Google Trends-Based Demand Forecasting for Indonesian MSMEs: A Comparative Model Evaluation
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This study presents the first systematic comparative evaluation of eight forecasting models applied to Google Trends data specifically for Indonesian digital MSME product categories, addressing a critical gap in accessible demand prediction tools for resource-constrained enterprises. Employing quantitative comparative experimental design, we analyzed 18 product keywords from fashion and traditional food sectors with weekly temporal resolution (2020-2024), evaluated via 80/20 train-test split with MAPE. Exponential Smoothing achieved superior overall performance (20.33% MAPE), while Moving Average proved optimal for traditional food products (16.21% MAPE). Category-specific model selection enables MSMEs to anticipate demand peaks, time market entry windows, optimize inventory positioning ahead of seasonal cycles, and prioritize promotional spending, translating search trend signals directly into actionable penetration decisions. These findings provide an evidence-based, zero-cost framework for demand-informed market strategy, accessible to resource-constrained enterprises without specialized infrastructure or advanced technical expertise.
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Aaronson, D., Brave, S. A., Butters, R. A., Fogarty, M., Sacks, D. W., & Seo, B. (2022). Forecasting unemployment insurance claims in real-time with Google Trends. International Journal of Forecasting, 38(2), 567–581. https://doi.org/10.1016/j.ijforecast.2021.04.001
Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723. https://doi.org/10.1109/TAC.1974.1100705
Armstrong, J. S., & Collopy, F. (1992). Error measures for generalizing about forecasting methods: Empirical comparisons. International Journal of Forecasting, 8(1), 69–80. https://doi.org/10.1016/0169-2070(92)90008-W
Bates, J. M., & Granger, C. W. J. (1969). The combination of forecasts. OR, 20(4), 451–468. https://doi.org/10.2307/3008764
Boone, T., Ganeshan, R., Hicks, R. L., & Sanders, N. R. (2018). Can Google Trends improve your sales forecast? Production and Operations Management, 27(10), 1770–1774. https://doi.org/10.1111/poms.12839
Box, G. E. P., & Jenkins, G. M. (1970). Time series analysis: Forecasting and control. Holden-Day.
Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control (5th ed.). Wiley.
Brockwell, P. J., & Davis, R. A. (2006). Introduction to time series and forecasting (2nd ed.). Springer.
Carta, S., Medda, A., Pili, A., Reforgiato Recupero, D., & Saia, R. (2019). Forecasting e-commerce products prices by combining an autoregressive integrated moving average (ARIMA) model and Google Trends data. Future Internet, 11(1), Article 5. https://doi.org/10.3390/fi11010005
Chaidir, M., Yulianti, G., & Santoso, S. (2024). Dampak digitalisasi terhadap inovasi teknologi pada usaha mikro, kecil, dan menengah. Jurnal Visi Manajemen, 10(2). https://doi.org/10.56910/jvm.v10i2.523
Choi, H., & Varian, H. (2012). Predicting the present with Google Trends. Economic Record, 88(s1), 2–9. https://doi.org/10.1111/j.1475-4932.2012.00809.x
Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7, 1–30.
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and practice (2nd ed.). OTexts.
Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688. https://doi.org/10.1016/j.ijforecast.2006.03.001
Hyndman, R. J., Koehler, A. B., Ord, J. K., & Snyder, R. D. (2008). Forecasting with exponential smoothing: The state space approach. Springer.
Jun, S.-P., Yoo, H. S., & Choi, S. (2018). Ten years of research change using Google Trends: From the perspective of big data utilizations and applications. Technological Forecasting and Social Change, 130, 69–87. https://doi.org/10.1016/j.techfore.2017.11.009
Kementerian Koordinator Bidang Perekonomian Republik Indonesia. (2025, January 30). Pemerintah dorong UMKM naik kelas, tingkatkan kontribusi terhadap ekspor Indonesia. https://www.ekon.go.id/publikasi/detail/6152/pemerintah-dorong-umkm-naik-kelas-tingkatkan-kontribusi-terhadap-ekspor-indonesia
Lusa, S., Purbo, O. W., & Lestari, T. (2024). Peran e-commerce dalam mendukung ekonomi digital Indonesia. Penerbit Andi.
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2020). The M4 competition: 100,000 time series and 61 forecasting methods. International Journal of Forecasting, 36(1), 54–74. https://doi.org/10.1016/j.ijforecast.2019.04.014
Massaron, L., & Boschetti, A. (2016). Regression analysis with Python. Packt Publishing.
Nosek, B. A., Alter, G., Banks, G. C., Borsboom, D., Bowman, S. D., Breckler, S. J., Buck, S., Chambers, C. D., Chin, G., Christensen, G., Contestabile, M., Dafoe, A., Eich, E., Freese, J., Glennerster, R., Goroff, D., Green, D. P., Hesse, B., Humphreys, M., Ishiyama, J., . . . Yarkoni, T. (2015). Promoting an open research culture. Science, 348(6242), 1422–1425. https://doi.org/10.1126/science.aab2374
Pattiasina, T. (2025). Studi kualitatif tentang adopsi cloud computing pada UMKM di Indonesia. Catha: Journal of Creative and Innovative Research, 2(1).
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
Praharjo, A., Marsudi, & Andharini, S. N. (2023). Optimasi Google advertising dalam promosi bisnis di era digital. Pustaka Peradaban.
Priyono, A., Moin, A., & Putri, V. N. A. O. (2020). Identifying digital transformation paths in the business model of SMEs during the COVID-19 pandemic. Journal of Open Innovation: Technology, Market, and Complexity, 6(4), Article 104. https://doi.org/10.3390/joitmc6040104
Purwana, D., Rahmi, R., & Aditya, S. (2017). Pemanfaatan digital marketing bagi usaha mikro, kecil, dan menengah (UMKM) di Kelurahan Malaka Sari, Duren Sawit. Jurnal Pemberdayaan Masyarakat Madani (JPMM), 1(1), 1–17. https://doi.org/10.21009/JPMM.001.1.01
Putra, B. P. P., Judijanto, L., Apriyanto, A., Susilo, A., Kusumastuti, S. Y., Jamaludin, J., Arifiyanti, A. A., & Sari, F. H. (2025). Tren bisnis digital: Transformasi dunia bisnis terkini. PT Sonpedia Publishing Indonesia.
Rahayu, W., & Veri, J. (2025). Penerapan sistem informasi manajemen berbasis digital dalam UMKM: Sebuah kajian literatur. Journal of Human and Education (JAHE), 5(2), 267–272. https://doi.org/10.31004/jh.v5i2.2340
Rahman, E. (2025). Disrupsi pemasaran (Disruption of marketing). Feniks Muda Sejahtera.
Ramadhani, D., Sonia, R., & Astuti, D. (2025). Analisis adopsi teknologi digital dalam studi kelayakan bisnis UMKM di era transformasi digital. Jurnal Ekonomi dan Bisnis Digital, 2(3).
Ramadhani, N., Sugesti, A. I., Sagita, D. N., & Purwanto, E. (2025). Pemanfaatan marketplace digital dalam meningkatkan kinerja UMKM melalui sarana komunikasi di era digital. Jurnal Bisnis dan Komunikasi Digital, 2(2), 20. https://doi.org/10.47134/jbkd.v2i2.3560
Ramadhi, Sirojudin, H. A., Maradidya, A., Septiani, S., Yuliastuti, H., Polimpung, L. J. C., Tarmizi, R., Baharuddin, R. A., Ratnasari, A. R., Gusnafitri, Soleh, O., Dharwiyanti, S., Taqwiem, A., Sunandar, E., Rachmadi, K. R., & Solihin, I. (2023). Ekonomi digital: Transformasi bisnis dalam era digital. Sada Kurnia Pustaka.
Rizky, M. F., & Permana, E. (2022). Analisis strategi bisnis menggunakan digital marketing pada UMKM pasca pandemi COVID-19. Jurnal Riset Pendidikan Ekonomi, 7(2), 142–150. https://doi.org/10.21067/jrpe.v7i2.6820
Seabold, S., & Perktold, J. (2010). Statsmodels: Econometric and statistical modeling with Python. In Proceedings of the 9th Python in Science Conference. https://doi.org/10.25080/Majora-92bf1922-011
Silva, E. S., Hassani, H., Madsen, D. Ø., & Gee, L. (2019). Googling fashion: Forecasting fashion consumer behaviour using Google Trends. Social Sciences, 8(4), Article 111. https://doi.org/10.3390/socsci8040111
Tashman, L. J. (2000). Out-of-sample tests of forecasting accuracy: An analysis and review. International Journal of Forecasting, 16(4), 437–450. https://doi.org/10.1016/S0169-2070(00)00065-0
Trisnawati, N. L. D. E., & Suryaningsih, K. (2025). Sistem pengelolaan persediaan barang dagang pada UMKM. Jurnal Akuntansi, Ekonomi dan Manajemen Bisnis, 5(3), 365–374. https://doi.org/10.55606/jaemb.v5i3.7651
Tukey, J. W. (1977). Exploratory data analysis. Addison-Wesley.
Wardani, S., Lubis, S. S., & Dewantoro, R. W. (2025). Analisis big data untuk prediksi permintaan produk dalam e-commerce. Jurnal Penelitian Teknologi Informasi dan Sains, 3(1). https://doi.org/10.54066/jptis.v3i1.3066
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