Flight anomaly detection is used to determine the abnormal state data on the flight route. This study focused on two groups: general aviation habits (C1) and anomalies (C2). Groups C1 and C2 are obtained through similarity test with references. The methods used are: 1) normalizing the training data form, 2) forming the training segment 3) calculating the log-likelihood value and determining the maximum log-likelihood (C1) and minimum log-likelihood (C2) values, 4) determining the percentage of data based on criteria C1 and C2 by grouping SVM, KNN, and K-means and 5) Testing with log-likelihood ratio. The results achieved in each segment are Log-likelihood value in C1Latitude is-15.97 and C1Longitude is-16.97. On the other hand, Log-likelihood value in C2Latitude is-19.3 (maximum) and-20.3 (minimum), and log-likelihood value in C2Longitude is-21.2 (maximum) and-24.8 (minimum). The largest percentage value in C1 is 96%, while the largest in C2 is 10%. Thus, the highest potential anomaly data is 10%, and the smallest is 3%. Also, there are performance tests based on F-measure to get accuracy and precision.

Original languageEnglish
Pages (from-to)285-296
Number of pages12
JournalInternational Journal of Advances in Intelligent Informatics
Issue number3
Publication statusPublished - Nov 2019


  • Accuracy
  • Anomaly detection
  • Grouping similarity
  • Log-likelihood ratio
  • Segment


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