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All Journal Jurnal Buana Informatika JURNAL ELEKTRO E-Dimas: Jurnal Pengabdian kepada Masyarakat J-Dinamika: Jurnal Pengabdian Kepada Masyarakat Building of Informatics, Technology and Science Jurnal Mantik Journal of Information Systems and Informatics Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Reswara: Jurnal Pengabdian Kepada Masyarakat Yumary: Jurnal Pengabdian kepada Masyarakat Jurnal Pengabdian Masyarakat Charitas JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Stephen Aprius Sutresno, Stephen Aprius
Universitas Katolik Indonesia Atma Jaya
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Public Health Social Sciences Other

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Journal : Building of Informatics, Technology and Science

 1 
Rekomendasi Content Creator Terbaik sebagai Pendukung Keputusan Penilaian pada Agensi Menggunakan Metode TOPSIS Eugenius Kau Suni; Stephen Aprius Sutresno
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.4082

Abstract

As an agency, it is necessary to evaluate and assign value to the content outcomes that have been published by content creators periodically. Aurora News Agency, which is one of the content creator agencies for Snack Video, has over 700 member content creators. This requires specialized techniques to facilitate and expedite the assessment of the performance of these content creators. Therefore, research was conducted on a decision support system using the TOPSIS method as a decision-making tool for evaluating the best content creators within the agency. Data was collected from a total of 630 content creators, and after undergoing data cleansing processes, a total of 10,916 content items were obtained. The research results present a ranking of the top 10 content creators based on their preference scores, ranging from the highest to the lowest. Anemz Tv is the content creator with the highest preference score of 0.4368, securing the first rank. On the other hand, Talenta.TV is the content creator with a preference score of 0.3203, earning the second rank. Upon analysis, differences in strategies for each content creator became apparent, with some focusing on quantity and others placing emphasis on the quality of content. In conclusion, the application of the TOPSIS method can be implemented relatively simply, with sufficiently fast computations, and it yields a diverse range of preference scores
Analisis Sentimen Masyarakat Indonesia Terhadap Dampak Penurunan Global Sebagai Akibat Resesi di Twitter Sutresno, Stephen Aprius
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3149

Abstract

A recession is a significant reduction in economic activity and is spread across the economy at its greatest for more than a few months, but it can also be seen in Real GDP, Real Income, Employment, Industrial Production, and Wholesale-Retail Sales. Recently, there has been a lot of public opinion regarding the recession that will occur in 2023, especially in Indonesia, on various social media such as Twitter. Based on these problems, sentiment analysis was carried out on tweets to obtain information on the positive or negative polarity of these opinions using the Naive Bayes and Support Vector Machine (SVM) methods to choose a more effective way in case studies to determine sentiment predictions. The steps are taken consist of data collection, processing data, weighting data, classification process, evaluation, validation, and results and discussion. The web scraping technique was used, and after going through the data cleaning stages, a total of 780 tweet data was obtained. The results of the classification test show that the SVM method has a greater accuracy rate with a proportion of 79.5% compared to the Naive Bayes method with a proportion of 72.5%. The SVM method's prediction results also show several 144 positive and 636 negative sentiments. Judging from the Wordcloud that was formed, it can be assumed that people are worried about their economic conditions, one of which is the unstable oil price which can trigger a recession.
Co-Authors Ade Iriani Airlangga, Gregorius Alvalent, Derrel Angkur, Lusiana V.G Artpinkan, Tamaria H Bata, Julius Victor Manuel Bernardus Alvin Rig Biafra Daffa Farabi Charmelita, Pauelina Christanto, Henoch Juli Christine Christine Christine Dewi Dagha, Willyam Chrisna Umbu Denny Jean Cross Sihombing Denny Jean Cross Sihombing Dominikus Vieri Tegar Linestyo Eko Sediyono Elfin Saputra Eugenius Kau Suni Eugenius Kau Suni Eugenius Kau Suni Gerald Edgard Laukon Gilberto Dennis G E Sidabutar Gilberto Dennis G E Sidabutar Henoch Juli Christanto Henoch Juli Christanto Henoch Juli Christanto Henoch Juli Christanto Henoch Juli Christanto Herlambang, Frederik Wiriawan Hiuredhy, Davin Kurnia Jesslyn Alvina Seah Juli Christanto, Henoch Julius Bata Julius Victor Manuel Bata Lim, Ricardo Jonathan Linestyo, Dominikus Vieri Tegar Mavish, Steven Octa Hutapea Othernima, Desi C Pedro Manuel Lamberto Buu Sada Philia, Gregoria Pijar Karisma Prihanto Piolo, Samuel Prihanto, Pijar Karisma Purwoko, Agus Radyan Rahmananta Raharjo, Julius Kevin Rudyanto, Robby Sentya Setiawan, Yohanes Evan Sidabutar, Gilberto Dennis G E Sidhunata, Billy Macarius Siemens Benyamin Tjhang Suni, Eugenius Kau Valentina, Vierena Vincentius Setiawan Suandi Widiarto, Sandra Octaviani B Widiarto, Sandra Oktaviani B Yerik Afrianto Singgalen Yoel Krisrian