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All Journal International Journal of Informatics and Computing
Rusnida Romli
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Computer Science & IT Electrical & Electronics Engineering

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Journal : International Journal of Informatics and Computing

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Optimizing Data Management in Web Applications through Google Drive API Integration and Synchronization Putri Septia Amalia; Erna Haerani; Rusnida Romli; Trisna Ari Roshinta
JICO: International Journal of Informatics and Computing Vol. 1 No. 1 (2025): May 2025
Publisher : IAICO

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Abstract

The rise of Web-based applications has created a demand for streamlined data management and automatic data synchronization. Even manually stored local data is often insufficient to meet these requirements, necessitating a solution that can efficiently manage data access and storage through Cloud technology. This study advocates for utilizing the Google Drive API to resolve these issues. By leveraging the benefits of Google Drive's Cloud storage, Web applications can seamlessly synchronize user-uploaded data to the Cloud. To initiate this integration, a Google account is required to authenticate the process and serve as a mediator for data exchange. This approach employs secure authentication and authorization mechanisms to ensure data privacy. The system is developed using an iteration-based approach starting with user requirements analysis, followed by interface design and API integration. Pilot tests were then conducted to validate system performance under various usage scenarios. The findings revealed a noteworthy advancement in the synchronization and administration of data through the Web-based application with a data transmission duration of under 60 seconds, contingent on internet speed. Google Drive's API integration enables users to access files and manage them in real-time, surpassing prior limitations. To meet the demands of progressively intricate Web-based applications, future research can concentrate on enhancing data security and optimizing performance.
GANS: Genetic Algorithm and Neural Network Integration for Optimal Brain Selection in Snake Game Bambang Pudjoatmodjo; Mugi Praseptiawan; Ulka Chandini Pendit; Rusnida Romli
JICO: International Journal of Informatics and Computing Vol. 1 No. 2 (2025): November 2025
Publisher : IAICO

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Snake games have emerged as an engaging subject in artificial intelligence and optimization research due to the growing interest in developing autonomous agents capable of controlling the snake intelligently. This study presents a hybrid approach by integrating a Genetic Algorithm (GA) with a Neural Network (NN) to enhance the snake game’s performance, effectively forming an adaptive and intelligent control system or “brain.” In this framework, the Snake game is modeled as an optimization problem, where the GA is employed to optimize the parameters of the NN to improve the decision-making process of the snake. The GA operates by evolving a population of individuals each representing a set of strategies through selection, crossover, and mutation. These operations are iteratively applied to discover optimal solutions within the vast parameter space. The integrated neural network enables the snake to make real-time decisions based on environmental stimuli, enhancing its survival and goal-seeking behavior. Fitness evaluation is performed based on everyone’s gameplay performance, where the most successful individuals contribute to the next generation. Experimental results demonstrate that the combination of GA and NN significantly improves snake gameplay performance. The fitness score acts as a performance indicator, showing that higher-generation populations tend to yield better results. For instance, snakes trained over 100 generations achieved scores around 8, while those trained over 500 generations exceeded scores of 15. This confirms the effectiveness of evolutionary optimization in training neural networks for game-based AI tasks.
Co-Authors Bambang Pudjoatmodjo Haerani, Erna Praseptiawan, Mugi Putri Septia Amalia Trisna Ari Roshinta Ulka Chandini Pendit