Sales Forecasting of the Local Cultural Product Tanjak Melayu in Rokan Hulu Using the Trend Moment Method to Support Sustainable UMKM Marketing Strategies

khairul sabri; Afrijal; Inal; Surnaida

doi:10.51454/decode.v5i3.1491

Authors

khairul sabri Information Systems Universitas Pasir Pengaraian
Afrijal Management Universitas Pasir Pengaraian
Inal Information Systems Universitas Pasir Pengaraian
Surnaida Information Systems Universitas Pasir Pengaraian

DOI:

https://doi.org/10.51454/decode.v5i3.1491

Keywords:

Cultural Product, MSMEs, Sales Forecasting, Sustainable Marketing Strategy, Trend Moment

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a crucial role in strengthening Indonesia’s regional economy and preserving cultural identity through creative industries. One of the prominent local cultural products is Tanjak Melayu, a traditional Malay headpiece produced in Rokan Hulu Regency, Riau. However, inconsistent market demand often leads to production inefficiencies and unstable marketing performance. This study aims to forecast the sales of Tanjak Melayu using the Trend Moment method integrated with seasonal adjustment analysis to support sustainable MSME marketing strategies. The dataset used consists of monthly sales records from January 2023 to December 2024, analyzed using quantitative forecasting techniques. The resulting trend equation, Y = 145.55 + 1.4087X, indicates an average monthly increase of 1.4 units in sales volume. Model validation produced a MAPE value of 3.78%—categorized as excellent accuracy—and an RMSE value of 9.08, reflecting a low prediction error compared to actual sales. The findings demonstrate that the Trend Moment method effectively captures both the upward sales trend and seasonal fluctuations, with demand peaks occurring in November and December. This research provides practical insights for MSME actors in optimizing production planning and marketing schedules, and theoretical contributions by highlighting the applicability of simple statistical forecasting for culture-based small enterprises toward sustainable economic development.

References

Afrijal, A., & Sabri, K. (2024). Peramalan Penjualan Kain Batik Pada Penjahit Ruslan Menggunakan Metode Autoregressive Integrated Moving Average (Arima). Jurnal Economica: Media Komunikasi ISEI Riau, 12(1), 82-88.

Azaria, D. P. (2019). Pengaruh Absorptive Capacity dan Kolaborasi Jaringan Inovasi Terhadap New Product Performance Pada UMKM Minuman di Yogyakarta. https://dspace.uii.ac.id/handle/123456789/15622

Azizah, I., Helmiah, F., & Latiffani, C.. (2024). Penerapan Metode Trend Moment Dalam Memprediksi Penjualan Rumah KPR Bersubsidi Pada Perumahan. Decode: Jurnal Pendidikan Teknologi Informasi, 4(3), 798–808. https://doi.org/10.51454/decode.v4i3.509

Chairunnisa, A. D. A., & Fauzan, A. (2023). Implementation of Panel Data Regression in the Analysis of Factors Affecting Poverty Levels in Bengkulu Province in 2017-2020. EKSAKTA: Journal of Sciences and Data Analysis 4(1), 40–45. https://doi.org/10.20885/eksakta.vol4.iss1.art5

Dada, J. A., Babatola, F. A., Adebusoye, A. B., Ajayi, C. O., & Dada, G. O. (2023). Financial Management Capabilities and Demographic Characteristics on Sustainable Sales Growth of Selected Small and Medium Enterprises (MSMEs) in Nigeria. AKSU Journal of Administration and Corporate Governance, 3(3), 128-143. https://doi.org/10.61090/aksujacog.2023.024

Elisa, E., Tukino, T., & Handoko, K. (2022). Penerapan Forecasting Methods Untuk Penjualan Produk Umkm Dengan Algoritma K-Nearest Neighbor. Jurnal Teknik Informasi Dan Komputer (Tekinkom), 5(2).

Ernayani, R. (2024). Peran Bank Syariah dan UMKM dalam Meningkatkan Perekonomian Indonesia. Al-Kharaj: Jurnal Ekonomi, Keuangan & Bisnis Syariah, 6(3), 1311–1316. https://doi.org/10.47467/alkharaj.v6i3.180

Hidayatullah, R., Widodo, E. A., Maulana, M. H., Asmita, I., Fatimah, F., Wahyuni, I., Meisaroh, M., Yanto, B., & Sabri, K. (2025). Pelatihan Pemanfaatan Website Aplikasi Destinasi Wisata untuk Meningkatkan Daya Tarik dan Kinerja Dinas Pariwisata Rokan Hulu. Jurnal Masyarakat Negeri Rokania, 6(1), 551–556. https://doi.org/10.56313/jmnr.v6i1.412

Khairina, D. M., Khairunnisa, R., Hatta, H. R., & Maharani, S. (2021). Comparison Of The Trend Moment And Double Moving Average Methods For Forecasting The Number Of Dengue Hemorrhagic Fever Patients. Bulletin of Electrical Engineering and Informatics, 10(2), 978~987. https://doi.org/10.11591/eei.v10i2.2711

Meitriana, M. A., Suwena, K. R., & Santi, N. W. A. (2023). Fintech: Its Effect on Business Sustainability. Jurnal Pendidikan Ekonomi Undiksha, 15(1), 226–231. https://doi.org/10.23887/jjpe.v15i1.64752

Nasution, T. F. T., Lubis, A. R., & Alkhowarizmi, A. (2022). Analisis Metode Trend Moment Sebagai Peramalan (Forecast) Penjualan UMKM Dimsum. Jurnal Ilmu Komputer dan Sistem Informasi, 2(1), 1-10.

Nissa, D. A., Supian, S., & Nahar, J. (2023). Inventory Control for MSME Products Using the Q Model with Lost Sales Condition Based on Products Sales Forecasting. International Journal of Quantitative Research and Modeling, 4(1), 20-29. https://doi.org/10.46336/ijqrm.v4i1.417

Pratama, J., & Zaki, M. (2021). Perancangan Dan Implementasi Augmented Reality Tanjak Melayu Menggunakan Metode MDLC. Conference on Business, Social Sciences and Technology, 1(1), 375-385.

Sari, I. P., Mursyida, E., & Lestari, R. (2020). E- Commerce Tanjak Melayu Hasil Kerajinan Tangan Kelurahan Agrowisata Sebagai Upaya Promosi Pasar Global. Jurnal Pengabdian Masyarakat Multidisiplin, 3(3), 167–172. https://doi.org/10.36341/jpm.v3i3.1010

Temür, A. S., & Yıldız, Ş. (2021). Comparison of Forecasting Performance of ARIMA LSTM and HYBRID Models for The Sales Volume Budget of a Manufacturing Enterprise. Istanbul Business Research, 50(1), 15-46. https://doi.org/10.26650/ibr.2021.51.0117

Ulfa, H., Susanti, R., Candra, C., Nahwiyah, S., & Murwindra, R. (2023). Implementasi Nilai – Nilai Songket Dan Tanjak Di Tk/Kb Cempaka Desa Koto Sentajo. Jurnal Pengabdian Kepada Masyarakat, 3(1), 109 - 116. https://doi.org/10.36378/bhakti_nagori.v3i1.3126

Zhao, D., Zhang, R., Zhang, H., & He, S. (2022). Prediction Of Global Omicron Pandemic Using ARIMA, MLR, And Prophet Models. Scientific Reports, 12(1), 1-13. https://doi.org/10.1038/s41598-022-23154-4