Analisis Peramalan Curah Hujan Menggunakan Metode Extreme Learning Machine (Studi Kasus: Stasiun Klimatologi Jawa Timur)
Kata Kunci:
analisis peramalan, curah hujan, jaringan syaraf tiruan, ELM, MSEAbstrak
Penelitian ini fokus pada analisis peramalan curah hujan di Indonesia, sebuah negara tropis yang terdapat curah hujan tinggi sehingga menyebabkan banjir dan tanah longsor. Stasiun Klimatologi Jawa Timur, terletak di Kabupaten Malang, merupakan pusat informasi cuaca dan sering terkena dampak banjir. Studi ini mengidentifikasi pola data historis secara sistematis untuk peramalan yang akurat. Metode peramalan yang digunakan adalah Jaringan Saraf Tiruan, khususnya Extreme Learning Machine (ELM). ELM dianggap efektif dengan tingkat kesalahan rendah dan kecepatan pelatihan yang tinggi. Penelitian ini membandingkan kinerja ELM dibandingkan metode tradisional seperti KNN dan SVM, menunjukkan superioritas ELM dalam kecepatan dan kinerja komputasi. Sebuah studi kasus menggunakan judul "Analisis Peramalan Curah Hujan Menggunakan Metode Extreme Learning Machine (Studi Kasus: Stasiun Klimatologi Jawa Timur)" menyoroti penerapan ELM dalam konteks ini. Temuan penelitian ini dapat mendukung upaya pencegahan banjir melalui peramalan yang lebih akurat. Kinerja peramalan curah hujan dengan metode ELM memperoleh nilai Mean Squared Error (MSE) 0,021 rasio parameter data pelatihan dan data pengujian sebesar 50% - 50% dan jumlah hidden neuron sebanyak 10 neuron.
Referensi
Aggarwal, C. C., 2018. Neural Networks and Deep Learning A Textbook. New York: Springer.
Ahmad, I., Basheri, M., Iqbal, M. J. & Rahim, A., 2018. Performance Comparison of Support Vector Machine, Random Forest, and Extreme Learning Machine for Intrusion Detection. SPECIAL SECTION ON SURVIVABILITY STRATEGIES FOR EMERGING WIRELESS NETWORKS, Volume 6, pp. 33789 - 33795.
Chy, T. S. & Rahaman, M. A., 2019. A Comparative Analysis by KNN, SVM & ELM Classification to Detect Sickle Cell Anemia. Dhaka, 2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST).
Henderi, Wahyuningsih, T. & Rahwanto, E., 2021. Comparison of Min-Max normalization and Z-Score Normalization in the K-nearest neighbor (KNN) Algorithm to Test the Accuracy of Types of Breast Cancer. International Journal of Informatics and Information System, 4(1), pp. 13-20.
Herawati, S. & Latif, M., 2020. Forecasting tourist visits using data decomposition technique and learning optimization of artificial neural network. Surabaya, Journal of Physics: Conference Series.
Huang, G.-B., Zhu, Q.-Y. & Siew, C.-K., 2006. Extreme learning machine: Theory and applications. Neurocomputing, 70(1-3), pp. 489-501.
Hyndman, R. J. & Athanasopoulos, G., 2018. Forecasting: Principles and Practice. 2nd ed. Melbourne: Otexts.
Kayabaşı, A., Yıldız, B. & Aslan, M. F., 2018. Comparison of ELM and ANN on EMG Signals Obtained for Control of Robotic-Hand. Iasi, ECAI 2018 - International Conference – 10th Edition.
Khair, U., Fahmi, H., Hakim, S. A. & Rahim, R., 2017. Forecasting Error Calculation with Mean Absolute Deviation and Mean Absolute Percentage Error. Melaka, International Conference on Information and Communication Technology (IconICT).
Panigrahi, S., Karali, Y. & Behera, H. S., 2013. Normalize Time Series and Forecast using Evolutionary Neural Network. International Journal of Engineering Research & Technology (IJERT), 2(9).
Petrusevich, D. A., 2021. Review of missing values procession methods in time series data. Journal of Physics: Conference Series, 1889(3), p. 032009.
Sharma, S., Sharma, S. & Athaiya, A., 2020. ACTIVATION FUNCTIONS IN NEURAL NETWORKS. International Journal of Engineering Applied Sciences and Technology, 4(12), pp. 310-316.
Sidiq, M., 2018. Forecasting Rainfall with Time Series Model. Bandung, IOP Conference Series.
Simamora, R. J. D., Tibyani & Sutrisno, 2019. Peramalan Curah Hujan Menggunakan Metode Extreme Learning Machine. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 3(10), pp. 9670-9676.
Sudarsono, A., 2016. JARINGAN SYARAF TIRUAN UNTUK MEMPREDIKSI LAJU PERTUMBUHAN PENDUDUK MENGGUNAKAN METODE BACPROPAGATION (STUDI KASUS DI KOTA BENGKULU). Jurnal Media Infotama, 12(1).
Wahyuni, N. P. M. S., Sumarjaya, I. W. & Srinadi, I. G. A. M., 2016. PERAMALAN CURAH HUJAN MENGGUNAKAN METODE ANALISIS SPEKTRAL. E-Jurnal Matematika, 5(4), pp. 183-193.
Wang, J., Lu, S., Wang, S.-H. & Zhang, Y.-D., 2021. A review on extreme learning machine. Multimedia-based Healthcare Systems using Computational Intelligence, Volume 81, pp. 41611-41660.
Widiarti, Pertiwi, R. R. & Sutrisno, A., 2017. Perbandingan Mean Squared Error (MSE) Metode Prasad-Rao dan Jiang-Lahiri-Wan Pada Pendugaan Area Kecil. Jakarta, Seminar Nasional Teknoka.
Zainuddin, A. Z., Mansor, W., Lee, K. Y. & Mahmoodin, Z., 2019. Comparison of Extreme Learning Machine and K-Nearest Neighbour Performance in Classifying EEG Signal of Normal, Poor and Capable Dyslexic Children. Berlin, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
Zhao, J. H., Dong, Z. & Xu, Z., 2006. Effective Feature Preprocessing for Time Series Forecasting. Xi'an, International Conference on Advanced Data Mining and Applications.
Aggarwal, C. C., 2018. Neural Networks and Deep Learning A Textbook. New York: Springer.
Ahmad, I., Basheri, M., Iqbal, M. J. & Rahim, A., 2018. Performance Comparison of Support Vector Machine, Random Forest, and Extreme Learning Machine for Intrusion Detection. SPECIAL SECTION ON SURVIVABILITY STRATEGIES FOR EMERGING WIRELESS NETWORKS, Volume 6, pp. 33789 - 33795.
Chy, T. S. & Rahaman, M. A., 2019. A Comparative Analysis by KNN, SVM & ELM Classification to Detect Sickle Cell Anemia. Dhaka, 2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST).
Henderi, Wahyuningsih, T. & Rahwanto, E., 2021. Comparison of Min-Max normalization and Z-Score Normalization in the K-nearest neighbor (KNN) Algorithm to Test the Accuracy of Types of Breast Cancer. International Journal of Informatics and Information System, 4(1), pp. 13-20.
Herawati, S. & Latif, M., 2020. Forecasting tourist visits using data decomposition technique and learning optimization of artificial neural network. Surabaya, Journal of Physics: Conference Series.
Huang, G.-B., Zhu, Q.-Y. & Siew, C.-K., 2006. Extreme learning machine: Theory and applications. Neurocomputing, 70(1-3), pp. 489-501.
Hyndman, R. J. & Athanasopoulos, G., 2018. Forecasting: Principles and Practice. 2nd ed. Melbourne: Otexts.
Kayabaşı, A., Yıldız, B. & Aslan, M. F., 2018. Comparison of ELM and ANN on EMG Signals Obtained for Control of Robotic-Hand. Iasi, ECAI 2018 - International Conference – 10th Edition.
Khair, U., Fahmi, H., Hakim, S. A. & Rahim, R., 2017. Forecasting Error Calculation with Mean Absolute Deviation and Mean Absolute Percentage Error. Melaka, International Conference on Information and Communication Technology (IconICT).
Panigrahi, S., Karali, Y. & Behera, H. S., 2013. Normalize Time Series and Forecast using Evolutionary Neural Network. International Journal of Engineering Research & Technology (IJERT), 2(9).
Petrusevich, D. A., 2021. Review of missing values procession methods in time series data. Journal of Physics: Conference Series, 1889(3), p. 032009.
Sharma, S., Sharma, S. & Athaiya, A., 2020. ACTIVATION FUNCTIONS IN NEURAL NETWORKS. International Journal of Engineering Applied Sciences and Technology, 4(12), pp. 310-316.
Sidiq, M., 2018. Forecasting Rainfall with Time Series Model. Bandung, IOP Conference Series.
Simamora, R. J. D., Tibyani & Sutrisno, 2019. Peramalan Curah Hujan Menggunakan Metode Extreme Learning Machine. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 3(10), pp. 9670-9676.
Sudarsono, A., 2016. JARINGAN SYARAF TIRUAN UNTUK MEMPREDIKSI LAJU PERTUMBUHAN PENDUDUK MENGGUNAKAN METODE BACPROPAGATION (STUDI KASUS DI KOTA BENGKULU). Jurnal Media Infotama, 12(1).
Wahyuni, N. P. M. S., Sumarjaya, I. W. & Srinadi, I. G. A. M., 2016. PERAMALAN CURAH HUJAN MENGGUNAKAN METODE ANALISIS SPEKTRAL. E-Jurnal Matematika, 5(4), pp. 183-193.
Wang, J., Lu, S., Wang, S.-H. & Zhang, Y.-D., 2021. A review on extreme learning machine. Multimedia-based Healthcare Systems using Computational Intelligence, Volume 81, pp. 41611-41660.
Widiarti, Pertiwi, R. R. & Sutrisno, A., 2017. Perbandingan Mean Squared Error (MSE) Metode Prasad-Rao dan Jiang-Lahiri-Wan Pada Pendugaan Area Kecil. Jakarta, Seminar Nasional Teknoka.
Zainuddin, A. Z., Mansor, W., Lee, K. Y. & Mahmoodin, Z., 2019. Comparison of Extreme Learning Machine and K-Nearest Neighbour Performance in Classifying EEG Signal of Normal, Poor and Capable Dyslexic Children. Berlin, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
Zhao, J. H., Dong, Z. & Xu, Z., 2006. Effective Feature Preprocessing for Time Series Forecasting. Xi'an, International Conference on Advanced Data Mining and Applications.
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