Penerapan Metode Monte Carlo untuk Simulasi Prediksi Tingkat Penjualan Coklat Khas Dubai
Main Article Content
Abstract
Article Summary
DarkChoco, which sells Dubai chocolate, has seen its sales increase every day, but this situation has affected the level of service to customers because the ingredients used to make the chocolate run out quickly. There are some customers who cannot taste and get the Dubai chocolate. This case will lead to fewer customers. So this research was conducted to predict sales in maintaining the availability of materials so that the services will increase again. The method that will be used in this research uses the Monte Carlo method by processing Dubai Viral Chocolate sales data in 2021, 2022 and 2023. This study aims to simulate the prediction of the sales level of Dubai specialty chocolate by applying the Monte Carlo method. Dubai specialty chocolate is a product with high market potential and significant demand fluctuations, so accurate sales prediction is very important for business decision making. The simulation of the sales forecast will show an average accuracy rate of about 94.5%, which means that the forecast is quite close to the actual data. Some months with large deviations (February, March, May, August, December) indicate potential seasonal variability or other factors that need attention. This simulation model is effective for sales forecasting and planning by taking uncertainty into account. Thus, this method is capable of producing forecasts that are more realistic and informative than conventional methods. The results show that the application of Monte Carlo method is able to provide a comprehensive overview of sales simulation and predict sales levels with sufficient accuracy, and this Monte Carlo method can also be an effective tool in helping companies manage inventory and marketing strategies for Dubai specialty chocolates to improve operational efficiency and business profits in the future.
Keywords
Article Keywords
Application ; Forecasting ; Sales ; Simulation ; Monte Carlo
Downloads
Article Details

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal 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 License (CC-BY 4.0) that allows others to share the work with an acknowledgement 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 acknowledgement 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.
Bourgeois, C. M., Soltanisehat, L., Barker, K., & González, A. D. (2023). Risk-based inventory scheduling framework to fulfill multi-product orders within a production network. Computers & Industrial Engineering, 182, 109343. https://doi.org/10.1016/j.cie.2023.109343.
Desi, E., Aliyah, S., Lubis, C. P., Nst, M. A. E., & Tahel, F. (2024). Simulasi Monte Carlo Dalam Meprediksikan Tingkat Lonjakan Pendaftaran Vaksin Booster Pada Puskesmas Martubung. Jurnal Teknologi Informasi dan Ilmu Komputer, 11(3), 579-586. https://doi.org/10.25126/jtiik.937570.
Fahdia, M. R., Kurniawati, I., Amsury, F., & Saputra, I. (2022). Pelatihan Digital Marketing Untuk Meningkatkan Penjualan Bagi UMKM Tajur Halang Makmur. Abdiformatika: Jurnal Pengabdian Masyarakat Informatika, 2(1), 34-39. https://doi.org/10.25008/abdiformatika.v2i1.147.
Geni, B. Y., & Santony, J. (2019). Prediksi Pendapatan Terbesar pada Penjualan Produk Cat dengan Menggunakan Metode Monte Carlo. Jurnal Informatika Ekonomi Bisnis, 15-20. https://doi.org/10.37034/infeb.v1i4.5.
Hidayah, H. (2022). Metode Monte Carlo untuk Memprediksi Jumlah Tamu Menginap. Jurnal Informasi dan Teknologi, 76-80. https://doi.org/10.37034/jidt.v4i1.193.
Hutahaean, H. D. (2018). Analisa simulasi monte carlo untuk memprediksi tingkat kehadiran mahasiswa dalam perkuliahan (studi kasus: STMIK pelita nusantara). Journal Of Informatic Pelita Nusantara, 3(1), 41-45.
Kang, W., & Shao, B. (2023). The impact of voice assistants’ intelligent attributes on consumer well-being: Findings from PLS-SEM and fsQCA. Journal of Retailing and Consumer Services, 70, 103130. https://doi.org/10.1016/j.jretconser.2022.103130.
Lin, J., & Michailidis, G. (2024). A multi-task encoder-dual-decoder framework for mixed frequency data prediction. International Journal of Forecasting, 40(3), 942-957.
Lubis, R. (2022). Simulasi Jenis Penyakit Pasien yang Berobat Menggunakan Metode Monte Carlo. Jurnal Sistim Informasi Dan Teknologi, 42-46. https://doi.org/10.37034/jsisfotek.v4i2.121.
Mahessya, R. A. (2017). Pemodelan dan Simulasi Sistem Antrian Pelayanan Pelanggan Menggunakan Metode Monte Carlo Pada PT Pos Indonesia (Persero) Padang. Jurnal Ilmu Komputer, 6(1), 15-24.
Manurung, K. H., & Santony, J. (2019). Simulasi Pengadaan Barang Menggunakan Metode Monte Carlo. Jurnal Sistim Informasi dan Teknologi, 1(3), 7-11.
Mikaeil, R., Amini Khoshalan, H., Nasrollahi, M. H., & Esmaeilzadeh, A. (2022). ANALIZA POUZDANOSTI STROJEVA ZA PUNOPROFILNI ISKOP TUNELA PRIMJENOM SIMULACIJSKE METODE MONTE CARLO. Rudarsko-geološko-naftni zbornik, 37(3), 149-160. https://doi.org/10.17794/rgn.2022.3.12.
Muhazir, A. (2022). Penerapan Metode Monte Carlo dalam Memprediksi Jumlah Penumpang Kereta Api (Studi Kasus: PT. Kai Wilayah Sumatra). Journal of Science And Social Research, 5(1), 151-158. https://doi.org/10.54314/jssr.v5i1.825.
Mulia, J. R., & Nurcahyo, G. W. (2022). Prediksi Pemakaian Obat Kronis Menggunakan Metode Monte Carlo. Jurnal Informasi Dan Teknologi, 81-85. https://doi.org/10.37034/jidt.v4i2.198.
Qiao, Y., Lan, Q., Wang, Y., Jia, S., Kuang, X., Yang, Z., & Ma, C. (2023). PEvaChain: Privacy-preserving ridge regression-based credit evaluation system using hyperledger fabric blockchain. Expert Systems with Applications, 223, 119844. https://doi.org/10.1016/j.eswa.2023.119844.
Santony, J., & Yunus, Y. (2019). Simulasi Monte Carlo untuk Memprediksi Hasil Ujian Nasional (Studi Kasus di SMKN 2 Pekanbaru). Jurnal Informasi Dan Teknologi, 1-6. https://doi.org/10.37034/jidt.v1i4.21.
Sapriadi, S., Yunus, Y., & Dari, R. W. (2022). Prediction of the Number of Arrivals of Training Students with the Monte Carlo Method. Jurnal Informasi dan Teknologi, 9-13. https://doi.org/10.37034/jidt.v4i1.168.
Satria, R., Sovia, R., & Gema, R. L. (2017). Pemodelan dan Simulasi Analisa Sistem Antrian Pelayanan Nasabah di PT Sarana Sumatera Barat Ventura SSBV Menggunakan Metode Monte Carlo. Komputer Teknologi Informasi, 4(1).
Simatupang, S. (2022). Simulasi Monte Carlo dalam Memprediksi Ketersediaan Barang (PT. Terang Abadi Pekanbaru). JURSIMA, 10(1), 176-184. https// doi.org/10.47024/js.v10i1.399 .
Sukrianto, D., Gunawan, A., & Oktarina, D. (2022). Implementasi Sistem Informasi Penjualan pada Pet Shop Mulya PS: AMIK Mahaputra Riau. Journal Intra Tech, 6(1), 50-62.
Syaputra, A. E., & Eirlangga, Y. S. (2022). Prediksi Tingkat Kunjungan Pasien dengan Menggunakan Metode Monte Carlo. Jurnal Informasi dan teknologi, 97-102.
Trisna, N., Safitri, W., & Pratiwi, M. (2019). Penerapan Sistem Antrian sebagai Upaya Pengoptimalkan Pelayanan terhadap Pasien pada Loket Pengambilan Obat di RSI. Ibnu Sina Pasaman Barat dengan menggunakan Metode Monte Carlo. Jurnal Teknologi Informasi, 3(1), 7-15.
Varera, O. J. (2022). Optimalisasi Prediksi Tingkat Pendapatan Desa Berdasarkan Jenis Usaha Menggunakan Metode Monte Carlo. Jurnal Informatika Ekonomi Bisnis, 23-27. https://doi.org/10.37034/infeb.v4i1.120.
WASKITO, F. J. (2022). Penerapan Simulasi Monte Carlo Dalam Upaya Perencanaan Bahan Baku Untuk Mengurangi Shortage (Studi Kasus: Ud Mekar Putra).
Xu, B., Wang, H., & Li, J. (2023). Evaluation of operation cost and energy consumption of ports: Comparative study on different container terminal layouts. Simulation Modelling Practice and Theory, 127, 102792. https://doi.org/10.1016/j.simpat.2023.102792.