lang="en">Data mining is a term to describe the process of moving through large databases in search of certain previously unknown patterns. In finding certain patterns, you need a supporting technique, called machine learning. Machine learning involves learning hidden patterns in data and further using patterns to classify or predict an event related to a problem. One of the problems can be solved with machine learning such as predicting the sales rate of tire products. This can help companies predict tire products that are selling well in the market. In producing an accurate prediction model, it will be compared with decision tree classification methods of CART, CART + Discrete Adaboost, and Naive Bayes applied to tire sales data by PT. Mitra Mekar Mandiri. The results of the study based on successive model performance evaluations are model Naive Bayes &amp;lt; model CART &amp;lt; model CART+Discrete Adaboost. The Discrete Adaboost model with a data proportion of 90:10 is the best model for predicting tire sales. The accuracy, sensitivity and specificity values for the model were 79.17%; 89.47%; and 68.84%. The AUC value is 0.8 which indicates the model is good</dc:description>
	<dc:description xml:lang="id">Data mining merupakan istilah untuk mendeskripsikan proses perpindahan melalui database besar untuk mencari pola tertentu yang sebelumnya tidak diketahui. Dalam menemukan pola tertentu maka perlu teknik yang mendukung yang disebut machine learning. Machine learning melibatkan pembelajaran pola tersembunyi dalam data dan selanjutnya menggunakan pola untuk mengklasifikasikan atau memprediksi suatu peristiwa yang terkait dengan masalah. Penelitian bertujuan untuk membandingkan metode klasifikasi decision tree tipe CART, discrete adaboost, dan naive bayes yang diaplikasikan pada data penjualan ban oleh PT. Mitra Mekar Mandiri. Hasil penelitian berdasarkan evaluasi kinerja model berturut-turut adalah model discrete adaboost &amp;lt; model naive bayes &amp;lt; model CART. Model CART dengan proporsi data 80:20 adalah model terbaik dalam memprediksi penjualan ban terlaris. Nilai akurasi, sensitifitas dan spesifisitas untuk model tersebut masing-masing 77,5%; 73,5%; dan 81,3%. Nilai AUC diperoleh 0,774 yang mengindikasikan model cukup baik</dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
	<dc:date>2024-05-15</dc:date>
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	<dc:identifier>10.20956/j.v20i3.33187</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 20 No. 3 (2024): May 2024; 596-605</dc:source>
	<dc:source>2614-8811</dc:source>
	<dc:source>1858-1382</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/jmsk/article/view/33187/11485</dc:relation>
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	<dc:rights xml:lang="en">Copyright (c) 2024 Author and publisher</dc:rights>
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