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All Journal Journal of Information Systems and Informatics Ranah Research : Journal of Multidisciplinary Research and Development
Tampinongkol, Felliks F.
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Transportation Other

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Integration of Hash Encoding Technique with Machine Learning for Employee Turnover Prediction Kamila, Ahya Radiatul; Andry, Johanes Fernandes; Lee, Francka Sakti; Tampinongkol, Felliks F.
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1129

Abstract

Employee turnover refers to the replacement of employees within an organization, which can lead to losses such as recruitment costs and decreased productivity. Predicting turnover is crucial for companies to anticipate and take appropriate actions to retain potential employees. This study aims to optimize the employee turnover prediction model by integrating hash encoding techniques and machine learning. The dataset used in this study is an open-source dataset obtained from Kaggle dataset. It consists of 14,994 rows and 10 columns (features) representing employee-related information such as satisfaction level, evaluation score, number of projects, average monthly hours, and whether the employee left the company. Among these features, some are of object data type. Since machine learning algorithms generally cannot work directly with object-type features, the use of hash encoding is proposed. This technique converts object-type data into numerical data. It is part of the preprocessing stage, aiming to reduce memory usage, speed up data preprocessing, and improve model performance. After preprocessing is completed, the prediction model is trained using the Random Forest algorithm to predict employee turnover. The evaluation is conducted using accuracy, recall, precision, and F1-score metrics, which yielded results of 0.988, 0.961, 0.988, and 0.974, respectively. These results indicate that the integration of hash encoding techniques and machine learning can produce a well-performing model for predicting employee turnover.
Analisis Sentimen Pengguna Aplikasi STEAM dengan Algoritma Naive Bayes Kho, Andy; Tampinongkol, Felliks F.
Ranah Research : Journal of Multidisciplinary Research and Development Vol. 7 No. 6 (2025): Ranah Research : Journal Of Multidisciplinary Research and Development
Publisher : Dinasti Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/rrj.v7i6.1803

Abstract

Steam is a widely used digital game distribution platform with millions of users worldwide. The abundance of user reviews on the Google Play Store serves as a valuable source for analyzing user perceptions and satisfaction regarding the application. Therefore, this study performs sentiment analysis to understand user opinions about the Steam app. This research employs the Naive Bayes algorithm to classify user reviews into two sentiment categories: positive and negative. The process begins with collecting user reviews from the Google Play Store using web scraping techniques. The data then undergo preprocessing steps such as case folding, cleaning, tokenization, stopwords removal, and stemming to improve its quality. TF-IDF is used for feature extraction from the review texts, which are then used as input for the Naive Bayes Classifier model. The model is trained with training data and evaluated with testing data that has been previously split. Model performance is evaluated using a Confusion Matrix and metrics such as accuracy, precision, recall, and F1-score. The results show that the Naive Bayes model achieves an average accuracy of 84% in classifying sentiment. These findings indicate that the method is effective in understanding user opinions about the Steam application. This research is expected to provide insights for developers to improve application quality based on user feedback
Co-Authors Francka Sakti Lee Johanes Fernandes Andry Kamila, Ahya Radiatul Kho, Andy