scholarly journals K-Nearest Neighbor for Classification of Tomato Maturity Level Based on Hue, Saturation, and Value Colors

2019 ◽  
Vol 2 (2) ◽  
pp. 101
Author(s):  
Suwanto Sanjaya ◽  
Morina Lisa Pura ◽  
Siska Kurnia Gusti ◽  
Febi Yanto ◽  
Fadhilah Syafria
Keyword(s):  
Cross Validation ◽  
Nearest Neighbor ◽  
Image Size ◽  
K Nearest Neighbor ◽  
Color Information ◽  
Maturity Level ◽  
Total Data ◽  
Fold Cross Validation ◽  
Selection Of

The selection of tomatoes can use several indicators. One of the indicators is the fruit color. In digital image processing, one of the color information that could be used in Hue, Saturation, and Value (HSV). In this research, HSV is proposed as a color model feature for information on the ripeness of tomatoes. The total data of tomato images used in this research were 400 images from four sides. The maturity level of tomatoes uses five levels, namely green, turning, pink, light red, and red. The process of divide data uses K-Fold Cross Validation with ten folds. The method used for classification is k-Nearest Neighbor (kNN). The scenario of the test performed is to combine the image size with the parameter value of the neighbor (k). The image sizes tested are 100x100 pixels, 300x300 pixels, 600x600 pixels and 1000x1000 pixels. The “k” values tested were 1, 3, 5, 7, 9, 11, and 13. The highest accuracy reached 92.5% in the image size 1000x1000 pixels with a parameter “k” is 3. The result of the experiment showed that the image size has a significant influence of accuracy, but the parameter value of neighbor (k) has an influence that is not too significant.

scholarly journals PERBANDINGAN KINERJA MUTUAL K-NEAREST NEIGHBOR (MKNN) DAN K-NEAREST NEIGHBOR (KNN) DALAM ANALISIS KLASIFIKASI KELAYAKAN KREDIT

2019 ◽  
Vol 8 (3) ◽  
pp. 366-376
Author(s):  
Annisa Sugesti ◽  
Moch. Abdul Mukid ◽  
Tarno Tarno
Keyword(s):  
Cross Validation ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
Classification Performance ◽  
K Nearest Neighbor ◽  
Classification Technique ◽  
Optimal Value ◽  
Fold Cross Validation

Credit feasibility analysis is important for lenders to avoid the risk among the increasement of credit applications. This analysis can be carried out by the classification technique. Classification technique used in this research is instance-based classification. These techniques tend to be simple, but are very dependent on the determination of  K values. K is number of nearest neighbor considered for class classification of new data. A small value of K is very sensitive to outliers. This weakness can be overcome using an algorithm that is able to handle outliers, one of them is Mutual K-Nearest Neighbor (MKNN). MKNN removes outliers first, then predicts new observation classes based on the majority class of their mutual nearest neighbors. The algorithm will be compared with KNN without outliers. The model is evaluated by 10-fold cross validation and the classification performance is measured by Gemoetric-Mean of sensitivity and specificity. Based on the analysis the optimal value of K is 9 for MKNN and 3 for KNN, with the highest G-Mean produced by KNN is equal to 0.718, meanwhile G-Mean produced by MKNN is 0.702. The best alternative to classifying credit feasibility in this study is K-Nearest Neighbor (KNN) algorithm with K=3.Keywords: Classification, Credit, MKNN, KNN, G-Mean.

scholarly journals KLASIFIKASI STATUS PEMBAYARAN PREMI MENGGUNAKAN ALGORITMA NEIGHBOR WEIGHTED K-NEAREST NEIGHBOR (NWKNN) (STUDI KASUS: PT. BUMIPUTERA KOTA SAMARINDA)

2020 ◽  
Vol 1 (2) ◽  
pp. 56-63
Author(s):  
Grassella Gunsyang ◽  
Ika Purnamasari ◽  
Fidia Deny Tisna Amijaya
Keyword(s):  
Cross Validation ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Fold Cross Validation

Algoritma Neighbor Weighted K-Nearest Neighbor (NWKNN) merupakan pengembangan dari algoritma K-Nearest Neighbor (KNN), dengan memberikan bobot pada setiap kelas yang akan diklasifikasikan. Penelitian ini membahas tentang klasifikasi menggunakan algoritma NWKNN yang diaplikasikan pada data status pembayaran premi. Tujuannya untuk mengetahui nilai eksponen (E) dan nilai ketetanggaan (K) yang optimal, serta nilai akurasi dari klasifikasi data status pembayaran Premi di PT. Bumiputera Kota Samarinda. Tahapan dalam penelitian ini yaitu menentukan nilai E dan nilai K menggunakan k-fold cross validation, menghitung jarak euclidean, menghitung bobot dan skor setiap kelas, melihat nilai skor terbesar untuk menentukan hasil klasifikasi, kemudian menghitung nilai akurasi klasifikasi. Hasil penelitian menunjukkan bahwa nilai K dan nilai E yang optimal untuk klasifikasi status pembayaran premi di PT. Bumiputera Kota Samarinda menggunakan NWKNN sebesar K=3 dan E=6 dengan nilai akurasi sebesar 75%.

scholarly journals Phishing Website Detection Using Machine Learning Classifiers Optimized by Feature Selection

2020 ◽  
Vol 37 (4) ◽  
pp. 563-569
Author(s):  
Dželila Mehanović ◽  
Jasmin Kevrić
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Security Threats ◽  
Selection Methods ◽  
K Nearest Neighbor ◽  
Machine Learning Classifiers ◽  
Time To Build ◽  
Fold Cross Validation

Security is one of the most actual topics in the online world. Lists of security threats are constantly updated. One of those threats are phishing websites. In this work, we address the problem of phishing websites classification. Three classifiers were used: K-Nearest Neighbor, Decision Tree and Random Forest with the feature selection methods from Weka. Achieved accuracy was 100% and number of features was decreased to seven. Moreover, when we decreased the number of features, we decreased time to build models too. Time for Random Forest was decreased from the initial 2.88s and 3.05s for percentage split and 10-fold cross validation to 0.02s and 0.16s respectively.

scholarly journals Perbandingan Akurasi dan Waktu Proses Algoritma K-NN dan SVM dalam Analisis Sentimen Twitter

2019 ◽  
Vol 6 (2) ◽  
pp. 226-235
Author(s):  
Muhammad Rangga Aziz Nasution ◽  
Mardhiya Hayaty
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Fold Cross Validation

Salah satu cabang ilmu komputer yaitu pembelajaran mesin (machine learning) menjadi tren dalam beberapa waktu terakhir. Pembelajaran mesin bekerja dengan memanfaatkan data dan algoritma untuk membuat model dengan pola dari kumpulan data tersebut. Selain itu, pembelajaran mesin juga mempelajari bagaimama model yang telah dibuat dapat memprediksi keluaran (output) berdasarkan pola yang ada. Terdapat dua jenis metode pembelajaran mesin yang dapat digunakan untuk analisis sentimen:  supervised learning dan unsupervised learning. Penelitian ini akan membandingkan dua algoritma klasifikasi yang termasuk dari supervised learning: algoritma K-Nearest Neighbor dan Support Vector Machine, dengan cara membuat model dari masing-masing algoritma dengan objek teks sentimen. Perbandingan dilakukan untuk mengetahui algoritma mana lebih baik dalam segi akurasi dan waktu proses. Hasil pada perhitungan akurasi menunjukkan bahwa metode Support Vector Machine lebih unggul dengan nilai 89,70% tanpa K-Fold Cross Validation dan 88,76% dengan K-Fold Cross Validation. Sedangkan pada perhitungan waktu proses metode K-Nearest Neighbor lebih unggul dengan waktu proses 0.0160s tanpa K-Fold Cross Validation dan 0.1505s dengan K-Fold Cross Validation.

scholarly journals Analisis Komparatif Evaluasi Performa Algoritma Klasifikasi pada Readmisi Pasien Diabetes

2016 ◽  
Vol 7 (4) ◽  
Author(s):  
Mochammad Yusa ◽  
Ema Utami ◽  
Emha T. Luthfi
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Kappa Statistic ◽  
Naïve Bayes ◽  
Validation Dataset ◽  
K Nearest Neighbor ◽  
Fold Cross Validation

Abstract. Readmission is associated with quality measures on patients in hospitals. Different attributes related to diabetic patients such as medication, ethnicity, race, lifestyle, age, and others result in the calculation of quality care that tends to be complicated. Classification techniques of data mining can solve this problem. In this paper, the evaluation on three different classifiers, i.e. Decision Tree, k-Nearest Neighbor (k-NN), dan Naive Bayes with various settingparameter, is developed by using 10-Fold Cross Validation technique. The targets of parameter performance evaluated is based on term of Accuracy, Mean Absolute Error (MAE), dan Kappa Statistic. The selected dataset consists of 47 attributes and 49.735 records. The result shows that k-NN classifier with k=100 has a better performance in term of accuracy and Kappa Statistic, but Naive Bayes outperforms in term of MAE among other classifiers. Keywords: k-NN, naive bayes, diabetes, readmissionAbstrak. Proses Readmisi dikaitkan dengan perhitungan kualitas penanganan pasien di rumah sakit. Perbedaan atribut-atribut yang berhubungan dengan pasien diabetes proses medikasi, etnis, ras, gaya hidup, umur, dan lain-lain, mengakibatkan perhitungan kualitas cenderung rumit. Teknik klasifikasi data mining dapat menjadi solusi dalam perhitungan kualitas ini. Teknik klasifikasi merupakan salah satu teknik data mining yang perkembangannya cukup signifikan. Di dalam penelitian ini, model algoritma klasifikasi Decision Tree, k-Nearest Neighbor (k-NN), dan Naive Bayes dengan berbagai parameter setting akan dievaluasi performanya berdasarkan nilai performa Accuracy, Mean AbsoluteError (MAE), dan Kappa Statistik dengan metode 10-Fold Cross Validation. Dataset yang dievaluasi memiliki 47 atribut dengan 49.735 records. Hasil penelitian menunjukan bahwa performa accuracy, MAE, dan Kappa Statistik terbaik didapatkan dari Model Algoritma Naive Bayes.Kata Kunci: k-NN, naive bayes, diabetes, readmisi

scholarly journals KLASIFIKASI DOKUMEN TUGAS AKHIR (SKRIPSI) MENGGUNAKAN K-NEAREST NEIGHBOR

2019 ◽  
Vol 4 (1) ◽  
pp. 69
Author(s):  
Kitami Akromunnisa ◽  
Rahmat Hidayat
Keyword(s):  
Test Data ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Data Distribution ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Electronic Documents ◽  
Digital Version ◽  
Abstract Data

Various scientific works from academicians such as theses, research reports, practical work reports and so forth are available in the digital version. However, in general this phenomenon is not accompanied by a growth in the amount of information or knowledge that can be extracted from these electronic documents. This study aims to classify the abstract data of informatics engineering thesis. The algorithm used in this study is K-Nearest Neighbor. Amount of data used 50 abstract data of Indonesian language, 454 data of English abstract and 504 title data. Each data is divided into training data and test data. Test data will be classified automatically with the classifier model that has been made. Based on the research conducted, the classification of the Indonesian essential data resulted in greater accuracy without going through a stemming process that had a 9: 1 ratio of 100.0% compared to an 8: 2 ratio of 90.0%, 7: 3 which was 80.0%, 6: 4 which is 60.0% and the data distribution using Kfold cross validation is 80.0%.

scholarly journals Efisiensi Big Data Menggunakan Improved Nearest Neighbor

2019 ◽  
Vol 6 (6) ◽  
pp. 665
Author(s):  
Aditya Hari Bawono ◽  
Ahmad Afif Supianto
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Efficient Method ◽  
Nearest Neighbor ◽  
Classification Methods ◽  
K Nearest Neighbor ◽  
Knn Classification ◽  
Degree Of Similarity ◽  
Selection Of

<p>Klasifikasi adalah salah satu metode penting dalam kajian data mining. Salah satu metode klasifikasi yang populer dan mendasar adalah k<em>-nearest neighbor</em> (kNN). Pada kNN, hubungan antar sampel diukur berdasarkan tingkat kesamaan yang direpresentasikan sebagai jarak. Pada kasus mayoritas terutama pada data berukuran besar, akan terdapat beberapa sampel yang memiliki jarak yang sama namun amat mungkin tidak terpilih menjadi tetangga, maka pemilihan parameter k akan sangat mempengaruhi hasil klasifikasi kNN. Selain itu, pengurutan pada kNN menjadi masalah komputasi ketika dilakukan pada data berukuran besar. Dalam usaha mengatasi klasifikasi data berukuran besar dibutuhkan metode yang lebih akurat dan efisien. <em>Dependent Nearest Neighbor</em> (dNN) sebagai metode yang diajukan dalam penelitian ini tidak menggunakan parameter k dan tidak ada proses pengurutan sampel. Hasil percobaan menunjukkan bahwa dNN dapat menghasilkan efisiensi waktu sebesar 3 kali lipat lebih cepat daripada kNN. Perbandingan akurasi dNN adalah 13% lebih baik daripada kNN.</p><p><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Classification is one of the important methods of data mining. One of the most popular and basic classification methods is k-nearest neighbor (kNN). In kNN, the relationships between samples are measured by the degree of similarity represented as distance. In major cases, especially on big data, there will be some samples that have the same distance but may not be selected as neighbors, then the selection of k parameters will greatly affect the results of kNN classification. Sorting phase of kNN becomes a computation problem when it is done on big data. In the effort to overcome the classification of big data a more accurate and efficient method is required. Dependent Nearest Neighbor (dNN) as method proposed in this study did not use the k parameters and no sample at the sorting phase. The proposed method resulted in 3 times faster than kNN. The accuracy of the proposed method is13% better results than kNN.</em></p><p class="Judul2" align="left"><em> </em></p>

scholarly journals Bayes Classifier dan Support Vector Machine dalam Klasifikasi Judul Karya Akhir Mahasiswa Program Studi PTIK UNJ

2019 ◽  
Vol 3 (1) ◽  
pp. 54-62
Author(s):  
Razi Aziz Syahputro ◽  
Widodo ◽  
Hamidillah Ajie
Keyword(s):  
Support Vector Machine ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Confusion Matrix ◽  
Vector Space Model ◽  
Support Vector ◽  
Bayes Classifier ◽  
K Nearest Neighbor ◽  
Space Model ◽  
Fold Cross Validation

Penelitian ini dilatarbelakangi dengan dibutuhkannya sistem pengklasifikasian untuk memudahkan pihak Jurusan Teknik Elektro khususnya Program Studi PTIK untuk mengklasifikasikan judul skripsi berdasarkan peminatan. Sebelum sistem dibuat diperlukan pertimbangan dari beberapa algoritma klasifikasi yang ada, maka dari itu penelitian ini memilih 3 algoritma dari 10 algoritma terbaik menurut ICDM tahun 2006. Klasifikasi terhadap dokumen teks pendek seperti judul skripsi mahasiswa memiliki kesulitan tersendiri daripada dokumen teks panjang karena semakin sedikit kata semakin sulit diklasifikasi. Sehingga tujuan dari penelitian ini adalah untuk mengetahui algoritma yang paling efektif untuk mengklasifikasi judul skripsi. Penelitian ini terdiri dari beberapa tahap yaitu pengumpulan data, pengelompokan data melalui angket oleh dosen ahli, pre-processing text, pembobotan kata menggunakan vector space model dan tf-idf, evaluasi dengan k-fold cross validation, klasifikasi menggunakan k-nearest neighbor, naïve bayes classifier, dan support vector machine, dan analisis dengan confusion matrix. Percobaan dilakukan dengan menggunakan 266 data judul skripsi mahasiswa PTIK UNJ dari angkatan 2010-2013, dengan data terakhir berasal dari sidang skripsi pada semester 105(semester ganjil 2016/2017). Hasil dari klasifikasi menggunakan algoritma tersebut didapatkan algoritma yang paling efisien yaitu support vector machine dengan akurasi 82% dari 10 kali percobaan.

scholarly journals PREDIKSI HASIL PEMILU LEGISLATIF MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR BERBASIS BACKWARD ELIMINATION

2020 ◽  
Vol 3 (1) ◽  
pp. 27-41
Author(s):  
Achmad Saiful Rizal ◽  
Moch. Lutfi
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Political Elite ◽  
Data Mining Algorithm ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Backward Elimination ◽  
K Nearest Neighbor Algorithm ◽  
Fold Cross Validation ◽  
Selection Of

Elections in Indonesia from period to period have undergone some changes. Elections legislative candidates not determined voters, but instead became a political elite authority in accordance with the order of the list of legislative candidates and their number sequence. To perform a prediction one of them with data mining. Data mining can be applied in the political sphere for example to predict the results of the legislative election and others. K-nearest neighbor algorithm is one of the data mining algorithm that performs classification based on learning object against which are closest to the object. Election-related research has been done with the k-nearest neighbor algorithm, but accuracy is obtained that method is still too low, so it takes an additional algorithm to improve accuracy. In this study, the proposed method, namely the method of k-nearest neighbor method combined with backward elimination as a selection of features. The dataset that will be used in the study comes from the KPU Sidoarjo that has special attributes 1 and 13 regular attributes. From the results of the analysis and computation of some methods, it can be concluded that the method of k-nearest neighbor method combined with backward elimination produced some conclusions. First, of the 14 attributes in the dataset, retrieved 8 most influential attribute. Second, the best accuracy are of 96.03% when k = 2 and tested by 10 fold cross validation.

scholarly journals Recommender System for Term Deposit Likelihood Prediction using Cross-validated Neural Network

2021 ◽  
pp. 21-28
Author(s):  
Shawni Dutta ◽  
Samir Kumar Bandyopadhyay
Keyword(s):  
Neural Network ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Automated System ◽  
K Nearest Neighbor ◽  
Decision Tree Classifier ◽  
Proposed Model ◽  
Tree Classifier ◽  
Customer Perspective ◽  
Fold Cross Validation

For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer details are influential while considering possibilities of taking term deposit subscription. An automated system is provided in this paper that approaches towards prediction of term deposit investment possibilities in advance. Neural network along with stratified 10-fold cross-validation methodology is proposed as predictive model which is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concluded that proposed model provides significant prediction results over other baseline models with an accuracy of 88.32% and MSE of 0.1168.

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