scholarly journals DETERMINING LECTURAL EVALUATION IN FACULTY OF ENGINEERING MALIKUSSALEH UNIVERSITY USING K-NN

2019 ◽  
Vol 11 (2) ◽  
pp. 307
Author(s):  
Asrianda Asrianda ◽  
Risawandi Risawandi ◽  
Gunarwan Gunarwan
Keyword(s):  
Nearest Neighbor ◽  
Predictor Variable ◽  
Training Data ◽  
Manhattan Distance ◽  
Similar Data ◽  
K Nearest Neighbor ◽  
Classification Result ◽  
Distance Method ◽  
Testing Data ◽  
Supervised Learning Algorithms

K-Nearest Neighbor is a method that can classify data based on the closest distance. In addition, K-NN is one of the supervised learning algorithms with learning processes based on the value of the target variable associated with the value of the predictor variable. In the K-NN algorithm, all data must have a label, so that when a new data is given, the data will be compared with the existing data, then the most similar data is taken by looking at the label of that data. Filling and processing many questionnaires to determining the results of lectural evaluation from the performance of lecturers certainly requires a lot of time and process. Therefore, it is necessary to apply the K-NN Manhattan Distance method. In this study, the testing data is taken from one of the training data and has a classification result that is "Very Good". After going through the K-NN Manhattan Distance method with k being the closest / smallest neighbor, then the following results are obtained: Distance 5.4, the classification result is "Very Good" and 74.03% of similarity value. Based on the results obtained, the result of the classification from K-NN Manhattan Distance method show similarities with the results of the pre-existing classification.

scholarly journals Helmet Monitoring System using Hough Circle and HOG based on KNN

2021 ◽  
Vol 12 (1) ◽  
pp. 13
Author(s):  
Rachmad Jibril Al Kautsar ◽  
Fitri Utaminingrum ◽  
Agung Setia Budi
Keyword(s):  
Police Officers ◽  
Nearest Neighbor ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Histogram Of Oriented Gradient ◽  
Surveillance Camera ◽  
Testing Data ◽  
Motorized Vehicles ◽  
Traffic Operation

 Indonesian citizens who use motorized vehicles are increasing every year. Every motorcyclist in Indonesia must wear a helmet when riding a motorcycle. Even though there are rules that require motorbike riders to wear helmets, there are still many motorists who disobey the rules. To overcome this, police officers have carried out various operations (such as traffic operation, warning, etc.). This is not effective because of the number of police officers available, and the probability of police officers make a mistake when detecting violations that might be caused due to fatigue. This study asks the system to detect motorcyclists who do not wear helmets through a surveillance camera. Referring to this reason, the Circular Hough Transform (CHT), Histogram of Oriented Gradient (HOG), and K-Nearest Neighbor (KNN) are used. Testing was done by using images taken from surveillance cameras divided into 200 training data and 40 testing data obtained an accuracy rate of 82.5%.

scholarly journals PREDIKSI KELULUSAN MAHASISWA MAGISTER TEKNIK INFORMATIKA UNIVERSITAS AMIKOM YOGYAKARTA MENGGUNAKAN METODE K-NEAREST NEIGHBOR

Respati ◽  
2018 ◽  
Vol 13 (2) ◽  
Author(s):  
Eri Sasmita Susanto ◽  
Kusrini Kusrini ◽  
Hanif Al Fatta
Keyword(s):  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Process Data ◽  
K Nearest Neighbors ◽  
Testing Data ◽  
Estimation Scheme ◽  
Student Graduation ◽  
Feasibility Test

INTISARIPenelitian ini difokuskan untuk mengetahui uji kelayakan prediksi kelulusan mahasiswa Universitas AMIKOM Yogyakarta. Dalam hal ini penulis memilih algoritma K-Nearest Neighbors (K-NN) karena K-Nearest Neighbors (K-NN) merupakan algoritma  yang bisa digunakan untuk mengolah data yang bersifat numerik dan tidak membutuhkan skema estimasi parameter perulangan yang rumit, ini berarti bisa diaplikasikan untuk dataset berukuran besar.Input dari sistem ini adalah Data sampel berupa data mahasiswa tahun 2014-2015. pengujian pada penelitian ini menggunakn dua pengujian yaitu data testing dan data training. Kriteria yang digunakan dalam penelitian ini adalah , IP Semester 1-4, capaian SKS, Status Kelulusan. Output dari sistem ini berupa hasil prediksi kelulusan mahasiswa yang terbagi menjadi dua yaitu tepat waktu dan kelulusan tidak tepat waktu.Hasil pengujian menunjukkan bahwa Berdasarkan penerapan k=14 dan k-fold=5 menghasilkan performa yang terbaik dalam memprediksi kelulusan mahasiswa dengan metode K-Nearest Neighbor menggunakan indeks prestasi 4 semester dengan nilai akurasi= 98,46%, precision= 99.53% dan recall =97.64%.Kata kunci: Algoritma K-Nearest Neighbors, Prediksi Kelulusan, Data Testing, Data Training ABSTRACTThis research is focused on knowing the feasibility test of students' graduation prediction of AMIKOM University Yogyakarta. In this case the authors chose the K-Nearest Neighbors (K-NN) algorithm because K-Nearest Neighbors (K-NN) is an algorithm that can be used to process data that is numerical and does not require complicated repetitive parameter estimation scheme, this means it can be applied for large datasets.The input of this system is the sample data in the form of student data from 2014-2015. test in this research use two test that is data testing and training data. The criteria used in this study are, IP Semester 1-4, achievement of SKS, Graduation Status. The output of this system in the form of predicted results of student graduation which is divided into two that is timely and graduation is not timely.The result of the test shows that based on the application of k = 14 and k-fold = 5, the best performance in predicting the students' graduation using K-Nearest Neighbor method uses 4 semester achievement index with accuracy value = 98,46%, precision = 99.53% and recall = 97.64%.Keywords: K-Nearest Neighbors Algorithm, Graduation Prediction, Testing Data, Training Data

scholarly journals Classification of Bird Based on Face Types Using Gray Level Co-Occurrence Matrix (GLCM) Feature Extraction Based on the k-Nearest Neighbor (K-NN) Algorithm

2021 ◽  
Vol 6 (2) ◽  
pp. 111-119
Author(s):  
Daurat Sinaga ◽  
Feri Agustina ◽  
Noor Ageng Setiyanto ◽  
Suprayogi Suprayogi ◽  
Cahaya Jatmoko
Keyword(s):  
Feature Extraction ◽  
Nearest Neighbor ◽  
Correlation Energy ◽  
Training Data ◽  
Gray Level ◽  
K Nearest Neighbor ◽  
Testing Data ◽  
Occurrence Matrix

Indonesia is one of the countries with a large number of fauna wealth. Various types of fauna that exist are scattered throughout Indonesia. One type of fauna that is owned is a type of bird animal. Birds are often bred as pets because of their characteristic facial voice and body features. In this study, using the Gray Level Co-Occurrence Matrix (GLCM) based on the k-Nearest Neighbor (K-NN) algorithm. The data used in this study were 66 images which were divided into two, namely 55 training data and 11 testing data. The calculation of the feature value used in this study is based on the value of the GLCM feature extraction such as: contrast, correlation, energy, homogeneity and entropy which will later be calculated using the k-Nearest Neighbor (K-NN) algorithm and Eucliden Distance. From the results of the classification process using k-Nearest Neighbor (K-NN), it is found that the highest accuracy results lie at the value of K = 1 and at an degree of 0 ° of 54.54%.

scholarly journals Comparison Analysis of K-Nearest Neighbor and Naïve Bayes in Determining Talent of Adolescence

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Yessi Jusman ◽  
Widdya Rahmalina ◽  
Juni Zarman
Keyword(s):  
Nearest Neighbor ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Combined Training ◽  
Testing Data ◽  
Bayes Algorithm ◽  
Children's Interests

Adolescence always searches for the identity to shape the personality character. This paper aims to use the artificial intelligent analysis to determine the talent of the adolescence. This study uses a sample of children aged 10-18 years with testing data consisting of 100 respondents. The algorithm used for analysis is the K-Nearest Neigbor and Naive Bayes algorithm. The analysis results are performance of accuracy results of both algorithms of classification. In knowing the accurate algorithm in determining children's interests and talents, it can be seen from the accuracy of the data with the confusion matrix using the RapidMiner software for training data, testing data, and combined training and testing data. This study concludes that the K-Nearest Neighbor algorithm is better than Naive Bayes in terms of classification accuracy.

scholarly journals Analysis of Braycurtis, Canberra and Euclidean Distance in KNN Algorithm

SinkrOn ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 74 ◽  
Author(s):  
Annisa Fadhillah Pulungan ◽  
Muhammad Zarlis ◽  
Saib Suwilo
Keyword(s):  
Euclidean Distance ◽  
Evaluation Method ◽  
Nearest Neighbor ◽  
Distance Matrix ◽  
Training Data ◽  
Distance Functions ◽  
Classification Model ◽  
K Nearest Neighbor ◽  
Distance Method ◽  
Canberra Distance

Classification is a technique used to build a classification model from a sample of training data. One of the most popular classification techniques is The K-Nearest Neighbor (KNN). The KNN algorithm has important parameter that affect the performance of the KNN Algorithm. The parameter is the value of the K and distance matrix. The distance between two points is determined by the calculation of the distance matrix before classification process by the KNN. The purpose of this study was to analyze and compare performance of the KNN using the distance function. The distance functions are Braycurtis Distance, Canberra Distance and Euclidean Distance based on an accuracy perspective. This study uses the Iris Dataset from the UCI Machine Learning Repository. The evaluation method used id 10-Fold Cross-Validation. The result showed that the Braycurtis distance method had better performance that Canberra Distance and Euclidean Distance methods at K=6, K=7, K=8 ad K=10 with accuracy values of 96 %.

Traffic Flow Prediction using SUMO Application with K-Nearest Neighbor (KNN) Method

2020 ◽  
Vol 12 (7) ◽  
Author(s):  
Fikri Aditya ◽  
◽  
Surya Michrandi Nasution ◽  
Agus Virgono ◽  
◽  
...  
Keyword(s):  
Traffic Flow ◽  
Public Transportation ◽  
Nearest Neighbor ◽  
Training Data ◽  
Routine Activity ◽  
Urban Mobility ◽  
K Nearest Neighbor ◽  
Traffic Flow Prediction ◽  
Testing Data ◽  
Returning To Work

In Indonesia, the density of traffic flow occurs at the time of leaving and returning to work, long holidays or national holidays such as the end of the year (New Year). This annual routine activity is mostly carried out especially in big cities in Indonesia such as Bandung. Because Bandung is a city that has a lot of tourism, Bandung is therefore always the center of visitors to enjoy weekends or long holidays. So from this problem, we want to create a traffic prediction application that can help to solve congestion problems that have become an annual routine. The several types of vehicles used in the prediction are private cars, motorcycles, taxis, public transportation, large buses, mini buses, and mini trucks. Research conducted using the K-Nearest Neighbor method is a prediction of short-term traffic flow on Jl. Riau Bandung. The input used in making predictions is historical data on the number of vehicles going on Jl. Riau Bandung. The output generated from the use of the K-Nearest Neighbor method is the level of the jam class that runs on Jl. Riau Bandung in 2018 used a simulation on the SUMO (Simulation of Urban Mobility) application. The resulting performance of KNN with k = 3 has an accuracy of 99.21%, k = 5 has an accuracy of 99.60%, and k = 7 has an accuracy rate of 99.21% on 90% training data and 10% testing data.

scholarly journals Comparison of PCA and 2DPCA Accuracy with K-Nearest Neighbor Classification in Face Image Recognition

2019 ◽  
Vol 6 (1) ◽  
pp. 64-72
Author(s):  
Sri Sutarti ◽  
Anggyi Trisnawan Putra ◽  
Endang Sugiharti
Keyword(s):  
Principal Component Analysis ◽  
Covariance Matrix ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Component Analysis ◽  
Face Image ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Testing Data ◽  
Pca Method

Face recognition is a special pattern recognition for faces that compare input image with data in database. The image has a variety and has large dimensions, so that dimension reduction is needed, one of them is Principal Component Analysis (PCA) method. Dimensional transformation on image causes vector space dimension of image become large. At present, a feature extraction technique called Two-Dimensional Principal Component Analysis (2DPCA) is proposed to overcome weakness of PCA. Classification process in 2DPCA using K-Nearest Neighbor (KNN) method by counting euclidean distance. In PCA method, face matrix is changed into one-dimensional matrix to get covariance matrix. While in 2DPCA, covariance matrix is directly obtained from face image matrix. In this research, we conducted 4 trials with different amount of training data and testing data, where data is taken from AT&T database. In 4 time testing, accuracy of 2DPCA+KNN method is higher than PCA+KNN method. Highest accuracy of 2DPCA+KNN method was obtained in 4th test with 96.88%. while the highest accuracy of PCA+KNN method was obtained in 4th test with 89.38%. More images used as training data compared to testing data, then the accuracy value tends to be greater.

scholarly journals PREDIKSI KELANCARAN PEMBAYARAN CICILAN CALON DEBITUR DENGAN METODE K-NEAREST NEIGHBOR

JURTEKSI ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 195-202
Author(s):  
Sri Ayu Rizky ◽  
Rolly Yesputra ◽  
Santoso Santoso
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Training Data ◽  
Mining Method ◽  
K Nearest Neighbor ◽  
Application System ◽  
K Value ◽  
Testing Data ◽  
Calculation Process ◽  
K Nearest Neighbor Algorithm

Abstract: In this research, a prediction system has been successfully developed to predict whether or not a prospective money borrower will run smoothly. Prospective borrowers who will borrow, some of the data that meet the criteria will be inputted by the office clerk into a prediction application system interface to be processed using the Data Mining method, namely the K-Nearest Neighbor Algorithm with the Codeigniter programming language 3. The results of the Euclidean calculation process are based on predetermined criteria Between training data (training) to testing data (test) will be displayed with a table that has been sorted from smallest to largest containing 9 closest neighbors according to the K value that has been determined, namely 9. The nine neighbors will be taken the dominant category. This dominant category can be used as a guideline that makes it easier for the leader to make a decision on the next borrower.            Keywords: Data Mining; Euclidean; K-Nearest Neighbor; Prospective Borrowers;  Abstrak: Dalam penelitian ini telah berhasil dibuat sebuah sistem prediksi untuk memprediksi lancar atau tidak lancarnya seorang calon peminjam uang. Calon peminjam uang yang akan meminjam, sebagian datanya yang memenuhi kriteria akan diinputkan petugas kantor ke dalam sebuah interface sistem aplikasi prediksi untuk diolah menggunakan metode Data Mining yaitu Algoritma K-Nearest Neighbor dengan bahasa pemrograman Codeigniter 3. Hasil proses perhitungan Euclidean berdasarkan kriteria yang sudah ditentukan antara data training (latih) ke data testing (uji) tersebut akan ditampilkan dengan sebuah tabel yang sudah diurutkan dari yang terkecil ke terbesar berisi 9 tetangga terdekat sesuai dengan nilai K yang sudah ditentukan yaitu 9.  Sembilan tetangga tersebut akan diambil kategori yang dominan. Kategori yang dominan tersebut bisa dijadikan suatu pedoman yang memudahkan pimpinan dalam mengambil sebuah keputusan kepada calon peminjam selanjutnya. Kata kunci: Debitur; Data Mining; Euclidean; K-Nearest Neighbor

scholarly journals Pengenalan Karakter Tulisan Tangan Dengan K-Support Vector Nearest Neighbor

2019 ◽  
Vol 9 (1) ◽  
pp. 33
Author(s):  
Aditya Surya Wijaya ◽  
Nurul Chamidah ◽  
Mayanda Mega Santoni
Keyword(s):  
Character Recognition ◽  
Nearest Neighbor ◽  
Recognition Accuracy ◽  
Training Data ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Zone Method ◽  
Handwritten Character ◽  
Testing Data ◽  
Handwritten Recognition

Handwritten characters are difficult to be recognized by machine because people had various own writing style. This research recognizes handwritten character pattern of numbers and alphabet using K-Nearest Neighbour (KNN) algorithm. Handwritten recognition process is worked by preprocessing handwritten image, segmentation to obtain separate single characters, feature extraction, and classification. Features extraction is done by utilizing Zone method that will be used for classification by splitting this features data to training data and testing data. Training data from extracted features reduced by K-Support Vector Nearest Neighbor (K-SVNN) and for recognizing handwritten pattern from testing data, we used K-Nearest Neighbor (KNN). Testing result shows that reducing training data using K-SVNN able to improve handwritten character recognition accuracy.

scholarly journals Implementasi Algoritma K-Nearest Neighbor Untuk Memprediksi Program Studi Bagi Calon Mahasiswa Baru

2021 ◽  
Vol 4 (2) ◽  
pp. 131-141
Author(s):  
Ratna Rahmawati Rahayu ◽  
◽  
Lidiawati Lidiawati ◽  
Keyword(s):  
Measurement Accuracy ◽  
Nearest Neighbor ◽  
High Accuracy ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Study Program ◽  
Testing Data ◽  
Study Programs ◽  
New Students ◽  
K Nearest Neighbor Algorithm

One of the factors for students graduating on time with good grades is that the study program they take is in accordance with their interests and competencies. For this reason, in the process of admitting new students, it is necessary to carry out selection, information and direction regarding the chosen study program. By using previous year's student data, data mining processing is carried out to produce classifications of study programs for prospective new students. To get maximum results, preprocessing data is carried out, after which the data is divided into training data and testing data. The two data are then processed with the K-Nearest Neighbor algorithm to determine the suitability of the Study Program class in the testing data and then the measurement accuracy value is calculated. Because it has a high accuracy value of 74%, using this training data it is developed in the form of an application with Java NetBeans which can be used to assist prospective new students in predicting the appropriate study program

Sign in / Sign up

Export Citation Format

Share Document