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.
Knowledge and utilization of digital images are growing rapidly not only in the fields of medicine and industry but also in the field of agriculture. This knowledge can apply it to a computer-based program that is used to detect agricultural products more effectively and efficiently. this research aims to build a system to detect the types of pests and diseases of cocoa pods because in general, an inspection of pests and diseases of cocoa pods is still manual based on the visual analysis of the color of the pods visually by the human eye which has limitations, which requires more energy to sort, the level of human consistency. In terms of assessing the symptoms of pests and fruit diseases, it is not guaranteed, because humans can experience fatigue, and humans also assess symptoms of pests and fruit diseases, sometimes it is subjective. This study utilizes digital image processing techniques to extract the color features of digital images of cocoa pods, the method used to extract the color features of Hue, Saturation, Value (HSV), and the classification algorithm used by K-Nearest Neighbor. The data used as many as 150 images divided into 70% training data and 30% testing data. Based on the results of trials using k values of 5,7,11 and 13 in the holdout method, the best accuracy is 84.44% with a value of k = 5. And in the k-5 cross-validation test, the best accuracy is also found at k = 5 with a value accuracy of 99.33%.
Anomaly detection of flight route can be analyzed with the availability of flight data set. Automatic Dependent Surveillance (ADS-B) is the data set used. The parameters used are timestamp, latitude, longitude, and speed. The purpose of the research is to determine the optimum area for anomaly detection through real time approach. The methods used are: a) clustering and cluster validity analysis; and b) False Identification Rate (FIR). The results archieved are four steps, i.e: a) Build segments based on waypoints; b) Partition area based on 3-Dimension features P<sub>1</sub> and P<sub>2</sub>; c) grouping; and d) Measurement of cluster validity. The optimum partition is generated by calculating the minimum percentage of FIR. The results achieved are: i) there are five partitions, i.e: (n/2, n/3, n/4, n/5) and ii) optimal partition of each 3D, that is: for P<sub>1</sub> was five partitions and the P<sub>2</sub> feature was four partitions
Persalinan merupakan proses mengeluarkan janin setelah kehamilan 20 minggu atau lebih untuk dapat hidup di luar kandungan melalui jalan lahir atau jalan lain, dengan atau tanpa bantuan. Angka Kematian Ibu di Indonesia masih cukup tinggi berdasarkan Buku Putih Reformasi Sistem Kesehatan Nasional pada Maret 2022, sebesar 305 per 100.000 kelahiran. Penyebab banyaknya Angka Kematian Ibu ialah proses persalinan berisiko bagi ibu dan janin. Prediksi klinis berkembang dengan mengadopsi ilmu komputer dan teknologi informasi dalam pengolahan datanya, disertai dengan metode data mining untuk teknik pengolahannya. Permasalahan ibu hamil dapat diantisipasi dengan menggunakan sistem prediksi status proses persalinan dengan implementasi data mining dan algoritma Naïve Bayes, dengan tujuan untuk membantu penurunan Angka Kematian Ibu, terutama diakibatkan proses persalinan berisiko. Penelitian ini menggunakan 600 data latih, lalu diuji menggunakan metode Confusion Matrix pada 100 data uji. Diperoleh nilai Precision sebesar 82.4%, nilai Recall sebesar 94%, nilai F-Measure sebesar 88.7%, nilai Accuracy sebesar 92%.
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