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All Journal Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Jurnal Teknik Informatika (JUTIF) Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Eduvest - Journal of Universal Studies SATIN - Sains dan Teknologi Informasi PADIMAS: Jurnal Pengabdian Masyarakat Abdi Karya: Jurnal Pengabdian Kepada Masyarakat International Journal of Informatics and Computing
Haerani, Erna
Siliwangi University, Tasikmalaya, Indonesia
Aerospace Engineering Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Medicine & Pharmacology Neuroscience Social Sciences Other

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Journal : International Journal of Informatics and Computing

 1 
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.
Improving Emotion Recognition Accuracy with Combination of Bidirectional and Long Short-Term Memory Models Haerani, Erna; Rahmatulloh, Alam; Rizal, Randi
JICO: International Journal of Informatics and Computing Vol. 1 No. 2 (2025): November 2025
Publisher : IAICO

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Emotions play a vital role in shaping human behavior and mental health, making accurate emotion recognition essential for mitigating potential negative impacts. This study explores the application of Bidirectional Long Short-Term Memory (Bi-LSTM) for recognizing emotions from text-based data. Bi-LSTM extends the standard LSTM by enabling the model to process input sequences in both forward and backward directions, thereby capturing contextual dependencies more effectively. The research methodology consists of data collection, manual emotion labeling, and pre-processing techniques, including stemming, tokenization, and one-hot encoding. Visualization of the dataset and the distribution of labeled emotions was conducted to gain deeper insights into the data. The Bi-LSTM model was trained for 25 epochs, achieving a training accuracy of 0.9954 and validation accuracy of 0.8790, along with a training loss of 0.0133 and validation loss of 0.658. A confusion matrix was used to further evaluate model performance and classification accuracy across various emotion categories. The experimental results confirm that the Bi-LSTM model is highly effective in recognizing emotions from textual input. Its ability to capture long-term dependencies in both directions contribute to improved learning and prediction. However, opportunities for enhancement remain, particularly in refining the model architecture, expanding the dataset, and exploring additional feature extraction techniques. This research demonstrates the potential of Bi-LSTM in building intelligent emotion-aware systems for applications in mental health monitoring, customer feedback analysis, and human-computer interaction.
Co-Authors Alam Rahmatulloh Anjar Ginanjar Christina Juliane Darmawan, Muhamad Aditya Elmeftahi, Souhayla Franciskus Antonius Alijoyo Genta Nazwar Tarempa Irfan Darmawan Neng Ika Kurniati Putri Septia Amalia Randi Rizal Rizal, Randi Rohmat Gunawan Rusnida Romli Selamat, Siti Rahayu Setiawan, Perdi Trisna Ari Roshinta