Strategi Meningkatkan Daya Saing Bawang Merah Melalui Prediksi Harga
Abstract
Shallots contribute significantly to the formation of food commodity inflation caused by high price fluctuations. Precise price forecasting is vital for all agribusiness actors, from farmers, traders, and consumers to production and inventory management. This research aims to identify the forecasting prices for shallot producers and consumers and formulate strategies to increase the competitiveness of shallots. This research uses the SARIMA method to capture seasonal elements in the data. The data used is time series data on shallot prices at the consumer and producer levels from January-November 2021. Determining the best SARIMA model uses the auto-arima technique, which shows that the best SARIMA for shallot prices at the producer level is ARIMA (2,1,2)(2,0,0)[12]. In contrast, the price of shallots at the consumer level is ARIMA (5,1,1)(1,0,1)[12]. The prediction results show that the dynamics of shallot prices in the future will continue to follow seasonal patterns as in previous years, namely, high prices during the lean season and religious holidays and low prices during the harvest season. The government needs to strengthen its policy of stabilizing shallot prices at both consumer and producer levels. Availability of agricultural inputs, adoption of technology for post-harvest and marketing, value-added innovation, and infrastructure improvements are strategic efforts to strengthen the competitiveness of shallots.
Keywords: consumer price, price fluctuation, producer price, shallot
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References
Adanacioglu H, Yercan M. 2012. An analysis of tomato prices at wholesale level in Turkey: An application of SARIMA model. Custos e Agronegocio. 8(4): 52–75.
Al-Hafid MS, Al-maamary GH. 2012. Short and medium Iraqi load forecast using holt-winter method and wavelet transformation. Canadian Journal on Electrical and Electronics. 3(5): 225–228.
Anderson K, Rausser G, Swinnen J. 2019. Political economy of public policies: Insights from distortions to agricultural and food markets. World Scientific Reference on Asia-Pacific Trade Policies. 51(2): 635–705. https://doi.org/10.1142/9789813274730_0016.
Anwar, M., Farooqi, S., Yahya Khan, G., & Javaid Iqbal Khan, S. 2015. Agriculture sector performance: An analysis through the role of agriculture sector share in GDP Fiscal Decentralization View project Convergence in SAARC countries View project. April. https://www.researchgate.net/publication/321481461
Ruslan AJ, Firdaus M, Studi Ilmu Ekonomi Pertanian, P., & Pascasarjana Institut Pertanian Bogor, S. (2016). Transmisi Harga Asimetri Dalam Rantai Pasok Bawang Merah Dan Hubungannya Dengan Impor Di Indonesia: Studi Kasus di Brebes dan Jakarta. https://doi.org/10.30908/bilp.v10i1.33
Astuti LTW, Daryanto A, Syaukat Y, Daryanto HK. 2020. Efficiency analysis of shallot farmer in Brebes, Central Java. International Journal of Research and Review. 7(11): 551–558. https://www.academia.edu/download/65139563/IJRR0074.pdf.
Azis MS, Dewanda F. 2023. Design model for empowering partner farmers of PT SBI through design thinking approach and triple layered BMC. Journal of Technopreneurship on Economics and Business. 4(2): 66–79. https://jtebr.unisan.ac. https://doi.org/10.37195/jtebr.v4i2.114
Bank Indonesia. 2021. Analisis Inflasi Februari 2021. 1–17. Jakarta (ID).
Basuki S, Wulanjari EM, Komalawati, Sahara D. 2021. The performance of production, price and marketing system of shallot in Central Java. E3S Web of Conferences. 316: 02004. https://doi.org/10.1051/e3sconf/202131602004.
Bhardwaj SP, Paul RK, Singh DR, Singh KN. 2014. An empirical investigation of arima and garch models in agricultural price forecasting. Economic Affairs. 59(3): 415. https://doi.org/10.5958/0976-4666.2014.00009.6.
Booranawong T, Booranawong A. 2017. An exponentially weighted moving average method with designed input data assignments for forecasting lime prices in thailand. Jurnal Teknologi. 79(6): 53–60. https://doi.org/10.11113/jt.v79.10096.
Boudrioua MS, Boudrioua A. 2020. Modeling and forecasting the Algerian Stock Exchange using the Box-Jenkins Methodology. Journal of Economics, Finance and Accounting Studies (JEFAS). 2(1): 1–15. https://doi.org/10.20944/preprints201909.0134.v1
[BPS] Badan Pusat Statistik. 2022. Data Harga Bawang Merah Tahun 2022. Jakarta (ID).
Ricketts DKG, Turvey C, Gómez MI. 2014. Value chain approaches to development. Journal of Agribusiness in Developing and Emerging Economies. 4(1): 2–22. https://doi.org/10.1108/JADEE-10-2012-0025
Destiarni RP, Zainuddin A, Jamil AS. 2021. Market Integration: How Does It Work in National Shallot Commodity Market in The Middle of Covid-19 Pandemic? E3S Web of Conferences. 316: 01006. https://doi.org/10.1051/e3sconf/202131601006.
Divisekara RW, Jayasinghe GJMSR, Kumari KWSN. 2021. Forecasting the red lentils commodity market price using SARIMA models. SN Business & Economics. 1(1): 1–13. https://doi.org/10.1007/s43546-020-00020-x.
Anjasari DH, Listiwikono, EFIY. 2018. perbandingan metode double exponential smoothing Holt dan metode triple exponential smoothing Holt-Winters untuk peramalan wisatawan Grand Watu Dodol. Jurnal Pendidikan Matematika & Matematika. 2(2): 12–25.
Elmunim NA, Abdullah M, Hasbi AM, Bahari SA. 2017. Comparison of GPS TEC variations with Holt-Winter method and IRI-2012 over Langkawi, Malaysia. Advances in Space Research. 60(2): 276–285. https://doi.org/10.1016/j.asr.2016.07.025.
Etwire PM, Dogbe W, Wiredu AN, Martey E, Etwire E, Owusu RK, Wahaga IE. 2013. Factors influencing farmer’s participation in agricultural projects: The case of the agricultural value chain mentorship project in the Northern Region of Ghana. Journal of Economics and Sustainable Development. 4(10): 36–43.
Fauzi NF, Ahmadi NS, Shafii NH, Halim HZ. 2020. A comparison study on fuzzy time series and Holt-Winter model in forecasting tourist arrival in Langkawi, Kedah. Journal of Computing Research and Innovation. 5(1): 34–43. https://doi.org/10.24191/jcrinn.v5i1.138.
Hansun S, Subanar. 2016. H-WEMA: A new approach of double exponential smoothing method. Telkomnika (Telecommunication Computing Electronics and Control). 14(2): 772–777. https://doi.org/10.12928/TELKOMNIKA.v14i1.3096.
Jadhav V, Chinnappa RBV, Gaddi GM. 2017. Application of ARIMA model for forecasting agricultural prices. Journal of Agricultural Science and Technology. 19(5): 981–992.
Kangogo D, Dentoni D, Bijman J. 2020. Determinants of farm resilience to climate change: The role of farmer entrepreneurship and value chain collaborations. Sustainability (Switzerland). 12(3): 1–15. https://doi.org/10.3390/su12030868.
Kementerian Pertanian. 2021. Laporan Kinerja Kementerian Pertanian Tahun 2020. Jakarta (ID).
Kumar P, Shinoj P, Raju SS, Kumar A, Rich KM, Msangi S. 2010. Factor demand, output supply elasticities and supply projections for major crops of India. Agricultural Economics Research Review. 23(June): 1–14.
Latifi Z, Fami HS. 2022. Forecasting Wheat Production in Iran Using Time Series Technique and Artificial Neural Network. 24.
Lee NU, Shim JS, Ju YW, Park SC. 2018. Design and implementation of the SARIMA–SVM time series analysis algorithm for the improvement of atmospheric environment forecast accuracy. Soft Computing. 22(13): 4275–4281. https://doi.org/10.1007/s00500-017-2825-y
Liu Y, Amin A, Rasool SF, Zaman QU. 2020. The role of agriculture and foreign remittances in mitigating rural poverty: Empirical evidence from Pakistan. Risk Management and Healthcare Policy. 13: 13–26. https://doi.org/10.2147/RMHP.S235580.
Mutwiri MR. 2019. Forecasting of tomatoes wholesale prices of Nairobi in Kenya: Time series analysis using Sarima model. International Journal of Statistical Distributions and Applications. 5(3): 46. https://doi.org/10.11648/j.ijsd.20190503.11.
Nurjati E. 2018. Price volatility of red chili peppers in Central Java. Jurnal Sosial Ekonomi dan Kebijakan Pertanian. 7(2): 176–187.
Panetto H, Lezoche M, Hormazabal JEH, Diaz MDMEA, Kacprzyk J. 2020. Special issue on Agri-Food 4.0 and digitalization in agriculture supply chains-New directions, challenges and applications. Computers in Industry. 116: 103188.
Pertiwi DD. 2020. Applied exponential smoothing Holt-Winter method for predict rainfall in Mataram City. Journal of Intelligent Computing and Health Informatics. 1(2): 45–49. https://doi.org/10.26714/jichi.v1i2.6330.
Rahmawati A, Fariyanti A, Rifin A. 2018. Spatial market integration of shallot in Indonesia. Jurnal Manajemen dan Agribisnis. 15(3): 258–267. https://doi.org/10.17358/jma.15.3.258.
Rao KB. 2011. Agriculture market price fluctuations, changing livestock systems and Vulnerability connect-a case of Mhaswandi watershed, Ahmednagar district, Maharashtra.
Revathy R, Balamurali S. 2019. Distinguishing SARIMA with extensive neural network model for forecasting sugarcane productivity. IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2019. 0–4. https://doi.org/10.1109/INCOS45849.2019.8951397.
Sabu KM, Kumar TKM. 2020. Predictive analytics in Agriculture: Forecasting prices of arecanuts in Kerala. Procedia Computer Science. 171(2019): 699–708. https://doi.org/10.1016/j.procs.2020.04.076.
Saptana, Gunawan E, Perwita AD, Sukmaya SG, Darwis V, Ariningsih E, Ashari. 2021. The competitiveness analysis of shallot in Indonesia: A policy analysis matrix. PLoS One. 16(9): 1–19. https://doi.org/10.1371/journal.pone.0256832.
Sharma H., Burark, SS. 2015. Bajra price forecasting in Chomu market of Jaipur district: An application of SARIMA model. Agricultural situation in India. 71(11): 7-12.
Sujarwo S. 2017. Factors affecting farmers’ acceptability toward agricultural insurance program in Malang, East Java, Indonesia. Agricultural Socio-Economics Journal. 17(3): 97–104. https://doi.org/10.21776/ub.agrise.2017.017.3.1.
Sujarwo S, Rukmi SMN. 2018. Factors Affecting Agricultural Insurance Acceptability of Paddy Farmers in East Java, Indonesia. Jurnal Manajemen Dan Agribisnis, 15(2): 143–149. https://doi.org/10.17358/jma.15.2.143.
Sukati MA. 2013. Measuring Maize Price Volatility in Swaziland using ARCH/GARCH Approach. Munic Personal REPEC Archive. 51840(51840): 1–19. https://mpra.ub.uni-muenchen.de/51840/.
Sunariadi NM, Intan KP, Candra D, Novitasari R, Hariningsih Y, Matematika P, Sunan U, Surabaya, A., 2022. Prediksi Produksi Bawang Merah Di Kabupaten Nganjuk Dengan Metode Seasonal Arima (SARIMA). Jurnal Pendidikan Matematika dan Matematika. 6(1): 49–60. https://doi.org/10.36526/tr.v%vi%i.1672.
Tenriawaru AN, Annisa AJ, Heliawaty, Salam M, Viantika NM. 2020. Trends of shallot retail prices at traditional market in Makassar. IOP Conference Series: Earth and Environmental Science. 575(1): 1–6. https://doi.org/10.1088/1755-1315/575/1/012058.
Virdaus D, Prasetyaningrum PT. 2020. Penerapan Data Mining Untuk Memprediksi Harga Bawang Merah Di Yogyakarta Menggunakan Metode K-Nearest Neighbor Prodi Sistem Informasi. Yogyakarta (ID): Fakultas Teknologi Informasi Universitas Mercu Buana Yogyakarta.
Wati S, Nendissa DR, Olviana T, Retang EUK. 2021. Shallot market cointegration between markets in Province West Southeast and East Nusa Tenggara. International Journal of Business, Technology and Organizational Behavior (IJBTOB). 1(3): 176–188. https://doi.org/10.52218/ijbtob.v1i3.92.
Wibowo AR, Ginting R, Ayu SF. 2014. Peramalan dan faktor faktor yang mempengaruhi harga bawang merah di Sumatera Utara. Journal on Social Economics of Agriculture and Agribusiness. 3(2): 24–37.
Windhy AM, Suci YT, Jamil AS. 2021. Analisis peramalan harga bawang merah nasional dengan pendekatan model Arima. Seminar Nasional Pembangunan Pertanian Berkelanjutan Berbasis Sumber Daya Lokal. 591–604.
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