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All Journal Cakrawala Pendidikan International Journal of New Media Technology Ultimacomm: Jurnal Ilmu Komunikasi
Samiaji Bintang Nusantara
Universitas Multimedia Nusantara
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media

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U-TAPIS: AUTOMATIC SPELLING FILTER AS AN EFFORT TO IMPROVE INDONESIAN LANGUAGE COMPETENCIES OF JOURNALISTIC STUDENTS Niknik Mediyawati; Julio Cristian Young; Samiaji Bintang Nusantara
Jurnal Cakrawala Pendidikan Vol 40, No 2 (2021): Cakrawala Pendidikan (June 2021)
Publisher : LPMPP Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/cp.v40i2.34546

Abstract

The problem of Indonesian language errors among students is of particular observation. This problem becomes an important concern for students majoring in journalism because one day the graduates will become journalists. A language error filtering application has been developed that can be used quickly and accurately in journalists’ work. This application, which involves statistical analysis, computational language, and artificial intelligence, is named U-Tapis. This study was aimed at finding out the feasibility and effectiveness measures of the U-Tapis model by focusing on the language of students’ journalistic works such as opinions, news items, and news articles. The study involved 30 students majoring in Journalism, a private university in Jakarta, Indonesia. It was found that the students’ error rate decreased after the use of the model. It can be concluded that, in addition to eligibility which reaches 92.31%, the U-Tapis application can help effectively increase students’ proficiency in the use of the Indonesian language.
A Comparison of Traditional Machine Learning Approaches for Supervised Feedback Classification in Bahasa Indonesia Andre Rusli; Alethea Suryadibrata; Samiaji Bintang Nusantara; Julio Christian Young
IJNMT (International Journal of New Media Technology) Vol 7 No 1 (2020): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (575.434 KB) | DOI: 10.31937/ijnmt.v1i1.1485

Abstract

The advancement of machine learning and natural language processing techniques hold essential opportunities to improve the existing software engineering activities, including the requirements engineering activity. Instead of manually reading all submitted user feedback to understand the evolving requirements of their product, developers could use the help of an automatic text classification program to reduce the required effort. Many supervised machine learning approaches have already been used in many fields of text classification and show promising results in terms of performance. This paper aims to implement NLP techniques for the basic text preprocessing, which then are followed by traditional (non-deep learning) machine learning classification algorithms, which are the Logistics Regression, Decision Tree, Multinomial Naïve Bayes, K-Nearest Neighbors, Linear SVC, and Random Forest classifier. Finally, the performance of each algorithm to classify the feedback in our dataset into several categories is evaluated using three F1 Score metrics, the macro-, micro-, and weighted-average F1 Score. Results show that generally, Logistics Regression is the most suitable classifier in most cases, followed by Linear SVC. However, the performance gap is not large, and with different configurations and requirements, other classifiers could perform equally or even better.
Setelah Guncangan Digital Samiaji Bintang Nusantara; Ignatius Haryanto; Albertus Magnus Prestianta
Ultimacomm: Jurnal Ilmu Komunikasi Vol 11 No 1 (2019): UltimaComm
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1404.371 KB) | DOI: 10.31937/ultimacomm.v11i1.1113

Abstract

Working as a journalist is still aspired by some of Indonesia’s young generation, called the millennials. However, the media industry landscape in Indonesia is rapidly changing in recent years due to digital disruption. Several printed media have to work hard to adopt digital tools as a publication platform to reach news consumer, while others have desperately managing to expand its audience applying convergence strategies. Meanwhile, some media industries which could not compete in the digital market have decided to stop their publication. Since 2014, there were several media stop operating their production. In many cases, the media closure is followed by termination of employment among journalists. This study finds that it has not only impacted the future of the former journalists but also has caused precariousness condition for a young journalist. These journalists were the young generation, or the millennials age 24-35 years. Some of the millennials had worked for more than a year and enjoyed the privilege as a journalist. But when digital disruption came and hit the media where they worked, most of these young age journalists had not registered nor joined in a labor union that could have helped advocate them when facing disagreement with the media company. Besides, from 2014 to 2017, it is a difficult time for the journalist to adapt in the midst of the transition from conventional media to digital media. Keyword: digital disruption, young journalist, media closure
Co-Authors Albertus Magnus Prestianta Alethea Suryadibrata Andre Rusli Ignatius Haryanto Julio Christian Young Julio Cristian Young Niknik Mediyawati