scholarly journals PENERAPAN TEXT MINING DALAM MENGANALISIS KEPRIBADIAN PENGGUNA MEDIA SOSIAL

2020 ◽  
Vol 5 (1) ◽  
pp. 63-71
Author(s):  
M. Pramadani Riyanis Putra ◽  
Kiki Rizky Nova Wardani
Keyword(s):  
Text Mining ◽  
Big Five ◽  
Nearest Neighbor ◽  
Quality Information ◽  
Big Five Personality ◽  
Classification Methods ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
High Quality Information ◽  
K Nearest Neighbor Algorithm

Facebook”is a social networking application where users reveal a lot about the mselves”through their posting pages. So the writer wants”to know what information can be taken about the user's”personality. Data mining plays an important role”which aims to”transform raw data into a structure that can be understood”for further use. ”Text mining refers to the process of”retrieving”high quality information from text”,one of the classification methods that can be used is the K-Nearest Neighbor algorithm. Based on the theory of big five personality”the results of the study concluded that”the accuracy rate obtained was 92.92%, from 550 data with the highest openness personality character value of 239, Conscientiouseness of 16 data, Extraversion of 173 data, Agreeableness of 50 data, Neuroticism of 33 data and 39 data that cannot be classified.

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 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 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 Performance comparison between naive bayes and k- nearest neighbor algorithm for the classification of Indonesian language articles

2021 ◽  
Vol 10 (2) ◽  
pp. 452
Author(s):  
Titin Winarti ◽  
Henny Indriyawati ◽  
Vensy Vydia ◽  
Febrian Wahyu Christanto
Keyword(s):  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Performance Comparison ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm ◽  
Bayes Algorithm

<span id="docs-internal-guid-210930a7-7fff-b7fb-428b-3176d3549972"><span>The match between the contents of the article and the article theme is the main factor whether or not an article is accepted. Many people are still confused to determine the theme of the article appropriate to the article they have. For that reason, we need a document classification algorithm that can group the articles automatically and accurately. Many classification algorithms can be used. The algorithm used in this study is naive bayes and the k-nearest neighbor algorithm is used as the baseline. The naive bayes algorithm was chosen because it can produce maximum accuracy with little training data. While the k-nearest neighbor algorithm was chosen because the algorithm is robust against data noise. The performance of the two algorithms will be compared, so it can be seen which algorithm is better in classifying documents. The comes about obtained show that the naive bayes algorithm has way better execution with an accuracy rate of 88%, while the k-nearest neighbor algorithm has a fairly low accuracy rate of 60%.</span></span>

scholarly journals Classification of Radical Web Content in Indonesia using Web Content Mining and k-Nearest Neighbor Algorithm

2018 ◽  
Vol 5 (2) ◽  
pp. 328-348
Author(s):  
Muh Subhan ◽  
Amang Sudarsono ◽  
Ali Ridho Barakbah
Keyword(s):  
Text Mining ◽  
Nearest Neighbor ◽  
Experimental Result ◽  
Web Content ◽  
K Nearest Neighbor ◽  
K Value ◽  
Content Mining ◽  
Result Show ◽  
K Nearest Neighbor Algorithm ◽  
Radical Content

Radical content in procedural meaning is content which have provoke the violence, spread the hatred and anti nationalism. Radical definition for each country is different, especially in Indonesia. Radical content is more identical with provocation issue, ethnic and religious hatred that is called SARA in Indonesian languange. SARA content is very difficult to detect due to the large number, unstructure system and many noise can be caused multiple interpretations. This problem can threat the unity and harmony of the religion. According to this condition, it is required a system that can distinguish the radical content or not. In this system, we propose text mining approach using DF threshold and Human Brain as the feature extraction. The system is divided into several steps, those are collecting data which is including at preprocessing part, text mining, selection features, classification for grouping the data with class label, simillarity calculation of data training, and visualization to the radical content or non radical content. The experimental result show that using combination from 10-cross validation and k-Nearest Neighbor (kNN) as the classification methods achieve 66.37% accuracy performance with 7 k value of kNN method[1].

scholarly journals Algoritma K-Nearest Neighbor Untuk Menentukan Kelayakan Keluarga Penerima Bantuan Pangan Non Tunai (Studi Kasus : Kelurahan Karya Jaya)

2020 ◽  
Vol 1 (2) ◽  
pp. 75-87
Author(s):  
Sastri Yani ◽  
Fithri Selva Jumeilah ◽  
Muhamad Kadafi
Keyword(s):  
Nearest Neighbor ◽  
Food Assistance ◽  
Optimal Distribution ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Classification Technique ◽  
K Nearest Neighbor Algorithm ◽  
F Measure ◽  
Environmental Units ◽  
Social Affairs

Non-cash Food Assistance (BPNT) is food social assistance in the form of non-cash. In its implementation, this program still encounters a number of obstacles, one of which is in the sub-optimal distribution of aid in several regions, including Karya Jaya Village. This is because the Ministry of Social Affairs is not optimal in determining BPNT recipients. One way to solve this problem is by utilizing one of the data mining concepts, namely the classification technique with the K-Nearest Neighbor algorithm. Where KPM data previously only accumulated can be used as useful information, one of which is to predict the eligibility of BPNT recipients in the next period. The results of this research are in the form of information on the results of predictions of appropriate KPM as BPNT recipients in 2021 and Local Environmental Units (SLS) which are the most receiving regions. This information can be used as evaluation material for the Ministry of Social Affairs in determining the more targeted BPNT recipients. The prediction results of BPNT recipients in Karya Jaya Village in 2021 are 511 recipients with an accuracy rate of 75.79%, 76.17% Precision, 89.24% Recall, and 82.19% F-measure. And it can be seen that the most BPNT recipient categories are in SLS RW 005, namely 74 recipients. Where there are variables that most influence, namely sta_kis

scholarly journals Klasifikasi Jenis Laporan Masyarakat Dengan K-Nearest Neighbor Algorithm

2020 ◽  
Vol 5 (1) ◽  
pp. 77-85
Author(s):  
Heru Pramono Hadi ◽  
Titien S. Sukamto
Keyword(s):  
Text Mining ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm

Feedback masyarakat terhadap pelayanan pemerintah merupakan elemen penting dalam proses evaluasi dan peningkatan kinerja. Maka dari itu pemerintah perlu untuk memiliki metode pelaporan yang efektif, efisien dan sistematis. Feedback masyarakat dapat berupa pengaduan, permintaan informasi dan aspirasi. Salah satu cara penyampain feedback masyarakat adalah melalui media sosial. Klasifikasi jenis laporan/feedback masyarakat ini penting dilakukan untuk mempercepat proses penanggapan laporan. Algoritma K-Nearest neighbor pada metode text mining ini merupakan salah satu solusi untuk dapat membantu proses klasifikasi jenis laporan. Dengan 930 data latih dan 100 data uji laporan masyarakat tahun 2017 yang disampaikan melalui media sosial, menghasilkan nilai akurasi tertinggi k=11 sebesar 82%.

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