Recognition of Traffic Weight Using Sobel Edge Detection Method and K-Nearest Neighbor Algorithm

10.17158/220 ◽  
2012 ◽  
Vol 18 (1) ◽  
Author(s):  
Eric John G. Emberda ◽  
Lovie Mae N. Dalagan ◽  
Christy Faith O. Baguio
Keyword(s):  
Edge Detection ◽  
Nearest Neighbor ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Software Application ◽  
K Nearest Neighbor Algorithm ◽  
Sobel Edge Detection ◽  
Edge Detection Method ◽  
The Given

This study explored the use of Sobel Edge Detection and K-Nearest Neighbor algorithm in classifying the traffic weight of a given captured image. A software application was created that accepts as input, a snapshot of a given intersection. The application could determine the traffic weight of the given snapshot, as whether it is light, moderate, or heavy by comparing it to a database of images using the K-Nearest Neighbor algorithm. The accuracy of the result was highly dependent on the training data and the quality of the snapshot. Overall, the use of Sobel Edge Detection and K-Nearest Neighbor algorithm gave significant results in recognizing the weight of a given snapshot of traffic.

scholarly journals Business Intelligence using the K-Nearest Neighbor Algorithm to Analyze Customer Behavior in Online Crowdfunding Systems

2020 ◽  
Vol 202 ◽  
pp. 16005
Author(s):  
Chashif Syadzali ◽  
Suryono Suryono ◽  
Jatmiko Endro Suseno
Keyword(s):  
Business Intelligence ◽  
Nearest Neighbor ◽  
Customer Behavior ◽  
Training Data ◽  
Business Strategies ◽  
Intelligence Analysis ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm

Customer behavior classification can be useful to assist companies in conducting business intelligence analysis. Data mining techniques can classify customer behavior using the K-Nearest Neighbor algorithm based on the customer's life cycle consisting of prospect, responder, active and former. Data used to classify include age, gender, number of donations, donation retention and number of user visits. The calculation results from 2,114 data in the classification of each customer’s category are namely active by 1.18%, prospect by 8.99%, responder by 4.26% and former by 85.57%. System accuracy using a range of K from K = 1 to K = 20 produces that the highest accuracy is 94.3731% at a value of K = 4. The results of the training data that produce a classification of user behavior can be used as a Business Intelligence analysis that is useful for companies in determining business strategies by knowing the target of optimal market.

scholarly journals Analysis K-Nearest Neighbor Algorithm for Improving Prediction Student Graduation Time

SinkrOn ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 42
Author(s):  
Rizki Muliono ◽  
Juanda Hakim Lubis ◽  
Nurul Khairina
Keyword(s):  
Higher Education ◽  
Nearest Neighbor ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Study Program ◽  
Sample Data ◽  
Student Graduation ◽  
K Nearest Neighbor Algorithm

Higher education plays a major role in improving the quality of education in Indonesia. The BAN-PT institution established by the government has a standard of higher education accreditation and study program accreditation. With the 4.0-based accreditation instrument, it encourages university leaders to improve the quality and quality of their education. One indicator that determines the accreditation of study programs is the timely graduation of students. This study uses the K-Nearest Neighbor algorithm to predict student graduation times. Students' GPA at the time of the seventh semester will be used as training data, and data of students who graduate are used as sample data. K-Nearest Neighbor works in accordance with the given sample data. The results of prediction testing on 60 data for students of 2015-2016, obtained the highest level of accuracy of 98.5% can be achieved when k = 3. Prediction results depend on the pattern of data entered, the more samples and training data used, the calculation of the K-Nearest Neighbor algorithm is also more accurate.

scholarly journals IMPLEMENTASI K-NEAREST NEIGHBORD PADA RAPIDMINER UNTUK PREDIKSI KELULUSAN MAHASISWA

2018 ◽  
Vol 10 (1) ◽  
pp. 35-41
Author(s):  
Sumarlin Sumarlin ◽  
Dewi Anggraini
Keyword(s):  
Cross Validation ◽  
Nearest Neighbor ◽  
Confusion Matrix ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Process Data ◽  
Nearest Neighbor Algorithm ◽  
Student Graduation ◽  
K Nearest Neighbor Algorithm ◽  
Auc Value

Data on graduate students is an important part in determining the quality of a private and public university. Graduate data is included in important assessments in the accreditation process. Data from Uyelindo Kupang STIKOM graduates every year will continue to grow and accumulate like neglected data because it is rarely used. To maximize student data into information that can be used by universities, the data must be processed in this case used as training data in a study using data mining to obtain information in the form of predictions of graduation from Kupang Uyelindo STIKOM students. The method used in this study is K-Nearest Neighbor using rapidminer software to measure K-Nearest Neighbor's accuracy against student graduate data. The criteria used were in the form of student names, gender, cumulative achievement index (GPA) from semester 1 to 6. In applying the K-Nearest Neighbor algorithm can be used to produce predictions of student graduation. To measure the performance of the k-nearest neighbor algorithm, the Cross Validation, Confusion Matrix and ROC Curves methods are used, in this study using a 5-fold cross validation to predict student graduation. From 100 student dataset records Uyelindo Kupang STIKOM graduates obtained accuracy rate reached 82% and included a very good classification because it has an AUC value between 0.90-1.00, which is 0.971, so it can be concluded that the accuracy of testing of student graduation models using K-Nearest Neighbor (K-NN) algorithm is influenced by the number of data clusters. Accuracy and the highest AUC value of 5-fold validation is to cluster data k = 4 with the accuracy value of 90%.

scholarly journals Performance comparison between naive bayes and k- nearest neighbor algorithm for the classification of Indonesian language articles

2021 ◽  
Vol 10 (2) ◽  
pp. 452
Author(s):  
Titin Winarti ◽  
Henny Indriyawati ◽  
Vensy Vydia ◽  
Febrian Wahyu Christanto
Keyword(s):  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Performance Comparison ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm ◽  
Bayes Algorithm

<span id="docs-internal-guid-210930a7-7fff-b7fb-428b-3176d3549972"><span>The match between the contents of the article and the article theme is the main factor whether or not an article is accepted. Many people are still confused to determine the theme of the article appropriate to the article they have. For that reason, we need a document classification algorithm that can group the articles automatically and accurately. Many classification algorithms can be used. The algorithm used in this study is naive bayes and the k-nearest neighbor algorithm is used as the baseline. The naive bayes algorithm was chosen because it can produce maximum accuracy with little training data. While the k-nearest neighbor algorithm was chosen because the algorithm is robust against data noise. The performance of the two algorithms will be compared, so it can be seen which algorithm is better in classifying documents. The comes about obtained show that the naive bayes algorithm has way better execution with an accuracy rate of 88%, while the k-nearest neighbor algorithm has a fairly low accuracy rate of 60%.</span></span>

scholarly journals Optimization of k value and lag parameter of k-nearest neighbor algorithm on the prediction of hotel occupancy rates

2020 ◽  
Vol 8 (3) ◽  
pp. 246-254
Author(s):  
Agus Subhan Akbar ◽  
R. Hadapiningradja Kusumodestoni
Keyword(s):  
Nearest Neighbor ◽  
Business Management ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Value ◽  
Sample Data ◽  
K Nearest Neighbor Algorithm ◽  
Occupancy Rates ◽  
Fold Cross Validation

Hotel occupancy rates are the most important factor in hotel business management. Prediction of the rates for the next few months determines the manager's decision to arrange and provide all the needed facilities. This study performs the optimization of lag parameters and k values of the k-Nearest Neighbor algorithm on hotel occupancy history data. Historical data were arranged in the form of supervised training data, with the number of columns per row according to the lag parameter and the number of prediction targets. The kNN algorithm was applied using 10-fold cross-validation and k-value variations from 1-30. The optimal lag was obtained at intervals of 14-17 and the optimal k at intervals of 5-13 to predict occupancy rates of 1, 3, 6, 9, and 12 months later. The obtained k-value does not follow the rule at the square root of the number of sample data.

Implementation of K-Nearest Neighbor Algorithm in Heart Disease Classification

2021 ◽  
Vol 10 (1) ◽  
pp. 39
Author(s):  
Ni Kadek Sukma Putri Rahayu ◽  
I Komang Ari Mogi
Keyword(s):  
Heart Disease ◽  
Nearest Neighbor ◽  
The Body ◽  
Disease Classification ◽  
Training Data ◽  
Main Function ◽  
Performance Measurements ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm

The heart is an important organ that exists in the human body. The main function of the heart is to pump blood throughout the body through blood vessels. The WHO states that as many as 7.3 million people die from heart disease. In this study heart disease will be classified using the K-Nearest Neighbor algorithm. K-Nearest Neighbor algorithm is a classification algorithm based on the distance from data testing against training data with a pre-defined number of k. The results were obtained from performance measurements for the classification of heart disease with the K-Nearest Neighbor algorithm measured using the K-Fold Cross Validation algorithm, from an accuracy rate of 65.89%, a precision level of 66.27%, and a recall of 74.67%.

Handwritten Balinesse Character Recognition using K-Nearest Neighbor

2018 ◽  
Author(s):  
I Wayan Agus Surya Darma
Keyword(s):  
Feature Extraction ◽  
Success Rate ◽  
Character Recognition ◽  
Nearest Neighbor ◽  
Recognition System ◽  
Extraction Process ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm ◽  
Character Feature

Balinese character recognition is a technique to recognize feature or pattern of Balinese character. Feature of Balinese character is generated through feature extraction process. This research using handwritten Balinese character. Feature extraction is a process to obtain the feature of character. In this research, feature extraction process generated semantic and direction feature of handwritten Balinese character. Recognition is using K-Nearest Neighbor algorithm to recognize 81 handwritten Balinese character. The feature of Balinese character images tester are compared with reference features. Result of the recognition system with K=3 and reference=10 is achieved a success rate of 97,53%.

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