Analysis of Slow Moving Goods Classification Technique: Random Forest and Naïve Bayes

Deny Jollyta(1*), Gusrianty Gusrianty(2), Darmanta Sukrianto(3),

(1) Sekolah Tinggi Ilmu Komputer (STIKOM) Pelita Indonesia
(2) Sekolah Tinggi Ilmu Komputer (STIKOM) Pelita Indonesia
(3) AMIK Mahaputra Riau
(*) Corresponding Author
DOI: https://doi.org/10.23917/khif.v5i2.8263

Abstract

Classifications techniques in data mining are useful for grouping data based on the related criteria and history. Categorization of goods into slow moving group or the other is important because it affects the policy of the selling. Various classification algorithms are available to predict labels or class labels of data. Two of them are Random Forest and Naïve Bayes. Both algorithms have the ability to describe predictions in detail through indicators of accuracy, precision, and recall. This study aims to compare the performance of the two algorithms, which uses testing data of snacks with labels for package type, size, flavor and categories. The study attempts to analyze data patterns and decides whether or not the goods fall into the slow moving category. Our research shows that Random Forest algorithm predicts well with accuracy of 87.33%, precision of 85.82% and recall of 100%. The aforementioned algorithm performs better than Naïve Bayes algorithm which attains accuracy of 84.67%, precision of 88.33% and recall of 92.17%. Furthermore, Random Forest algorithm attains AUC value of 0.975 which is slightly higher than that attained by Naïve Bayes at 0.936. Random Forest algorithm is considered better based on the value of the metrics, which is reasonable because the algorithm does not produce bias and is very stable.

Keywords

slow moving; random forest; naïve bayes

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References

Rajahstan, Reading Material Drug Store Management Rational Drug Use For Medical Officers , Nurses & Pharmacists, no. December. 2010.

D. Janari, M. M. Rahman, and A. R. Anugerah, “Analisis Pengendalian Persediaan Menggunakan Pendekatan Music 3D (Muti Unit Spares Inventory Control- Three Dimensional Approach) Pada Warehouse Di PT Semen Indonesia (PERSERO) TBK Pabrik Tuban,” Teknoin, vol. 22, no. 4, pp. 261–268, 2016.

G. Chodak, “The Nuisance of Slow Moving Products in Electronic Commerce,” MPRA Munich Pers. RePEc Arch., vol. 70141, no. 3, pp. 1–7, 2016.

B. Lowe and A. Kulkarni, “Multispectral Image Analysis Using Random Forest,” Int. J. Soft Comput., vol. 6, no. 1, pp. 1–14, 2015.

V. Y. Kullarni and P. K. Sinha, “Random Forest Classifier: A Survey and Future Research Directions,” Int. J. Adv. Comput., vol. 36, no. 1, pp. 1144–1156, 2013.

N. Horning, “Random Forests: An algorithm for image classification and generation of continuous fields data sets,” in International Conference on Geoinformatics for Spatial Infrastructure Development in Earth and Allied Sciences 2010, 2010, pp. 1–6.

Susanto, E. D. S. Mulyani, and I. R. Nurhasanah, “Penerapan Data Mining Classification Untuk Prediksi Perilaku Pola Pembelian Terhadap Waktu Transaksi Menggunakan Metode Naïve Bayes,” in Konferensi Nasional Sistem dan Informatika (KNS&I), 2015, pp. 313–318.

Ardiyansyah, P. A. Rahayuningsih, and R. Maulana, “Analisis Perbandingan Algoritma Klasifikasi Data Mining Untuk Dataset Blogger Dengan Rapid Miner,” J. Khatulistiwa Inform., vol. VI, no. 1, pp. 20–28, 2018.

I. Oktanisa and A. A. Supianto, “Perbandingan Teknik Klasifikasi Dalam Data Mining Untuk Bank Direct Marketing,” J. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 5, pp. 567–576, 2018.

N. H. Niloy and M. A. I. Navid, “Naïve Bayesian Classifier and Classification Trees for the Predictive Accuracy of Probability of Default Credit Card Clients,” Am. J. Data Min. Knowl. Discov., vol. 3, no. 1, pp. 1–12, 2018.

N. Sagala and H. Tampubolon, “Komparasi Kinerja Algoritma Data Mining pada Dataset Konsumsi Alkohol Siswa,” Khazanah Inform. J. Ilmu Komput. dan Inform., vol. 4, no. 2, pp. 98–103, 2018.

A. K. Mishra and B. K. Ratha, “Study of Random Tree and Random Forest Data Mining Algorithms for Microarray Data Analysis,” Int. J. Adv. Electr. Comput. Eng., vol. 3, no. 4, pp. 5–7, 2016.

A. Cutler, D. R. Cutler, and J. R. Stevens, “Ensemble Machine Learning,” in Random Forest, no. January, 2011, p. 21.

E. Goel and E. Abhilasha, “Random Forest: A Review,” Int. J. Adv. Res. Comput. Sci. Softw. Eng., vol. 7, no. 1, pp. 251–257, 2017.

S. Taheri and M. Mammadov, “Learning the naive bayes classifier with optimization models,” Int. J. Appl. Math. Comput. Sci., vol. 23, no. 4, pp. 787–795, 2013.

S. Dixit and S. Kr, “Collaborative Analysis of Customer Feedbacks using Rapid Miner,” Int. J. Comput. Appl., vol. 142, no. 2, pp. 29–36, 2016.

K. . Ghose, R. Pradhan, and S. S. Ghose, “Decision Tree Classification of Remotely Sensed Satellite Data using Spectral Separability Matrix,” Int. J. Adv. Comput. Sci. Appl., vol. 1, no. 5, pp. 93–101, 2010.

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