scholarly journals Security System Aided by Voice Fingerprint

2021 ◽  
Vol 14 (1) ◽  
pp. 24-29
Author(s):  
Gabriel Popan ◽  
Lorena Muscar ◽  
Lacrimioara Grama
Keyword(s):  
Private Information ◽  
Nearest Neighbor ◽  
Confusion Matrix ◽  
Training Model ◽  
Security System ◽  
Fingerprint Recognition ◽  
K Nearest Neighbor ◽  
Mel Frequency Cepstral Coefficients ◽  
K Nearest Neighbor Algorithm ◽  
Audio Files

Abstract The goal of this paper is to create a security system to identify a specific person who wants to access private information or enter a building using their voice. To perform this system, we identified a database containing the audio files of the users who will be able to authenticate with this system. Several steps were sequentially performed in order to extract the characteristics of the Mel Frequency Cepstral Coefficients from the audio files. Based on the k-Nearest Neighbor algorithm with an Euclidean distance and 4 neighbors, a training model was created. Through experimental results we prove in two ways, using confusion matrix and scatter plot, that the overall voice fingerprint recognition is 100%, for this particular configuration.

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 Aplikasi Prediksi Kelulusan Mahasiswa Berbasis K-Nearest Neighbor (K-NN)

2019 ◽  
Vol 1 (1) ◽  
pp. 30-36 ◽  
Author(s):  
Lalu Abd Rahman Hakim ◽  
Ahmad Ashril Rizal ◽  
Dwi Ratnasari
Keyword(s):  
Nearest Neighbor ◽  
Educational Institution ◽  
Confusion Matrix ◽  
K Nearest Neighbor ◽  
Study Program ◽  
K Value ◽  
Student Graduation ◽  
K Nearest Neighbor Algorithm ◽  
Communication Planning ◽  
Fold Cross Validation

Students are important assets for an educational institution and for this reason, it is necessary to pay attention to the student's graduation rate on time. Presentation of the ups and downs of students' ability to complete their studies on time is one of the elements of campus accreditation assessment. Based on data from the Study Program Section in the last 3 years the student graduation presentation is only 25% of the total students who can complete their studies on time. In this study using the K-Nearest Neighbor algorithm which aims to be able to identify student graduation in new cases by adapting solutions from previous cases that have closeness to new cases. This algorithm has the role to get the value of the closeness of the new case to the old case, which in turn the most population in area K with the closest value obtained by the student is predicted whether to pass on time or not on time. This study uses Roger S. Pressman's waterfalll method, namely Communication, Planning, Modeling, and Construction. Based on the tests carried out using K-Fold Cross Validation, the highest accuracy in the third model was 80% when folded 4th and 61% when the K value = 1. While testing using the Confusion Matrix obtained the highest accuracy of 98% at K = 1 for classification "Timely", and 98% at K = 2 for classification "Not Timely"

scholarly journals Detecting Abnormalities in Heart Sounds

2021 ◽  
pp. 137-143
Author(s):  
Muhammed Telceken ◽  
Yakup Kutlu
Keyword(s):  
Early Diagnosis ◽  
Nearest Neighbor ◽  
Classification Performance ◽  
Heart Sounds ◽  
K Nearest Neighbor ◽  
Mel Frequency Cepstral Coefficients ◽  
Important Data ◽  
Data Set ◽  
K Nearest Neighbor Algorithm ◽  
Selection Of

Heart sounds are important data that reflect the state of the heart. It is possible to prevent larger problems that may occur with early diagnosis of abnormalities in heart sounds. Therefore, in this study, the detection of abnormalities in heart sounds has been studied. In order to detect abnormalities in heart sounds, the heartbeat-sounds data set obtained free of charge from the kaggle.com website was examined. Mel frequency cepstral coefficients (MFCCs) were used in the selection of the characteristics of the sounds. Parameters such as the number of filters to be applied for MFCCs, the number of attributes to be extracted are examined separately with different values. The classification performance of heart sounds with feature matrices extracted in different parameters of MFCCs with K-nearest neighbor algorithm was investigated. The classification performance of different feature extractions was compared and the best case was tried to be determined. Two different records that make up the data set were examined separately as normal and abnormal. Then, the new data set obtained by combining the two records was examined as normal and abnormal.

scholarly journals Design and Implementation The Learning of Classification Aromatherapy made from Indonesian Spices using K-Nearest Neighbor (KNN)

2019 ◽  
Vol 1 (1) ◽  
pp. 21
Author(s):  
Maulia Wijiyanti Hidayah ◽  
Muhammad Ashar ◽  
I Made Wirawan
Keyword(s):  
Medicinal Plants ◽  
Nearest Neighbor ◽  
Confusion Matrix ◽  
Android Application ◽  
K Nearest Neighbor ◽  
Design And Implementation ◽  
Front End ◽  
Accuracy Result ◽  
K Nearest Neighbor Algorithm ◽  
Accuracy Data

Indonesia is country that has various plants which have many benefits for human. There are more than 31 types of medicinal plants as one of material that needed by industry as traditional medicine and spices. Traditional spiceal medicine comes from spiceal plants that used from Indonesian spices. This spices can produce aromatherapy include essential oils. Aromatherapy can help in maintaning the healthy of human body. This is necessary that aromatherapy can be classsified using K-Nearest Neighbor to classify aromatherapy from Indonesian spices. The accuracy result which shown by K-Nearest Neighbor algorithm is 97.5 percent, the accuracy data testing using confusion matrix which will be followed by front end and back end testing that shown the valid result for application design and valid using weka application with an accuracy result of 97.5 percent. This research will produce product such as android application that can be accessed by android users.

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%.

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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