THE STRATEGY TO ENHANCE SHALLOT COMPETITIVENESS BASED ON PREDICTIVE PRICES
Abstract
Bawang merah berkontribusi signifikan dalam pembentukan inflasi komoditas pangan yang disebabkan oleh fluktuasi harga yang tinggi. Peramalan harga yang presisi berfungsi bagi semua aktor agribisnis mulai dari petani, pedagang, dan konsumen untuk manajemen produksi dan persediaan. Penelitian ini menggunakan metode SARIMA yang mampu menangkap unsur seasonal pada data. Data yang digunakan adalah data time series harga bawang merah ditingkat konsumen dan produsen pada Bulan Januari 2011 sampai November 2021. Penentuan model SARIMA terbaik menggunakan teknik auto-arima yang menunjukkan bahwa SARIMA terbaik untuk harga bawang merah dilevel produsen adalah ARIMA (2,1,2)(2,0,0)[12], sedangkan untuk harga bawang merah dilevel konsumen adalah ARIMA (5,1,1)(1,0,1)[12]. Hasil prediksi menunjukkan bahwa dinamika harga bawang merah kedepannya akan tetap mengikuti pola musiman seperti pada tahun-tahun sebelumnya, yaitu harga tinggi pada musim paceklik dan hari besar keagamaan serta harga akan rendah pada musim panen raya. Pemerintah perlu menguatkan kebijakan yang komprehensif mulai dari hulu sampai hilir agribisnis untuk peningkatan daya saing bawang merah. Adopsi teknologi untuk pascapanen dan permasaran, inovasi nilai tambah, dan perbaikan infrastruktur merupakan upaya strategis untuk penguatan daya saing bawang merah
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References
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Kumar, P., Shinoj, P., Raju, S. S., Kumar, A., Rich, K. M., & 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, H. S. (2022). Forecasting Wheat Production in Iran Using Time Series Technique and Artificial Neural Network. 24.
Lee, N.U., Shim, J.S., Ju, Y.W. and Park, S. C. (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.
Liu, Y., Amin, A., Rasool, S. F., & Zaman, Q. U. (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
Masood 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
Mathenge Mutwiri, R. (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. https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=2ahUKEwi5o7uH5JrgAhUiSY8KHTnYAyoQFjAAegQIAhAB&url=http://journal.trunojoyo.ac.id/agriekonomika/article/view/1758&usg=AOvVaw32xzWSKyPFs0NfbjZJfqFv
Pertiwi, D. D. (2020). Applied Exponential Smoothing Holt-Winter Method for Predict Rainfall in Mataram City. Journal of Intelligent Computing and Health Informatics, 1(2), 45. 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, K. B., & Trust, W. O. (2011). Agriculture market price fl uctuations, changing livestock systems and Vulnerability Connect – a case of.
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, K. M., & Kumar, T. K. M. (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, A. D., Sukmaya, S. G., Darwis, V., Ariningsih, E., & Ashari. (2021). The competitiveness analysis of shallot in Indonesia: A Policy Analysis Matrix. PLoS ONE, 16(9 September), 1–19. https://doi.org/10.1371/journal.pone.0256832
Sharma, H. (2015). Bajra Price Forecasting in Choumu Market of Jaipur District : An Application of SARIMA Model Bajra Price Forecasting in Chomu Market of Jaipur District : An Application of. Agricultural Situation in India.
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, S. M. N. (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, M. A. (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/
Tenriawaru, A. N., Annisa, A. J., Heliawaty, Salam, M., & Viantika, N. M. (2020). Trends of shallot retail prices at traditional market in Makassar. IOP Conference Series: Earth and Environmental Science, 575(1), 0–6. https://doi.org/10.1088/1755-1315/575/1/012058
Wati, S., Nendissa, D. R., Olviana, T., & Retang, E. U. K. (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, A. R., Ginting, R., & Ayu, S. F. (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, A. M., Suci, Y. T., & Jamil, A. S. (2019). Analisis Peramalan Harga Bawang Merah Nasional Dengan Pendekatan Model Arima. Seminar Nasional Pembangunan Pertanian Berkelanjutan Berbasis Sumber Daya Lokal, 591–604.
Al-Hafid, M. S., & Al-maamary, G. H. (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 (In 2 Volumes), 51(2), 635–705. https://doi.org/10.1142/9789813274730_0016
Astuti, L. T. W., Daryanto, A., Syaukat, Y., & Daryanto, H. K. (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
Bank Indonesia. (2021). Analisis Inflasi Februari 2021. 1–17.
Basuki, S., Eti Wulanjari, M., 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, S. P., Paul, R. K., Singh, D. R., & Singh, K. N. (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, M. S., & 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/10.5281/zenodo.3903241
D. Ricketts, K., G. Turvey, C., & I. Gómez, M. (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, R. P., Zainuddin, A., & Jamil, A. S. (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, R. W., Jayasinghe, G. J. M. S. R., & Kumari, K. W. S. N. (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
Dwi Hilda Anjasari, Eko Listiwikono, F. I. Y. (2018). Perbandingan Metode Double Exponential Smoothing Holt Dan Metode Triple Exponential Smoothing Holt-Winters Untuk Peramalan Wisatawan Grand Watu Dodol PERBANDINGAN. Jurnal Pendidikan Matematika & Matematika, 2(2), 12–25.
Elmunim, N. A., Abdullah, M., Hasbi, A. M., & Bahari, S. A. (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, P. M., Dogbe, W., Wiredu, A. N., Martey, E., Etwire, E., Owusu, R. K., & Wahaga1, E. (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.
Eva, M., & Diaz, A. (2020). Agri-Food 4.0 and Digitalization in Agriculture Supply Chains - New directions, challenges and applications Hervé.
Fauzi, N. F., Ahmadi, N. S., Shafii, N. H., & Ab Halim, H. Z. (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 Reddy, B. V., & Gaddi, G. M. (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). https://doi.org/10.3390/su12030868
Kementerian Pertanian. (2021). Laporan Kinerja Kementerian Pertanian Tahun 2020.
Kumar, P., Shinoj, P., Raju, S. S., Kumar, A., Rich, K. M., & 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, H. S. (2022). Forecasting Wheat Production in Iran Using Time Series Technique and Artificial Neural Network. 24.
Lee, N.U., Shim, J.S., Ju, Y.W. and Park, S. C. (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.
Liu, Y., Amin, A., Rasool, S. F., & Zaman, Q. U. (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
Masood 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
Mathenge Mutwiri, R. (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. https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=2ahUKEwi5o7uH5JrgAhUiSY8KHTnYAyoQFjAAegQIAhAB&url=http://journal.trunojoyo.ac.id/agriekonomika/article/view/1758&usg=AOvVaw32xzWSKyPFs0NfbjZJfqFv
Pertiwi, D. D. (2020). Applied Exponential Smoothing Holt-Winter Method for Predict Rainfall in Mataram City. Journal of Intelligent Computing and Health Informatics, 1(2), 45. 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, K. B., & Trust, W. O. (2011). Agriculture market price fl uctuations, changing livestock systems and Vulnerability Connect – a case of.
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, K. M., & Kumar, T. K. M. (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, A. D., Sukmaya, S. G., Darwis, V., Ariningsih, E., & Ashari. (2021). The competitiveness analysis of shallot in Indonesia: A Policy Analysis Matrix. PLoS ONE, 16(9 September), 1–19. https://doi.org/10.1371/journal.pone.0256832
Sharma, H. (2015). Bajra Price Forecasting in Choumu Market of Jaipur District : An Application of SARIMA Model Bajra Price Forecasting in Chomu Market of Jaipur District : An Application of. Agricultural Situation in India.
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, S. M. N. (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, M. A. (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/
Tenriawaru, A. N., Annisa, A. J., Heliawaty, Salam, M., & Viantika, N. M. (2020). Trends of shallot retail prices at traditional market in Makassar. IOP Conference Series: Earth and Environmental Science, 575(1), 0–6. https://doi.org/10.1088/1755-1315/575/1/012058
Wati, S., Nendissa, D. R., Olviana, T., & Retang, E. U. K. (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, A. R., Ginting, R., & Ayu, S. F. (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, A. M., Suci, Y. T., & Jamil, A. S. (2019). Analisis Peramalan Harga Bawang Merah Nasional Dengan Pendekatan Model Arima. Seminar Nasional Pembangunan Pertanian Berkelanjutan Berbasis Sumber Daya Lokal, 591–604.
Published
2024-03-15
How to Cite
NurjatiE., & Susanti WiryawanF. (2024). THE STRATEGY TO ENHANCE SHALLOT COMPETITIVENESS BASED ON PREDICTIVE PRICES. Jurnal Ilmu Pertanian Indonesia, (00). Retrieved from https://jurnal.ipb.ac.id/index.php/JIPI/article/view/49525
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