Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : Journal of Applied Information, Communication and Technology (JAICT)

Strengthening campus finance by analyzing attribute attributes for student registration classifications M Adib Al Karomi; Much. Rifqi Maulana; Slamet Joko Prasetiono; Ivandari Ivandari; Arochman Arochman
JAICT Vol 4, No 2 (2019)
Publisher : Politeknik Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (206.394 KB) | DOI: 10.32497/jaict.v4i2.1431

Abstract

Students are the most valuable assets in a private college. Assets like this that really need to be maintained and maintained, because most of the income from the private campus is derived from the tuition fees of students. The large number of students who resigned and did not conduct registration would have an impact on the financial institutions. STMIK Widya Pratama is the only computer science campus in Pekalongan City. Data from the last 5 years obtained from the new student admissions committee at STMIK Widya Pratama Pekalongan shows that out of 2670 prospective students who enroll, there are at least 514 prospective students who do not register. This means that around 20% of students do not register. Several analyzes related to the classification for student registration were conducted. In this case the best method that can be used is C45. In the process of calculating the C45 algorithm, information gain method is used to determine the importance of data attributes. The calculation results show that the attribute with the highest level of importance is the city_district attribute from the prospective student's residence, followed by the attributes of education, parental education, and tuition. These results can later be used and developed to create a system to support campus policy.
Improved Decission Tree Performance using Information Gain for Classification of Covid-19 Survillance Datasets Ivandari Ivandari; Much. Rifqi Maulana; M. Adib Al Karomi
JAICT Vol 7, No 1 (2022)
Publisher : Politeknik Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32497/jaict.v7i1.3501

Abstract

One of the most feared infectious diseases today is COVID-19. The transmission of this disease is quite fast. Patients also sometimes do not have the same symptoms. Overcoming the spread of the pandemic has been widely carried out throughout the world. Apart from the medical method, there are also many other methods, including computerization. Data mining is a discipline that can project data into new knowledge. One of the main functions of data mining is classification. Decision tree is one of the best models to solve classification problems. The number of data attributes can affect the performance of an algorithm. This study uses information gain to select the attribute features of the Covid-19 surveillance dataset. This study proves that there is an increase in the accuracy of the decision tree algorithm by adding information gain feature selection. Previously, the decision tree only had an accuracy rate of 65% for the classification of the Covid-19 surveillance dataset. After pre-processing using information gain, the accuracy rate increased to 75%.
Improved C45 performance with gain ratio for credit approval dataset Ivandari Ivandari; M Adib Al Karomi; Much. Rifqi Maulana
JAICT Vol 7, No 2 (2022)
Publisher : Politeknik Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32497/jaict.v7i2.3978

Abstract

Abstract— People's shopping behavior has undergone many changes after the COVID-19 pandemic. Many people have switched to using the marketplace to make buying and selling transactions. The payment process in the marketplace is relatively easy, especially when using a credit card. The increase in demand for credit must be addressed better by financial providers to minimize bad loans. The best thing in minimizing bad credit is to be more selective in choosing credit customers. Data mining is a field that can study old data to become new knowledge in the future. In data mining, the classification of bad credit customers is mostly done. One of the algorithms that excels in handling credit approval datasets is C45. The C45 model is widely used because it has an output decision tree that is easier to understand in human language. The number of data attributes can affect the performance of the algorithm. Feature selection is a form of attribute reduction to improve data quality and improve classification algorithm performance. Gain ratio is the development of information gain and is the best feature selection model and is widely used by researchers. This study performs a classification using C45 and uses a gain ratio for the selection of credit approval data features. By using the gain ratio, the accuracy of the C45 classification algorithm increased from the previous 94.12% to 95.29%.