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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics bit-Tech Information Technology Education Journal
Shaifudin Zuhdi
Department Of Informatics, Sebelas Maret University
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Indonesian Automated Essay Scoring: A Comparative Study of Pretrained Transformer Models Pulung Hendro Prastyo; Eddy Tungadi; Shaifudin Zuhdi
Information Technology Education Journal Vol. 4, No. 2, May (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v4i2.8069

Abstract

Manual essay scoring is often characterized by inefficiency and inconsistency. This process is notably time-consuming, leading to delayed feedback and increased susceptibility to evaluator fatigue and subjective bias, thereby posing significant challenges. Automated Essay Scoring (AES) offers a scalable, robust, and consistent solution to these issues. However, the performance of AES models can vary considerably depending on the specific application. Therefore, this study evaluated ten Indonesian pretrained transformer models from Hugging Face for AES tasks, using 300 essay responses from a Research Methodology quiz at Politeknik Negeri Ujung Pandang. Performance was assessed using Root Mean Square Error (RMSE) and Quadratic Weighted Kappa (QWK). Among the evaluated models, Indobenchmark/indobert-base-p2 (BERT-02) demonstrated superior performance. It achieved the lowest RMSE of 5.664 and the highest QWK score of 0.6745. The findings suggest that BERT-02 is the most effective model for Indonesian AES tasks. Future research could explore larger datasets and different models to further enhance the performance and understanding of Indonesian AES systems.
Efficient VGA-Net Modification Using ConvNeXt-Tiny and GATv2 for Retinal Vessel Segmentation Billie Zandra Widiyanto; Wiharto Wiharto; Shaifudin Zuhdi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 3 (2026): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i3.1747

Abstract

Retinal blood vessel segmentation plays a crucial role in the early detection of ocular diseases such as diabetic retinopathy, glaucoma, and macular degeneration. Existing hybrid architectures, such as VGA-Net, suffer from high computational complexity due to the VGG-16 backbone and limited attention expressiveness due to its static GAT module, yet no prior work has examined replacing both components within a patch-based graph architecture in which backbone feature quality directly conditions graph attention effectiveness. This study aims to improve the computational efficiency and topological modeling of VGA-Net by replacing VGG-16 with ConvNeXt-Tiny and substituting GAT with GATv2. The primary contribution is a 55% parameter reduction through the ConvNeXt-Tiny backbone substitution and improved vessel topology modeling through GATv2's dynamic attention mechanism, which produces fully dynamic attention coefficients per query node. Experiments were conducted on the DRIVE and STARE datasets using a consistent preprocessing pipeline, one-factor-at-a-time hyperparameter tuning, and a unified evaluation protocol across all compared methods. The proposed model achieves the lowest parameter count (5.3M) and GFLOPs (3.2443), with a competitive inference time of 61.00 ms per image, among all compared methods, while achieving competitive performance in sensitivity and topological continuity. On the DRIVE dataset, the model achieved the highest sensitivity of 0.8718 and the highest clDice of 0.8446. On the STARE dataset, the model achieved the highest sensitivity of 0.9383 and the highest clDice of 0.9055. These results demonstrate that the proposed model achieves a favorable efficiency-performance trade-off, leading to sensitivity and topological continuity at the lowest computational cost among all compared methods, at the expense of lower specificity, accuracy, Dice, and MCC relative to certain compared methods.
Analisis Peramalan IHSG Menggunakan Model ARIMA-ARCH untuk Mengatasi Efek Heteroskedastisitas Nadia Puspita Adinda; Fairuz Izzaty Salamy; Salsabilla Fatika Subagyo; Dewi Puspita Sari; Shaifudin Zuhdi
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3515

Abstract

The Indeks Harga Saham Gabungan (IHSG) is widely recognized as a central indicator of the Indonesian capital market and reflects overall market performance, investor sentiment, and macroeconomic conditions. Accurate forecasting of the IHSG is essential for investors, financial institutions, and policymakers; however, financial time series data are often characterized by non-stationarity and volatility clustering, which limit the effectiveness of conventional forecasting models. This study applies a hybrid Autoregressive Integrated Moving Average–Autoregressive Conditional Heteroskedasticity (ARIMA–ARCH) model to forecast the IHSG by simultaneously modeling the conditional mean and time-varying volatility. The ARIMA model is used to capture linear temporal dependence in the mean process, while the ARCH component addresses heteroskedasticity in the residuals by allowing conditional variance to change over time. Daily IHSG closing price data from September 2024 to September 2025 are analyzed using the Box–Jenkins methodology, including stationarity analysis, model selection, parameter estimation, and diagnostic validation. The empirical results indicate that the hybrid ARIMA–ARCH model provides improved forecasting accuracy compared to a standalone ARIMA model, particularly in periods of heightened market volatility. The ARCH component successfully captures volatility clustering and enables the construction of dynamic volatility-based prediction intervals, offering additional risk-related insights beyond point forecasts. These findings demonstrate that the ARIMA–ARCH framework is effective for modeling IHSG dynamics and can support better risk management, portfolio optimization, and decision-making processes in the Indonesian capital market.
Co-Authors Billie Zandra Widiyanto Dewi Puspita Sari Eddy Tungadi Fairuz Haniyah Ramadhani Nadia Puspita Adinda Pulung Hendro Prastyo Salsabilla Fatika Subagyo Wiharto Wiharto