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%

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 Framing Twitter Public Sentiment on Nigerian Government COVID-19 Palliatives Distribution Using Machine Learning

2021 ◽  
Vol 13 (6) ◽  
pp. 3497
Author(s):  
Hassan Adamu ◽  
Syaheerah Lebai Lutfi ◽  
Nurul Hashimah Ahamed Hassain Malim ◽  
Rohail Hassan ◽  
Assunta Di Vaio ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Primary Objective ◽  
Support Vector ◽  
Standard English ◽  
Emotion Classification ◽  
K Nearest Neighbor ◽  
The Public ◽  
The Government

Sustainable development plays a vital role in information and communication technology. In times of pandemics such as COVID-19, vulnerable people need help to survive. This help includes the distribution of relief packages and materials by the government with the primary objective of lessening the economic and psychological effects on the citizens affected by disasters such as the COVID-19 pandemic. However, there has not been an efficient way to monitor public funds’ accountability and transparency, especially in developing countries such as Nigeria. The understanding of public emotions by the government on distributed palliatives is important as it would indicate the reach and impact of the distribution exercise. Although several studies on English emotion classification have been conducted, these studies are not portable to a wider inclusive Nigerian case. This is because Informal Nigerian English (Pidgin), which Nigerians widely speak, has quite a different vocabulary from Standard English, thus limiting the applicability of the emotion classification of Standard English machine learning models. An Informal Nigerian English (Pidgin English) emotions dataset is constructed, pre-processed, and annotated. The dataset is then used to classify five emotion classes (anger, sadness, joy, fear, and disgust) on the COVID-19 palliatives and relief aid distribution in Nigeria using standard machine learning (ML) algorithms. Six ML algorithms are used in this study, and a comparative analysis of their performance is conducted. The algorithms are Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM), Random Forest (RF), Logistics Regression (LR), K-Nearest Neighbor (KNN), and Decision Tree (DT). The conducted experiments reveal that Support Vector Machine outperforms the remaining classifiers with the highest accuracy of 88%. The “disgust” emotion class surpassed other emotion classes, i.e., sadness, joy, fear, and anger, with the highest number of counts from the classification conducted on the constructed dataset. Additionally, the conducted correlation analysis shows a significant relationship between the emotion classes of “Joy” and “Fear”, which implies that the public is excited about the palliatives’ distribution but afraid of inequality and transparency in the distribution process due to reasons such as corruption. Conclusively, the results from this experiment clearly show that the public emotions on COVID-19 support and relief aid packages’ distribution in Nigeria were not satisfactory, considering that the negative emotions from the public outnumbered the public happiness.

scholarly journals Deteksi Batik Parang Menggunakan Fitur Co-Occurence Matrix Dan Geometric Moment Invariant Dengan Klasifikasi KNN

2016 ◽  
pp. 40
Author(s):  
Ni Luh Wiwik Sri Rahayu Ginantra
Keyword(s):  
Matrix Method ◽  
Nearest Neighbor ◽  
Texture Features ◽  
Sufficient Information ◽  
K Nearest Neighbor ◽  
Moment Invariant ◽  
The Public ◽  
Geometric Moment ◽  
Visual Identification ◽  
Occurrence Matrix

Batik motifs are the base or the blueprint of batik patterns which serve as the core of the batik image design, and therefore the meaning of a sign, symbol or logo in a batik work can be revealed through its motifs. Visual identification requires visual skills and knowledge in classifying patterns formed in a batik image. Lack of media providing information on batik motifs makes the public unable to have sufficient information about batik motifs. Looking at this phenomenon, this study is conducted in order to perform visual identification using a computer that can assist and facilitate in identifying the types of batik. The methods used for batik image recognition are the Co-occurrence Matrix method to provide extraction of batik texture features, and the Geometric Moment Invariant method, while K Nearest Neighbor is used to classify batik images. The results on the accuracy values obtained reveal that the of 80%, compared to the accuracy value result using the Co-occurrence Matrix method that is 70%.  

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 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.

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%

scholarly journals Giving more insight for automatic risk prediction during pregnancy with interpretable machine learning

2021 ◽  
Vol 10 (3) ◽  
Author(s):  
Muhammad Irfan ◽  
Setio Basuki ◽  
Yufis Azhar
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Classification Model ◽  
K Nearest Neighbor ◽  
Pregnancy Risk ◽  
The Public ◽  
Risk Monitoring ◽  
Machine Learning Model ◽  
Union Operation ◽  
Correlation Based Feature Selection

Maternal mortality rate (MMR) in Indonesia intercensal population survey (SUPAS) was considered high. For pregnancy risk detection, the public health center (puskesmas) applies a Poedji Rochjati screening card (KSPR) demonstrating 20 features. In addition to KSPR, pregnancy risk monitoring has been assisted with a pregnancy control card. Because of the differences in the number of features between the two control cards, it is necessary to make agreements between them. Our objectives are determining the most influential features, exploring the links among features on the KSPR and pregnancy control cards, and building a machine learning model for predicting pregnancy risk. For the first objective, we use correlation-based feature selection (CFS) and C5.0 algorithm. The next objective was answered by the union operation in the features produced by the two techniques. By performing the machine learning experiment on these features, the accuracy of the XGBoost algorithm demonstrated the hightest results of 94% followed by random forest, Naïve Bayes, and k-Nearest neighbor algorithms, 87%, 66%, and 60% respectively. Interpretability aspects are implemented with SHAP and LIME to provide more insight for classification model. In conclusion, the similarity feature generated in the two interpretation approaches confirmed that Cesar was dominant in determining pregnancy risk.

Collaborative filtering recommendation algorithm based on class correlation distance

2019 ◽  
Vol 13 ◽  
Author(s):  
Hanfei Zhang ◽  
Yumei Jian ◽  
Ping Zhou
Keyword(s):  
Collaborative Filtering ◽  
Nearest Neighbor ◽  
Experimental Result ◽  
K Nearest Neighbor ◽  
Recommendation Algorithm ◽  
K Value ◽  
Correlation Distance ◽  
Training Samples ◽  
The Difference ◽  
Better Than

: A class correlation distance collaborative filtering recommendation algorithm is proposed to solve the problems of category judgment and distance metric in the traditional collaborative filtering recommendation algorithm, which is using the advantage of the distance between the same samples and the class related distance. First, the class correlation distance between the training samples is calculated and stored. Second, the K nearest neighbor samples are selected, the class correlation distance of training samples and the difference ratio between the test samples and training samples are calculated respectively. Finally, according to the difference ratio, we classify the different types of samples. The experimental result shows that the algorithm combined with user rating preference can get lower MAE value, and the recommendation effect is better. With the change of K value, CCDKNN algorithm is obviously better than KNN algorithm and DWKNN algorithm, and the accuracy performance is more stable. The algorithm improves the accuracy of similarity and predictability, which has better performance than the traditional algorithm.

PUBLIC SENTIMENT ANALYSIS OF PASAR LAMA TANGERANG USING K-NEAREST NEIGHBOR METHOD AND PROGRAMMING LANGUAGE R

2019 ◽  
Vol 24 (2) ◽  
pp. 129-133
Author(s):  
Hustinawaty ◽  
Rama Al Azis Dwiputra ◽  
Tavipia Rumambi
Keyword(s):  
Sentiment Analysis ◽  
Programming Languages ◽  
Nearest Neighbor ◽  
Tourist Attraction ◽  
K Nearest Neighbor ◽  
The Public ◽  
Public Sentiment ◽  
A Value ◽  
Negative Comments ◽  
The City

Pasar Lama Tangerang is a tourist attraction in the city of Tangerang. With the development of current technology, the public can provide an overview of how the facilities and services are provided by expressing opinions on the internet. However, it is difficult to distinguish which opinions belong to positive or negative opinions. Sentiment analysis is needed to overcome this problem. The stage in sentiment analysis starts with collecting data first, then the data is processed. Furthermore, the data that has been propagated is given a sentiment classification using the K-Nearest Neighbor (KNN) algorithm. Then the classification results obtained an accuracy of 83% with a value of k = 1 of 120 data divided by 92 positive and 28 negative comments. Sentiment analysis is made using the R and Rstudio programming languages as supporting software.

Sign in / Sign up

Export Citation Format

Share Document