scholarly journals Penerapan Algoritma K-Nearest Neighbor untuk Prediksi Kelulusan Siswa pada SMK Anak Bangsa

2019 ◽  
Vol 1 ◽  
pp. 670
Author(s):  
Sekar Rizkya Rani ◽  
Sundari Retno Andani ◽  
Dedi Suhendro
Keyword(s):  
Nearest Neighbor ◽  
Vocational School ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Value Of Knowledge ◽  
Student Graduation ◽  
Education Achievement ◽  
Value Of Accreditation

Student graduation is very important for world education achievement, student graduation also influences the value of accreditation of an educational unit itself, therefor research on graduation prediction becomes a very interesting thing study, this study proposes the use of the K-Nearest Neighbor method to do predict student graduation at Anak Bangsa Private Vocational School. The result of the research is the value of k=5 with an accuracy rate of 93.55% which is determined as K-Optimal. The value of k=5 is applied to the K-NN algoritma to predict student graduation based on attendance, attitude, and value of knowledge.

Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

2015 ◽  
Vol 1 (4) ◽  
pp. 270
Author(s):  
Muhammad Syukri Mustafa ◽  
I. Wayan Simpen
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Sample Data ◽  
New Students ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

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%.

scholarly journals COMPARING NAIVE BAYES, K-NEAREST NEIGHBOR, AND NEURAL NETWORK CLASSIFICATION METHODS OF SEAT LOAD FACTOR IN LOMBOK OUTBOUND FLIGHTS

2019 ◽  
Vol 16 (2) ◽  
pp. 187
Author(s):  
Mega Luna Suliztia ◽  
Achmad Fauzan
Keyword(s):  
Neural Network ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Load Factor ◽  
Classification Methods ◽  
Affecting Factors ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Ticket Prices

Classification is the process of grouping data based on observed variables to predict new data whose class is unknown. There are some classification methods, such as Naïve Bayes, K-Nearest Neighbor and Neural Network. Naïve Bayes classifies based on the probability value of the existing properties. K-Nearest Neighbor classifies based on the character of its nearest neighbor, where the number of neighbors=k, while Neural Network classifies based on human neural networks. This study will compare three classification methods for Seat Load Factor, which is the percentage of aircraft load, and also a measure in determining the profit of airline.. Affecting factors are the number of passengers, ticket prices, flight routes, and flight times. Based on the analysis with 47 data, it is known that the system of Naïve Bayes method has misclassifies in 14 data, so the accuracy rate is 70%. The system of K-Nearest Neighbor method with k=5 has misclassifies in 5 data, so the accuracy rate is 89%, and the Neural Network system has misclassifies in 10 data with accuracy rate 78%. The method with highest accuracy rate is the best method that will be used, which in this case is K-Nearest Neighbor method with success of classification system is 42 data, including 14 low, 10 medium, and 18 high value. Based on the best method, predictions can be made using new data, for example the new data consists of Bali flight routes (2), flight times in afternoon (2), estimate of passenger numbers is 140 people, and ticket prices is Rp.700,000. By using the K-Nearest Neighbor method, Seat Load Factor prediction is high or at intervals of 80% -100%.

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 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 NEWS TOPIC CLASSIFICATION ON TRIBUNNEWS ONLINE MEDIA USING K-NEAREST NEIGHBOR ALGORITHM

2018 ◽  
Vol 1 (2) ◽  
pp. 38
Author(s):  
Nfn Herman
Keyword(s):  
Nearest Neighbor ◽  
Online Media ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm ◽  
Manual Classification

Online media journalists like tribunnews journalists usually determine the news category when make news input. Unfortunately, often the topic submitted is not in accordance with what is expected by the editor. These errors will make it difficult for news searches by customers. To eliminate these errors, editors can be assisted by an application that able to classify topics. Thus, editors is no longer too dependent on journalist input. This study aims to design applications that able to classify topics based on the texts contained in the news. The method used is the K-Nearest Neighboor algorithm. This design has produced a system that able to classify news topics automatically. To measure the accuracy of the application, several test were carried out by comparing between its results and the results of manual classification by the editor. The tests those carried out with several scenarios produce an accuracy rate of 82%

Deteksi Tingkat Kesegaran Daging Ayam Menggunakan K-Nearest Neighbor

CCIT Journal ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 177-185
Author(s):  
Irfan Purwanto ◽  
M. Afriansyah ◽  
Kusrini Kusrini
Keyword(s):  
Nearest Neighbor ◽  
Chicken Meat ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
High Demand ◽  
Fresh Meat ◽  
The Public ◽  
Limited Availability ◽  
Color Sensor ◽  
The Difference

The high demand for meat and the limited availability of meat on the market, make the price of meat become expensive and more and more traders are mixing rotten meat into fresh meat. To avoid risk, the public as consumers must be aware and know the characteristics of rotten meat and the difference with fresh meat. This study developed a fresh meat detection device using the TCS-230 RGB color sensor. The tool works by measuring the composition of RGB colors in identified meat and comparing with the reference composition of fresh meat RGB color. K-Neirest Neighbor as a method for introducing the freshness of chicken meat tested. The input used in the K-Neirest Neighbor is in the form of RGB color values ??obtained from the color sensor.In this study, meat freshness was tested using TCS-230 color sensor with an accuracy rate of 87% with a positive precision of 92% and negative precision of 67%

Temporal Prediction on Students’ Graduation using Naïve Bayes and K-Nearest Neighbor Algorithm

2021 ◽  
Vol 5 (2) ◽  
pp. 682
Author(s):  
Ahmad Marzuqi ◽  
Kusuma Ayu Laksitowening ◽  
Ibnu Asror
Keyword(s):  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Temporal Prediction ◽  
Study Programs ◽  
Level 3 ◽  
Student Graduation ◽  
K Nearest Neighbor Algorithm

Accreditation is a form of assessment of the feasibility and quality of higher education. One of the accreditation assessment factors is the percentage of graduation on time. A low percentage of on-time graduations can affect the assessment of accreditation of study programs. Predicting student graduation can be a solution to this problem. The prediction results can show that students are at risk of not graduating on time. Temporal prediction allows students and study programs to do the necessary treatment early. Prediction of graduation can use the learning analytics method, using a combination of the naïve bayes and the k-nearest neighbor algorithm. The Naïve Bayes algorithm looks for the courses that most influence graduation. The k-nearest neighbor algorithm as a classification method with the attribute limit used is 40% of the total attributes so that the algorithm becomes more effective and efficient. The dataset used is four batches of Telkom University Informatics Engineering student data involving data index of course scores 1, level 2, level 3, and level 4 data. The results obtained from this study are 5 attributes that most influence student graduation. As well as the results of the presentation of the combination naïve bayes and k-nearest neighbor algorithm with the largest percentage yield at level 1 75.40%, level 2 82.08%, level 3 81.91%, and level 4 90.42%.

Preliminary Study on Quantification of Duck Color Based on Fuzzy K – Nearest Neighbor Method

2010 ◽  
Vol 39 ◽  
pp. 210-215 ◽  
Author(s):  
Dong Cheng Tu ◽  
Jin Hui Zhao ◽  
Mu Hua Liu ◽  
Jie Shen ◽  
Fang Yu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Nearest Neighbor ◽  
Color Space ◽  
K Nearest Neighbor ◽  
Breast Meat ◽  
Accuracy Rate ◽  
Average Value ◽  
Image Acquisition System ◽  
Quantification Model

In order to quantify duck color quickly and accurately, a visible light image acquisition system was developed in this study. Firstly, duck breast meat was extraced from the original image using over-threshold segmentation method; and then image fleshcolor features including average value of L*，a* and b* were extracted through color space conversion; finally, fleshcolor features were used as characteristic parameters to establish quantification model for duck color based on the method of Fuzzy k-nearest neighbor (F KNN) and BP neural network and the experimental datas gotten from the two methods were analyzd in contrast. The results showed the accuracy rate of test samples by F KNN method was higher than that of BP neural network, the accuracy rate was 83.78% and 78.38% respectively. Therefore, F KNN method was selected for quantification of duck color in this research.

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

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