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All Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Tekno Kompak Building of Informatics, Technology and Science Jurnal Teknik Informatika (JUTIF) Jurnal Telematics and Information Technology (TELEFORTECH)
Parjito, Parjito
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Computer Science & IT Education

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Journal : Jurnal Teknik Informatika (JUTIF)

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COMBINATION OF AHP AND MAUT METHOD TO DETERMINE SCHOLARSHIP RECIPIENTS IN HIGHER EDUCATION (CASE STUDY: UNIVERSITAS TEKNOKRAT INDONESIA) Romdoni, Muhammad; Parjito, Parjito
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.1940

Abstract

Universitas Teknokrat Indonesia is an educational institution located in the city of Bandar Lampung. Every new academic year a new student admission selection is carried out. Selection is carried out with two channels, namely regular and scholarship. One of the scholarship pathways is the Indonesia Smart Lecture Card (KIP-K). The acceptance of the scholarship pathway is done conventionally. This method certainly has obstacles, namely less effective time efficiency. The solution offered is through research by applying a combination of Analytical Hierarchy Process (AHP) and Multi Attribute Utility Theory (MAUT) methods to the Decision Support System. To assist in making decisions to determine prospective students who are eligible for KIP-K Scholarships, the right criteria are needed. The criteria used include economic status, achievement, parents' income, number of dependents, housing conditions, previous scholarships, parental assistance, organizational experience, test scores, and parents' status. The purpose of this research is to apply the AHP and MAUT methods in a decision support system that can assist the campus in determining scholarship recipients quickly, precisely and efficiently. The stages of this research are data collection, application of AHP and MAUT methods, and system implementation. Based on calculations carried out using a combination of AHP and MAUT methods, the highest preference value is Destia Putri with a value of 0.7791 and the lowest preference value is Pramutya Galuh 0.0444. Judging from the ranking results, it can be concluded that the combination of AHP and MAUT methods can be used to assist in decision making to determine prospective student recipients of KIP-K scholarships at Universitas Teknokrat Indonesia.
COMPARISON OF NAIVE BAYES AND RANDOM FOREST METHODS IN SENTIMENT ANALYSIS ON THE GETCONTACT APPLICATION Arisula, Juan Pala; Parjito, Parjito
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2004

Abstract

The rapid growth in the use of social media and instant messaging platform apps has significantly changed the way people communicate. One of the most popular apps is GetContact, a platform focused on identifying the phone numbers of irresponsible people and reducing the impact of spam calls. In cases like this, sentiment analysis is important to understand user responses to the service. In performing sentiment analysis, there are two classification methods that will be used, namely the Naive Bayes and Random Forest methods. This research utilizes the SMOTE technique to handle data imbalance, and the results show that the application of SMOTE successfully improves classification accuracy. The Random Forest model performed better than Naive Bayes, with 80% accuracy, 84% precision, 77% recall, and 80% F1 score for positive sentiments, while Naive Bayes achieved 77% accuracy, 79% precision, 79% recall, and 79% F1 score. Although Random Forest is superior in precision, recall , and F1 score for positive sentiments, it performs almost on par with Naive Bayes in classifying negative sentiments, with 76% precision , 84% recall, and 80% F1 score for Random Forest, and 76% precision, 76% recall , and 76% F1 score for Naive Bayes. This shows that both models provide similar results in identifying negative sentiment overall.
Co-Authors Arisula, Juan Pala Dani, Jemmi Rama Erwanto Megawaty, Dyah Ayu Muhamad Azhari Pratama, Edvan Agus Putra, M. Pajar Kharisma Romdoni, Muhammad S. Samsugi Saputra, M Rafli Tono, Jimmy Julian Wijaya, Feri Aldi