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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Buletin Ilmiah Sarjana Teknik Elektro Journal of Innovation Information Technology and Application (JINITA) Sistem Pendukung Keputusan dengan Aplikasi Control Systems and Optimization Letters ASEAN Journal for Science and Engineering in Materials ASEAN Journal of Educational Research and Technology
Hewa Majeed Zangana
Duhok Polytechnic University
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

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Genetic algorithm-optimized BERTopic with SHAP explainability for institutional research trend analysis Muhammad Dedi Irawan; Yustria Handika Siregar; Hewa Majeed Zangana; Ali Ikhwan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3818-3826

Abstract

Institutional research grant titles constitute short-text grey literature characterized by heterogeneous semantic structures, making topic identification and research trend analysis challenging. This study proposes an integrated bidirectional encoder representation from transformers-based topic modeling (BERTopic) framework combining genetic algorithm (GA)-based hyperparameter optimization and Shapley additive explanations (SHAP)-based interpretability to improve semantic topic quality and model transparency. GA was applied to optimize dimensionality-reduction and density-based clustering parameters, while SHAP was used to estimate the contribution of bigram features to the surrogate classifier’s predictions of BERTopic-generated topic labels. Experimental results demonstrated that the proposed framework improved topic coherence from 0.367 to 0.543 while reducing the outlier ratio from 21.12% to 13.55%. In addition, the number of topics decreased from 46 to 10, resulting in a more compact and less fragmented topic structure. The resulting topic structure revealed dominant themes related to higher education, religious moderation, Islamic counseling, halal tourism, and sharia banking. Overall, the proposed framework contributes to the development of more coherent, interpretable, and semantically robust topic modeling for institutional short-text grey literature analysis.
Performance Evaluation of Sensor Data Filtering Methods for Signal Processing in TVET Learning Applications Farid Baskoro; Hisham A. Shehadeh; Hewa Majeed Zangana; Tri Wrahatnolo; Puput Wanarti Rusimamto; Fendi Achmad; Aristyawan Putra Nurdiansyah
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 4 (2026): August
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i4.16883

Abstract

Technical and Vocational Education and Training (TVET) learning requires sensor measurement data that are stable, accurate, and easy to interpret. Raw LiDAR sensor data often contain fluctuations that may interfere with the readability results. This study employed an experimental-comparative design by comparing Moving Average, Median Filter, Savitzky-Golay, Butterworth, and Simple Kalman Filter. The data acquisition system used a VL53L0X LiDAR sensor and ESP32 microcontroller. Data processing was conducted in MATLAB on 10,500 samples at a sampling frequency of 50 Hz. The evaluation was carried out based on error metrics, signal stability, noise reduction, and filter responsiveness. The raw data had a standard deviation of 111.26 and still showed fluctuations that required reduction. A Greenhouse–Geisser-corrected repeated-measures ANOVA showed a significant effect of filtering method on segment-level residual RMSE, F(1.10,44.92)=26.23, p<0.001, partial η2=0.390. Bonferroni-adjusted comparisons showed that Savitzky–Golay produced significantly lower residual RMSE than the other methods, indicating stronger preservation of the raw-signal pattern. The results showed that Savitzky–Golay achieved the best overall trade-off, with the lowest residual deviation, the highest estimated SNR of 32.154 dB, and good pattern preservation without excessive smoothing. Butterworth and Simple Kalman provided stronger fluctuation reduction, although Kalman introduced greater deviation and a 39-sample delay. Moving Average offered simple smoothing, whereas the Median Filter was more suitable for impulsive noise and outliers. This study contributes a comparative evaluation of filtering methods from both signal-processing and TVET pedagogical perspectives, supporting filter selection based on smoothness, readability, noise reduction, and responsiveness in signal processing.
Co-Authors Agis Abhi Rafdhi Ali Ikhwan Aristyawan Putra Nurdiansyah Azar Abid Salih Bobi Kurniawan, Bobi Dian Dharmayanti Dostnazar Ximmataliyev Eddy Soeryanto Soegoto Farid Baskoro Fendi Achmad Goh Kang Wen Hisham A. Shehadeh Hisham Shehadeh Jamal N. Al-Karaki Juliana Karin Marwan Oma Marwan Omar Maryam A. Sulaiman Maulana, Hanhan Mohammed Aquil Mirza Mohd. Kamir Yusof Muhammad Dedi Irawan Natheer Yaseen Ali Noor Salah Hassan Puput Wanarti Rusimamto Riska Endah Septiani Senny Luckyardi Sharyar Wani Sri Dwiyanti Tomas Chochole Tri Wrahatnolo Wahyu Sasongko Putro Widi Aribowo Xinwei Cao Yustria Handika Siregar