scholarly journals Perbaikan Missing value Menggunakan Pendekatan Korelasi Pada Metode K-Nearest Neighbor

2017 ◽  
Vol 9 (3) ◽  
Author(s):  
Novta Dany'el Irawan ◽  
Wijono Wijono ◽  
Onny Setyawati
Keyword(s):  
Social Science ◽  
Natural Science ◽  
Nearest Neighbor ◽  
Classification Method ◽  
K Nearest Neighbor ◽  
Missing Value ◽  
K Value ◽  
Data Value ◽  
Science Major ◽  
Measuring Tool

Missing value often occur in classification method that is caused by information on the object is not given, it is difficult to find, or because of the information is unavailable. It will cause the decrement of accuracy and data quality during it is analyzed. Correlation approach was conducted because it should be known the existence and the strength of variable correlation in related to an object or subject studied. Classification method used is K-NN method. It is because this method is included in classification method that has strong consistency by finding the case through calculation on the closeness between the case with the old one based on K value or the nearest neighbor. Correlation approach can be done to overcome missing value, as evidenced by the increasing classification results and the loss of unclassified data. Questionnaire as a measuring tool, the questionnaire contains some questions given to the respondent, from the results of questionnaires conducted data analysis to determine the level of correlation of data backup. After getting the level of backup data correlation, then the backup data is used as a substitute for missing data value. Before the replacement of data there is missing value classification of 500 data classified natural science major 88 students, social science major 126 students, the language major 271 students, and unclassified / false 15 students. After the replacement of data there is missing value from 500 data, it can be classified into natural science major 102 students, social science major 316 students, the language major 82 students, and no unclassified data. Based on the experimental results, the value of k = 3, 5, 7, 9, and 11. It can be seen that k = 5 has a high accuracy of 97.0%, so in this study majors using K-NN method set k value used is 5.

scholarly journals Mangrove Forest Classification in Drone Images Using HSV Color Moment and Haralick Features Extraction with K-Nearest Neighbor

2019 ◽  
Vol 1 (3) ◽  
pp. 1-12
Author(s):  
Agus Wahyu Widodo ◽  
Deo Hernando ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Mangrove Forest ◽  
Nearest Neighbor ◽  
Classification Method ◽  
Mangrove Forests ◽  
K Nearest Neighbor ◽  
Distance Method ◽  
K Value ◽  
Nearest Neighbor Classification ◽  
Haralick Features ◽  
Neighbor Classification

Due to the problems with uncontrolled changes in mangrove forests, a forest function management and supervision is required. The form of mangrove forest management carried out in this study is to measure the area of mangrove forests by observing the forests using drones or crewless aircraft. Drones are used to take photos because they can capture vast mangrove forests with high resolution. The drone was flown over above the mangrove forest and took several photos. The method used in this study is extracting color features using mean values, standard deviations, and skewness in the HSV color space and texture feature extraction with Haralick features. The classification method used is the k-nearest neighbor method. This study conducted three tests, namely testing the accuracy of the system, testing the distance method used in the k-nearest neighbor classification method, and testing the k value. Based on the results of the three tests above, three conclusions obtained. The first conclusion is that the classification system produces an accuracy of 84%. The second conclusion is that the distance method used in the k-nearest neighbor classification method influences the accuracy of the system. The distance method that produces the highest accuracy is the Euclidean distance method with an accuracy of 84%. The third conclusion is that the k value used in the k-nearest neighbor classification method influences the accuracy of the system. The k-value that produces the highest accuracy is k = 3, with an accuracy of 84%.

scholarly journals Optimization of Missing Value Data Imputation Automatic Dependent Surveillance Broadcasting (ADS-B) Based on K-Nearest Neighbor and Genetic Algorithm

2020 ◽  
Vol 9 (12) ◽  
pp. 327-331
Author(s):  
Didik Hariyanto ◽  
Sholeh Hadi Pramono ◽  
Erni Yudaningtyas
Keyword(s):  
Genetic Algorithm ◽  
Monte Carlo ◽  
Technology Use ◽  
Missing Values ◽  
Nearest Neighbor ◽  
Data Imputation ◽  
Flow Data ◽  
K Nearest Neighbor ◽  
Missing Value ◽  
K Value

The flight navigation equipments technology use still conventional, namely using radar, now slowly starting to switch to Automatic Dependent Surveillance-Broadcast (ADS-B [6]. In this study, using RTL-SDR to detect aircraft and carry out tests through the Monte Carlo alltitude method, latitude, and longitude only [3]. However, in this system there is a problem regarding the missing value in the preprocessed data results / ADS-B flow data. In handling missing values, the KNN method is the most popular, but the weakness in the KNN method, can reduce the performance[9]. So a Genetic Algorithm (GA) is proposed to optimize the k value in the KNN method. The results of this study obtained a better MSE value in the imputation process. Altitude k = 3, with MSE 128668.96, Speed k = 6, with the MSE value = 457.5201, while the k value in the Heading variable k = 61 with MSE = 752.1429. For Lattitude and Longitude, the value of k = 3, MSE 9.16E-05 and k = 2 and MSE 1.68E-05.

scholarly journals Design and Analysis System of KNN and ID3 Algorithm for Music Classification based on Mood Feature Extraction

2017 ◽  
Vol 7 (1) ◽  
pp. 486
Author(s):  
Made Sudarma ◽  
I Gede Harsemadi
Keyword(s):  
Feature Extraction ◽  
Processing Time ◽  
Nearest Neighbor ◽  
Extraction Process ◽  
Performance Comparison ◽  
K Nearest Neighbor ◽  
K Value ◽  
Id3 Algorithm ◽  
Analysis System ◽  
Music Information

Each of music which has been created, has its own mood which is emitted, therefore, there has been many researches in Music Information Retrieval (MIR) field that has been done for recognition of mood to music.  This research produced software to classify music to the mood by using K-Nearest Neighbor and ID3 algorithm.  In this research accuracy performance comparison and measurement of average classification time is carried out which is obtained based on the value produced from music feature extraction process.  For music feature extraction process it uses 9 types of spectral analysis, consists of 400 practicing data and 400 testing data.  The system produced outcome as classification label of mood type those are contentment, exuberance, depression and anxious.  Classification by using algorithm of KNN is good enough that is 86.55% at k value = 3 and average processing time is 0.01021.  Whereas by using ID3 it results accuracy of 59.33% and average of processing time is 0.05091 second.

scholarly journals IMPLEMENTASI ALGORITMA K-NEAREST NEIGHBOR UNTUK PENENTUAN KELULUSAN MAHASISWA TEPAT WAKTU

2020 ◽  
Vol 2 (1) ◽  
pp. 1-14
Author(s):  
Torkis Nasution
Keyword(s):  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Academic Quality ◽  
K Value ◽  
Entrance Exam ◽  
Maximum Accuracy ◽  
Data Clusters ◽  
New Students ◽  
Data Group

The selection was an attempt College to get qualified prospective students. Test data for new students able to describe the quality of academic and connect to graduate on time. Recognizing the academic quality of students is required in the implementation of the lecture to obtain optimal results. Real conditions today, timely graduation has not achieved optimally, need to be improved to reach the limits of reasonableness. Data that has no need to do a classification based on academic quality, in order to obtain predictions timely graduation. Therefore, proposed an effort to resolve the problem by applying the K-Nearest Neighbor algorithm to re-clustering the test result data for new students. The procedure is to determine the amount of data clusters, determining the center point of the cluster, calculate the distance of the object with the centroid, classifying objects. If the new data group calculation results together with the results of calculation of new data group then finished its calculations. The data will be used in clustering is the result of the entrance exam for new students 3 years old, and has been declared STMIK Amik Riau. This study aims to predict the graduation on time or not. Results of research on testing the value of k, maximum accuracy is obtained when k = 5, reaching 99.25%. Accuracy will decline if the k value the greater the more inaccurate results. The data will be used in clustering is the result of the entrance exam for new students 3 years old, and has been declared STMIK Amik Riau. This study aims to predict the graduation on time or not. Results of research on testing the value of k, maximum accuracy is obtained when k = 5, reaching 99.25%. Accuracy will decline if the k value the greater the more inaccurate results. The data will be used in clustering is the result of the entrance exam for new students 3 years old, and has been declared STMIK Amik Riau. This study aims to predict the graduation on time or not. Results of research on testing the value of k, maximum accuracy is obtained when k = 5, reaching 99.25%. Accuracy will decline if the k value the greater the more inaccurate results.  

scholarly journals Tone Classification Matches Kodàly Handsign with the K-Nearest Neighbor Method at Leap Motion Controller

2020 ◽  
Vol 5 (2) ◽  
pp. 40
Author(s):  
Muhammad Croassacipto ◽  
Muhammad Ichwan ◽  
Dina Budhi Utami
Keyword(s):  
Music Education ◽  
Nearest Neighbor ◽  
Human Interaction ◽  
Training Data ◽  
Leap Motion ◽  
K Nearest Neighbor ◽  
Motion Controller ◽  
K Value ◽  
Leap Motion Controller ◽  
Natural Function

<p>Hands can produce a variety of poses in which each pose can have a meaning or purpose that can be used as a form of communication determined according to a general agreement or who communicate. Hand pose can be used as human interaction with the computer is faster, intuitive, and in line with the natural function of the human body called Handsign. One of them is Kodàly Handsign, made by a Hungarian composer named Zoltán Kodály, which is a concept in music education in Hungary. This hand sign is used in interactive angklung performances in determining the tone that will be played by the K-Nearest Neighbor (KNN) algorithm classification process based on hand poses. This classification process is performed on the extracted data from Leap Motion Controller, which takes Pitch, Roll, and Yaw values based on basic aircraft principle. The results of the research were conducted five times with the value of k periodically 1,3,5,7,9 with test data consisting pose of 874 Do', 702 Si, 913 La, 612 Sol, 661 Fa, 526 Mi, 891 Re, and 1004 Do punctuation on 21099 training data. The test results can recognize hand poses with the optimal k value k=1 with an accuracy level of 94.87%.</p>

scholarly journals Application of Data Mining Classification Method for Student Graduation Prediction Using K-Nearest Neighbor (K-NN) Algorithm

2018 ◽  
Vol 1 (1) ◽  
pp. 1-8
Author(s):  
Mohammad Imron ◽  
Satia Angga Kusumah
Keyword(s):  
Data Mining ◽  
Graduation Rate ◽  
Nearest Neighbor ◽  
High Accuracy ◽  
Classification Method ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Study Program ◽  
Mining Technique ◽  
Student Graduation

The student graduation rate is one of the indicators to improve the accreditation of a course. It is needed to monitor and evaluate student graduation tendencies, timely or not. One of them is to predict the graduation rate by utilizing the data mining technique. Data Mining Classification method used is the algorithm K-Nearest Neighbor (K-NN). The data used comes from student data, student value data, and student graduation data for the year 2010-2012 with a total of 2,189 records. The attributes used are gender, school of origin, IP study program Semester 1-6. The results showed that the K-NN method produced a high accuracy of 89.04%.

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