scholarly journals C5.0 Algorithm Implementation on Web-Based Software and Usability Evaluation

SISTEMASI ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 394
Author(s):  
Deny Jollyta
Keyword(s):  
Data Mining ◽  
Decision Makers ◽  
Classification Algorithms ◽  
Test Results ◽  
Classification Problems ◽  
Process Data ◽  
Web Based ◽  
Classification Technique ◽  
Software Classification ◽  
Algorithm Implementation

AbstrakPerangkat lunak merupakan alat bantu yang memudahkan pengguna dalam pengolahan data dengan cepat dan tepat. Para pengambil keputusan membutuhkan alternatif perangkat lunak yang dapat digunakan setiap saat dengan teknik klasifikasi data algoritma C5.0 sesuai kriteria yang diinginkan. Namun perangkat lunak yang ada umumnya terdiri dari sejumlah teknik dan belum dapat digunakan secara online. Sebagai salah satu algoritma klasifikasi yang popular dalam ilmu data mining, C5.0 dapat memberikan hasil yang lebih baik. Penelitian bertujuan untuk membangun perangkat lunak yang dapat melakukan klasifikasi data menggunakan algoritma C5.0 berbasis web. Perangkat lunak dapat digunakan oleh siapa saja, terutama para pengambil keputusan. Penelitian ini juga dilengkapi dengan pengujian perangkat lunak usability sebelum digunakan. Hasil pengujian memperlihatkan bahwa perangkat lunak yang dibangun dapat diterima dengan nilai usability 76,892% dan berada pada predikat Baik. Diharapkan melalui penelitian ini, dapat memberikan alternatif perangkat lunak yang mampu menyelesaikan masalah klasifikasi menggunakan algoritma C5.0.Kata kunci: perangkat lunak, klasifikasi, algoritma c5.0, usability AbstractSoftware is a tool that makes it easy for users to process data quickly and precisely. Decision makers need an alternative software that can be used at any time with the C5.0 algorithm data classification technique according to the desired criteria. However, the existing software generally consists of a number of techniques and cannot be used online. As one of the popular classification algorithms in data mining science, C5.0 can provide better results. This study aims to build software that can classify data using the web-based C5.0 algorithm. Software can be used by anyone, especially decision makers. This research is also complemented by testing Usability software before used. The test results showed that the software built can be accepted with a Usability value of 76.892% and is in the Good predicate. It is hoped that through this research, it can provide alternative software that is able to solve classification problems using the C5.0 algorithm.Keywords: software, classification, c5.0 algorithm, usability

scholarly journals APRIORI ALGORITHM IMPLEMENTATION TO DETERMINE PURCHASE PATTERNS OF RAW MATERIALS AT PT PENJALINDO NUSANTARA

2021 ◽  
Vol 6 (2) ◽  
pp. 151-158
Author(s):  
Dedi Rozaq Prastyo ◽  
Sri Dianing Asri
Keyword(s):  
Data Mining ◽  
Raw Materials ◽  
Raw Material ◽  
Apriori Algorithm ◽  
Customer Demand ◽  
Web Based ◽  
Material Supply ◽  
Purchase Patterns ◽  
Algorithm Implementation ◽  
Data Mining Application

PT. Penjalindo Nusantara is a manufacturing company in the packaging field where production depends on customer demand or what is commonly known as job orders so that timely production work and availability of sufficient materials are mandatory for the company. There was a problem in the implementation of the raw material supply strategy by PT. Penjalindo Nusantara caused delays in the supply of raw material stocks. The solution to this problem is to apply the Apriori algorithm to find out what raw materials are being purchased simultaneously so that it can be the basis for implementing a purchasing strategy in supporting the effectiveness of procurement of raw material stocks and also saving time in sending raw materials by suppliers. This research uses a Web-based data mining application to find the raw material purchase pattern. The result of this research is obtained 11 patterns of purchasing raw materials using a minimum value of 90% support and a minimum of 100% confidence with a lift ratio of 1 as a reference for determining which raw materials will be purchased at the same time.

Prediction of Heart Diseases Using Data Mining Techniques

2018 ◽  
Vol 3 (2) ◽  
pp. 1-9 ◽  
Author(s):  
Nancy Masih ◽  
Sachin Ahuja
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Heart Diseases ◽  
Complete Analysis ◽  
Support Vector ◽  
Classification Algorithms ◽  
Process Data ◽  
Data Mining Techniques ◽  
Healthcare Data ◽  
Hidden Patterns

Health care organizations accumulate large amount of healthcare data, but it is not ‘extracted' to draw hidden patterns which can prove efficient for the decision making process. Data mining techniques can be used to gain insights by discovering hidden patterns which remain undetected manually. Data analytics proves to be useful in detection and identification of the diseases. A complete analysis has been conducted on the FHS (Framingham Heart Study) using various data analytic techniques viz. Decision tree, Naïve Bayes, Support vector machine (SVM) and Artificial neural network (ANN) and the results were ranked according to the accuracy. ANN produce better results than other classification algorithms. The output helps to find out the prominent features that cause heart disease and also identifies the most common features that must be analyzed for prediction of deaths due to heart disease. Despite various studies carried out on heart diseases, the main focus of this study is prediction of heart disease on the dataset of FHS by using various classification algorithms to achieve high accuracy.

scholarly journals Social Media Data using Various Classification Algorithms in Datamaning

2019 ◽  
Vol 8 (12S) ◽  
pp. 588-589
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Training Data ◽  
Classification Algorithms ◽  
Data Set ◽  
Social Media Data ◽  
The Past ◽  
Classification Technique ◽  
Mathematical Techniques ◽  
Media Data

Data Mining is one of the most successful domains in research. It describes the past and speculates the future for analysis. There are several techniques used in data mining. Among them classification is one of the main data mining techniques based on machine learning. In classification technique data set is classified into predefined set of groups or classes. Mathematical techniques such as decision tree, linear regression, neural networks and statistics are used for classification methods. Classification is a problem to identify which set of categories the new observation belongs to using training data set. This paper analyses the data taken from social media and uses the classification algorithm for making a comparative study on social advertisement using python.

Research on Text Classification Technique

2013 ◽  
Vol 278-280 ◽  
pp. 2081-2084
Author(s):  
Shi Min Wang ◽  
Xian Zhe Cao
Keyword(s):  
Data Mining ◽  
Text Classification ◽  
The Other ◽  
Classification Algorithms ◽  
Classification Technique

At first the paper will introduce the basic conception and the generic progress of text classification, after that it will introduce three text classification algorithms in detail and finally it will verify NB, SVM and KNN by experiment with the data mining software-weka. The result of experiment shows that KNN is more efficient than the other two algorithms in recall and precision.

A Comparative Analysis of Classification Algorithms on Students’ Performance

2020 ◽  
Vol 8 (2) ◽  
pp. 20-34
Author(s):  
Nilar Aye
Keyword(s):  
Data Mining ◽  
Comparative Analysis ◽  
Comparative Evaluation ◽  
Classification Algorithms ◽  
Large Dataset ◽  
Classification Technique ◽  
Education Data ◽  
Future Events ◽  
Student’S Performance ◽  
Events Data

Recently educational system, many features control a student’s performance. Students should be well stimulated to study their education. Motivation leads to interest, interest leads to success in their lives. Appropriate assessment of abilities encourages the students to do better in their education. Data mining is to find out patterns by analyzing a large dataset and apply those patterns to predict the possibility of the future events. Data mining is a very critical field in educational area and it provides high potential for the schools and universities. In data mining, there are various classification techniques with various levels of accuracy. This paper focuses to make comparative evaluation of four classifiers such as J48, Naive Bayesian, Bayesian Network and Decision Stump by using WEKA tool.  This study is to investigate and identify the best classification technique to analyze and predict the students’ performance of University of Jordan.

scholarly journals IMPLEMENTASI ALGORITMA APRIORI DALAM MENENTUKAN PERSEDIAAN OBAT

2017 ◽  
Vol 2 (1) ◽  
Author(s):  
Gusti Ahmad Syaripudin ◽  
Edi Faizal
Keyword(s):  
Data Mining ◽  
Programming Language ◽  
Association Rules ◽  
A Priori ◽  
Marketing Strategies ◽  
Apriori Algorithm ◽  
Drug Supply ◽  
Test Results ◽  
Computer Based ◽  
Algorithm Implementation

Computer-based transaction resulting in the accumulation of data in the database of an application. The data can be reprocessed to obtain important information. Data mining can be used to obtain valuable information for management purposes. The technique can be used are the rules of the association. One type of association rules is a priori algorithm. Application of a priori algorithm has been done in the analysis of sales. The research will be applied to the application pharmacies RMC. The programming language used for the algorithm implementation language is Java with Netbeans Platform 7.4 .DBMS used is MySQL. The test results showed a priori algorithm can be used to identify drugs that may be purchased in conjunction with other drugs, as well as showing the drug most widely sold and least by the set of combinations of items. Such recommendations can be used for management in determining drug supply and design marketing strategies quickly, accurately and efficiently.Keywords: java, apriori algorithm, netbeans, MySQL

scholarly journals PENERAPAN DATA MINING DENGAN METODE INTERPOLASI UNTUK ESTIMASI PREMI BERDASARKAN PENDAPATAN NASABAH ASURANSI PRUDENTIAL DI YOGYAKARTA

Compiler ◽  
2012 ◽  
Vol 1 (2) ◽  
Author(s):  
Pandapotan Pandapotan ◽  
Anto Setiawan Honggowibowo ◽  
Dwi Nugraheny
Keyword(s):  
Data Mining ◽  
Test Results ◽  
Application System ◽  
Web Based ◽  
Insurance Benefits ◽  
Prudential Value

Premi in a definition can be interpreted as a fee to be paid by the customer, which is a combination of the overall cost of insurance benefits that used, and sometimes also include the amount of money invested by customers. The output of the data mining is a customers is accepted according to the prudential value of the estimated premi be gained from the number of customer revenue.Final project is, created a program of web-based application system to estimate the premi to make it easier for admin (prudential employee)  to determine the amount of premi to be paid by the customer. In this program, admin (prudential employee) just need to know the amount of customer’s income that the program will then process the results of the estimated premi.The test results is concluded that the program estimates the premi can be used easily by the admin (prudential employee) and simplify admin decisions premi estimate to the customer.

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%.

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