Prediksi Visibility Pada Penerbangan Menggunakan Metode Gate Recurrent Unit Berbasis Seleksi Fitur Cross Correlation Function
DOI:
https://doi.org/10.21111/fij.v10i2.14685Abstract
Abstrak Visibility yang rendah menyebabkan terganggunya penerbangan terutama pada keterlambatan atau pembatalan jadwal penerbangan. Keadaan tersebut sulit diprediksi karena kondisinya yang relatif berubah-ubah tiap jamnya. Salah satu cara untuk mengetahui rendahnya visibility adalah dengan melihat jarak pandang aman di udara > 5-kilometer sesuai aturan Dinas Perhubungan. Oleh karena itu, tujuan dari penelitian ini adalah membangun model prediksi visibility untuk mengantisipasi rendahnya visibility dengan menggunakan metode deep learning yaitu Gate Recurrent Unit (GRU). Variabel independen atau fitur yang digunakan dalam penelitian ini adalah suhu titik embun, suhu bola kering, kelembapan, arah dan kecepatan angin, serta curah hujan mulai awal hingga akhir tahun 2023. Hasil Cross Correlation Function (CCF) menunjukkan korelasi fitur yang memiliki nilai tinggi digunakan untuk masuk model pelatihan adalah kecepatan angin, suhu bola kering, dan kelembapan. Beberapa uji coba parameter dilakukan terbukti bahwa model memiliki performa yang baik dalam memprediksi visibility. Hasil model yang memiliki tingkat kesalahan prediksi menggunakan MAPE sebesar 12.75%. Kata kunci: Prediksi, Visibility, Cross Correlation Function, Gate Recurrent Unit Abstract [Visibility Prediction in Flight Using Gate Recurrent Unit Method Based on Cross Correlation Function Feature Selection] Low visibility disrupts flights, especially delays or cancellations of flight schedules. This situation is difficult to predict because the conditions change relatively every hour. One way to find out the low visibility is to see the safe visibility in the air > 5-kilometer according to the rules of the Department of Transportation. Therefore, the purpose of this research is to build a visibility prediction model to anticipate low visibility using the deep learning Gate Recurrent Unit (GRU) method. The independent variables or features used in this study are dew point temperature, dry bulb temperature, humidity, wind direction and speed, and rainfall from the beginning to the end of 2023. The Cross Correlation Function (CCF) results show that the feature correlations that have high values used to enter the training model are wind speed, dry bulb temperature, and humidity. Several parameter tests were conducted to prove that the model has good performance in predicting visibility. Model results that have a prediction error rate using MAPE of 12.75%. Keywords: Prediction, Visibility, Cross-Correlation Function, Gate Recurrent UnitDownloads
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Copyright (c) 2025 Rahmat Wahyu Widodo, Nurissaidah Ulinnuha, Dian Candra Rini Novitasari
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