Metode Fuzzy Time Series Markov Chain Untuk Peramalan Curah Hujan Harian
Abstract views: 131
,
PDF downloads: 155
Abstract
Cilacap Regency has diverse topography and geographical conditions which cause this region to have rainfall that varies spatially and temporally; therefore, a forecasting method to overcome these uncertainties and fluctuations is needed. Fuzzy Time Series Markov Chain utilizes Fuzzy logic which provides flexibility in handling uncertain and unstructured data. Moreover, the addition of Markov chain elements that utilize Fuzzy logic concepts provides flexibility in handling data allowing the model to capture inter-time relationships and changes in system state that depend on previous states. Therefore, the research aims to see the suitability of the Fuzzy Time Series Markov Chain for predicting daily rainfall in Cilacap Regency. The method is suitable for predicting rainfall data for Cilacap Regency. The accuracy value in this study can be seen from the RMSE and SMAPE values on the training data (in-sample), respectively, which are 58.76469 and 0.7227493. Meanwhile, the testing data (out sample) was 56.01818 and 0.7055117.
References
M. Yustiana, M. Zainuri, D. N. Sugianto, M. P. N. Batubara, and A. M. Hidayat, “Dampak Variabilitas Iklim Inter-Annual (El Niño, La Niña) Terhadap Curah Hujan dan Anomali Tinggi Muka Laut di Pantai Utara Jawa Tengah,” Bul. Oseanografi Mar., vol. 12, no. 1, pp. 109–124, 2023, doi: 10.14710/buloma.v12i1.48377.
Stasiun BMKG Kabupaten Cilacap, “Banyaknya Curah Hujan dan Hari Hujan/Number of Preticipatios and Rainy days 2017-2019.” https://cilacapkab.bps.go.id/indicator/151/324/1/banyaknya-curah-hujan-dan-hari-hujan-number-of-preticipatios-and-rainy-days.html
Desy Ika Puspitasari and Mochammad Arif Afianto, “Implementasi Fuzzy Time Series Markov Chain Model (Ftsmcm) Dalam Prediksi Jumlah Produksi Ayam Potong,” J. Teknol. Inf. Univ. Lambung Mangkurat, vol. 2, no. 2, pp. 45–50, 2017, doi: 10.20527/jtiulm.v2i2.19.
A. A. Tama and D. R. S. Saputro, “Algoritme Average-Based Fuzzy Time Series Markov-Chain,” Pros. Semin. Nas. Mat., vol. 5, pp. 711–715, 2022, [Online]. Available: https://journal.unnes.ac.id/sju/index.php/prisma/
Rachim. F, Tarno, and Sugito, “Perbandingan Fuzzy Time Series dengan Metode Chen dan Metode S. R. Singh,” J. Gaussian, vol. Vol.9, pp. 306–315, 2020.
Agan and Teti Sofia Yanti, “Perbandingan Metode Fuzzy Time Series Markov Chain dan Fuzzy Time Series Chen Average Based untuk Peramalan Volume Impor Migas.,” Bandung Conf. Ser. Stat., vol. 2, no. 2, pp. 207–216, 2022, doi: 10.29313/bcss.v2i2.3853.
I. Muhandhis, A. S. Ritonga, and M. H. Murdani, “Peramalan Curah Hujan Menggunakan Metode Average-Based Fuzzy Time Series Markov Chain,” Pros. Semin. Nas. Penelit. dan Pengabdi. Masayarakat, vol. 5, no. 1, pp. 118–122, 2020, [Online]. Available: http://prosiding.unirow.ac.id/index.php/SNasPPM
R. C. Tsaur, “A fuzzy time series-Markov chain model with an application to forecast the exchange rate between the Taiwan and us Dollar,” Int. J. Innov. Comput. Inf. Control, vol. 8, no. 7 B, pp. 4931–4942, 2012.
D. L. Rahakbauw, A. Afriananda, and H. W. M. Patty, “Perbandingan Logika Fuzzy Metode Sugeno dan Metode Mamdani Untuk Deteksi Dini Penyakit Stroke,” Tensor Pure Appl. Math. J., vol. 3, no. 1, pp. 11–22, 2022, doi: 10.30598/tensorvol3iss1pp11-22.
I. Admirani, “Model Ruey Chyn Tsaur Fuzzy Time Series Untuk Prediksi Pendaftaran Mahasiswa Baru,” 56 J. JUPITER, vol. 12, no. 2, pp. 56–64, 2020.
B. Hidayah, R. V, Budhiyati, and P. Hendikawati, “Aplikasi diagonalisasi matriks pada rantai Markov ( Application of matrix diagonalization on Markov chain ),” Sain Dasar, vol. 3, no. 1, pp. 20–24, 2014.
I. Fikri, Admi Salma, Dodi Vionanda, and Zilrahmi, “Comparison of Fuzzy Time Series Markov Chain and Fuzzy Time Series Cheng to Predict Inflation in Indonesia,” UNP J. Stat. Data Sci., vol. 1, no. 4, pp. 306–312, 2023, doi: 10.24036/ujsds/vol1-iss4/76.
S. Hariyanto, Y. D. Sumanto, S. Khabibah, and Zaenurrohman, “Average-Based Fuzzy Time Series Markov Chain Based on Frequency Density Partitioning,” J. Appl. Math., vol. 2023, 2023, doi: 10.1155/2023/9319883.
T. Indayani and M. Y. Darsyah, “Pemilihan Model Peramalan Terbaik Menggunakan Model Arima dan Winters Untuk Meramalkan Indeks LQ45,” Pros. Semin. Nas. Mhs. Unimus, vol. 1, pp. 336–342, 2018.
A. Heryana and U. E. Unggul, “UJI MCNEMAR DAN UJI WILCOXON ( Uji Hipotesa Non-Parametrik Dua Sampel,” no. May, 2020, doi: 10.13140/RG.2.2.17682.48325.
Copyright (c) 2024 Laura Sari, Annisa Romadloni, Rostika Listyaningrum, Fadhilla Hazrina
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
