scholarly journals A Spatial Improved-kNN-Based Flood Inundation Risk Framework for Urban Tourism under Two Rainfall Scenarios

2021 ◽  
Vol 13 (5) ◽  
pp. 2859
Author(s):  
Shuang Liu ◽  
Rui Liu ◽  
Nengzhi Tan
Keyword(s):  
Nearest Neighbor ◽  
Scientific Information ◽  
Climate Extremes ◽  
Extreme Rainfall ◽  
Tourism Industry ◽  
Urban Tourism ◽  
Evaluation Framework ◽  
Economic Losses ◽  
Flood Inundation ◽  
K Nearest Neighbor

Urban tourism has been suffering socio-economic challenges from flood inundation risk (FIR) triggered by extraordinary rainfall under climate extremes. The evaluation of FIR is essential for mitigating economic losses, and even casualties. This study proposes an innovative spatial framework integrating improved k-nearest neighbor (kNN), remote sensing (RS), and geographic information system (GIS) to analyze FIR for tourism sites. Shanghai, China, was selected as a case study. Tempo-spatial factors, including climate, topography, drainage, vegetation, and soil, were selected to generate several flood-related gridded indicators as inputs into the evaluation framework. A likelihood of FIR was mapped to represent possible inundation for tourist sites under a moderate-heavy rainfall scenario and extreme rainfall scenario. The resultant map was verified by the maximum inundation extent merged by RS images and water bodies. The evaluation outcomes deliver the baseline and scientific information for urban planners and policymakers to take cost-effective measures for decreasing and evading the pressure of FIR on the sustainable development of urban tourism. The spatial improved-kNN-based framework provides an innovative, effective, and easy-to-use approach to evaluate the risk for the tourism industry under climate change.

scholarly journals IMPLEMENTASI K-NEAREST NEIGHBOR PADA PENENTUAN KELUARGA MISKIN BAGI DINAS SOSIAL KABUPATEN TABANAN

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
I Wayan Supriana ◽  
Luh Gede Astuti
Keyword(s):  
Poverty Alleviation ◽  
Nearest Neighbor ◽  
Central Government ◽  
Fundamental Problem ◽  
System Development ◽  
Poverty Rate ◽  
Tourism Industry ◽  
Regional Government ◽  
K Nearest Neighbor ◽  
The Government

ABSTRACT Poverty is one of the problems prioritized for completion by the central government or the regions. This condition seems to have no limits because every year the problem of poverty is an issue that has always been a discussion of the government. As in Bali, even though the tourism industry is growing very rapidly, until now the problem of poverty is still a fundamental problem that needs to be resolved. Based on data from the Central Statistics Agency in 2016 the poverty rate of the province of Bali is 4.25% and one of the districts that has a higher poverty rate than the province is Tabanan Regency [1]. Various poverty alleviation programs have been implemented to break the cycle of poverty. However, poverty alleviation programs that have been implemented by the Tabanan regional government are still not optimal. In overcoming these problems, this study has the aim of creating an application system that can identify the conditions of households in Tabanan regency. The system built will identify a family based on 5 welfare categories so that it will provide an easy assessment for the poverty program survey officers. The system development model uses the K-Nearest Nighbor algorithm in modeling and classifying households. The results showed the system had an assessment accuracy rate of 83% Keywords: Poverty, Poor Households, K-Nearest Neighbor ABSTRAK Kemiskinan menjadi salah satu permsalahan yang diprioritaskan untuk di selesaikan oleh pemerintah pusat maupu daerah. Kondisi ini seakan tidak ada batasnya karena setiap tahun permasalahan kemiskinan merupakan isu yang selalu menjadi pembahasan pemerintah. Seperti halnya di provinsi bali, meskipun industri pariwisata berkembang sangat pesat namu sampai saat ini permasalahan kemiskinan masih menjadi permasalahan mendasar yang perlu diselesaikan. Berdasarkan data Badan Pusat Statistik tahun 2016 tingkat kemiskinan provinsi bali sebesar 4,25% dan salah satu kabupaten yang memiliki tingkat kemiskinan lebih tinggi dari provinsi adalah Kabupaten Tabanan [1]. Berbagai program pengentasan kemiskinan sudah dilaksanakan untuk memutus siklus kemiskinan yang terjadi. Namun program-program pengentasan kemiskinan yang sudah dilaksanakan pemerintah daerah Tabanan masih belum optimal. Dalam mengatasi permasalahan tersebut, pada penelitian ini memiliki tujuan untuk membuat sistem aplikasi yang dapat mengidentifikasi kondisi rumah tangga yang ada di kabupaten Tabanan. Sistem yang dibangun akan mengidentifikasi sebuah keluarga berdasarkan 5 katagori kesejahteraan sehingga akan memberikan kemudahan penilaian untuk petugas pendata program kemiskinan. Model pengembangan sistem menggunakan algoritma K-Nearest Nighbor dalam memodelkan dan mengklasifikasi rumah tangga. Hasil penelitian menunjukkan sistem memiliki tingkat akurasi penilaian sebesar 83% Kata Kunci : Kemiskinan, Rumah Tangga Miskin, K-Nearest Neighbor

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.

PENENTUAN DAERAH PRIORITAS PELAYANAN AKTA KELAHIRAN DENGAN METODE K-NN DAN K-MEANS

2020 ◽  
Vol 17 (1) ◽  
pp. 319-328
Author(s):  
Ade Muchlis Maulana Anwar ◽  
Prihastuti Harsani ◽  
Aries Maesya
Keyword(s):  
Nearest Neighbor ◽  
Information Gain ◽  
Birth Certificate ◽  
Population Data ◽  
Community Services ◽  
Birth Certificates ◽  
Similar Data ◽  
K Nearest Neighbor ◽  
Civil Registration ◽  
The Family

Population Data is individual data or aggregate data that is structured as a result of Population Registration and Civil Registration activities. Birth Certificate is a Civil Registration Deed as a result of recording the birth event of a baby whose birth is reported to be registered on the Family Card and given a Population Identification Number (NIK) as a basis for obtaining other community services. From the total number of integrated birth certificate reporting for the 2018 Population Administration Information System (SIAK) totaling 570,637 there were 503,946 reported late and only 66,691 were reported publicly. Clustering is a method used to classify data that is similar to others in one group or similar data to other groups. K-Nearest Neighbor is a method for classifying objects based on learning data that is the closest distance to the test data. k-means is a method used to divide a number of objects into groups based on existing categories by looking at the midpoint. In data mining preprocesses, data is cleaned by filling in the blank data with the most dominating data, and selecting attributes using the information gain method. Based on the k-nearest neighbor method to predict delays in reporting and the k-means method to classify priority areas of service with 10,000 birth certificate data on birth certificates in 2019 that have good enough performance to produce predictions with an accuracy of 74.00% and with K = 2 on k-means produces a index davies bouldin of 1,179.

Optimizing Error Rate in Intrusion Detection System Using Artificial Neural Network Algorithm

2018 ◽  
Vol 6 (9) ◽  
pp. 152
Author(s):  
S. Vijaya Rani ◽  
G. N. K. Suresh Babu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Error Rate ◽  
Learning Process ◽  
Nearest Neighbor ◽  
Detection System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Artificial Neural

The illegal hackers  penetrate the servers and networks of corporate and financial institutions to gain money and extract vital information. The hacking varies from one computing system to many system. They gain access by sending malicious packets in the network through virus, worms, Trojan horses etc. The hackers scan a network through various tools and collect information of network and host. Hence it is very much essential to detect the attacks as they enter into a network. The methods  available for intrusion detection are Naive Bayes, Decision tree, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks. A neural network consists of processing units in complex manner and able to store information and make it functional for use. It acts like human brain and takes knowledge from the environment through training and learning process. Many algorithms are available for learning process This work carry out research on analysis of malicious packets and predicting the error rate in detection of injured packets through artificial neural network algorithms.

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%.

Prediksi Kelompok UKT Mahasiswa Menggunakan Algoritma K-Nearest Neighbor

2020 ◽  
Vol 8 (1) ◽  
pp. 121
Author(s):  
Sukamto Sukamto ◽  
Yanti Adriyani ◽  
Rizka Aulia
Keyword(s):  
Nearest Neighbor ◽  
K Nearest Neighbor

DRSA: a non-hierarchical clustering algorithm using k-NN graph and its application in vegetation classification

2015 ◽  
pp. 125-138 ◽  
Author(s):  
I. V. Goncharenko
Keyword(s):  
Cluster Analysis ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Clustering Algorithms ◽  
Protein Structures ◽  
Hierarchical Cluster ◽  
Vegetation Classification ◽  
K Nearest Neighbor ◽  
Neighbor Graph ◽  
Nearest Neighbor Graph

In this article we proposed a new method of non-hierarchical cluster analysis using k-nearest-neighbor graph and discussed it with respect to vegetation classification. The method of k-nearest neighbor (k-NN) classification was originally developed in 1951 (Fix, Hodges, 1951). Later a term “k-NN graph” and a few algorithms of k-NN clustering appeared (Cover, Hart, 1967; Brito et al., 1997). In biology k-NN is used in analysis of protein structures and genome sequences. Most of k-NN clustering algorithms build «excessive» graph firstly, so called hypergraph, and then truncate it to subgraphs, just partitioning and coarsening hypergraph. We developed other strategy, the “upward” clustering in forming (assembling consequentially) one cluster after the other. Until today graph-based cluster analysis has not been considered concerning classification of vegetation datasets.

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