Kombinasi Logika Fuzzy dan Jaringan Syaraf Tiruan untuk Prakiraan Curah Hujan Timeseries di Area Puspo – Jawa Timur
DOI:
https://doi.org/10.25126/jtiik.201743299Abstrak
Abstrak
Prakiraan curah hujan merupakan salah satu tanggung jawab penting yang dilakukan oleh layanan meteorologi di seluruh dunia. Permasalahan utama dalam hal analisis dan prakiraan adalah tingkat kesalahan yang semakin meningkat dari waktu ke waktu. Hal ini dapat terjadi karena kondisi ketidakpastian juga meningkat seiring dengan perubahan musim dan iklim. Penelitian ini mencoba mengombinasikan dua metode yaitu Logika Fuzzy untuk menghadapi kondisi-kondisi yang tidak pasti dan Jaringan Syaraf Tiruan multi-layer untuk menghadapi kondisi dengan ketidakpastian yang terus meningkat. Penelitian ini juga menggunakan algoritma Particle Swarm Optimization untuk menentukan kebutuhan secara otomatis. Kebutuhan yang perlu ditentukan secara otomatis adalah bobot-bobot awal dalam Jaringan Syaraf Tiruan multi-layer sebelum akhirnya melakukan proses pelatihan algoritma. Penelitian ini menggunakan studi kasus di empat area Jawa Timur yaitu Puspo, Tutur, Tosari, dan Sumber untuk memprakirakan curah hujan di area Puspo. Data yang digunakan merupakan curah hujan timeseries yang dicatat selama 10 tahun oleh Badan Meteorologi Klimatologi dan Geofisika (BMKG). Hasil penelitian ini menunjukkan bahwa kombinasi dari Logika Fuzzy dengan Jaringan Syaraf Tiruan multi-layer mampu memberikan tingkat RMSE sebesar 2.399 dibandingkan dengan hanya menggunakan regresi linear dengan tingkat RMSE sebesar 7.211.
Kata kunci: fuzzy, hujan, hybrid, jaringan syaraf, optimasi, timeseries
Abstract
Rainfall forecasting is one of the important responsibilities that carried out by meteorological services in the worldwide. The main problem in terms of analysis and forecasting is the error rate is almost increasing from time to time. This caused by the uncertainty conditions are also increasing with the change of seasons and climate. This study tried to combine two methods of Fuzzy Logic for the problem solved of uncertain conditions and multi-layer Artificial Neural Network for the problem solved of the uncertainty that continues to increase. Particle Swarm Optimization algorithm also is used to determine the requirement automatically. The requirement that needs to be determined automatically is initial weights in multi-layer Artificial Neural Networks before the process of algorithm training. This study uses a case study in four areas of East Java that are Puspo, Tutur, Tosari, and Sumber. The data are a time series of rainfall rate that recorded in the 10 years by Badan Meteorologi Klimatologi dan Geofisika (BMKG). The results of this study indicate that the combination of Fuzzy Logic with Multi-Layer Neural Networks is capable of providing an RMSE level of 2,399 compared to only using linear regression with an RMSE level of 7,211.
Keywords: fuzzy, hybrid, neural networks, optimization, rainfall, time series
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