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All Journal Prosiding SNATIF Prosiding Semnastek I-STATEMENT International Journal of New Media Technology Jurnal ULTIMATICS Building of Informatics, Technology and Science Kalbiscientia Jurnal Sains dan Teknologi Jurnal Minfo Polgan (JMP) Prioritas : Jurnal Pengabdian Kepada Masyarakat
Waworuntu, Alexander
Universitas Multimedia Nusantara
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Social Sciences

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Public Sentiment Analysis on the Transition from Analog to Digital Television Using the Random Forest Classifier Algorithm Samudera, Elfajar Bintang; Waworuntu, Alexander; Lumba, Ester
ULTIMATICS Vol 16 No 1 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i1.3653

Abstract

Television is one of the most popular media for entertainment and information. Analog television is the most widely used type among the public. However, with technological advancements, analog television is becoming obsolete and is being replaced by digital television, which offers better performance. On November 2, 2022, the Government officially mandated the transition from analog to digital broadcasting. This Analog Switch Off program has elicited various pro and con opinions among the public. Twitter, a widely used social media platform, facilitates rapid communication and information dissemination among users. This study aims to classify public sentiment regarding the Analog Switch Off policy as either positive or negative. The classification model used is the Random Forest algorithm, with the Lexicon Inset for data labeling, Count Vectorizer and TF-IDF Vectorizer for data vectorization and weighting, and various train-test data splits. The study achieved the best classification performance using the Count Vectorizer method, with an 80%:20% train-test data ratio, yielding an accuracy of 88%, precision of 88%, recall of 88%, and an F1-score of 88%. Index Terms”Analog Television; Digital Television; Sentiment; Twitter; Random Forest
Leveraging Content-Based Filtering for Personalized Game Recommendations: A Flutter-Based Mobile Application Development Waworuntu, Alexander
ULTIMATICS Vol 16 No 2 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i2.3936

Abstract

The background of this study stems from the need for a recommendation system to assist users in finding games that match their interests. With the rapid growth of the gaming market, an increasing number of people engage in gaming activities. In 2022, the personal computer (PC) gaming market accounted for 37.9% of all gamers worldwide. One of the largest PC gaming platforms is Steam, developed by Valve Corporation, which boasts over 184 million active users. However, the overwhelming number of options can lead users to lose interest in purchasing games. Therefore, a recommendation system is required to help users find games that align with their preferences. The methods/theories employed in this study include data from the Steam Web API, SteamSpy API, and local JSON files. The Content-Based Filtering method, using the Cosine Similarity algorithm, was implemented to determine the similarity index between games and user preferences. Flutter was used for application development and to display the recommendation results to users. The results of this study show that the application was successfully developed, and the Content-Based Filtering method provided recommendations that met expectations. The highest cosine similarity factor achieved was 0.6454972244, indicating a fairly good level of accuracy. Application evaluation using the Technology Acceptance Model revealed positive reception, with a "Perceived Usefulness" score of 82.6% and a "Perceived Ease of Use" score of 86.2%, indicating that users found the application both useful and easy to use.
Co-Authors Adhi Kusnadi Astika AyuningTyas, Astika Dennis Gunawan Destriana Widyaningrum Dirgantara, Harya Bima Dirgantara, Harya Bima Ester Lumba Fenina Twince Tobing Istiono, Wirawan Ivan Hermawan Kandoko, Charoline Leon Christopher Lukman Nadjamuddin Maria Irmina Prasetiyowati Marvelio, Christopher Matthew Novia Rahman Nisa Rofiq Harun Samudera, Elfajar Bintang Syam, Syahriani Tashia, Bella Yaman Khaeruzzaman