cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Aji Prasetya Wibawa
Contact Email
aji.prasetya.ft@um.ac.id
Phone
-
Journal Mail Official
businta.2017@gmail.com
Editorial Address
Sudah terakreditasi SINTA 2. Editorial Office of Bulletin of Social Informatics Theory and Application Association for Scientific Computing and Electrical, Engineering (ASCEE)-Indonesia Section Jln. Supriyadi, Kel. Surodakan, Kec. Trenggalek, Kota Trenggalek, Propinsi Jawa Timur, 66316 Indonesia Email: businta.2017@gmail.com
Location
Kab. trenggalek,
Jawa timur
INDONESIA
Bulletin of Social Informatics Theory and Application
Published by Association for Scientific Computing, Electronics, and Engineering
ISSN : 26140047     EISSN : 26140047     DOI : https://doi.org/10.31763/businta.v6i2.601
Core Subject : Science, Social,
Computer Science & IT Social Sciences
Bulletin of Social Informatics Theory and Application (ISSN 2614-0047) is an interdisciplinary scientific journal for researchers from Computer Science, Informatics, Social Sciences, and Management Sciences to share ideas and opinions, and present original research work on studying the interplay between socially-centric platforms and social phenomena. Bulletin of Social Informatics Theory and Application is the first Asia-Pacific journal in social informatics. The journal aims to create a better understanding of novel and unique socially-centric platforms not just as a technology, but also as a set of social phenomena and to provide a media to help scholars from the two disciplines define common research objectives and explore methodologies. Bulletin of Social Informatics Theory and Application offers an opportunity for the dissemination of knowledge between the two communities by publishing of original research papers and experience-based case studies in computer science, sociology, psychology, political science, public health, media & communication studies, economics, linguistics, artificial intelligence, social network analysis, and other disciplines that can shed light on the open questions in the growing field of computational social science. To that end, we are inviting interdisciplinary papers, on applying information technology in the study of social phenomena, on applying social concepts in the design of information systems, on applying methods from the social sciences in the study of social computing and information systems, on applying computational algorithms to facilitate the study of social systems and human social dynamics, and on designing information and communication technologies that consider social context.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 9 Documents
 1 
Search results for , issue "Vol. 7 No. 1 (2023)" : 9 Documents clear
The use of social media for earthquake and Tsunami information Anik Nur Handayani; Reski Dwi Suciati; Ulfa Qomaria; Widya Lestari; Youngga Rega Nugraha; Paul Igunda Machumu
Bulletin of Social Informatics Theory and Application Vol. 7 No. 1 (2023)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

As one of the countries being passed by the Pacific Fire Circle (Ring of Fire), Indonesia is prone to earthquakes. Recently, an earthquake and tsunami occurred. A new disaster occurred in Central Sulawesi, Indonesia, resulting in the destroyed buildings and houses. This occurrence of the disaster was quite shocking to various countries. News of the occurrence of natural disasters immediately went viral through various social media due to the residents' and BMKG's posts. Social media is greatly involved in natural disaster cases as it facilitates analysis on the conditions and situations of earthquake-affected areas. Besides, it also functions to disseminate earthquake information. By using social media, such as Twitter and Facebook, we may rescue the directly affected individuals. It can also be means of sharing information and ways for people in and outside disaster-affected areas to volunteer and provide support-based information to affected individuals. Additionally, social media can perform important assistance functions such as identification of safety, placement of displaced people, provision of damage information, support for disabled people, volunteer organizations, fundraising, and a moral support system. This study discusses the potential usage of social media in disaster preparedness and response, especially in Central Sulawesi, Indonesia, earthquakes and tsunamis.
A decade evolution of virtual and remote laboratories Nurul Fajriah Andini; Popy Maulida Dewi; Tyas Agung Cahyaning Marida; Aji Prasetya Wibawa; Andrew Nafalski
Bulletin of Social Informatics Theory and Application Vol. 7 No. 1 (2023)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The conventional-experimental laboratory has problems of times, expenses, risks, distances, and spaces. A potential solution to such problems is a virtual and remote laboratory. It uses web or mobile applications and internet networks for virtual learning. This paper discusses the recent development of virtual and remote laboratories in the last decade. An in-depth literature review is performed to discover any facts about VR laboratory development. The results of this review may lead to the future development of virtual and remote laboratories.
Internship and Entrepreneurship in computer science Muhammad Bahauddin Alfan; Sabri Sangjaya; Muhammad Rizal Rusdiansyah; Fandi M Arfabuma; Gulsun Kurubacak
Bulletin of Social Informatics Theory and Application Vol. 7 No. 1 (2023)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Graduated students of State University of Malang majoring in Computer Science have required to do an internship in industry. Generally, the chosen private industry is in the form of a startup such as learning media applications and games. In this case, the authors would like to conduct research related to the impact of an internship program and another Computer Science subject related to entrepreneurial skills. It is expected that this research can improve the quality of student graduates in order to have an entrepreneurial spirit.
Social Media and e-government to prevent corruption in Indonesia Villages Adjie Rosyidin; Arief Yoga Pangestu; Austin Fascal Iskandar; Darusalam
Bulletin of Social Informatics Theory and Application Vol. 7 No. 1 (2023)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Examining the anti-corruption movement in Indonesia, this paper will give a review of the topic. The primary goal of this analysis is to discover whether or not e-government and social media can serve as an efficient weapon in the fight against corruption in Indonesian village budgets. This research evaluates corruption from several behavioral angles. To evaluate the efficacy of social media and e-government in combating corruption in Indonesia's village funds, researchers conducted a comprehensive literature review and normative juridical analysis using a conceptual approach. The study demonstrates that ICT's rapid expansion and improvement in Indonesia has had far-reaching consequences. The participants share and discuss corruption-related news and information. Because traditional punishment-oriented tactics have not been effective in eradicating corruption in Indonesia, incorporating technology into future efforts to do so might boost the likelihood of success.
Twitter sentiment analysis about economic recession in indonesia Fauzan Prasetyo Eka Putra; Fairuz Iqbal Maulana; Nawawi Muhammad Akbar; Wicaksono Febriantoro
Bulletin of Social Informatics Theory and Application Vol. 7 No. 1 (2023)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v7i1.592

Abstract

As one of the most popular social media platforms, Twitter enables users to express their opinions on diverse concepts, products, and services. Large quantities of data shared as tweets can be mined for user feedback and used to improve the quality of products and services. Using Twitter data and social media sentiment analysis, tracking how people feel about the recession in real time is possible. As a consequence, relevant organizations or governments can take preventative measures against the disinformation and unlawful conduct caused by the effects of the recession. This study aims to determine if there is a correlation between how people on Twitter feel about the recession. This study's data acquisition utilized "Recession"-tagged Twitter remarks from 2023. This study analyses filtered tweets for sentiment, emotion, word usage, and trends. According to the findings, 94% of tweets had benign sentiments, 4% had positive sentiments, and 2% had negative sentiments. Tweets with moderate subjective valence cluster in the middle of the polarity scale (between 1 and +1), while tweets with strong subjective valence are dispersed throughout the scale
Artificial intelligence in malnutrition research: a bibliometric analysis Herman Yuliansyah; Sulistyawati; Tri Wahyuni Sukesi; Surahma Asti Mulasari; Wan Nur Syamilah Wan Ali
Bulletin of Social Informatics Theory and Application Vol. 7 No. 1 (2023)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v7i1.605

Abstract

Malnutrition is a nutritional imbalance in a child’s body. Currently, there have been many reviews done on malnutrition in children. However, reviews on artificial intelligence linked with malnutrition are yet to be done. Thus, this study aims to identify the implementation of artificial intelligence in predicting malnutrition using bibliometric analysis. The bibliometric analysis consists of four stages: determining the purpose and scope, selecting the analytical technique, collecting data, and presenting the findings. Data used for this analysis is sourced from the Scopus database. The investigation was conducted using VOSviewer and “Publish or Perish” software. Based on five searched words: malnutrition, artificial intelligence, machine learning, neural networks, and deep learning, it was found that machine learning is the most widely used artificial intelligence approach for malnutrition research. Deep learning techniques are reported to grow as it is introduced as a new method in artificial intelligence. Malnutrition prediction tasks are the most studied problem. The use of deep learning, reinforcement learning, and transfer learning methods are used tremendously in malnutrition prediction research. This analysis’s results help improve the quality of the review by showing the mapping areas for malnutrition research.
Uncovering negative sentiments: a study of indonesian twitter users' health opinions on coffee consumption Laksono Budiarto; Nissa Mawada Rokhman; Wako Uriu
Bulletin of Social Informatics Theory and Application Vol. 7 No. 1 (2023)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v7i1.606

Abstract

The increase in coffee consumption among the public is due to several reasons, including health and lifestyle reasons. Awareness of the positive and negative effects of coffee consumption has also increased in society. This research is a sentiment analysis that aims to investigate Twitter users' opinions about the impact of coffee consumption on their health. The method used involves data collection using the RapidMiner application, utilizing the Twitter Application Programming Interface (API) function connected to a prepared Twitter account. The obtained data underwent data cleaning, saved as an Excel file type, training and testing, and model evaluation. Then, the data was classified into three categories: Negative Opinion, Neutral Opinion, and Positive Opinion. The results showed that less than 10% of opinions were positive, 19% were neutral, and 73% were negative. The opinions obtained are useful information for stakeholders in the coffee industry. They can also be used to determine better steps in educating the public about coffee.
Optimizing AWS lambda code execution time in amazon web services Muh Awal Arifin; Ramdan Satra; Lukman Syafie; Ahmad Mursyidun Nidhom
Bulletin of Social Informatics Theory and Application Vol. 7 No. 1 (2023)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v7i1.609

Abstract

One of the problems in providing infrastructure is the lack of interest in managing infrastructure. AWS Lambda is a FaaS (Function as a Service) service that allows users to run code automatically in an environment managed by Amazon Web Services. In this study, the method used is to collect data on code execution time at various input sizes, then perform an analysis of the factors that affect execution time. Furthermore, optimization is carried out by selecting the appropriate memory size and proper coding techniques to improve performance. The results show that optimizing memory size and coding can improve code execution time performance by up to 30%, depending on the type of service used. This can help AWS Lambda users improve code performance and save on operational costs.
Comparing neural network with linear Regression for stock market prediction Fachrul kurniawan; Yunifa Miftachul Arif; Fresy Nugroho; Mohammed Ikhlayel
Bulletin of Social Informatics Theory and Application Vol. 7 No. 1 (2023)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v7i1.621

Abstract

There are both gains and losses possible in stock market investing. Brokerage firms' stock investments carry a higher risk of loss since their stock prices are not being tracked or analyzed, which might be problematic for businesses seeking investors or individuals. Thanks to progress in information and communication technologies, investors may now easily collect and analyze stock market data to determine whether to buy or sell. Implementing machine learning algorithms in data mining to obtain information close to the truth from the desired objective will make it easier for an individual or group of investors to make stock trades. In this study, we test hypotheses on the performance of a financial services firm's stock using various machine learning and regression techniques. The relative error for the neural network method is only 0.72 percentage points, while it is 0.78 percentage points for the Linear Regression. More training cycles must be applied to the Algortima neural network to achieve more accurate results.

Page 1 of 1 | Total Record : 9

 1 

Filter by Year

2023 2023


Reset
Filter By Issues
All Issue Vol. 7 No. 1 (2023) Vol. 6 No. 2 (2022) Vol. 6 No. 1 (2022) Vol. 5 No. 2 (2021) Vol. 5 No. 1 (2021) Vol. 4 No. 2 (2020) Vol. 4 No. 1 (2020) Vol. 3 No. 2 (2019) Vol. 3 No. 1 (2019) Vol. 2 No. 2 (2018) Vol. 2 No. 1 (2018) Vol. 1 No. 2 (2017) Vol. 1 No. 1 (2017) More Issue