Abstract
Classification is the process of grouping data based on observed variables to predict new data whose class is unknown. There are some classification methods, such as Naïve Bayes, K-Nearest Neighbor and Neural Network. Naïve Bayes classifies based on the probability value of the existing properties. K-Nearest Neighbor classifies based on the character of its nearest neighbor, where the number of neighbors=k, while Neural Network classifies based on human neural networks. This study will compare three classification methods for Seat Load Factor, which is the percentage of aircraft load, and also a measure in determining the profit of airline.. Affecting factors are the number of passengers, ticket prices, flight routes, and flight times. Based on the analysis with 47 data, it is known that the system of Naïve Bayes method has misclassifies in 14 data, so the accuracy rate is 70%. The system of K-Nearest Neighbor method with k=5 has misclassifies in 5 data, so the accuracy rate is 89%, and the Neural Network system has misclassifies in 10 data with accuracy rate 78%. The method with highest accuracy rate is the best method that will be used, which in this case is K-Nearest Neighbor method with success of classification system is 42 data, including 14 low, 10 medium, and 18 high value. Based on the best method, predictions can be made using new data, for example the new data consists of Bali flight routes (2), flight times in afternoon (2), estimate of passenger numbers is 140 people, and ticket prices is Rp.700,000. By using the K-Nearest Neighbor method, Seat Load Factor prediction is high or at intervals of 80% -100%.
Highlights
Classification is the process of grouping data based on observed variables to predict new data
Naïve Bayes classifies based on the probability value of the existing properties
K-Nearest Neighbor classifies based on the character of its nearest neighbor
Summary
Salah satu alat transportasi yang menjadi andalan masyarakat karena dapat beroperasi secara efektif dari segi waktu adalah pesawat terbang. Penurunan jumlah penumpang juga akan mengurangi Seat Load Factor atau SLF yang mana inilah yang akhirnya berimbas pada pendapatan maskapai yang akhirnya anjlok hingga 60%. Sehingga dari uraian tersebut peneliti akan membandingkan beberapa metode klasifikasi seperti K- Nearest Neighbor, Naïve Bayes, dan Artificial Neural Network terhadap nilai Seat Load Factor atau SLF [1]. Beberapa penelitian sebelumnya yang berkaitan dengan analisis dan studi kasus antara lain, Amin [3] menganalisis pengaruh tarif penerbangan, jumlah penerbangan, dan pendapatan perkapita dalam meningkatkan jumlah penumpang. Selain itu Susanto [12] juga melakukan penelitian dengan membandingkan metode Algoritma Neural Network, K-Nearest Neighbor, dan Naïve Bayes untuk memprediksi pendonor darah potensial. Tujuan utama adalah mengetahui metode terbaik yang digunakan dalam klasifikasi SLF yang kemudian akan digunakan untuk melakukan prediksi. Dari tujuan penelitian tersebut hasil yang diperoleh dapat dimanfaatkan sebagai bahan pertimbangan dalam mengambil kebijakan untuk meningkatkan penjualan tiket guna memaksimumkan revenue perusahaan
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