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Jurusan Teknik Informatika, Fakultas Teknik Universitas Negeri Surabaya Kampus Ketintang, Gedung A10, lt.2, Surabaya-Indonesia, 60231.
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JIEET (Journal of Information Engineering and Educational Technology)
Published by Universitas Negeri Surabaya
ISSN : -     EISSN : 2549869X     DOI : http://dx.doi.org/10.26740/
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Journal Description: JIEET (Journal of Information Engineering and Educational Technology) is a scientific journal that publishes the peer-reviewed research papers in the field of Computer Engineering, Distributed and Parallel Systems, Business Informatics, Computer Science, Computer Security, System & Software Engineering and Educational Technology.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 234 Documents
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NVIDIA Stock Price Forecasting in 2025 Using a Time-Series Based LSTM Model Sheila Nur’Aeni Afidah; Adik Nur Halimah; Febri Liantoni
JIEET (Journal of Information Engineering and Educational Technology) Vol. 10 No. 1 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v10n01.p62-73

Abstract

Advances in computing technology, artificial intelligence, and graphics accelerators have driven significant growth in the value of NVIDIA Corporation (NVDA) shares over the past decade. The high volatility and non-linear patterns in NVDA's stock price require the use of forecasting methods that can capture long-term dependencies in time-series data. This study proposes a Long Short-Term Memory (LSTM) model to predict NVDA's stock price using historical daily data up to 2025. The training process was carried out using a two-layer LSTM architecture, Adam optimizers, and a 60-day time window. The evaluation results show that the model is capable of achieving a Mean Absolute Percentage Error (MAPE) of 4%, which indicates a high level of accuracy in predicting price movement patterns. Visualization of the prediction results confirms that the model successfully follows actual trends consistently, with relatively small deviations at extreme fluctuation points. Additionally, the model was used to project NVDA stock prices ten steps ahead, showing that price movement trends remain consistent with historical patterns. These findings contribute methodologically to studies on high-risk stock forecasting through deep learning approaches and provide a basis for data-driven decision-making in the modern financial sector.
Cardiovascular Disease Classification Using Support Vector Machine with Feature Selection Yuwatsiqul Aqwam; Irwan Budi Santoso Santoso; ur Fitriyah Ayu Tunjung Sari Sari
JIEET (Journal of Information Engineering and Educational Technology) Vol. 10 No. 1 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v10n01.p74-87

Abstract

Cardiovascular disease is one of the most serious health problems and remains the leading cause of death worldwide each year. This condition shows that early diagnosis and classification of cardiovascular disease are very important to increase the chances of recovery and prolong the life expectancy of patients. This study aims to develop a cardiovascular disease classification system using the Support Vector Machine (SVM) method and analyze the effect of Recursive Feature Elimination (RFE) and Chi-Square feature selection on model computational performance. The dataset used is the Heart Disease Dataset from IEEE Dataport, which consists of 1,190 data points. The RFE and Chi-Square methods were used to select the most relevant features, resulting in seven main features used in the classification process with various SVM kernels. The results showed that the application of feature selection significantly improved model stability and accelerated the computational process, especially for kernels with high complexity. The linear kernel provided the highest accuracy of 85.84%, while the seventh-degree polynomial kernel increased from 77.23% to 84.31%, and the RBF kernel increased from 83.66% to 84.86% after feature selection. The combination of the RFE and Chi-Square methods proved to be effective in improving stability and computational efficiency, making it a potential alternative to support the cardiovascular disease classification process
Data and Algorithm-Driven Strategies for Building Artificial Intelligence Startups: A Narrative Literature Review Nabil Hanif Achmaddiredja Achmaddiredja; Herbert Siregar
JIEET (Journal of Information Engineering and Educational Technology) Vol. 10 No. 1 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v10n01.p51-61

Abstract

Data and Algorithm-Driven Strategies for Building Artificial Intelligence Startups: A Narrative Literature Review
Application of YOLOv11s Algorithm to Automate Egg Counting in Poultry Farms Muh. Hakkam Zakka Robbani; Akhfa Bagas Alfarizi; Adrian Fardan Andi; Gema Parasti Mindara; Endang Purnama Giri
JIEET (Journal of Information Engineering and Educational Technology) Vol. 10 No. 1 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v10n01.p39-50

Abstract

Chicken eggs are a strategic food commodity and a globally affordable source of animal protein. In the industrial poultry farming sector, accurate daily production data is fundamental for evaluating biological performance, feed efficiency, and supply chain management. However, the conventional manual counting process is highly susceptible to human error caused by physical fatigue and dynamic environmental conditions, resulting in significant data inaccuracies. To address these inefficiencies, this study aims to develop a precise automated egg counting system by implementing the You Only Look Once (YOLO) deep learning algorithm version 11s. This study employed an experimental approach that included data collection, manual annotation, and extensive augmentation techniques to ensure the model's robustness to variations in lighting and spatial orientation. The model was trained using a specific egg dataset and evaluated against standard object detection performance metrics. The experimental results demonstrated superior performance, with the model achieving 97.80% Precision, 98.60% Recall, and 99.10% Mean Average Precision at intersection over union 50%. Furthermore, the F1-Score reached 98.20%, indicating a strong balance between detection accuracy and sensitivity. The system successfully detected and counted eggs in a real-time environment while maintaining stability across various visual scenarios. In conclusion, the proposed architecture proved to be a highly effective solution for automating production recording in poultry farms. These findings offer a practical transition towards digitized precision farming, significantly reducing operational inefficiencies associated with manual monitoring while enabling sound managerial decision-making.

Page 24 of 24 | Total Record : 234

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