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Talent Management Employee Development by Using Certainty Factor Method of Expert System Hamid, Aditia Putra; Al Hakim, Rosyid Ridlo; Sungkowo, Aming; Trikolas, Trikolas; Purnawan, Hendra; Jaenul, Ariep
ARRUS Journal of Engineering and Technology Vol. 1 No. 1 (2021)
Publisher : Lembaga Penelitian dan Pengembangan Teknologi dan Rekayasa, Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/jetech568

Talent management is a factor that determines success in the business environment because talent management requires quantitative and qualitative skills. This study aims to implement the certainty factor (CF) method of an expert system for employee development talent management. This research using a certainty factor (CF) method to design an expert system framework. Due to the focus on our research aim, we provide a certainty factor calculation with mathematical modeling for calculating talent management employee development in X Company. The confidence level is 93.55% for a recommendation of not promotion of the job; for 52.38% is a recommendation that can be proposed for promotion, but HRD will evaluate in some time; for 98.73% is a recommendation for promotion of the job. We used CF calculation that can provide the level of confidence (in %). The calculation of the certainty factor (CF) method can be used for recommending job promotion in some companies.

Talent Management Employee Development by Using Certainty Factor Method of Expert System Aditia Putra Hamid; Rosyid Ridlo Al Hakim; Aming Sungkowo; Trikolas Trikolas; Hendra Purnawan; Ariep Jaenul
ARRUS Journal of Engineering and Technology Vol. 1 No. 1 (2021)
Publisher : Lembaga Penelitian dan Pengembangan Teknologi dan Rekayasa, Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/jetech568

Talent management is a factor that determines success in the business environment because talent management requires quantitative and qualitative skills. This study aims to implement the certainty factor (CF) method of an expert system for employee development talent management. This research using a certainty factor (CF) method to design an expert system framework. Due to the focus on our research aim, we provide a certainty factor calculation with mathematical modeling for calculating talent management employee development in X Company. The confidence level is 93.55% for a recommendation of not promotion of the job; for 52.38% is a recommendation that can be proposed for promotion, but HRD will evaluate in some time; for 98.73% is a recommendation for promotion of the job. We used CF calculation that can provide the level of confidence (in %). The calculation of the certainty factor (CF) method can be used for recommending job promotion in some companies.

Recent Updates on Intelligent System for Talent Management: Does That Become a Helpful System? Aditia Putra Hamid; Rosyid Ridlo Al-Hakim; Yanuar Zulardiansyah Arief; ‪Brainvendra Widi Dionova; Mahmmoud Hussein A. Alrahman
SATESI: Jurnal Sains Teknologi dan Sistem Informasi Vol. 3 No. 1 (2023): April 2023
Publisher : Yayasan Pendidikan Penelitian Pengabdian ALGERO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/satesi.v3i1.1590

The use of information technology in human management today has increased, along with the implementation of intelligent systems that generally use artificial intelligence (AI) in their application. One of the areas of human management that has adopted AI is talent management (TM). TM is crucial for companies to identify, manage, determine, assess, and recommend talent (in this case, it can be employees) for their company's sustainability. The application of family planning in TM is not as extensive as thought, but this study tries to review the latest research that adapts AI to a very complex TM process. The results of this review are at least 11 articles involved in the use of family planning. These 11 articles certainly discuss one or more processes in TM, such as talent identification, talent matching or mapping, and talent recommendations. Some critical studies in the future are that in practice, AI needs to be widely used, especially to handle large-scale data management (data intelligence), in addition to intelligent system methods, and AI can be used for all processes in TM, proven to be accurate, efficient, safe, and fast in practice.

Predict the thyroid abnormality particular disease likelihood of the symptoms’ certainty factor value and its confidence level: A regression model analysis Rosyid Ridlo Al-Hakim; Yanuar Zulardiansyah Arief; Agung Pangestu; Hexa Apriliana Hidayah; Aditia Putra Hamid; Aviasenna Andriand; Nur Fauzi Soelaiman; Machnun Arif; Mahmmoud Hussein Abdel Alrahman
Sistemasi: Jurnal Sistem Informasi Vol 12, No 2 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i2.2542

The traditional expert system (TES) in the medical field commonly uses a certainty factor (CF) rule-based algorithm that can be calculated several symptoms to determine the inference solutions. The main issue for this TES included a prediction for some particular disease likelihood in the cases of new patients. CF is calculated based on symptoms related to clinical signs in patients’ diagnoses. For some reason, this TES probably won’t predict uncertain things, such as particular disease likelihood of some diseases. So, supervised learning, such as linear regression, can solve this problem. We tried to analyse the existing TES for thyroid disorders due to modelling the regression equation to predict the thyroid abnormality particular disease likelihood, based on the symptoms’ CF value and its confidence level. We used multiple linear regression (MLR) and multiple polynomial regression (MPR) to analyse the best regression model to solve the problem. The results show that the MPR model indicates the best regression model for predicting particular disease likelihood of thyroid abnormality, supported by R-squared 94.7%, R-squared adjusted 94.4%, F-value 265.925, and p-value < 0.05, which are higher than MLR model. Our study proposed a foundation for expert system development by focusing more on machine learning expert system (MLES) analysis approaches than TES.

Predictive Machine Learning Model to Predict the Price Movements of Cryptocurrency Meme Coin in the Solana Ecosystem Aditia Putra Hamid
International Journal of Integrated Science and Technology Vol. 2 No. 9 (2024): September 2024
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijist.v2i9.2546

The meme coin ecosystem on the Solana blockchain is showing rapid growth in 2024, thanks to its superior blockchain technology and strong community support. Meme coin projects such as BONK, and DOGWIFHAT have leveraged these advantages to thrive in the Solana ecosystem. This study aims to build a prediction model for the price movement of meme coin cryptocurrencies in the Solana ecosystem using the Long Short-Term Memory (LSTM) method, with Adam optimization. Historical meme coin price data is taken as the research dataset, and the model is trained using LSTM with several epoch variations to obtain the best results. The model is evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE). The experimental results show that the LSTM model with Adam optimization can provide fairly accurate predictions, with the best performance at epoch 75 where the model successfully achieves a balance between training and testing data performance, without experiencing overfitting. This study provides valuable insights for investors, developers, and policymakers into the dynamics of the meme coin ecosystem on Solana and its potential use in the development of blockchain technology. With a better unders

Blockchain Technology for Employee Verification and Background Checks in the Human Resources Recruitment Process Aditia Putra Hamid; Edi Sugiono; Hasanudin
International Journal of Educational and Life Sciences Vol. 2 No. 7 (2024): July 2024
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijels.v2i7.2294

Blockchain technology offers an innovative solution for employee verification and background checks in the human resources (HR) recruitment process. This research aims to explore the application of blockchain technology in employee verification and background checks in the human resource (HR) recruitment process through prototype development and evaluation. This research methodology uses a qualitative approach with a focus on prototype design and testing. With a decentralised and transparent system, blockchain can reduce costs, increase trust, and protect personal data. The results show that the adoption of blockchain in recruitment can optimise operational efficiency and minimise the risk of data fraud.

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