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All Journal UPGRADE: Jurnal Pendidikan Teknologi Informasi Journal of Science and Technology: Alpha
Gede Yogi Pratama
Universitas Bumigora
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Social Sciences Other

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Augmentasi Data Berbasis GAN dan Ekstraksi Fitur EfficientNetB0 dengan XGBoost untuk Meningkatkan Klasifikasi Penyakit Daun Jagung Ririn Anugerah Ikasatya; Cahya Apriliani; Fathir Jannatul Firdaus; Gede Yogi Pratama
Upgrade : Jurnal Pendidikan Teknologi Informasi Vol 3 No 2 (2026): Februari
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/upgrade.v3i2.6224

Abstract

Corn leaf diseases are one of the main factors contributing to decreased corn productivity. Manual identification of leaf diseases remains subjective, time-consuming, and highly dependent on individual experience. This study aims to improve the performance of image-based corn leaf disease classification through the integration of data balancing techniques, deep feature extraction, and machine learning-based classification methods. The dataset consists of four classes with an imbalanced distribution, namely  Blight with 802 images, Common Rust with 914 images, Gray Leaf Spot with 401 images, and Healthy with 813 images, where GrayLeaf Spot represents the minority class. Data balancing is performed by generating synthetic images using  a convolution-based generative model to increase the number of samples in the minority class. Furthermore, feature extraction is carried out using the EfficientNetB0 architecture, and classification is performed using a gradient boosting-based algorithm. There sults show that the proposed approach improves accuracy from 92.49 percent to 93.29 percent and enhances the model’s ability to recognize the minority class, as indicated by an increase in recall from 69 percent to 78 percent and an improvement in performance balance from 0.76to 0.84. These findings indicate that the proposed method is effective in improving classification performance, particularly for the minority class, without reducing performance on majority classes.
Perbandingan Metode Long Short-Term Memory (LSTM) dan Gated Recurrent Unit (GRU) dalam Memprediksi Harga Saham Telkom Gede Yogi Pratama; Onis Alamsyah; Hanif Aljauziah; Muh Sohibul Ihsania; Mohammad Mirza; Lathifah Laili Andita
Journal of Science and Technology: Alpha Vol. 2 No. 2 (2026): Journal of Science and Technology: Alpha, April 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/alpha.v2i2.475

Abstract

Accurate stock price prediction remains a challenging task due to the highly volatile nature of financial markets and the influence of various macroeconomic factors and market sentiment. PT Telkom Indonesia Tbk (TLKM), one of the largest publicly listed companies in Indonesia, has attracted significant attention from investors because of its substantial market capitalization and active stock trading. This study aims to compare the performance of the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models in predicting TLKM stock prices using time series data. The dataset consists of historical TLKM stock data, including the Open, High, Low, Close, Adjusted Close, and Volume variables. Data preprocessing involved data cleaning, normalization using the Min-Max Scaling technique, and time series sequence generation through the sliding window approach. Both LSTM and GRU models were developed using comparable network architectures and trained with the Adam optimizer and the Mean Squared Error (MSE) loss function. Model performance was evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The experimental results demonstrate that both models effectively capture historical stock price patterns. However, the GRU model consistently outperformed the LSTM model by achieving lower prediction errors while requiring lower computational complexity and training time. These findings suggest that GRU is a more effective and computationally efficient approach for predicting TLKM stock prices based on time series data.
Co-Authors Cahya Apriliani Fathir Jannatul Firdaus Hanif Aljauziah Lathifah Laili Andita Mohammad Mirza Muh Sohibul Ihsania Onis Alamsyah Ririn Anugerah Ikasatya