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All Journal Aptisi Transactions on Management Computer Science and Information Technologies International Transactions on Education Technology (ITEE)
Nurtino, Tio
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Social Sciences

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Information Technology Development's Impact On Library Services fauziyyah, syifaa; Febriyanti, Rahma Ayu; Nurtino, Tio; Huzaifah, Muhamad Lutfi; Kusumawardhani, Dhiyah Ayu Rini
International Transactions on Education Technology (ITEE) Vol. 2 No. 1 (2023): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v2i1.397

Abstract

The purpose of this study is to find out how library services will be affected by advances in information technology. This research examines the various services offered by the library as well as the facilities and technology used there. A descriptive qualitative method was used in this study. Direct observation and interviews were used as data collection methods in this study. In addition, there was an opportunity for the researchers to ask some questions regarding the research that had been conducted. The library director participated in this research. The services provided by the library are a combination of manual and technological services. The main way information technology affects library services is by making it easier for library staff to do their jobs, attracting more visitors, and modernizing the facilities offered by the library.
Enhancing Organizational Efficiency through the Integration of Artificial Intelligence in Management Information Systems Bhima, Bhima; Rahmania Az Zahra, Achani; Nurtino, Tio; Firli, M. Zaki
APTISI Transactions on Management (ATM) Vol 7 No 3 (2023): ATM (APTISI Transactions on Management: September)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/atm.v7i3.2146

Abstract

This research delves into AI's role in enhancing Management Information Systems for organizational efficiency. It employs cross-sector case studies to showcase AI's potential in automating tasks, offering predictive insights from historical data, and bolstering decision-making. While AI promises substantial benefits, it also poses technical and ethical challenges during implementation. AI integration emerges as a game-changer, liberating organizations from mundane tasks through automation. Predictive analytics empowers firms to foresee trends, fostering a competitive edge in decision-making. Yet, obstacles include algorithm compatibility with existing systems and the demand for heightened technical proficiency. Ethical considerations loom large, demanding robust privacy and fairness guidelines in AI data usage. This research underscores the importance of employee AI training and multidisciplinary teams for tackling technical hurdles. Ethical principles should permeate AI development and utilization. The study recommends a three-fold strategy: First, prioritize employee AI training for seamless adoption. Second, establish cross-disciplinary teams to navigate technical complexities. Third, embed ethics in every AI facet to maintain trust. In conclusion, a holistic approach allows organizations to seamlessly integrate AI into Management Information Systems, yielding operational efficiencies, superior decision-making, and a competitive edge in a dynamic business landscape.
Trends in sentiment of Twitter users towards Indonesian tourism: analysis with the k-nearest neighbor method Purnama Harahap, Eka; Dwi Purnomo, Hindriyanto; Iriani, Ade; Sembiring, Irwan; Nurtino, Tio
Computer Science and Information Technologies Vol 5, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i1.p19-28

Abstract

This research analyzes the sentiment of Twitter users regarding tourism in Indonesia using the keyword "wonderful Indonesia" as the tourism promotion identity. The aim of this study is to gain a deeper understanding of the public sentiment towards "wonderful Indonesia" through social media data analysis. The novelty obtained provides new insights into valuable information about Indonesian tourism for the government and relevant stakeholders in promoting Indonesian tourism and enhancing tourist experiences. The method used is tweet analysis and classification using the K-nearest neighbor (KNN) algorithm to determine the positive, neutral, or negative sentiment of the tweets. The classification results show that the majority of tweets (65.1% out of a total of 14,189 tweets) have a neutral sentiment, indicating that most tweets with the "wonderful Indonesia" tagline are related to advertising or promoting Indonesian tourism. However, the percentage of tweets with positive sentiment (33.8%) is higher than those with negative sentiment (1.1%). This study also achieved training results with an accuracy rate of 98.5%, precision of 97.6%, recall of 98.5%, and F1-score of 98.1%. However, reassessment is needed in the future as Twitter users' sentiment can change along with the development of Indonesian tourism itself.
Co-Authors Ade Iriani Bhima, Bhima fauziyyah, syifaa Febriyanti, Rahma Ayu Firli, M. Zaki Hindriyanto Dwi Purnomo Huzaifah, Muhamad Lutfi Irwan Sembiring Kusumawardhani, Dhiyah Ayu Rini Purnama Harahap, Eka Rahmania Az Zahra, Achani