Prakiraan Tinggi Gelombang Air Laut Menggunakan Data Mining

Luky Agus Hermanto

Abstract


Melakukan prakiraan cuaca memerlukan banyak komponen data cuaca, record dalam jumlah yang besar, serta kemampuan pelaku prakiraan. Keadaan ini mengakibatkan keakuratan dan kecepatan prakiraan menjadi kurang terpenuhi ketika kesimpulan diambil. Untuk mengatasi masalah tersebut, dilakukan penelitian pemodelan prediksi menggunakan teknik yang ada dalam konsep penambangan data, association rule, klasifikasi, serta Random Forest. Penelitian ini menggunakan data dari stasiun pengamatan maritim Cilacap mulai Agustus 2012 sampai dengan Agustus 2016. Data tersebut terdiri atas tanggal, waktu, kecepatan angin, arah angin, arah arus, kecepatan arus, arah gelombang, dan kecepatan gelombang. Data pengujian adalah sebagian data yang diambil secara acak dari keseluruhan data yang digunakan. Dari pengujian model, didapatkan bahwa Association Rule menghasilkan akurasi 79%, sedangkan Classification Tree menghasilkan akurasi 88%.


Keywords


Penambangan data; Association rule; Classification tree; Random Forest; Gelombang laut

References


B. Santosa. Data mining: Teknik Pemanfaatan Data untuk Keperluan Bisnis, Edisi Pertama. Yogyakarta: Graha Ilmu, 2007.

S. Nandagopal, S. Karthik, and V.P. Arunachala. "Mining of Meteorological Data Using Modified Apriori Algorithm," European Journal of Scientific Research, vol. 47, no. 2, pp. 295-308, 2010.

A. Mcgovern, et al. "Understanding Severe Weather Processes through Spatiotemporal Relational Random Forest," Proceedings of Conference on Intelligent Data Understanding, pp. 213-227.

J. K. Williams, D. A. Ahijevych, C. J. Kessinger, T. R. Saxen, M. Steiner, and S. Dettling. "A Machine Learning Approach to Finding Weather Regimes and Skillful Predictor Combinations for Short-term Storm Forecasting," 2008. Available: http://nldr.library.ucar.edu/repository/collections/OSGC-000-000-003-270.

X. Li, et al. "Real-Time Storm Detection and Weather Forecast Activation through Data Mining and Events Processing," Journal of Earth Science Informatics, vol. 1, no. 2, pp. 49-57, 2008.

C. B. C. Latha, S. Paul, E. Kirubakaran, and Sathianarayanan. "A Service Oriented Architecture for Weather Forecasting Using Data Mining," International Journal of Advanced Networking and Applications, vol. 2, no. 2, pp. 608-613, 2010.

G. C. Onwubolu, et al. "Self-Organizing Data Mining for Weather Forecasting," Proceedings of IADIS European Conference Data Mining, pp. 81-88, 2007.

C.T. Dhanya and N. Kumar. "Data Mining for Evolving Fuzzy Association Rules for Predicting Monsoon Rainfall of India," Journal of Intelligent System, vol. 18, no. 3, pp. 193-209, 2009.

S. N. Kohail and A. M. El-Halees. "Implementation of Data Mining Techniques for Meteorological Data Analysis (A case study for Gaza Strip)," International Journal of Informatics and Communication Technology Research, vol. 1, no. 3, pp. 96-100, 2011.

L. Ingsrisawang, S. Ingsrisawang, S. Somchit, P. Aungsuratana, and W. Khantiyanan. "Machine Learning Techniques for Short-Time Rain Forecasting System in the Northeastern Part of Thailand," International Journal of World Academy of Science, Engineering and Technology, pp. 248-253, 2008.

M. Hahsler, "A Probabilistic Comparison of Commonly Used Interest Measures for Association Rules," Feb. 9, 2018. [Online]. Available: http://michael.hahsler.net/research/association_rules/measures.html

Iqbal. "Penerapan Data mining di Badan Meteorologi dan Geofisika untuk Memprediksi Cuaca di Jakarta," MTI Thesis, Fakultas Ilmu Komputer, Univeristas Indonesia, 2007.

Orange Data Mining. Education in Data Science. [Online]. Available: https://orange.biolab.si/features/interactive-data-visualization.

Turban, et al. Decision Support and Business Intelligence Systems, 2009.




DOI: https://doi.org/10.31284/j.iptek.2018.v22i1.232

Refbacks

  • There are currently no refbacks.


Copyright (c) 2018 Jurnal IPTEK

 

Indexed by:

Sinta S3 Google Scholar GARUDA Garba Rujukan Digital