FUZZY TIME SERIES IN FORECASTING EXPORT PERFORMANCE OF INDONESIAN SEAWEED PRODUCTS
Abstract
This study applies the Fuzzy Time Series method to forecast the export performance of Indonesian processed seaweed, one of the country's main export commodities, contributing significantly to foreign exchange earnings. The Fuzzy Time Series method is employed for its simplicity and effectiveness in handling time series data with high variability and uncertainty—characteristics often found in export data. Unlike traditional statistical methods, Fuzzy Time Series does not require strict assumptions such as stationarity or normality, making it suitable for real-world applications. Although more appropriate for short-term forecasting, the method still provides meaningful insights for planning and policy. The analysis uses monthly export data from January 2013 to December 2021 to generate forecasts for January to December 2022. The results indicate a positive trend in export performance, with projections showing an increase from 1,707,070 kg in December 2021 to approximately 1,759,763 kg in January 2022. Despite Indonesia's processed seaweed still lagging behind some competitors in terms of competitiveness, its steady growth and rising demand abroad highlight its strong development potential. The forecasting results can be a strategic reference to optimize the commodity's development, increase its added value, and ultimately enhance the country's foreign exchange income.
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N. Fauziah, S. Wahyuningsih, and Y. N. Nasution, “CHEN FUZZY TIME SERIES FORECASTING (CASE STUDY: SAMARINDA CITY RAINFALL),” Journal of Statistics University of Muhammadiyah Semarang, vol. 4, no. 2, pp. 52–61, 2016, doi: https://doi.org/10.26714/jsunimus.4.2.2016.%25p
L. Chairani, T. Chandra, and F. Udin, “RANKING OF SEAWEED AGRO-INDUSTRY IN INDONESIA USING FUZZY OWA METHOD,” IOP Conf Ser Mater Sci Eng, vol. 507, no. 1, pp. 1–5, 2019, doi: https://doi.org/10.1088/1757-899X/507/1/012016
M. Yulisti, E. Luhur, F. Arthatiani, and I. Mulyawan, “COMPETITIVENESS ANALYSIS OF INDONESIAN SEAWEEDS IN GLOBAL MARKET,” IOP Conf Ser Earth Environ Sci, vol. 860, no. 1, 2021, doi: https://doi.org/10.1088/1755-1315/860/1/012061
United Nations Statistics, “UNITED NATIONS COMMODITY TRADE (COMTRADE) STATISTICS DATABASE,” 2022.
I. A. Asshidiq and N. Agustina, “EXPORT PERFORMANCE ANALYSIS OF INDONESIAN PROCESSED SEAWEED TO THE SEVEN MAIN DESTINATION COUNTRIES FROM 2010 TO 2019,” Asian Journal of Business Environment, vol. 12, no. 3, pp. 13–22, 2022, doi: https://doi.org/10.13106/ajbe.2022.vol12.no3.13
F. Y. Arthatiani, B. Wardono, E. S. Luhur, and T. Apriliani, “EXPORT PERFORMANCE SITUATION ANALYSIS OF INDONESIAN SEAWEED DURING THE OUTBREAK OF COVID-19,” Journal of Socio-Economic Policy Minister of Marine Affairs and Fisheries of the Republic of Indonesia, vol. 11, no. 1, pp. 1–12, 2021, doi: http://dx.doi.org/10.15578/jksekp.v11i1.9501
B. Ryu, Y.-S. Kim, and Y.-J. Jeon, “SEAWEEDS AND THEIR NATURAL PRODUCTS FOR PREVENTING CARDIOVASCULAR ASSOCIATED DYSFUNCTION,” Mar Drugs, vol. 19, no. 9, p. 507, 2021, doi: https://doi.org/10.3390/md19090507
L. Stokvis, M. M. Van Krimpen, R. P. Kwakkel, and P. Bikker, “EVALUATION OF THE NUTRITIONAL VALUE OF SEAWEED PRODUCTS FOR BROILER CHICKENS’ NUTRITION,” Anim Feed Sci Technol, vol. 280, pp. 1–12, 2021, doi: https://doi.org/10.1016/j.anifeedsci.2021.115061
P. Gullón, G. Astray, B. Gullón, D. Franco, P. C. B. Campagnol, and J. M. Lorenzo, “INCLUSION OF SEAWEEDS AS HEALTHY APPROACH TO FORMULATE NEW LOW-SALT MEAT PRODUCTS,” Curr Opin Food Sci, vol. 40, pp. 20–25, 2021, doi: https://doi.org/10.1016/j.cofs.2020.05.005
P. M. Slegers, R. J. K. Helmes, M. Draisma, R. Broekema, M. Vlottes, and S. W. K. van den Burg, “ENVIRONMENTAL IMPACT AND NUTRITIONAL VALUE OF FOOD PRODUCTS USING THE SEAWEED SACCHARINA LATISSIMA,” J Clean Prod, vol. 319, pp. 1–12, 2021, doi: https://doi.org/10.1016/j.jclepro.2021.128689
P. S. Shukla, T. Borza, A. T. Critchley, and B. Prithiviraj, “SEAWEED-BASED COMPOUNDS AND PRODUCTS FOR SUSTAINABLE PROTECTION AGAINST PLANT PATHOGENS,” Mar Drugs, vol. 19, no. 2, p. 59, 2020, doi: https://doi.org/10.3390/md19020059
V. A. Extrilia, B. E. Diniari, B. N. Iman, Z. T. A. Sainsi, R. D. Ramadhani, and Z. Nurfadilah, “FORECASTING OF AGRICULTURAL LAND, RICE PRODUCTION, AND RICE PRODUCTIVITY ON ECONOMIC GROWTH SUSTAINABLE,” Bengkulu International Conference on Economics, Management, Business and Accounting (BICEMBA), vol. 2, pp. 589–604, 2024, doi: https://doi.org/10.33369/bicemba.2.2024.137
R. E. Caraka et al., “MICRO, SMALL, AND MEDIUM ENTERPRISES’ BUSINESS VULNERABILITY CLUSTERS IN INDONESIA: AN ANALYSIS USING OPTIMIZED FUZZY GEODEMOGRAPHIC CLUSTERING,” Sustainability, vol. 13, no. 14, p. 7807, 2021, doi: https://doi.org/10.3390/su13147807
Jasmadi et al., “PRELIMINARY CHARACTERIZATION OF SEAWEED AGAR SHEET PRODUCTS: CASE STUDY OF LOCAL SMALL AND MEDIUM ENTERPRISE IN JAVA ISLAND,” IOP Conf Ser Earth Environ Sci, vol. 934, pp. 1–7, 2021, doi: https://doi.org/10.1088/1755-1315/934/1/012083
N. Henidar and Firmansyah, “ANALYSIS OF COMPETITIVENESS AND DETERMINANT OF SEAWEED EXPORT VALUES (HS 121221) INDONESIATO EXPORT DESTINATION IN THE PERIOD OF 2012-2018,” AFEBI Economic and Finance Review (AEFR), vol. 5, no. 2, pp. 29–43, 2020, doi: https://doi.org/10.47312/aefr.v5i02.491
B. Endaryati and R. Kurniawan, “COMPARISON OF FORECASTING METHODS AUTOMATIC CLUSTERING TECHNIQUE AND FUZZY LOGICAL RELATIONSHIPS WITH SINGLE EXPONENTIAL SMOOTHING,” Media Statistics, vol. 8, no. 2, pp. 93–101, 2015, doi: https://doi.org/10.14710/medstat.8.2.93-101
P. Singh, “A BRIEF REVIEW OF MODELING APPROACHES BASED ON FUZZY TIME SERIES,” International Journal of Machine Learning and Cybernetics, vol. 8, no. 2, pp. 397–420, 2017, doi: https://doi.org/10.1007/s13042-015-0332-y
I.-J. Wen and C.-H. Yang, “USING FUZZY TIME-SERIES PREDICTION FOR FORECASTING RAINFALL IN TAIWAN,” The International Journal of Organizational Innovation, vol. 13, no. 1, 2020, [Online]. Available: http://www.ijoi-online.org/http://www.ijoi-online.org/
D. Septiyana, “WATER CONSUMPTION PREDICTION USING FUZZY TIME SERIES - A CASE STUDY IN PRIVATE COMPANY OF TANGERANG DISTRICT INDONESIA,” Journal of Industrial Engineering, vol. 15, no. 3, 2020, doi: https://doi.org/10.14710/jati.15.3.203-208
L. Patria, “FUZZY TIME SERIES APPLICATION IN PREDICTING THE NUMBER OF CONFIRMATION CASES OF COVID-19 PATIENTS IN INDONESIA,” International Journal of Quantitative Research and Modeling, vol. 2, no. 4, pp. 193–200, 2021, doi: https://doi.org/10.46336/ijqrm.v2i4.194
M. Ula, Bahtiar, D. Yulisda, Badriana, and A. Bintoro, “APPLICATION OF THE FUZZY TIME SERIES MODEL IN CLOTHING MATERIAL STOCK FORECASTING,” Journal of Information Systems and Computer Science Prima, vol. 6, no. 1, 2022, doi: https://doi.org/10.34012/jurnalsisteminformasidanilmukomputer.v6i1.2862
V. Vivianti, M. K. Aidid, and M. Nusrang, “IMPLEMENTATION OF THE FUZZY TIME SERIES METHOD FOR FORECASTING THE NUMBER OF VISITORS AT FORT ROTTERDAM,” VARIANCE: Journal of Statistics and Its Application on Teaching and Research, vol. 2, no. 1, pp. 1–12, 2020, doi: https://doi.org/10.35580/variansiunm12904
A. Qalbi, K. Nurfadilah, and W. Alwi, “COMPARISON OF FUZZY TIME SERIES METHODS AND AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) FOR INFLATION DATA,” Eigen Mathematics Journal, no. 2, pp. 40–50, 2021, doi: https://doi.org/10.29303/emj.v4i2.122
M. Biringallo and B. Abapihi, “COMPARISON ACCURACY OF USING FUZZY TIME SERIES MARKOV-CHAIN AND CHENG METHODS IN FORECASTING THE NUMBER OF TRAFFIC ACCIDENTS IN KENDARY CITY,” Proceedings of the National Seminar Science and Application VI, vol. 6, pp. 85–99, 2022.
H. N. Sofhya, “COMPARISON OF FUZZY TIME SERIES CHEN AND CHENG TO FORECAST INDONESIA RICE PRODUCTIVITY,” Eduma : Mathematics Education Learning and Teaching, vol. 11, no. 1, pp. 119–128, 2022, doi: https://doi.org/10.24235/eduma.v11i1.
S. Julida and D. Murni, “COMPARISON OF FUZZY TIME SERIES METHODS CHEN MODEL AND MARKOV CHAIN MODEL TO PREDICT RAINFALL IN PADANG CITY,” Tambusai Education Journal, vol. 8, no. 1, pp. 15809–15821, 2024, doi: https://doi.org/10.31004/jptam.v8i1.14613
I. Fikri, A. Salma, D. Vionanda, and Zilrahmi, “COMPARISON OF FUZZY TIME SERIES MARKOV CHAIN AND FUZZY TIME SERIES CHENG TO PREDICT INFLATION IN INDONESIA,” UNP Journal of Statistics and Data Science, vol. 1, no. 4, pp. 306–312, Aug. 2023, doi: https://doi.org/10.24036/ujsds/vol1-iss4/76
W. Widiyani, Y. Setyawan, and M. T. Jatipaningrum, “COMPARISON OF FUZZY TIME SERIES- CHEN AND WEIGHTED FUZZY INTEGRATED TIME SERIES METHOD FOR PREDICTING JOINT STOCK PRICE INDEX DATA,” Journal of Industrial and Computational Statistics, vol. 7, no. 1, pp. 81–87, 2022, doi: https://doi.org/10.30743/jsik.v7i1.81
L. A. Zadeh, “FUZZY SETS AS A BASIS FOR A THEORY OF POSSIBILITY,” Fuzzy Sets Syst, vol. 1, pp. 3–28, 1978, doi: https://doi.org/10.1016/0165-0114(78)90029-5
F. Chellai, “FORECASTING USING FUZZY TIME SERIES,” Munich Personal RePEc Archive, no. 113848, 2022, doi: https://mpra.ub.uni-muenchen.de/113848/
Rahmawati, E. P. Cynthia, and K. Susilowati, “CHENG’S FUZZY TIME SERIES METHOD IN PREDICTING NUMBER OF TOURISTS IN WEST SUMATRA PROVINCE,” Journal of Education Informatic Technology and Science (JeITS), vol. 1, no. 1, pp. 11–23, 2019, doi: https://ejurnal.umri.ac.id/index.php/JeITS/article/view/1222/733
A. N. Nur, E. S. Sababan, P. Gultom, and Sutarman, “APPLICATION OF FUZZY TIME SERIES METHOD CHENG MODEL IN FORECASTING STOCK PRICES PT BUKIT ASAM TBK,” Journal of Science, Technology and Industry, vol. 21, no. 1, pp. 88–97, 2023, doi: https://doi.org/10.24014/sitekin.v21i1.22910
S. N. Rizki, “FUZZY IMPLEMENTATION IN THE SELECTION OF A CHIEVING EMPLOYEES AT PT. SUMBER BARKAH USING MATLAB SOFTWARE,” International Journal of Information System & Technology Akreditasi, vol. 5, no. 2, pp. 170–178, 2021, doi: http://dx.doi.org/10.30645/ijistech.v5i2.128
R. Aliyev, S. Salehi, and R. Aliyev, “DEVELOPMENT OF FUZZY TIME SERIES MODEL FOR HOTEL OCCUPANCY FORECASTING,” Sustainability (Switzerland), vol. 11, no. 3, Feb. 2019, doi: https://doi.org/10.3390/su11030793
C. D. Lewis, Industrial and Business Forecasting Methods: A PRACTICAL GUIDE TO EXPONENTIAL SMOOTHING AND CURVE FITTING. London: Butterworth Scientific, 1982.
D. C. Montgomery, C. L. Jennings, and M. Kulahci, INTRODUCTION TO TIME SERIES ANALYSIS AND FORECASTING. John Wiley & Sons, 2015.
R. I. Khaldun, “POLICY STRATEGY FOR INCREASING INDONESIAN SEAWEED COMPETITIVENESS IN THE GLOBAL MARKET,” J. Sospol, vol. 3, no. 1, pp. 99–125, 2017, doi: https://doi.org/10.22219/sospol.v3i1.4403
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