Naïve Bayes Classification Model for the Producer Price Index Prediction

Melisa Winda Pertiwi; Mira Kusmira; Rezkiani Rezkiani; Bambang Kelana Simpony; Yanti Apriyani; Iqbal Dzulfiqar Iskandar; Taufik Wibisono; Imam Amirulloh

doi:10.32520/stmsi.v11i1.1669

Naïve Bayes Classification Model for the Producer Price Index Prediction

Melisa Winda Pertiwi, Mira Kusmira, Rezkiani Rezkiani, Bambang Kelana Simpony, Yanti Apriyani, Iqbal Dzulfiqar Iskandar, Taufik Wibisono, Imam Amirulloh

Abstract

Producer Price Index is an index number that describes the level of price change at the producer level. Data users can take advantage of the development of producer prices as an early indicator of wholesale and retail prices. In addition, it can also be used to assist in the preparation of the economic balance, distribution of goods, trade margins, and so on. Every year the Badan Pusat Statistika (BPS) updates data on the producer price index to facilitate producer price standards, including rice and grain producers. To determine the Price Prediction Index, a prediction algorithm is needed, namely Naive Bayes based on data from Quarters I and II of 2021. The Naïve Bayes Algorithm, can be used to predict the Producer Price Index. This prediction is made to provide an overview of Quarter III, considering that in 1 year BPS updates the Producer Price Index’s data up to Quarter IV in 1 year. The prediction obtained is an increase for Quarter III with a maximum value between 0.961 – 0.980 based on data from Quarters I and II.

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References

Badan Pusat Statistik, “Indeks Harga Produsen,” 2021. [Online]. Available: https://www.bps.go.id/subject/36/harga-produsen.html#subjekViewTab3. [Accessed: 28-Sep-2021].

Badan Pusat Statistik, “Indeks Harga Produsen (IHP),” Badan Pusat Statistik, 2021. [Online]. Available: https://www.bps.go.id/subject/36/harga-produsen.html#subjekViewTab2. [Accessed: 27-Sep-2021].

DQLab, “Belajar Algotirma Naive Bayes.” [Online]. Available: https://www.dqlab.id/belajar-algotirma-naive-bayes. [Accessed: 11-Aug-2021].

A. Tripathi, S. Yadav, and R. Rajan, “Naive Bayes Classification Model for the Student Performance Prediction,” 2019 2nd Int. Conf. Intell. Comput. Instrum. Control Technol. ICICICT 2019, pp. 1548–1553, 2019.

X. Wang, L. Luo, Q. Zou, F. Liu, J. Liu, and D. Huang, “Constructing Naive Bayesian Classification Model by Spark for Big Data,” 2020 17th Int. Comput. Conf. Wavelet Act. Media Technol. Inf. Process. ICCWAMTIP 2020, pp. 306–309, 2020.

G. Zheng, H. Zhang, J. Han, C. Zhuang, and L. Xi, “The research on agricultural product price forecasting service based on combination model,” IEEE Int. Conf. Cloud Comput. CLOUD, vol. 2020-Octob, pp. 4–9, 2020.

N. N. Faizah, D. Saepudin, and ..., “Prediksi Kenaikan atau Penurunan Indeks Pasar Keuangan Indonesia dengan Menggunakan Bayesian Network dan Prediksi Perubahan Kenaikan Pasar Keuangan …,” eProceedings …, vol. 6, no. 2, pp. 9946–9954, 2019.

T. I. Andini, W. Witanti, and F. Renaldi, “Prediksi Potensi Pemasaran Produk Baru dengan Metode Naïve Bayes Classifier dan Regresi Linear,” Semin. Nas. Apl. Teknol. Inf., pp. 27–32, 2016.

Kuncahyo Setyo Nugroho, “Tutorial 1: Menggunakan Model Machine Learning untuk Klasifikasi,” 2020. [Online]. Available: https://ksnugroho.medium.com/menerapkan-model-machine-learning-pada-rapidminer-142259846e13. [Accessed: 06-Oct-2021].

RapidMiner Studio, “RapidMiner Documentations.” [Online]. Available: https://docs.rapidminer.com/latest/studio/operators/blending/attributes/names_and_roles/set_role.html. [Accessed: 06-Oct-2021].

A. Kika, L. Leka, S. Maxhelaku, and A. Ktona, “Using Data Mining Techniques on Moodle Data for Classification of Student’S Learning Styles,” no. November, 2019.

D. T. Barus, R. Elfarizy, F. Masri, and P. H. Gunawan, “Parallel Programming of Churn Prediction Using Gaussian Naïve Bayes,” 2020 8th Int. Conf. Inf. Commun. Technol. ICoICT 2020, 2020.

A. W. Case, “Gaussian Discriminant Analysis , Naive Bayes and EM Algorithm Probability Theory Review,” 2021.

RapidMiner Studio, “RapidMiner Documentation.” [Online]. Available: https://docs.rapidminer.com/latest/studio/operators/scoring/apply_model.html. [Accessed: 11-Oct-2021].

RapidMiner Studio, “RapidMiner Documentations.” [Online]. Available: https://docs.rapidminer.com/latest/studio/operators/validation/performance/performance.html. [Accessed: 10-Nov-2021].

PadaKuu, “Algorithm Evaluation.” [Online]. Available: https://padakuu.com/article/140-algorithm-evaluation. [Accessed: 13-Oct-2021].

DOI: https://doi.org/10.32520/stmsi.v11i1.1669

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