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Firdaus, Wigananda
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Computer Science & IT Electrical & Electronics Engineering

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Exploring Opportunities and Challenges in Multi-Cloud and Hybrid Cloud Implementation Firdaus, Wigananda; Sukmaaji, Anjik
Information Technology International Journal Vol. 2 No. 2 (2024): Information Technology International Journal
Publisher : Magister Teknologi Informasi UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/itij.v2i2.30

Abstract

This study reviews the opportunities and challenges of implementing Multi-Cloud and Hybrid Cloud models that focus on security and data management. Multi-Cloud implementation offers flexibility, but also brings challenges related to security and privacy. Data security in multi-cloud can be improved by implementing encryption such as Homomorphic Encryption and Hybrid Crypto which combines DES and RSA algorithms. The Hybrid Cloud model allows integration between public and private clouds. where the implementation of Zero Trus can improve the security of the cloud network. The results of this Literature Review emphasize the importance of security policies at every layer of the cloud from infrastructure to applications. this is done to protect sensitive data in the cloud environment. of course, the right strategy is needed in cloud data management so that the implementation of cloud computing is more effective
A Tracer Study Design With Whatsapp Chatbot Integration Using Natural Language Processing Firdaus, Wigananda
Information Technology International Journal Vol. 2 No. 2 (2024): Information Technology International Journal
Publisher : Magister Teknologi Informasi UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/itij.v2i2.24

Abstract

Tracer study is a method used by educational institutions to track alumni and assess the effectiveness of the education provided. A key challenge in conducting these studies is the low participation rate of respondents, often due to lengthy surveys and a lack of interactive engagement. To address this issue, a WhatsApp chatbot system powered by Natural Language Processing (NLP) was developed. This system facilitates an interactive and user-friendly survey experience, allowing respondents to complete the survey directly through WhatsApp without needing to visit a website. Responses are automatically stored in Google Sheets via an API. By using a microservices architecture, the project efficiently separates crucial components such as WhatsApp API, NLP services, and Google Sheets API, leading to improved data collection efficiency and a more convenient survey process for respondents.
Co-Authors Anjik Sukmaaji