Sales and Stock Purchase Prediction System Using Trend Moment Method and FIS Tsukamoto

Authors

  • Riko Firmansyah Universitas Nurdin Hamzah, Indonesia
  • Sukma Puspitorini Universitas Nurdin Hamzah, Indonesia http://orcid.org/0000-0003-2555-7246
  • Pariyadi Pariyadi Universitas Nurdin Hamzah, Indonesia
  • Tamrin Syah Universitas Nurdin Hamzah

DOI:

https://doi.org/10.29240/arcitech.v1i1.3057

Keywords:

Prediction, Sales, Stock Purchase, Trend Moment, Fuzzy Tsukamoto, Fuzzy Inference System, Decision Support, Fuzzy variable, Aggregration, Defuzzification

Abstract

The purpose of this research is to build decisions support model to predict sales and stock purchase using Trend Moment method and Tsukamoto Fuzzy Inference System. Trend moment is a simple statistical-based forecasting method widely used to forecast sales in a company using historical data. Tsukamoto is a fuzzy inference system that uses monotonic reasoning to determine output. The object of this research is sales and purchase data for Ice Cream X Depo Jambi products from August 2019 to April 2020. The study aims to build a decision support model web-based to predict sales and purchases of ice cream X stock at Jambi depots. Fuzzy Tsukamoto in this study will be used to predict product stock purchases after predicting future sales using trend moments. The system input is in product data form, data of ice cream sales history, and data of ice cream stock purchase. Sales history data will be use to calculate slope and constanta that will predict future sales trends. Stocks  purchase history data along with sales trend prediction value will be use to calculate the membership degree of fuzzy variables, perform the aggregation process on fuzzy rules, and then carry out the defuzzification process to produce output prediction values for future ice cream stock purchases. from the prediction model implemented in the decision support system, sales prediction data has an accuracy of 71% while stock purchase predictions have an accuracy of 85%.

Downloads

Download data is not yet available.

Author Biographies

Riko Firmansyah, Universitas Nurdin Hamzah

Prodi Teknik Informatika. Fakultas Ilmu Komputer. Universitas Nurdin Hamzah

Sukma Puspitorini, Universitas Nurdin Hamzah

Prodi Teknik Informatika. Fakultas Ilmu Komputer. Universitas Nurdin Hamzah

Pariyadi Pariyadi, Universitas Nurdin Hamzah

Prodi Teknik Informatika. Fakultas Ilmu Komputer. Universitas Nurdin Hamzah

Tamrin Syah, Universitas Nurdin Hamzah

Prodi Teknik Informatika. Fakultas Ilmu Komputer. Universitas Nurdin Hamzah

References

Aengchuan, P., & Phruksaphanrat, B. (2018). Comparison of fuzzy inference system (FIS), FIS with artificial neural networks (FIS + ANN) and FIS with adaptive neuro-fuzzy inference system (FIS + ANFIS) for inventory control. Journal of Intelligent Manufacturing, 29(4), 905-923.

Bag, S., Tiwari, M. K., & Chan, F. T. (2019, January). Predicting the consumer's purchase intention of durable goods: An attribute-level analysis. Journal of Business Research, 94, 408-419.

Chen, T. e. (2018, November). TADA: Trend Alignment with Dual-Attention Multi-task Recurrent Neural Networks for Sales Prediction. 2018 IEEE International Conference on Data Mining (ICDM),

Fauzi, A., Fitri, I., & Benrahman. (2021, Maret). Sistem Informasi Monitoring Penjualan Dan Prediksi. Jurnal Teknik Informatika dan Sistem Informasi, 8(1), 26-40.

Ilyas, Marisa, F., & Purnomo, D. (2018, Mei). “Implementasi Metode Trend Moment (Peramalan) Mahasiswa Baru Universitas Widyagama Malang. Journal of Information Technology and Computer Science (JOINTECH), 3(2), 69-74.

Izyudin, A., & Wibisono, S. (2020, Juni). APLIKASI PREDIKSI PENJUALAN ACMENGGUNAKAN DECISION TREE DENGAN ALGORITMA C4.5. Jurnal ManajemenInformatika & Sistem Informasi, 3(2), 146-156.

Karmila, D., & Rusda, D. (2019). E-MARKETPLACE PENJUALAN DAN PEMASARAN BARANG FURNITURE PADA TOKO MEBEL MENGGUNAKAN PHP DAN MYSQL SERVER. Jurnal Penelitian Dosen FIKOM (UNDA), 8(1), 26-40.

Martinez, A. e. (2020, March). A machine learning framework for customer purchase prediction in the non-contractual setting. European Journal of Operational Research, 281(3), 588-596.

Pavlyshenko, B. M. (2019, January). Machine-Learning Models for Sales Time Series Forecasting . Data, 4(1), 1.

Ramadhan, G. K., & Utama, D. N. (2019, November). Fuzzy Tsukamoto based Decision Support Model For Purchase Decision in Pharmacy Company. International Journal of Recent Technology and Engineering (IJRTE), 8(4), 1.

Velastegui , R. (2020, October). Time Series Prediction by Using Convolutional Neural Networks. Proceedings of the Future Technologies Conference, 2.

Downloads

Published

30-06-2021

How to Cite

Firmansyah, R., Puspitorini, S., Pariyadi, P., & Syah, T. (2021). Sales and Stock Purchase Prediction System Using Trend Moment Method and FIS Tsukamoto. Arcitech: Journal of Computer Science and Artificial Intelligence, 1(1), 15–24. https://doi.org/10.29240/arcitech.v1i1.3057

Issue

Vol. 1 No. 1 (2021): June 2021

Section

Articles

Citation Check

License

Authors who publish with Arcitech: Journal of Computer science and Artificial Intelligence agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).