Sistem Klasifikasi Cuaca Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS) Berdasarkan Data Weather Monitoring

Pebrianti, Ayu Nurhafiza (2024) Sistem Klasifikasi Cuaca Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS) Berdasarkan Data Weather Monitoring. Diploma thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Salah satu perusahaan yang bergerak di bidang penyaluran Bahan Bakar Minyak (BBM) melalui jalur laut, khususnya melalui fasilitas Single Point Mooring (SPM). Fasilitas tersebut merupakan sebuah titik tambat di lepas pantai yang berfungsi untuk memudahkan distribusi minyak dari kapal tanker. Salah satu tantangan utama dalam distribusi BBM melalui SPM adalah kondisi cuaca yang dapat mempengaruhi keamanan dan efisiensi proses distribusi. Untuk mengatasi hal ini, perusahaan tersebut telah menggembangkan alat pemantau cuaca real-time yang disebut Weather Monitoring (WeMon), yang mengukur berbagai parameter cuaca seperti kecepatan angin, arah angin, suhu, curah hujan, tinggi gelombang laut. Saat ini, data yang dikumpulkan dari WeMon belum dikelompokkan berdasarkan jenis cuaca, sehingga perlu dikembangkan sistem klasifikasi cuaca yang lebih terstruktur. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi cuaca menggunakan metode Adaptive Neuro Fuzzy Inference System (ANFIS), yang merupakan kombinasi dari jaringan saraf tiruan dan logika fuzzy, untuk mengatasi data yang tidak pasti dan kompleks. Hasil penelitian menunjukkan bahwa ANFIS dapat mengklasifikasikan data cuaca dengan efektif. Dari hasil training, epoch ke-150 terbukti menjadi jumlah epoch yang optimal dengan nilai akurasi sebesar 17% dan nilai korelasi sebesar 0,37. Meskipun akurasi tertinggi tercapai pada epoch ke-200 dengan nilai akurasi 26%, penurunan korelasi menjadi 0,31 menunjukkan adanya overfitting, sehingga epoch ke-150 dipilih untuk menjaga kemampuan generalisasi model.
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One of the companies operating in the field of fuel oil distribution via sea routes, specifically through the Single Point Mooring (SPM). That facility is an offshore mooring point that facilitates oil distribution from tanker ships. One of the main challenges in BBM distribution through SPM is the weather conditions that can affect the safety and efficiency of the distribution process. To address this, the company has developed a real-time weather monitoring tool called Weather Monitoring (WeMon), which measures various weather parameters such as wind speed, wind direction, temperature, rainfall, and wave height. Currently, the data collected from WeMon has not been classified based on weather types, necessitating the development of a more structured weather classification system. This research aims to develop a weather classification system using the Adaptive Neuro Fuzzy Inference System (ANFIS) method, which is a combination of artificial neural networks and fuzzy logic, to handle uncertain and complex data. The research results show that ANFIS can effectively classify weather data. From the training results, the 150th epoch proved to be the optimal number of epochs with an accuracy value of 17% and a correlation value of 0.37. Although the highest accuracy was achieved at the 200th epoch with an accuracy value of 26%, the correlation decreased to 0.31, indicating overfitting. Therefore, the 150th epoch was chosen to maintain the model's generalization capability.

Item Type:	Thesis (Diploma)
Uncontrolled Keywords:	Adaptive Neuro Fuzzy Inference System (ANFIS), Klasifikasi Cuaca, Single Point Mooring (SPM), Weather Classification. Weather Monitoring (WeMon)
Subjects:	T Technology > T Technology (General) > T57.5 Data Processing T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7878 Electronic instruments
Divisions:	Faculty of Vocational > 36304-Automation Electronic Engineering
Depositing User:	AYU NURHAFIZA PEBRIANTI
Date Deposited:	19 Aug 2024 02:45
Last Modified:	19 Aug 2024 02:45
URI:	http://repository.its.ac.id/id/eprint/115453

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