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All Journal Electronic Integrated Computer Algorithm Journal
Fitria Widianingsih
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Social Sciences

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SolarWinds Attack: Stages, Implications, and Mitigation Strategies in the Cyber Age Gia Anisa; Fitria Widianingsih
Electronic Integrated Computer Algorithm Journal Vol. 2 No. 1 (2024): VOLUME 2, NO 1: OCTOBER 2024
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/enigma.v2i1.31

Abstract

SolarWinds is a software company based in the United States that provides IT monitoring and management tools. Founded in 1999, SolarWinds offers a variety of products that help organizations manage networks, systems, IT infrastructure, applications and cloud-based services. SolarWinds products are used for performance monitoring, log management, IT security, and data analysis. The company became widely known after a major cybersecurity incident came to light in late 2020, in which their network management software, Orion, was used as a vector for attacks by a state-backed hacking group. These attacks affected many organizations, including government agencies and private companies, and led to an increased focus on software supply chain security. This paper has reviewed stages, Implications, and mitigation strategies of SolarWinds.
AI and ML Integration Using Collaborative Filtering in Movie Recommendations Fitria Widianingsih; Ledi Diniyatullah
Electronic Integrated Computer Algorithm Journal Vol. 2 No. 2 (2025): VOLUME 2, NO 2: APRIL 2025
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/enigma.v2i2.55

Abstract

This study aims to integrate Artificial Intelligence (AI) and Machine Learning (ML) technologies with Collaborative Filtering (CF) to build a more accurate and personalized movie recommendation system. This system uses the Singular Value Decomposition (SVD) algorithm to reduce the dimensionality of data and generate rating predictions for users of movies they have not watched. This study implements a dataset from MovieLens to test the effectiveness of the model in providing recommendations. The experimental results show that the system successfully predicts user ratings with fairly high accuracy, reflected in the average Root Mean Square Error (RMSE) value of 0.85 for the five users tested. Although these results show good performance, challenges such as cold start problems and data sparsity are still major obstacles in producing more optimal recommendations. Therefore, this study also proposes the use of hybrid filtering, deep learning, and the use of external data to improve prediction accuracy and overcome these limitations.
Co-Authors Gia Anisa Ledi Diniyatullah