Prediksi Penjualan Kopi Bubuk Menggunakan Extreme Learning Machine (Studi Kasus: Kafe Tjap Daoen Bondowoso)

Muhammad Ariful Furqon, Anis Madani, Yanuar Nurdiansyah, Gama Wisnu Fajarianto

Abstract


Cafe Tjap Daoen belum menerapkan metode yang mendukung peramalan penjualan, sehingga keputusan terkait penjualan hanya didasarkan pada data periode sebelumnya. Hal ini menyebabkan terjadinya underproduksi saat permintaan tinggi dan overproduksi ketika permintaan menurun. Penelitian ini menganalisis dua produk utama, yaitu Arabica Specialty Coffee dan Arabica Wine, menggunakan data penjualan dari tahun 2019 hingga 2022. Algoritma Extreme Learning Machine (ELM) dipilih karena kinerjanya yang sangat baik dalam memprediksi data deret waktu. Hasil penelitian menunjukkan bahwa algoritma ELM mampu menghasilkan nilai Mean Absolute Percentage Error (MAPE) sebesar 1,8450%. Sementara itu, Arabica Wine menghasilkan MAPE sebesar 10,373%. Penelitian ini menunjukkan bahwa algoritma ELM efektif dalam meningkatkan akurasi peramalan penjualan untuk kedua produk tersebut. @font-face {font-family:"Cambria Math"; panose-1:2 4 5 3 5 4 6 3 2 4; mso-font-charset:0; mso-generic-font-family:roman; mso-font-pitch:variable; mso-font-signature:-536870145 1107305727 0 0 415 0;}@font-face {font-family:Calibri; panose-1:2 15 5 2 2 2 4 3 2 4; mso-font-charset:0; mso-generic-font-family:swiss; mso-font-pitch:variable; mso-font-signature:-469750017 -1040178053 9 0 511 0;}p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-unhide:no; mso-style-qformat:yes; mso-style-parent:""; margin:0cm; mso-pagination:widow-orphan; font-size:12.0pt; mso-bidi-font-size:11.0pt; font-family:"Times New Roman",serif; mso-fareast-font-family:Calibri; mso-ansi-language:IN;}.MsoChpDefault {mso-style-type:export-only; mso-default-props:yes; font-size:10.0pt; mso-ansi-font-size:10.0pt; mso-bidi-font-size:10.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-fareast-font-family:Calibri; mso-hansi-font-family:Calibri; mso-font-kerning:0pt; mso-ligatures:none; mso-fareast-language:EN-ID;}div.WordSection1 {page:WordSection1;}

Keywords


Extreme Learning Machine; Prediksi Penjualan; Analisis Deret Waktu

References


S. D. Hastutik and D. W. Handani, “Studi kelayakan bisnis startup berbasis teknologi pada bidang pengolahan kopi arabika (studi kasus kelompok tani argopuro walida),” Jurnal Ilmiah Inovasi, vol. 23, no. 2, pp. 180–187, Aug. 2023, doi: 10.25047/JII.V23I2.3944.

M. Hafezd As’ad, J. Murti, and M. Aji, “FAKTOR yang mempengaruhi preferensi konsumen kedai kopi modern di bondowoso,” JSEP (Journal of Social and Agricultural Economics), vol. 13, no. 2, pp. 182–199, Jul. 2020, doi: 10.19184/JSEP.V13I2.16441.

S. Sutrisno, “Kondisi sosial petani kopi desa ujung bulu, kecamatan rumbia, kabupaten jeneponto,” Al-Din: Jurnal Dakwah dan Sosial Keagamaan, vol. 5, no. 2, pp. 120–141, 2019.

E. S. Sintiya, A. Kusumawardana, M. A. Furqon, N. F. Najwa, A. C. Puspitaningrum, and A. S. Afrah, “SARIMA and holt-winters seasonal methods for time series forecasting in tuberculosis case,” in 2020 4th International Conference on Vocational Education and Training (ICOVET), 2020, pp. 1–5.

H. S. Irawan, N. O. Adiwijaya, and M. ‘Ariful Furqon, “Implementasi metode holt-winters multiplicative pada sistem peramalan pengunjung objek wisata kawah ijen kabupaten bondowoso,” Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer, vol. 14, no. 2, pp. 209–216, Nov. 2023, doi: 10.24176/SIMET.V14I2.9549.

A. S. Pranata, N. O. Adiwijaya, and M. Furqon, “Screen printing t-shirt stock forecasting system with weight moving average,” Jurnal Komputer Terapan, vol. 9, no. 1, pp. 50–57, Jun. 2023, doi: 10.35143/jkt.v9i1.5834.

V. Komaria, N. El Maidah, and M. A. Furqon, “Prediksi harga cabai rawit di provinsi jawa timur menggunakan metode fuzzy time series model lee,” Komputika : Jurnal Sistem Komputer, vol. 12, no. 2, pp. 37–47, Sep. 2023, doi: 10.34010/KOMPUTIKA.V12I2.10644.

I. A. Putri, N. El Maidah, and M. A. Furqon, “Penerapan metode fuzzy time series cheng pada peramalan inflasi di indonesia,” Komputika : Jurnal Sistem Komputer, vol. 13, no. 2, pp. 183–191, Oct. 2024, doi: 10.34010/KOMPUTIKA.V13I2.12108.

G. Bin Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, vol. 70, no. 1–3, pp. 489–501, Dec. 2006, doi: 10.1016/J.NEUCOM.2005.12.126.

E. Cambria, P. Gastaldo, F. Bisio, and R. Zunino, “An elm-based model for affective analogical reasoning,” Neurocomputing, vol. 149, no. Part A, pp. 443–455, Feb. 2015, doi: 10.1016/J.NEUCOM.2014.01.064.

J. Wang, S. Lu, S. H. Wang, and Y. D. Zhang, “A review on extreme learning machine,” Multimedia Tools and Applications 2021 81:29, vol. 81, no. 29, pp. 41611–41660, May 2021, doi: 10.1007/S11042-021-11007-7.

M. Alizamir, S. Kim, M. Zounemat-Kermani, S. Heddam, N. W. Kim, and V. P. Singh, “Kernel extreme learning machine: an efficient model for estimating daily dew point temperature using weather data,” Water 2020, Vol. 12, Page 2600, vol. 12, no. 9, p. 2600, Sep. 2020, doi: 10.3390/W12092600.

P. S. G. De Mattos Neto et al., “Energy consumption forecasting for smart meters using extreme learning machine ensemble,” Sensors 2021, Vol. 21, Page 8096, vol. 21, no. 23, p. 8096, Dec. 2021, doi: 10.3390/S21238096.

Q. Yu, Y. Miche, E. Séverin, and A. Lendasse, “Bankruptcy prediction using extreme learning machine and financial expertise,” Neurocomputing, vol. 128, pp. 296–302, Mar. 2014, doi: 10.1016/J.NEUCOM.2013.01.063.

A. de Myttenaere, B. Golden, B. Le Grand, and F. Rossi, “Mean absolute percentage error for regression models,” Neurocomputing, vol. 192, pp. 38–48, Jun. 2016, doi: 10.1016/J.NEUCOM.2015.12.114.

V. N. G. Raju, K. P. Lakshmi, V. M. Jain, A. Kalidindi, and V. Padma, “Study the influence of normalization/transformation process on the accuracy of supervised classification,” Proceedings of the 3rd International Conference on Smart Systems and Inventive Technology, ICSSIT 2020, pp. 729–735, Aug. 2020, doi: 10.1109/ICSSIT48917.2020.9214160.

P. J. M. Ali, R. H. Faraj, E. Koya, P. J. M. Ali, and R. H. Faraj, “Data normalization and standardization: a technical report,” Mach Learn Tech Rep, vol. 1, no. 1, pp. 1–6, 2014.

M. M. Bejani and M. Ghatee, “Regularized deep networks in intelligent transportation systems: a taxonomy and a case study,” Artificial Intelligence Review 2021 54:8, vol. 54, no. 8, pp. 6391–6438, Nov. 2019, doi: 10.1007/s10462-021-09975-1.




DOI: https://doi.org/10.31284/p.snestik.2025.7066

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Muhammad Ariful Furqon, Anis Madani, Yanuar Nurdiansyah, Gama Wisnu Fajarianto

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.