scholarly journals Usage Pattern Exploration of Effective Contraception Tool

2019 ◽  
Vol 1 (1) ◽  
pp. 1-6
Author(s):  
Endang Wahyu Handamari
Keyword(s):  
Data Mining ◽  
Family Planning ◽  
Behavior Pattern ◽  
Early Stage ◽  
Nearest Neighbors ◽  
Training Data ◽  
Usage Pattern ◽  
K Nearest Neighbors ◽  
Advantages And Disadvantages ◽  
Effective Contraception

Determination of methods or contraception tool used by acceptors to support the Family Planning (“Keluarga Berencana”) is a problematic. In choosing methods or contraception tool, the acceptor must consider several factors, namely health factor, partner factor, and contraceptive method. Each method or contraception tool which is used has its advantages or disadvantages. Although it has been considering the advantages and disadvantages, it is still difficult to control fertility safely and effectively. Consequently acceptor change the method or a contraception tool that is used more than once. In order acceptors get the appropriate contraception tool then the patterns of changing in the usage of effective methods or contraception tool is determined. One of the methods that can be used to look for the patterns of changing in the usage of contraception tool is data mining. Data mining is an interesting pattern extraction of large amounts of data. A pattern is said to be interesting if the pattern is not trivial, implicit, previously unknown, and useful. The patterns presented should be easy to understand, can be applied to data that will be predicted with a certain degree, useful, and new. The early stage before applying data mining is using k nearest neighbors algorithm to determine the factors shortest distance selecting the contraception tool. The next step is applying data mining to usage changing data of method or contraception tool of family planning acceptors which is expected to dig up information related to acceptor behavior pattern in using the method or contraception tool. Furthermore, from the formed pattern, it can be used in decision making regarding the usage of effective contraception tool. The results obtained from this research is the k nearest neighbors by using the Euclidean distance can be used to determine the similarity of attributes owned by the acceptors of Family Planning to the training data is already available. Based on available training data, it can be determined the usage pattern of contraceptiion tool with the concept of data mining, where the acceptors of Family Planning are given a recommendation if the pattern is on the training data pattern. Conversely, if the pattern is none match, then the system does not provide recommendations of contraception tool which should be used.

scholarly journals Sistem Pendukung Keputusan Kredit Usaha Rakyat PT. Bank Rakyat Indonesia Unit Kaliangkrik Magelang

2015 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Agung Nugroho ◽  
Kusrini Kusrini ◽  
M. Rudyanto Arief
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Maker ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
Training Data ◽  
Classification Rule ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors ◽  
Viable Solution

Banyak faktor dan variabel yang mempengaruhi risiko kredit dalam pengambilan keputusan pada permasalahan Kredit Usaha Rakyat (KUR). Faktor-faktor yang digunakan sebagai dasar penilaian Kredit Usaha Rakyat pada PT.Bank Rakyat Indonesia Unit Kaliangkrik menggunakan prinsip dasar yang dikenal dengan prinsip “5 of Credit” yaitu Character, Capacity, Capital, Condition dan Collateral. Dari factor-faktor yang digunakan sebagai dasar penilaian kredit, digunakan metode Mining Classification Rule dalam membuat Sistem Pendukung Keputusan pemberian KUR. Terdapat beberapa algoritma yang dapat digunakan dalam data mining untuk metode klasifikasi salah satunya adalah algoritma k-nearest neightbor. Konsep sistem pendukung keputusan pemberian KUR ini dirancang dapat melakukan klasifikasi terhadap objek berdasarkan data pembelajaran yang jaraknya paling dekat dengan objek tersebut dan memberikan solusi nasabah yang layak menerima KUR berdasarkan masukan dari user dengan menggunakan metode k-nearest neighbors (knn). Data-data transaksi pembayaran nasabah lama akan dijadikan sebagai data training dimana sebelumnya akan ditentukan kelasnya terlebih dahulu. Penentuan kelas dilakukan dengan proses klasifikasi data berdasarkan kategori status nasabah sesuai jumlah tunggakan pembayaran kreditnya. Dari hasil perhitungan kemiripan kasus antara data calon nasabah baru dengan nasabah lama atau data training menggunakan algoritma K-Nearest Neighbor, hasil dengan nilai tertinggi akan dijadikan acuan seorang decision maker dalam mengambil keputusan.Many factors and variables that affect credit risk in decision-making on issues People's Business Credit (KUR). The factors are used as the basis of assessment of the People's Business Credit Unit at PT Bank Rakyat Indonesia Kaliangkrik using basic principle known as the principle of "5 of Credit" ie Character, Capacity, Capital, Collateral Condition and. Of the factors that are used as a basis for credit assessment, Classification Rule Mining method used in making the administration of KUR Decision Support Systems. There are several algorithms that can be used in data mining for classification methods one of which is the k-nearest algorithm neightbor. The concept of the provision of decision support system is designed KUR can perform the classification of objects based on distance learning data that is closest to the object and provide a viable solution customers receive KUR based on input from the user by using the k-nearest neighbors (KNN). Payment transaction data will be used as a customer long training data which will be determined prior to first class. Grading is done with the data classification process based on customer status categories according to the amount of credit outstanding payments. From the calculation of the similarity between the case of data with prospective new customers or old customers training data using the K-Nearest Neighbor algorithm, the results with the highest scores will be used as a reference to a decision maker in making decisions.

Klasifikasi Gerakan Jatuh Berbasis Accelerometer dan Gyroscope Menggunakan K-Nearest Neighbors

2020 ◽  
Vol 4 (2) ◽  
pp. 24-29
Author(s):  
Adlian Jefiza ◽  
Indra Daulay ◽  
Jhon Hericson Purba
Keyword(s):  
Real Time ◽  
Root Mean Square ◽  
Nearest Neighbors ◽  
Training Data ◽  
Mean Square ◽  
K Nearest Neighbors ◽  
Testing Data ◽  
Total Data

Permasalahan utama pada penelitian ini merujuk kepada semakin menurunnya daya tahan tubuh lanjut usia (lansia). Hal ini membutuhkan sistem monitoring aktivitas lansia secara real time. Untuk mendeteksi kegiatan para lansia, dirancang sebuah perangkat monitoring dengan accelerometer 3-sumbu dan gyroscope 3-sumbu. Data sensor diperoleh dari lima partisipan. Setiap partisipan melakukan lima gerakan yaitu terjatuh, duduk, tidur, rukuk dan sujud. Gerakan yang dipilih adalah gerakan yang menyerupai gerakan jatuh. Total data yang diperoleh dari partisipan adalah 75 data yang terbagi menjadi training data dan testing data. Penelitian ini menggunakan metode transformasi Wavelet untuk mengenali fitur dari gerakan. Untuk pengklasifikasian setiap gerakan, digunakan metode K-nearest neighbors (KNN). Hasil klasifikasi gerakan menggunakan lima kelas menghasilkan nilai root mean square sebesar 0.0074 dengan akurasi 100%.

Personality Based Lipstick Color Recommender System using K-Nearest Neighbors Algorithm

2019 ◽  
Vol 6 (1) ◽  
pp. 55-59
Author(s):  
Ryan Adiputra ◽  
Ni Made Satvika Iswari ◽  
Wella Wella
Keyword(s):  
Recommender System ◽  
Euclidean Distance ◽  
Nearest Neighbors ◽  
Training Data ◽  
Personality Test ◽  
Myers Briggs Type Indicator ◽  
K Nearest Neighbors ◽  
Type Indicator ◽  
Myers Briggs ◽  
The Right

Lipstick is a lip color which available in many colors. A research said instant valuation of woman personality can be figured by their lipstick color choice. Therefore there is a necessity to use the right lipstick color to obtain a harmony between personality and appearance. This experiment was conducted to give lipstick color recommendation by using K-Nearest Neighbors algorithm, and Myers-Briggs Type Indicator (MBTI) personality test instrument. The system was built on Android application. Euclidean distance value is affected by 5 factors which are age, introvert, sensing, thinking, and judging. Lipstick color recommendation is obtained by fetching 7 training data with nearest Euclidean distance when compared to personality test result. The colors used in this experiment are nude, pink, red, orange, and purple. After evaluation, it is obtained the application’s accuracy of 87.38% which considered as good classification, both precision and recall with 75.68% which considered as fair classification. The score for software quality is 79.13% which considered as good quality. Keywords—K-Nearest Neighbors, Data Mining, Myers-Briggs Type Indicator,Recommender System, Lipstick.  

scholarly journals Detecting Dengue Fever In Children Using A Combined Scheme Compared With Individual Algorithms: An APP Development And Usability Study

2021 ◽  
Author(s):  
Julie Chi Chow ◽  
Tsair-Wei Chien ◽  
Lin-Yen Wang ◽  
Willy Chou
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Dengue Fever ◽  
Prediction Accuracy ◽  
Early Stage ◽  
Statistical Significance ◽  
Nearest Neighbors ◽  
Public Health Issue ◽  
K Nearest Neighbors ◽  
Artificial Neural

Abstract Background: Dengue fever (DF) is an important public health issue in Asia. However, the disease is extremely hard to detect using traditional dichotomous (i.e., absent vs. present) evaluations of symptoms. Convolution neural network (CNN) and artificial neural networks(ANN) can improve prediction accuracy on account of its usage of a large number of parameters for modeling. A hypothesis using a combined scheme of algorithms, including convolutional neural networks(CNN), artificial neural networks(ANN), K-nearest Neighbors Algorithm(KNN), and logis-tical regression(LR), was made to improve the prediction DF accuracy for children. Methods: We extracted 19 feature variables of DF-related symptoms from 177 pediatric patients (69 diagnosed with DF). A 11-variables were eligible by observing the statistical significance in predicting DF risk. The prediction accuracy was based on two training (80%) and testing (20%) sets on model accuracy of the area under the receiver operating characteristic curve (AUC) greater than 0.80 and 0.70, respectively, for discriminating DF+ and DF− in the two sets. Two scenarios of the combined scheme and individual algorithms were compared using the training set to predict the testing set. Results: We observed that (i) k-nearest neighbors algorithm has poorer AUC(<0.50), (ii)LR has relatively higher AUC(=0.70), and (ii) the three alternatives have almost equal AUC(=0.68), but smaller than the individual algorithms of NaiveBayes, Logistic regression in raw data and NaiveBayes in normalized data. Conclusion: An LR-based APP was designed to detect DF in children. The 11-item model is suggested to develop the APP for helping patients, family members, and clinicians discriminate DF from other febrile illnesses at an early stage.

scholarly journals Data Mining Techniques for Identification and Classification of Various Diseases in Plants

2019 ◽  
Vol 9 (2S) ◽  
pp. 676-680
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Nearest Neighbors ◽  
Crop Productivity ◽  
Vital Role ◽  
Support Vector ◽  
Data Sets ◽  
K Nearest Neighbors ◽  
Data Mining Techniques

Data mining is currently being used in various applications; In research community it plays a vital role. This paper specify about data mining techniques for the preprocessing and classification of various disease in plants. Since various plants has different diseases based on that each of them has different data sets and different objectives for knowledge discovery. Data Mining Techniques applied on plants that it helps in segmentation and classification of diseased plants, it avoids Oral Inspection and helps to increase in crop productivity. This paper provides various classification techniques Such as K-Nearest Neighbors, Support Vector Machine, Principle component Analysis, Neural Network. Thus among various techniques neural network is effective for disease detection in plants.

scholarly journals Analisis Sentimen pada Ulasan pengguna Aplikasi Bibit Dan Bareksa dengan Algoritma KNN

2021 ◽  
Vol 8 (2) ◽  
pp. 636-646
Author(s):  
Aluisius Dwiki Adhi Putra
Keyword(s):  
Data Mining ◽  
Nearest Neighbors ◽  
Standard Process ◽  
K Nearest Neighbors ◽  
Industry Standard ◽  
Google Play

Investasi online merupakan kegiatan menanam modal baik langsung maupun tidak dengan harapan pada suatu waktu pemilik modal mendapatkan sejumlah keuntungan yang dilakukan secara online. Terdapat contoh aplikasi investasi online yang sudah banyak diunduh masyarakat menurut google play store yepenaitu bibit dan bareksa. Sehingga Tujuan penelitian ini adalah untuk menganalisa sentimen pada ulasan pengguna aplikasi investasi online yaitu bibit dan bareksa. Jumlah ulasan yang akan digunakan pada penelitian ini sebanyak  998 yang terdiri dari 484 sentimen positif dan 514 sentimen negatif untuk aplikasi bareksa sedangkan untuk aplikasi bibit menggunakan 1063 data yang terdiri dari 541 sentimen positif dan 522 sentimen negatif. Data tersebut juga melewati tahapan preprocessing dan modelling. Pada penelitian ini menggunakan model CRISP-DM (Cross Industry Standard Process for Data Mining) dan algoritma yang digunakan pada penelitian ini adalah K-Nearest Neighbors. Berdasarkan hasil yang diperoleh dari tahapan modelling dengan menggunakan algoritma k-nearest neighbors dan perbandingan 60:40 untuk data training dan data testing, maka nilai akurasi precision dan recall yang dihasilkan dari tiap aplikasi yaitu untuk bibit 85,14% , 91,91%, dan 76,44% sedangkan untuk bareksa yaitu 81,70% , 87,15%, 75,73%.

scholarly journals Determining and Clustering Potential Legislative Candidate in West Java District Using K-Nearest Neighbors Algorithm

2017 ◽  
Vol 2 (4) ◽  
pp. 35
Author(s):  
Faiza Renaldi ◽  
Alfin Dhuhawan Bagja ◽  
Gunawan Abdillah
Keyword(s):  
Data Mining ◽  
Party System ◽  
Nearest Neighbors ◽  
General Election ◽  
West Java ◽  
K Nearest Neighbors ◽  
Members Of Parliament ◽  
Public Figures ◽  
Using Data ◽  
The Right

Indonesia held its first general election in 1955 to elect legislatures from all provinces. The latest was held in 2014, which elected 560 members to the People's Representative Council (Dewan Perwakilan Rakyat, DPR) and 128 to the Regional Representative Council (Dewan Perwakilan Daerah, DPD). The PRC was elected by proportional representation from multi-candidate constituencies/districts. Currently, there are 77 constituencies in Indonesia, each of which returns 3-10 Members of Parliament based on population. Under Indonesia's new multi-party system, no party has been able to secure an outright victory; hence, selecting the right candidate for the right constituencies has been a major effort for all participating parties. Many combinations have been tried; popularities, intelligence, public figures, ‘putera daerah’ are all variables that can only show a fraction of winning pattern where no general conclusion can be drawn. This research used data mining techniques to create an unfound pattern, and to suggest which particular legislative candidate is most suitable for which constituency. Using 11 West Java constituencies (11 clusters), K-Nearest Neighbors (K-NN) algorithms, we found out that an 83.33% accuracy using data from 2014 general election.

Semi-Supervised Clustering Algorithm Based on Small Size of Labeled Data

2011 ◽  
Vol 121-126 ◽  
pp. 4675-4679
Author(s):  
Ming Wei Leng ◽  
Xiao Yun Chen ◽  
Jian Jun Cheng ◽  
Long Jie Li
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Nearest Neighbors ◽  
Experimental Results ◽  
K Nearest Neighbors ◽  
Supervised Clustering ◽  
The Core ◽  
Knn Classification ◽  
Core Problem

In many data mining domains, labeled data is very expensive to generate, how to make the best use of labeled data to guide the process of unlabeled clustering is the core problem of semi-supervised clustering. Most of semi-supervised clustering algorithms require a certain amount of labeled data and need set the values of some parameters, different values maybe have different results. In view of this, a new algorithm, called semi-supervised clustering algorithm based on small size of labeled data, is presented, which can use the small size of labeled data to expand labeled dataset by labeling their k-nearest neighbors and only one parameter. We demonstrate our clustering algorithm with three UCI datasets, compared with SSDBSCAN[4] and KNN, the experimental results confirm that accuracy of our clustering algorithm is close to that of KNN classification algorithm.

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

I-Way

2020 ◽  
pp. 23-34
Author(s):  
Chetna Gupta ◽  
Surbhi Singhal ◽  
Astha Kumari
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Knowledge Discovery ◽  
Learning Algorithm ◽  
Nearest Neighbors ◽  
Machine Learning Algorithm ◽  
K Nearest Neighbors ◽  
Inverse Document Frequency ◽  
Data Mining Techniques ◽  
Document Frequency

This study addresses the problem of effectively searching and selecting relevant requirements for reuse meeting stakeholders' objectives through knowledge discovery and data mining techniques maintained over a cloud platform. Knowledge extraction of similar requirement(s) is performed on data and meta-data stored in central repository using a novel intersective way method (i-way), which uses intersection results of two machine learning algorithm namely, K-nearest neighbors (KNN) and term frequency-inverse document frequency (TF-IDF). I-way is a two-level extraction framework which represents win-win situation by considering intersective results of two different approaches to ensure that selection is progressing towards desired requirement for reuse consideration. The validity and effectiveness of results of proposed framework are evaluated on requirement dataset, which show that proposed approach can significantly help in reducing effort by selecting similar requirements of interest for reuse.

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