Integrasi Metode Decision Tree dan SMOTE untuk Klasifikasi Data Kecelakaan Lalu Lintas
DOI:
https://doi.org/10.26418/justin.v8i3.40982Keywords:
Data Mining, Classification, Decision Tree, SMOTE, Traffic AccidentAbstract
Kecelakaan lalu lintas merupakan suatu peristiwa yang tidak dapat diprediksi dengan pasti dan dapat mengakibatkan korban jiwa, korban luka ringan, korban luka berat atau kerugian materil seperti benda berharga. Permasalahan ini terjadi di seluruh dunia, tidak terkecuali Australia Selatan yang merupakan salah satu wilayah di Australia. Tercatat bahwa wilayah tersebut memiliki total kecelakaan yang memakan korban 4.953 pada tahun 2018. Oleh karena itu, dibutuhkan analisis untuk mengantisipasi kecelakaan agar tidak terulang kembali kejadian dengan faktor yang sama. Salah satu solusi untuk permasalahan ini yaitu diperlukan metode klasifikasi untuk mengelompokkan faktor-faktor yang mempengaruhi kecelakaan lalu lintas. Metode klasifikasi yang digunakan sebagai pengolah data adalah metode Decision Tree. Metode pada permasalahan ketidakseimbangan kelas menggunakan metode Synthetic Minority Over-sampling Technique (SMOTE). Untuk proses dalam meningkatkan evaluasi pada penelitian ini menggunakan proses Knowledge Discovery in Database (KDD). Pengujian dilakukan dengan tiga desain model yaitu Split Validation Decision Tree dan SMOTE diperoleh akurasi 69.23%. Pengujian menggunakan Cross Validation Decision Tree dan SMOTE diperoleh akurasi 63.56%. Pengujian menggunakan Decision Tree dan SMOTE Split Data diperoleh akurasi 71.12% dengan perbandingan 1:9. Sehingga, setelah ketiga desain model tersebut dibandingkan, maka Decision Tree dan SMOTE Split Data mendapatkan akurasi yang paling baik. Selain itu diperoleh pula presisi 89.71% (3:7) dan area under curve (AUC) sebesar 0.773 (1:9). Penelitian ini masuk kedalam kategori fair classification (cukup).
Traffic accidents are events that cannot be predicted with certainty and can result in casualties, minor injuries, serious injuries, or material losses such as valuable objects. This problem occurs throughout the world, including South Australia which is one of the regions in Australia. It is recorded that the area had a total of 4,953 casualties in 2018. Therefore an analysis is needed to anticipate the accident so that it does not happen again with the same factors. One solution to this problem is the classification method needed to classify the factors that affect traffic accidents. The classification method used for data processing is the Decision Tree method. The method for class imbalance problems uses the method of Synthetic Minority Over-sampling Technique (SMOTE). For the process of increasing evaluation in this study using the Knowledge Discovery in Database (KDD) process. The test was carried out with three model designs namely Split Validation Decision Tree and SMOTE model design obtained an accuracy of 69.23%. Testing using Cross Validation Decision Tree and SMOTE obtained an accuracy of 63.56%. Testing using the Decision Tree and SMOTE Split Data obtained an accuracy 71.12% with ratio of 1:9. So, after the three design models are compared, the split Decision Tree and SMOTE Split Data gets the best accuracy. Also, a precision of 89.71% (3:7) and area under curve (AUC) were obtained of 0.773 (1:9). This research belongs to the fair classification category.
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