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Esther Irawati Setiawan
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Kampus Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya) Ngagel Jaya Tengah 73-77, Surabaya, Indonesia
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Insyst : Journal of Intelligent System and Computation
Published by Sekolah Tinggi Teknik Surabaya
ISSN : 26219220     EISSN : 27221962     DOI : https://doi.org/10.52985/insyst
Core Subject : Science,
Computer Science & IT
The Intelligent System and Computation Journal will be published for 2 editions in a year, every April and October. The Intelligent System and Computation Journal is an open access journal where full articles in this journal can be accessed openly. Review in this journal will be conducted with a blind review system. All articles in this journal will be indexed by Google Scholar. The topics contained in this journal consist of several fields (but not limited to): Algorithms and complexity Artificial Intelligence Big Data Analytics Biomedical Instrumentation Computational logic Computer Vision and Biometric Data and Web Mining Digital Signal Processing Image Processing Information Retrieval & Information Extraction Intelligence Embedded Systems Machine Learning Mathematics and models of computation Natural Language Processing Parallel & Distributed Computing Pattern Recognition Programming languages and semantics Speech Processing Virtual Reality & Augmented Reality
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 85 Documents
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Comparative Analysis of Neural Network Architecture Optimization: A Study on Genetic Algorithm, Random Search, Grid Search, and Adaptive Search Methods for Digit Classification Swastika, Windra
Intelligent System and Computation Vol 7 No 1 (2025): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v7i1.409

Abstract

This research presents a comprehensive comparative analysis of four neural network architecture optimization methods: Genetic Algorithm (GA), Random Search, Grid Search, and Adaptive Search. Using the MNIST digits dataset, a systematic evaluation was performed based on accuracy, computational efficiency, and architectural complexity. The experimental results demonstrate that the Genetic Algorithm achieved the highest accuracy at 98.33%, while Grid Search demonstrated computational efficiency with the fastest execution time at just 31.06 seconds. Random Search and Adaptive Search showed competitive performance with accuracies of 97.78% and 97.22% respectively, with varying computational requirements. The study revealed that simpler architectures with one or two layers often performed comparably to more complex structures, challenging the common assumption that deeper networks necessarily yield better results. The Genetic Algorithm converged to an optimal single-layer architecture with 119 neurons and ReLU activation, while Adaptive Search explored a more complex three-layer solution. The research identified a non-linear relationship between accuracy gains and computational costs, indicating that substantial increases in computational investment may yield diminishing returns in performance improvement. The convergence patterns of each method provided additional insights, with GA showing steady improvement across generations while Random Search achieved early discovery of good solutions. These findings contribute to both theoretical understanding and practical applications of neural network optimization, offering valuable insights into the trade-offs between methods and practical guidelines for selecting appropriate architecture optimization strategies based on specific requirements for accuracy and computational constraints.
Comparative Analysis of Large Red Chili Price Forecasting Models in Malang Regency Using Long Short-Term Memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) Indrawan, Yogi Fradika; Larasati, Aisyah; Purnama, Agus Rachmad; Sholikha, Nikmatus
Intelligent System and Computation Vol 7 No 1 (2025): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v7i1.419

Abstract

Large red chili is a strategic food commodity with high demand, yet its price often fluctuates due to factors such as weather, harvest seasons, and market dynamics. In Malang Regency, these fluctuations impact inflation and economic stability, necessitating an accurate forecasting model to support decision-making. This study aims to develop a price forecasting model using Long Short-Term Memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) methods and compare their performance using daily time series data on large red chili prices from January 2022 to August 2024, obtained from the Representative Office of Bank Indonesia in Malang. The data underwent preprocessing, where LSTM data was transformed using MinMaxScaler, while ARIMA data was differenced to meet stationarity assumptions, then split into 80% training and 20% testing data, with optimal parameters obtained through Grid Search for both models. The results show that the LSTM model with three layers (150, 150, 150 units) and a dropout of 0.2 achieved an RMSE of 2.326 and MAPE of 3.65%, whereas the best ARIMA configuration (4,1,3) achieved an RMSE of 2.455 and MAPE of 3.80%. Although both models performed competitively and yielded promising results, LSTM demonstrated superior accuracy in forecasting large red chili prices in dynamic market conditions.
A Hybrid Machine Learning and Deep Learning Approach for In-Game Assistance Dianaris, Audrey Ayu; Vincent; Setiono, Kevin; Setiawan, Mikhael; Pranoto, Yuliana Melita; Dewi, Grace Levina
Intelligent System and Computation Vol 7 No 1 (2025): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v7i1.430

Abstract

The rapid development of artificial intelligence (AI) has opened new possibilities for enhancing user interaction within video games. This study presents the design and implementation of a button-based assistant system for the simulation game Story of Seasons: Friends of Mineral Town, aimed at simplifying repetitive player tasks and improving the overall gameplay experience. The proposed system leverages a hybrid approach that combines Machine Learning and Deep Learning techniques, specifically Optical Character Recognition (OCR) with Tesseract, object detection using a custom-trained YOLOv7 model, the A* pathfinding algorithm for navigation, and automated input control through scripting. The assistant is capable of reading in-game time, weather, and events directly from screen captures, recognizing non-player characters (NPCs), and automatically directing the player’s character to desired locations or NPCs based on contextual data such as day, time, and weather conditions. A database-driven module stores key information such as NPC schedules, favorite gifts, and daily events to enable informed decision-making and interaction automation. Comprehensive testing was conducted, including comparisons of pathfinding algorithms, model accuracy assessments, and user experience evaluations involving volunteers. Results showed high detection accuracy with YOLOv7 and positive user feedback on the assistant's interface and usability. Users reported a more streamlined and enjoyable gaming experience, especially in managing daily tasks and character interactions. This research demonstrates how a hybrid AI-based approach can be effectively applied to traditional video games, offering a foundation for future development in intelligent game assistance systems. The proposed methodology not only improves convenience but also provides insights into the practical integration of AI in user-centric game design.
Multi View Natural Network for Cross-Project Software Defect Prediction Setiawan, Boy; Subekti, Agus
Intelligent System and Computation Vol 7 No 1 (2025): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v7i1.436

Abstract

Software Defect Prediction (SDP) plays a critical role in software engineering by enabling early identification of potentially defective modules, to assist developers and testers in prioritizing testing and inspection efforts to improve software quality and reliability. Driven by rapidly changing business requirements, defect prediction models have become increasingly essential in quality assurance workflows. Traditional approaches to SDP focused on Within-Project Defect Prediction (WPDP), where models are trained on historical data from the same project and effective under sufficient data conditions. This challenge motivates the adoption of Cross-Project Defect Prediction (CPDP), which leverages data from different projects. However, CPDP faces notable challenges including datasets distributional differences and class imbalance, which can degrade prediction performance and bias. To address these issues, recent studies have proposed data transformation, resampling, and domain adaptation techniques. In this study, we explore a multi-view learning approach using Neural Networks (NN) to enhance generalization and performance in CPDP scenarios. By leveraging multiple views of the same dataset—generated through concatenation of heterogeneous software metrics, imputation for missing values, normalization using Box-Cox transformation, and embedding-based feature transformation—we aim to construct a robust Multi-View Neural Network (MVNN). This architecture enables the integration of diverse information while mitigating the limitations of single-view learning in CPDP. Our method preserves more in-formation compared to conventional approaches that rely only on shared features. Experimental validation using benchmark SDP repositories demonstrates the competitiveness of our approach, offering improved performance over existing CPDP models and highlighting the potential of multi-view learning in defect prediction tasks.
Automated Data Extraction from Aircraft Fuel Invoices Using PaddleOCR Hutapea, Reinaldy; Harwanto, Vanessa; Situmeang, Samuel
Intelligent System and Computation Vol 7 No 1 (2025): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v7i1.427

Abstract

This study presents an automated data extraction system for aircraft fuel invoice documents using PaddleOCR, a deep learning-based optical character recognition (OCR) technology. The system is designed to address the challenges of extracting information from complex and unstructured document formats, which traditionally require extensive manual processing. To enhance performance, the system incorporates image pre-processing techniques and artificial intelligence-based validation methods, ensuring higher accuracy in recognizing aviation-specific details such as flight identifiers and fuel data. Evaluation of the system demonstrates notable improvements in both time efficiency and accuracy. On average, documents can be processed in under 60 seconds with high recognition rates for clean, standard-quality inputs. While performance decreases with noisy or small-text documents, results indicate that accuracy can be further improved through deep learning-based denoising and training with aviation-specific datasets. The system also proves scalable, successfully handling up to 640 documents without compromising performance, suggesting its feasibility for large-scale industrial deployment. Beyond technical efficiency, the system delivers tangible economic benefits by reducing operational costs, minimizing transaction discrepancies, and enabling staff to focus on higher-value strategic tasks. Furthermore, it establishes a foundation for future enhancements, including integration with ERP systems, multilingual OCR support, and handwriting recognition. Overall, this research highlights the potential of PaddleOCR-based automation to significantly transform document management in the aviation industry and offers promising opportunities for adoption across other data-intensive sectors.

Page 9 of 9 | Total Record : 85

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