scholarly journals Classification of Fish Species with Image Data Using K-Nearest Neighbor

2021 ◽  
Vol 2 (2) ◽  
pp. 54-58
Author(s):  
Kaharuddin Kaharuddin ◽  
Eka Wahyu Sholeha
Keyword(s):  
Computer Science ◽  
Everyday Life ◽  
Fish Species ◽  
Nearest Neighbor ◽  
Image Data ◽  
Test Results ◽  
Shape Features ◽  
K Nearest Neighbor ◽  
The World

Abstract— Classification is a technique that many of us encounter in everyday life, classification science is also growing and being applied to various types of data and cases in everyday life, in computer science classification has been developed to facilitate human work, one example of its application is to classify fish species in the world, the number of fish species in the world is very much so that there are still many people who are sometimes confused to distinguish them, therefore in this study a study will be conducted to classify fish species using the K-Nearest Neighbor Method. 4 types of fish, all data totaling 160 data. The purpose of this study was to test the K-Nearest Neighbor method for classifying fish species based on color, texture, and shape features. Based on the test results, the accuracy value of the truth is obtained using the value of K = 7 with a percentage of the truth of 77.50%, the second-highest accuracy value is the value of K = 10, namely 76.88%. Based on the results of this study, it can be concluded that the K-Nearest Neighbor method has a good enough ability to classify, but it can be done by adding variables or adding more amount of data, and using other types of fish.

scholarly journals Classification of Hadith Topic of Indonesian Translation Using K-Nearest Neighbor and Chi-Square

2021 ◽  
Vol 7 (2) ◽  
pp. 11-22
Author(s):  
Ghinaa Zain Nabiilah ◽  
Said Al Faraby ◽  
Mahendra Dwifebri Purbolaksono
Keyword(s):  
Feature Selection ◽  
Everyday Life ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Way Of Life ◽  
Chi Square ◽  
The Law ◽  
Test Scenarios

Hadith is the main way of life for Muslims besides the Qur'an whose can be applied in everyday life. Hadith also contains all the words or deeds of the Prophet Muhammad which are used as a source of the law of Islam. Therefore, many readers, especially Muslims, are interested in studying hadith. However, the large number of hadiths makes it difficult for readers or those who are still unfamiliar with Islam to read them. Therefore, we conducted a study to classify hadith textually based on the type of teaching, so that readers can get an overview or other reference in reading and searching for hadith based on the type of teaching more easily. This study uses KNN and chi-square methods as feature selection. We also carried out several test scenarios, including implementing stopword removal modifications in preprocessing and experimenting with selecting k values ​​for KNN to determine the best performance. The best performance was obtained by using the value of k = 7 on KNN without implementing chi-square and with stopword removal modification with a hammer loss value of 0.1042 or about 89.58% of the data correctly classified.

scholarly journals Classification of lung condition for early diagnosis of pneumonia and tuberculosis based on embedded system

2021 ◽  
Vol 10 (3) ◽  
pp. 1262-1270
Author(s):  
Rizal Maulana ◽  
Alfatehan Arsya Baharin ◽  
Hurriyatul Fitriyah
Keyword(s):  
Carbon Dioxide ◽  
Embedded System ◽  
Nearest Neighbor ◽  
Image Data ◽  
Early Research ◽  
K Nearest Neighbor ◽  
X Ray ◽  
Lung Condition ◽  
Chest X Ray

The lungs are the main organs in the respiratory system that have a function as a place for exchange of oxygen and carbon dioxide. Due to the importance of lung function, indications of lung disorders must be detected and diagnosed early. Research on the classification of lung conditions generally uses chest x-ray image data. Where a time-consuming procedure is needed to obtain the data. In this research, an embedded system to diagnose lung conditions was designed. The system was made to be easy to use independently and provides real-time examination results. This system uses parameters of body temperature, oxygen saturation, fingernail color and lung volume in classifying lung conditions. There are three conditions that can be classified by the system, that is healthy lungs, pneumonia and tuberculosis. The k-nearest neighbor method was used in the classification process in the designed system. The dataset used was 51 data obtained from the hospital. Each data already has a label in the form of lung condition based on the doctor’s diagnosis. The proposed system has an accuracy of 88.24% in classifying lung conditions.

scholarly journals Implementasi K-Nearest Neighbor Dalam Prediksi Mahasiswa Berhenti Kuliah

2021 ◽  
Vol 5 (3) ◽  
pp. 866
Author(s):  
Yunita Yunita
Keyword(s):  
Computer Science ◽  
Nearest Neighbor ◽  
Dropping Out ◽  
K Nearest Neighbor ◽  
Internal Factors ◽  
Public And Private ◽  
Student Data ◽  
Data Accumulation ◽  
The World

A student is a student who sits and is registered in one of the universities, both public and private, being a student is the dream of many students around the world and being a student is the starting gate to determine someone will be in the world of science in what field, be it computer science, medicine, world of education and others. However, there are many reasons why students decide to stop attending lectures suddenly due to several factors, both external and internal factors. This causes its own losses that will be faced by the campus, one of which is the reduction in the quantity of student data and resulting in data accumulation, it is necessary to predict students who have the potential to stop studying unilaterally by looking at several criteria and digging up information on the data of students who have the potential to quit college by applying the K-algorithm. NN. In this study, the K-NN algorithm records old data and sees similarities to new data in an effort to recognize patterns of students dropping out of college, the results obtained from new lecture data show that the data is similar to the old data of students who dropped out of college with the closest similarity of values from other cases, namely 17 .3815 with 19.98875 so that the results obtained by the new data student decision decided the possibility of dropping out of college

scholarly journals Classification of batik patterns using K-Nearest neighbor and support vector machine

2020 ◽  
Vol 9 (3) ◽  
pp. 1260-1267
Author(s):  
Agus Eko Minarno ◽  
Fauzi Dwi Setiawan Sumadi ◽  
Hardianto Wibowo ◽  
Yuda Munarko
Keyword(s):  
Support Vector Machine ◽  
Data Sharing ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Experimental Result ◽  
Support Vector ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Average Accuracy

This study is proposed to compare which are the better method to classify Batik image between K-Nearest neighbor and Support Vector Machine using minimum features of GLCM. The proposed steps are started by converting image to grayscale and extracting colour feature using four features of GLCM. The features include Energy, Entropy, Contras, Correlation and 0o, 45o, 90o, and 135o. The classifier features consist of 16 features in total. In the experimental result, there exist comparison of previous works regarding the classification KNN and SVM using multi texton histogram (MTH). The experiments are carried out in the form of calculation of accuracy with data sharing and cross-validation scenario. From the test results, the average accuracy for KNN is 78.3% and 92.3% for SVM in the cross-validation scenario. The scenario for the highest accuracy of data sharing is at 70% for KNN and at 100% for SVM. Thus, it is apparent that the application of the GLCM and SVM method for extracting and classifying batik motifs has been effective and better than previous work.

scholarly journals Perbandingan Performansi Model pada Algoritma K-NN terhadap Klasifikasi Berita Fakta Hoaks Tentang Covid-19

2021 ◽  
Vol 5 (2) ◽  
pp. 167-176
Author(s):  
Wahyu Hidayat ◽  
◽  
Ema Utami ◽  
Ahmad Fikri Iskandar ◽  
Anggit Dwi Hartanto ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Performance Comparison ◽  
Test Results ◽  
K Nearest Neighbor ◽  
K Value ◽  
Jaccard Distance ◽  
Comparison Results ◽  
Class 1 ◽  
Correct Data

During Covid-19 pandemic, there was various hoax news about Covid-19. There are truth-clarification platforms for hoax news about Covid-19 such as Jala Hoax and Saber Hoax which categorize into misinformation and disinformation. Classification of supervised learning methods is applied to carry out learning from fact labels. Dataset is taken from Jala Hoax and Saber Hoax as many as 559 data which are made into Class 1 (Misleading Content, Satire/Parody, False Connection), Class 2 (False Context, Imposter Content), Class 3 (Fabricated and Manipulated Content). K-Nearest Neighbor (K-NN) is used to classify categories of misinformation and disinformation. Dissimilarity measure Jaccard Distance is compared with Euclidean, Manhattan, and Minkowski and uses k-value variance in the K-NN to determine the performance comparison results for each test. Results of Jaccard Distance at the value of k = 4 get a higher value than other model with an accuracy 0.696, precision 0.710, recall 0.572, and F1-Score. Maximum results tend to be on label of the most data class in Class 1 (Misleading Content, Satire or Parody, False Connection) with a total of 58 correct data from 61 test data.

scholarly journals Assamese Text Classification using k Nearest Neighbor

2019 ◽  
Vol 8 (4) ◽  
pp. 8185-8188
Keyword(s):  
Nearest Neighbor ◽  
Document Classification ◽  
K Nearest Neighbor ◽  
Political News ◽  
Search Results ◽  
Wide Range ◽  
The World ◽  
Sports News ◽  
Law And Science

Knowledge is the most powerful weapon of a society. And in today’s world it is just a click away from the mouse. There is abundance of knowledge and information in the form of newspaper , electronic newspaper ,articles, online journals, webpages , search results etc. And there is a wide range of news from all over the world. But then the choice of news varies from person to person. Some people may prefer sports news to amusement news and some people may prefer political news over sports news and likewise there can be a number of other choices. It completely relies on individual’s decision. Document Classification is the process of classifying a document into a number of predefined classes. In this paper we have done document classification of Assamese text using k-Nearest Neighbor. We have considered only four classes sports , politics , law and science. Our dataset consists of 200 documents collected from major Assamese newspaper . We have divided our data into 3:1. Majority of our datasets that is 75% data from datasets is used for training and the rest 25% of the datasets is considered for testing.

Machine Learning Verdict of EEG Signals in Brain Computer Interface

2018 ◽  
pp. 429-441
Author(s):  
M. Jeyanthi ◽  
C. Velayutham
Keyword(s):  
Nearest Neighbor ◽  
Technology Development ◽  
Vital Role ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Data Set ◽  
Eeg Data ◽  
Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

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%.

Hydroponic Nutrient Control System Based on Internet of Things

2019 ◽  
Vol 13 (2) ◽  
Author(s):  
Herman Herman ◽  
Demi Adidrana ◽  
Nico Surantha ◽  
Suharjito Suharjito
Keyword(s):  
Urban Areas ◽  
Human Population ◽  
Mineral Water ◽  
Nearest Neighbor ◽  
Total Dissolved Solids ◽  
K Nearest Neighbor ◽  
Turn On ◽  
Nutrient Film Technique ◽  
Planting Method

The human population significantly increases in crowded urban areas. It causes a reduction of available farming land. Therefore, a landless planting method is needed to supply the food for society. Hydroponics is one of the solutions for gardening methods without using soil. It uses nutrient-enriched mineral water as a nutrition solution for plant growth. Traditionally, hydroponic farming is conducted manually by monitoring the nutrition such as acidity or basicity (pH), the value of Total Dissolved Solids (TDS), Electrical Conductivity (EC), and nutrient temperature. In this research, the researchers propose a system that measures pH, TDS, and nutrient temperature values in the Nutrient Film Technique (NFT) technique using a couple of sensors. The researchers use lettuce as an object of experiment and apply the k-Nearest Neighbor (k-NN) algorithm to predict the classification of nutrient conditions. The result of prediction is used to provide a command to the microcontroller to turn on or off the nutrition controller actuators simultaneously at a time. The experiment result shows that the proposed k-NN algorithm achieves 93.3% accuracy when it is k = 5.

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