Posts
Dwi Ekasari
Harmadji1, Solikhin Solikhin2*, Uky Yudatama3, Agus Purwanto4
Abstrak
Masa depan biofarmasi semakin cerah. Akibat mahalnya harga obat modern, maka permintaan tanaman obat meningkat di dalam dan luar negeri. Hal ini karena biofarmaka banyak digunakan di industri lain, seperti makanan, minuman, dan kosmetik. Konsumen di seluruh dunia termasuk di Indonesia bergerak menuju produk makanan dan kesehatan yang lebih sehat dengan slogan "kembali ke alam". Dengan demikian permintaan tanaman obat sebagai bahan baku industri lainnya juga meningkat. Untuk mengatasi masalah tersebut diperlukan suatu prediksi untuk menentukan besaran kenaikan atau penurunan jumlah produksi komoditas strategis biofarmaka untuk beberapa tahun ke depan, sehingga Memungkinkan analisis pergerakan tren dari perkembangan data sebelumnya. Saat ini belum dijumpai studi peramalan deret waktu untuk memprediksi produksi biofarmaka dengan tingkat akurasi baik. Dalam eksperimen ini kami mengusulkan model peramalan fuzzy time series berdasarkan pendekatan percentage change sebagai himpunan semesta dan frequency-based partition yang dapat memberikan tingkat akurasi peramalan yang tinggi. Prediksi difokuskan pada biofarmaka untuk empat jenis rimpang yaitu Jahe, Lengkuas, Kencur, dan Kunyit yang dinilai menjadi prioritas utama pengembangan tanaman obat di Indonesia. Dalam penelitian ini menggunakan data sekunder yang diperoleh dari Badan Pusat Statistika tahun 1997-2020. Tujuan dari survei adalah untuk memprediksi dan menganalisa perkembangan produksi biofarmaka untuk empat jenis rimpang. Hasil prediksi menunjukan akurasi luar biasa dengan nilai Mean Absolute Percentage Error yang sangat kecil yakni Jahe 0,03%, Lengkuas 0,02%, Kencur 0,14%, dan Kunyit 0,03%. Dengan demikian hasil eksperimen ini dapat berkontribusi dan digunakan bagi pihak yang berkompeten untuk membantu dalam menentukan kebijakan strategis di masa depan.
Abstract
Biopharmaceuticals' future is brightening. Due to the exorbitant cost of modern treatment, the desire for medicinal herbs is growing. due to their widespread use in different industries such as food, beverages, and cosmetics. Consumers worldwide, especially in Indonesia, are gravitating towards healthier food and health goods. So the demand for medicinal plants as raw materials increases. To solve this issue, a forecast is required for the next few years on the increase or decline in production of strategic biopharmaca commodities. Currently, no reliable time series forecasting study exists for biopharmaca production. To achieve high predicting accuracy, we present a fuzzy time series forecasting model based on percentage change as a universal set and frequency-based partition. Ginger, Galangal, Kencur, and turmeric are predicted to be the most important rhizomes for biopharmaca research in Indonesia. Secondary statistics from the Central Statistics Agency for 1997–2020 This study's goal was to anticipate and analyze biopharmaca synthesis in four rhizomes. The prediction results are incredibly accurate, with Mean Absolute Percentage Error values of just 0.03%, 0.02%, 0.14%, and 0.03% for Ginger, Galangal, Kencur, and Turmeric, respectively. Thus, competent parties can use the outcomes of this experiment to help determine future strategic policies.
DOI : https://doi.org/10.25126/jtiik.20231016267
Full text : PDF
Referensi
GARG, B., BEG, M. S., & ANSARI, A. Q., 2012. August. A new computational fuzzy time series model to forecast number of outpatient visits. In 2012 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS) (pp. 1-6). IEEE. doi: 10.1109/NAFIPS.2012.6290977.
Direktorat Jenderal Hortikultura Kementerian Pertanian (Dirjen Hortikultura Kementan), 2021. Sejarah. [online] Tersedia di:<https://hortikultura.pertanian.go.id/?page_id=5905> [Diakses 18 Februari 2022]
Program Studi Doktor Ilmu Pertanian Universitas Medan Area (Doktor Pertanian UMA), 2020. Pertanian Hortikultura Dunia. [online] Tersedia di:<https://doktor.pertanian.uma.ac.id/2020/09/07/pertanian-hortikultura-dunia/> [Diakses 18 Februari 2022]
Fakultas Ilmu Pertanian Universitas Medan Area (Pertanian UMA), 2020. Tanaman Hortikultura. [online] Tersedia di:<https://pertanian.uma.ac.id/tanaman-hortikultura/> [Diakses 19 Februari 2022]
Trop BRC (Tropical Biopharmaca Research Center), 2013. Quality of Herbal Medicine Plants and Traditional Medicine. [online] Tersedia di:<http://biofarmaka.ipb.ac.id/brc-news/brc-article/587-quality-of-herbal-medicine-plants-and-traditional-medicine-2013> [Diakses 19 Februari 2022]
SALIM, Z., & MUNADI, E., 2017. Info Komoditi Tanaman Obat. Badan Pengkajian dan Pengembangan Perdagangan Kementerian Perdagangan Republik Indonesia.
Badan Penelitian dan Pengembangan Pertanian (Litbang Pertanian) Departemen Pertanian, 2007. Prospek dan Arah Pengembangan Agribisnis Tanaman Obat. Edisi Kedua. Jakarta Barat, Pusat Penelitian dan Pengembangan Perkebunan. [online] Tersedia di:<https://www.litbang.pertanian.go.id/special/publikasi/doc_perkebunan/tanamanobat/tan-obat-bagian-a.pdf> [Diakses 19 Februari 2022]
LOGAN, T. M., MCLEOD, S., & GUIKEMA, S., 2016. Predictive models in horticulture: A case study with Royal Gala apples. Scientia Horticulturae, 209, pp.201-213, doi: 10.1016/j.scienta.2016.06.033.
IVANOV, D., TSIPOULANIDIS, A., & SCHÖNBERGER, J., 2019. Digital supply chain, smart operations and industry 4.0. In Global Supply Chain and Operations Management (pp. 481-526). Springer, Cham, doi: 10.1007/978-3-319-94313-8_16.
CHRISTYAWAN, T. Y., SYAUQI HARIS, M., RODY, R., & MAHMUDY, W., 2018. Optimization of Fuzzy Time Series Interval Length Using Modified Genetic Algorithm for Forecasting. International Conference on Sustainable Information Engineering and Technology (SIET), pp. 60-65, doi: 10.1109/SIET.2018.8693219.
KUMAR, S., & GANGWAR, S. S., 2016. Intuitionistic Fuzzy Time Series: An Approach for Handling Nondeterminism in Time Series Forecasting. in IEEE Transactions on Fuzzy Systems, vol. 24, no. 6, pp. 1270-1281, Dec. 2016, doi: 10.1109/TFUZZ.2015.2507582.
TELEZHKIN, V., RAGOZIN, A., & SAIDOV, B., 2021. Prediction of Signals in Control Systems Based on Fuzzy Time Series.
International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), pp. 950-954, doi: 10.1109/ICIEAM51226.2021.9446390.
SONG, Q., & CHISSOM, B. S., 1993. Forecasting enrollments with fuzzy time series—Part I. Fuzzy sets and systems, 54(1), pp.1-9, doi: 10.1016/0165-0114(93)90355-L.
JIANG, P., YANG, H., LI, R., & LI, C., 2020. Inbound tourism demand forecasting framework based on fuzzy time series and advanced optimization algorithm. Applied Soft Computing, 92, p.106320, doi: 10.1016/j.asoc.2020.106320.
SUN, S., WEI, Y., TSUI, K. L., & WANG, S., 2019. Forecasting tourist arrivals with machine learning and internet search index. Tourism Management, 70, pp.1-10 doi: 10.1016/j.tourman.2018.07.010.
YOLCU, O. C., & ALPASLAN, F., 2018. Prediction of TAIEX based on hybrid fuzzy time series model with single optimization process. Applied Soft Computing, 66, pp.18-33, doi: 10.1016/j.asoc.2018.02.007.
E SILVA, P. C. D. L., JUNIOR, C. A. S., ALVES, M. A., SILVA, R., COHEN, M. W., & GUIMARÃES, F. G., 2020. Forecasting in non-stationary environments with fuzzy time series. Applied Soft Computing, 97, p.106825, doi: 10.1016/j.asoc.2020.106825.
TANUWIJAYA, B. et al., 2020. A Novel Single Valued Neutrosophic Hesitant Fuzzy Time Series Model: Applications in Indonesian and Argentinian Stock Index Forecasting. in IEEE Access, vol. 8, pp. 60126-60141, doi: 10.1109/ACCESS.2020.2982825.
SOLIKHIN, S., LUTFI, S., PURNOMO, P., & HARDIWINOTO, H., 2021. Prediction of passenger train using fuzzy time series and percentage change methods. Bulletin of Electrical Engineering and Informatics, 10(6), pp.3007-3018, doi: 10.11591/eei.v10i6.2822.
SOLIKHIN, S., LUTFI, S., PURNOMO, P., & HARDIWINOTO, H., 2022. A machine learning approach in Python is used to forecast the number of train passengers using a fuzzy time series model. Bulletin of Electrical Engineering and Informatics, 11(5), doi: 10.11591/eei.v11i5.3518.
SOLIKHIN, S., & YUDATAMA, U., 2019. Fuzzy Time Series dan Algoritme Average Based Length untuk Prediksi Pekerja Migran Indonesia. Jurnal Teknologi Informasi dan Ilmu Komputer, 6(4), pp.369-376, doi: 10.25126/jtiik.2019641177.
CHENG, C. H., CHENG, G. W., & WANG, J. W., 2008. Multi-attribute fuzzy time series method based on fuzzy clustering. Expert systems with applications, 34(2), pp.1235-1242, doi: 10.1016/j.eswa.2006.12.013.
SINGH, S. R., 2009. A computational method of forecasting based on high-order fuzzy time series. Expert Systems with Applications, 36(7), pp.10551-10559, doi: 10.1016/j.eswa.2009.02.061.
BURNEY, S. M. A., & ALI, S. M., 2019. Sales Forecasting for Supply Chain Demand Management - A Novel Fuzzy Time Series Approach. 2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS), pp. 1-4, doi: 10.1109/MACS48846.2019.9024810.
RUBIO, A., BERMÚDEZ, J. D., & VERCHER, E., 2017. Improving stock index forecasts by using a new weighted fuzzy-trend time series method. Expert Systems with Applications, 76, pp.12-20, doi: 10.1016/j.eswa.2017.01.049.
SINGH, P., 2017. An efficient method for forecasting using fuzzy time series. In Emerging research on applied fuzzy sets and intuitionistic fuzzy matrices (pp. 287-304). IGI Global, doi: 10.4018/978-1-5225-0914-1.ch013.
CHEN, S. M., & PHUONG, B. D. H., 2017. Fuzzy time series forecasting based on optimal partitions of intervals and optimal weighting vectors. Knowledge-Based Systems, 118, pp.204-216, doi: 10.1016/j.knosys.2016.11.019.
DEL CAMPO, R. G., GARMENDIA, L., RECASENS, J., & MONTERO, J., 2017, July. Hesitant fuzzy sets and relations using lists. In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1-6). IEEE, doi: 10.1109/FUZZ-IEEE.2017.8015516.
CHENG, S. H., CHEN, S. M., & JIAN, W. S., 2015, October. A novel fuzzy time series forecasting method based on fuzzy logical relationships and similarity measures. In 2015 IEEE International Conference on Systems, Man, and Cybernetics (pp. 2250-2254). IEEE, doi: 10.1109/SMC.2015.393.
CHENG, S. H., CHEN, S. M., & JIAN, W. S., 2016. Fuzzy time series forecasting based on fuzzy logical relationships and similarity measures. Information Sciences, 327, pp.272-287, doi: 10.1016/j.ins.2015.08.024.
CHEN, S. M., & CHEN, S. W., 2014. Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and the probabilities of trends of fuzzy logical relationships. IEEE Transactions on Cybernetics, 45(3), pp.391-403, doi: 10.1109/TCYB.2014.2326888.
LIU, G., XIAO, F., LIN, C. T., & CAO, Z., 2020. A fuzzy interval time-series energy and financial forecasting model using network-based multiple time-frequency spaces and the induced-ordered weighted averaging aggregation operation. IEEE Transactions on Fuzzy Systems, 28(11), 2677-2690, doi: 10.1109/TFUZZ.2020.2972823.
JILANI, T. A., BURNEY, S. M. A., & ARDIL, C., 2010. Fuzzy metric approach for fuzzy time series forecasting based on frequency density based partitioning. International Journal of Computer and Information Engineering, 4(7), pp.1194-1199, doi: 10.5281/zenodo.1077541.
Badan Pusat Statistik (BPS), 2022. Produksi Tanaman Biofarmaka (Obat). [online] Tersedia di:<https://www.bps.go.id/indicator/55/63/1/produksi-tanaman-biofarmaka-obat-.html> [Diakses 18 Februari 2022]
CHANG, P.C., WANG, Y.W. and LIU, C.H., 2007. The development of a weighted evolving fuzzy neural network for PCB sales forecasting. Expert Systems with Applications, 32(1), pp.86-96, doi: 10.1016/j.eswa.2005.11.021.
