Perbandingan Algoritma Naive Bayes, Decision Tree, dan KNN untuk Klasifikasi Produk Populer Adidas US dengan Confusion Matrix

Lazuardi Firdaus, Tedy Setiadi

Abstract


Adidas America, Inc. (also known as Adidas US) is a company that produces shoes, clothing, and accessories as a subsidiary of Adidas AG, which is known worldwide for its trademark three stripes on its products. Product popularity is very important in increasing sales, especially for products that are frequently purchased, positively reviewed by customers, and reviewed by customers. In this case, there are Adidas US products whose popularity is still unknown. Therefore, in this case, popular Adidas US products will be classified as business needs of Adidas US. The classification algorithm used to classify popular products is Naive Bayes, Decision Tree, and KNN for classifying the popularity of Adidas products in the United States using the CRISP-DM method on the dataset. The data mining process is performed to discover patterns in the data set with the stages of business understanding, data pre-processing, and classification modeling with three different algorithms. The results of the three algorithms are tested with a confusion matrix, and the highest accuracy value is achieved by Decision Tree with 92.42%, making it the best algorithm for classifying popular Adidas US products.

Keywords


Comparison; Classification; Naive Bayes; Decision Tree; KNN

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References


P. B. N. Setio, D. R. S. Saputro, dan B. Winarno, “Klasifikasi dengan Pohon Keputusan Berbasis Algoritme C4.5,†dalam PRISMA, Prosiding Seminar Nasional Matematika 3, Jurusan Matematika, FMIPA Universitas Negeri Semarang, Feb 2020, hlm. 64–71.

C. Schröer, F. Kruse, dan J. M. Gómez, “A systematic literature review on applying CRISP-DM process model,†dalam Procedia Computer Science, Elsevier B.V., Feb 2021, hlm. 526–534. doi: 10.1016/j.procs.2021.01.199.

M. Calero Peréz, M. B. Calisto, S. Bonilla, dan D. Riofrío, “Application of Machine Learning algorithms for the prediction of payment by agreement in a debt collection company with the CRISP-DM methodology,†dalam 3rd South American International Conference on Industrial Engineering and Operations Management, Asuncion, Paraguay: IEOM Society International, Jul 2022, hlm. 474–485. doi: 10.46254/SA03.20220112.

J. W. Iskandar dan Y. Nataliani, “Perbandingan Naïve Bayes, SVM, dan k-NN untuk Analisis Sentimen Gadget Berbasis Aspek,†Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 6, hlm. 1120–1126, Des 2021, doi: 10.29207/resti.v5i6.3588.

A. P. Permana, K. Ainiyah, dan K. F. H. Holle, “Analisis Perbandingan Algoritma Decision Tree, kNN, dan Naive Bayes untuk Prediksi Kesuksesan Start-up,†JISKA (Jurnal Informatika Sunan Kalijaga), vol. 6, no. 3, hlm. 178–188, Sep 2021, doi: 10.14421/jiska.2021.6.3.178-188.

D. Tri Wiyanti dan Ainurrohmah, “Analisis Performa Algoritma Decision Tree, Naïve Bayes, K-Nearest Neighbor untuk Klasifikasi Zona Daerah Risiko Covid-19 di Indonesia,†Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), vol. 10, no. 1, hlm. 115–122, 2023, doi: 10.25126/jtiik.2023105935.

C. Anam dan H. B. Santoso, “Perbandingan Kinerja Algoritma C4.5 dan Naive Bayes untuk Klasifikasi Penerima Beasiswa,†Jurnal ENERGY, vol. 8, no. 1, hlm. 2088–4591, Mei 2018.

Y. I. Kurniawan, “Perbandingan Algoritma Naive Bayes dan C.45 dalam Klasifikasi Data Mining,†Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 5, no. 4, hlm. 455, Okt 2018, doi: 10.25126/jtiik.201854803.

S. Raschka, “Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning,†arXiv preprint, hlm. 1–49, Nov 2018.

M. A. Hasanah, S. Soim, dan A. S. Handayani, “Implementasi CRISP-DM Model Menggunakan Metode Decision Tree dengan Algoritma CART untuk Prediksi Curah Hujan Berpotensi Banjir,†Journal of Applied Informatics and Computing (JAIC), vol. 5, no. 2, hlm. 103–108, Okt 2021, doi: https://doi.org/10.30871/jaic.v5i2.3200.

V. K. Singh, A. Singh, dan K. Joshi, “Fair CRISP-DM: Embedding Fairness in Machine Learning (ML) Development Life Cycle,†dalam Proceedings of the 55th Hawaii International Conference on System Sciences, 2022, hlm. 1531–1540.

S. Marzukhi, N. Awang, S. N. Alsagoff, dan H. Mohamed, “RapidMiner and Machine Learning Techniques for Classifying Aircraft Data,†dalam Journal of Physics: Conference Series, IOP Publishing Ltd, Agu 2021, hlm. 1–8. doi: 10.1088/1742-6596/1997/1/012012.

N. Hidayati, J. Suntoro, dan G. G. Setiaji, “Perbandingan Algoritma Klasifikasi untuk Prediksi Cacat Software dengan Pendekatan CRISP-DM,†Jurnal Sains dan Informatika, vol. 7, no. 2, hlm. 117–126, Nov 2021, doi: 10.34128/jsi.v7i2.313.

I. Alfitra Salam, K. Wahyudi Putra, S. Yuliatina, dan B. Nurina Sari, “Application of Naïve Bayes for Classification of Criteria for Potable Water with the CRISP-DM Method,†Paradigma, vol. 25, no. 1, hlm. 1–5, Maret 2023, doi: 10.31294/paradigma.v25i1.1754.

H. Annur, “Klasifikasi Masyarakat Miskin Menggunakan Metode Naive Bayes,†ILKOM Jurnal Ilmiah, vol. 10, no. 2, hlm. 160–165, Agu 2018, doi: 10.33096/ilkom.v10i2.303.160-165.

I. Sutoyo, “IMPLEMENTASI ALGORITMA DECISION TREE UNTUK KLASIFIKASI DATA PESERTA DIDIK,†PILAR Nusa Mandiri, vol. 14, no. 2, hlm. 217–224, 2018, [Daring]. Tersedia pada: www.bsi.ac.id

H. Hafizan dan A. N. Putri, “Penerapan Metode Klasifikasi Decision Tree Pada Status Gizi Balita Di Kabupaten Simalungun,†KESATRIA: Jurnal Penerapan Sistem Informasi (Komputer & Manajemen), vol. 1, no. 2, hlm. 68–72, 2020.

P. B. N. Setio, D. R. S. Saputro, dan B. Winarno, “PRISMA, Prosiding Seminar Nasional Matematika Klasifikasi dengan Pohon Keputusan Berbasis Algoritme C4.5,†vol. 3, hlm. 64–71, 2020, [Daring]. Tersedia pada: https://journal.unnes.ac.id/sju/index.php/prisma/

N. Djamsi, D. Rizki Chandranegara, dan Z. Sari, “Mendeteksi Ekspresi Wajah dengan Meninjau Iris Mata Menggunakan Metode Transformasi Hough dan K-Nearest Neighbor (KNN),†REPOSITOR, vol. 5, no. 1, hlm. 575–580, 2023.

A. Nikmatul Kasanah, U. Pujianto, dan Muladi, “Penerapan Teknik SMOTE untuk Mengatasi Imbalance Class dalam Klasifikasi Objektivitas Berita Online Menggunakan Algoritma KNN,†JURNAL RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 1, no. 3, hlm. 196–201, Agu 2019, Diakses: 11 Maret 2023. [Daring]. Tersedia pada: http://jurnal.iaii.or.id/index.php/RESTI/article/view/945

A. Khumaidi, “DATA MINING FOR PREDICTING THE AMOUNT OF COFFEE PRODUCTION USING CRISP-DM METHOD,†Jurnal Techno Nusa Mandiri, vol. 17, no. 1, hlm. 1–8, Feb 2020, doi: 10.33480/techno.v17i1.1240.

Crawl Feeds, “Adidas US retail products dataset,†data.world, 23 Oktober 2021. https://data.world/crawlfeeds/adidas-us-retail-products-dataset (diakses 19 April 2023).

D. N. Yunita, A. H. S. Jones, dan D. Soyusiawaty, “Classification of Farmer’s Eligibility as Recipients of Subsidized Fertilizer Assistance with C4.5 Algorithm,†dalam IOP Conference Series: Materials Science and Engineering, Institute of Physics Publishing, Mar 2020. doi: 10.1088/1757-899X/771/1/012029.

Y. Azhar, A. Khoiriyah Firdausy, dan P. J. Amelia, “Perbandingan Algoritma Klasifikasi Data Mining Untuk Prediksi Penyakit Stroke,†SINTECH (Science and Information Technology), vol. 5, no. 2, hlm. 191–197, Okt 2022, doi: https://doi.org/10.31598.




DOI: https://doi.org/10.30865/json.v5i2.6124

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