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All Journal JIMEBIS Bulletin of Information Technology (BIT) Jurnal Riset Inossa : Media Hasil Riset Pemerintahan, Ekonomi dan Sumber Daya Alam Variable Research Journal
Yulindawati
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Journal : Bulletin of Information Technology (BIT)

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Application of The Naïve Bayes Algorithm for Employee Performance Prediction Based on SIMPEG at TVRI East Kalimantan Station Hanani, Ishmah; Lailiyah, Siti; Yulindawati
Bulletin of Information Technology (BIT) Vol 6 No 4 (2025): Desember 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i1.2294

Abstract

Employee performance evaluation is a crucial aspect of public organizational management, including at the public broadcasting institution TVRI East Kalimantan Station. To date, attendance indicators obtained from the Employee Management Information System (SIMPEG) have often been used as the primary benchmark, as the data are objectively and structurally available. However, a single attendance-based approach risks overlooking more substantive aspects of work achievement. Therefore, this study integrates attendance data with the Employee Performance Targets (SKP) to construct a more representative performance label. The method employed is a classification approach using the Naïve Bayes (GaussianNB) algorithm. The research dataset consists of attendance records (normal attendance, leave, official duty, study assignment, early departure, absence, and total working days) and quantized SKP scores. Performance labels were generated using a composite score (0.30 × attendance percentage + 0.70 × normalized SKP), which was then categorized into three classes: Excellent, Good, and Needs Improvement. The model was trained using SIMPEG and SKP data that had undergone preprocessing, data partitioning, and class balancing. Experimental results show that the model achieved an accuracy of 0.83, with a precision of 0.86, recall of 0.84, and F1-score of 0.83 on the test data. These results indicate that the model can consistently recognize employee performance patterns across all categories. Practically, this study offers a simple, efficient, and easily implementable predictive framework to support more objective processes of coaching, monitoring, and reward allocation within TVRI East Kalimantan Station.
Analisis Dan Prediksi Hasil Pertandingan Dota 2 Menggunakan Fuzzy Tsukamoto Tan, Muhammad Arief Adidharma; Yulindawati; Fahmi, Muhammad
Bulletin of Information Technology (BIT) Vol 6 No 4 (2025): Desember 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i4.2382

Abstract

Predicting the outcome of a Dota 2 match is a complex problem because it is influenced by many dynamic variables that change at each stage of the game. This study aims to analyze and predict the probability of winning a Dota 2 match using the Fuzzy Tsukamoto method based on three main variables: Hero Win Rate, Number of Kills, and Tower Destroyed. The fuzzy model was constructed using triangular and trapezoidal membership functions, with variable weights adjusted for the early game, mid game, and late game. Test results show that in the early game, the Hero Win Rate variable has the most dominant influence on the probability of winning, with a weight of 0.7. In the mid game, the number of kills and tower destruction begin to have a significant impact, while in the late game, towers and kills become the primary determinants of the probability of winning. The proposed system is able to generate different percentages of the probability of winning at each stage of the game and logically reflect the dynamics of the Dota 2 game. Based on these results, the Fuzzy Tsukamoto method is considered capable of handling uncertainty in Dota 2 match prediction and provides more flexible results than deterministic approaches, although it still depends on the quality of the dataset and the fuzzy rules used.
Co-Authors Ferdian, Aan Muhammad Hanani, Ishmah MUHAMMAD FAHMI Pahrudin, Pajar Ridla Tarigan, Ismail Rasyid Siti Lailiyah, Siti Tan, Muhammad Arief Adidharma Veran Goraldy Cosmas