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Journal : IJITEE (International Journal of Information Technology and Electrical Engineering)

Ontology-Based Social Media Talks Topic Classification (Twitter Case) Fransisca Julia Kusuma Deviyanti; Sri Suning Kusumawardani; Paulus Insap Santosa
IJITEE (International Journal of Information Technology and Electrical Engineering) Vol 3, No 1 (2019): March 2019
Publisher : Department of Electrical Engineering and Information Technology,Faculty of Engineering UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (984.569 KB) | DOI: 10.22146/ijitee.46534

Abstract

In the era of digital communication, the use of Twitter as a customer service has been widely encountered. Companies have started to develop strategies around effective use of Twitter, one of which was to identify problems that customers frequently complain about. Twitter, with its straightforward tweet characteristics, will certainly contain sentences with very specific and easily recognizable keywords. These characteristics can be used as a basis for classifying tweets into certain topics. With a help of ontology, classification with keywords can be done automatically. The purpose of this paper is to design an ontology used as a basis for classifying tweets into certain topics related to the 4G telecommunications network in Indonesia and to evaluate performance of proposed classifier model.
A Multi Criteria Decision Making to Support Major Selection of Senior High School Adhistya Erna Permanasari; Marsetyo Wisaksono; Sri Suning Kusumawardani
IJITEE (International Journal of Information Technology and Electrical Engineering) Vol 3, No 4 (2019): Desember 2019
Publisher : Department of Electrical Engineering and Information Technology,Faculty of Engineering UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijitee.54427

Abstract

Senior high school students need to select a specialization, such as Mathematics and Natural Sciences, Social Sciences, or Language and Culture. This selection process can be improved by using Multi Criteria Decision Making (MCDM) methods. When MCDM methods are implemented, AHP method has accuracy of 61%, whereas AHP-Fuzzy TOPSIS 1 and AHP-Fuzzy TOPSIS 2 have accuracy of 75%. This research implements tests and analyzes new MCDM method, which is Hybrid MCDM Model, in helping aforementioned specialization selection process. There are four basic steps in Hybrid MCDM Model: performing experimental design to obtain attributes' weight and criteria, evaluating MCDM with the three existing methods, performing RSM regression to derive mathematical model, and decision making. This research introduces data normalization to the mathematical model which results in better implementation of Hybrid MCDM Model in the senior high school students' specialization selection process. Hybrid MCDM Model in the senior high school student specialization selection has accuracy of 86%, which includes 11% accuracy improvements compared to other applied MCDM methods.