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All Journal Building of Informatics, Technology and Science
Aris Rakhmadi
Universitas Muhammadiyah Surakarta, Surakarta
Computer Science & IT

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Perbandingan Kinerja Model ARIMA dan LSTM pada Multi-Horizon Forecasting Harga Emas dengan Evaluasi Mean Directional Accuracy Muhamad Prasetyo Bayu Aji; Aris Rakhmadi
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.10010

Abstract

Precious metals, particularly gold, represent one of the most sought-after value-preserving investment instruments, yet their dynamic price fluctuations present significant challenges, making gold a difficult-to-predict yet crucial asset for investment decision-making. This study aims to forecast gold prices by comparing the performance of Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) forecasting models through three testing scenarios: single-step, multi-step forecasting, and rolling forecasting. This study utilizes 40 years of historical gold price data obtained from the public source Kaggle. The ARIMA model was implemented on stationary data, while LSTM was optimized with additional lag, volatility, and momentum features. Experimental results indicate that in the single-step scenario, both models produced equivalent accuracy with a MAPE below 1%. In the multi-step scenario, LSTM significantly outperformed ARIMA with a MAPE of 1.89% compared to 3.54%. In the rolling scenario, LSTM again performed better with a MAPE of 1.83% versus 3.52% for ARIMA. Conversely, ARIMA consistently recorded higher Mean Directional Accuracy (MDA) values across all scenarios, reaching 57.30% in the rolling forecast compared to LSTM's 46.07%, indicating ARIMA's advantage in identifying trend direction. This study concludes that the LSTM approach is more optimal for achieving numerical prediction precision over medium-term horizons, while the statistical ARIMA method is more reliable for accurately projecting market movement direction.
Analisis Ketahanan Model ResNet-50 pada Klasifikasi Bahasa Isyarat Arab terhadap Degradasi Citra Bawah Air Muhammad Ilham; Aris Rakhmadi
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9479

Abstract

Automatic sign language recognition using deep learning, particularly Convolutional Neural Networks (CNNs), has shown significant potential. The ResNet architecture, through transfer learning, is frequently reported to achieve high accuracy for Arabic Sign Language Alphabet classification under ideal conditions. However, the robustness of these models against real-world visual distortions remains a significant, yet under-explored challenge. This research aims to develop a ResNet-50-based classification model while comprehensively analyzing its robustness. The primary contribution of this research is mapping the tolerance limits and the extent of performance degradation of the ResNet architecture when facing image degradation. Evaluation was conducted on both ideal test data and test data digitally modified to simulate underwater visual effects. This underwater simulation was selected as an extreme stress test scenario because it technically represents an accumulation of simultaneous real-world optical distortions, such as contrast reduction, turbidity (haziness), and light refraction. Quantitative evaluation results show that the model performs excellently with an accuracy of 96.95% under ideal conditions. However, exposure to underwater distortion resulted in an accuracy drop of 4.24%, reducing it to 92.71%. Despite this noticeable performance reduction, the model maintained an F1-Score of 92.79%. These findings provide empirical evidence regarding the capability limits of the ResNet architecture when facing visual degradation, while also emphasizing the importance of robustness testing before deep learning models can be reliably deployed in non-ideal environments full of visual uncertainties.
Co-Authors Muhamad Prasetyo Bayu Aji Muhammad Ilham