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All Journal Journal of Information Systems and Informatics IJAIT (International Journal of Applied Information Technology)
Yualinda, Sherli
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Computer Science & IT

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Implementing IT-Based Succession Planning in University IT Units: Enhancing Operational Continuity Yualinda, Sherla; Adi, Taufik Nur; Fakhurroja, Hanif; Yualinda, Sherli
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.1023

Abstract

One of the accredited universities in Indonesia is committed to quality education through the use of information technology. However, the university's IT unit often experiences vacancies in key positions due to high employee turnover, which impacts workload and business processes, especially in handling Request for Change (RFC). While application X supports performance appraisals, it has not been optimized for succession planning. This study explores the potential of application X as a tool for succession planning by integrating the Rothwell and Integrated Talent Management models. The design includes identifying key positions, assessing candidate competencies, preparing development plans, and establishing a structured knowledge transfer system to sustain organizational leadership. Additionally, integrating Large Language Models (LLMs) like ChatGPT is expected to enhance assessment objectivity, provide individual development recommendations, and ensure a more effective leadership transition. The system's role in improving assessment objectivity is vital for unbiased, data-driven decisions, while its contribution to leadership transitions ensures a smoother, more systematic process for maintaining leadership continuity. With features such as candidate search and staff assessment, the system is expected to help organizations select the right replacement and maintain university operations.
Developing an IT-Based Knowledge Sharing System for University IT Units: Integrating Large Model Language Yualinda, Sherli; Adi, Taufik Nur; Fakhurroja, Hanif; Yualinda, Sherla
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.1024

Abstract

Information technology companies in Indonesia face the challenge of high employee turnover, which leads to the loss of important knowledge and has an impact on productivity and innovation. This research aims to develop conceptual knowledge sharing and knowledge sharing systems in university IT units, which do not yet have an integrated system for documenting knowledge. Observations show that the ticketing system used can be optimized as a long-term knowledge sharing platform. The designed model includes strengthening the culture of sharing, utilizing social networks within the organization, applying information technology, reward systems, and the SECI model approach. In addition to knowledge repository features, role systems, documentation automation, search, and collaboration modules, the integration of Large Language Models (LLM) such as ChatGPT is expected to improve information search, documentation automation. LLMs play a crucial role in enhancing user interactions by enabling natural language queries, improving search accuracy, and automating knowledge classification. Moreover, they facilitate knowledge extraction from unstructured data, assist in summarizing key insights, and provide adaptive learning capabilities. By leveraging LLMs, the system can increase efficiency, reduce the time required to find relevant information, and ensure knowledge continuity within the organization.
Poverty Level Prediction Based on E-Commerce Data Using Naïve Bayes Algorithm and Similarity-Based Feature Selection Aji, Pramuko; Wijaya, Dedy Rahman; Hernawati, Elis; Yualinda, Sherla; Yualinda, Sherli; Frasanta, Muhammad Akbar Haikal; Kannan, Rathimala
IJAIT (International Journal of Applied Information Technology) Vol 07 No 02 (November 2023)
Publisher : School of Applied Science, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/ijait.v7i02.5374

Abstract

The poverty rate is an important measure of any country because it indicates how well the economy develops and how well the economic prosperity distributes among citizens. The Central Statistics Agency, or BPS, measures the poverty rates in Indonesia using the concept of the ability to meet demands (basic needs approach). Using this approach, spending becomes a measure of poverty, defined as an economic incapacity to satisfy food and non-food requirements. Thus, the poor are individuals whose monthly per capita spending is less than the poverty threshold. In this study, the machine learning method using Naive Bayes with similarity-based feature selection and e-commerce data has been proposed to predict the poverty level in Indonesia. We proposed the method to be used as a complement to the results of the costly surveys and censuses conducted by BPS. Our experiments show that the classifier shows little relevance between the predicted and the original values or actual poverty prediction based on BPS data. A limited number of features does not necessarily result in poor accuracy, however great accuracy is not always achieved if a lot of features are being used.
Co-Authors Dedy Rahman Wijaya Elis Hernawati Fakhurroja, Hanif Frasanta, Muhammad Akbar Haikal Kannan, Rathimala Pramuko Aji Taufik Nur Adi Yualinda, Sherla