Klasifikasi Loyalitas Pengguna Sistem E-Learning Menggunakan Net Promoter Score dan Machine Learning

Supriyadi, Didi; Safitri, Sisilia Thya; Kristiyanto, Daniel Yeri

doi:10.26418/jp.v8i1.49300

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…The ultimate decision of the tree is made at the leaf nodes. Decision trees provide easily understandable decision rules and can discern significant features for predicting student academic performance [37], [38].…”

Section: Decision Tree (Tree)mentioning

confidence: 99%

Analysis of Attack Detection on Log Access Servers Using Machine Learning Classification: Integrating Expert Labeling and Optimal Model Selection

Ridwan,

Sembiring,

Setiawan

et al. 2024

SJI

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Purpose: As the complexity and diversity of cyberattacks continue to grow, traditional security measures fall short in effectively countering these threats within web-based environments. Therefore, there is an urgent need to develop and implement innovative, advanced techniques tailored specifically to detect and address these evolving security risks within web applications.Methods: This research focuses on analyzing attack detection in log access servers using machine learning classification with two primary approaches: expert labeling integration and best model selection. Expert labeling determines whether log entries are safe or indicate an attack.Result: Validation in labeling was applied using different datasets to minimize errors and increase confidence in the resulting dataset. Experimental results show that the Decision Tree and Random Forest models have nearly identical accuracy rates, around 89.3%-89.4%, while the ANN model has an accuracy of 81%.Novelty: This study proposes a fusion of expert knowledge in labeling log entries with a rigorous process of selecting the best classification model. This integration has not been extensively explored in previous research, offering a novel approach to enhancing attack detection within web applications. The research contribution lies in the integration of expert security assessment and the selection of the best model for detecting attacks on server access logs, along with validating labels using various datasets from different log devices to enhance confidence in the analysis results.

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Section: Decision Tree (Tree)mentioning

confidence: 99%

Analysis of Attack Detection on Log Access Servers Using Machine Learning Classification: Integrating Expert Labeling and Optimal Model Selection

Ridwan,

Sembiring,

Setiawan

et al. 2024

SJI

View full text Add to dashboard Cite

show abstract

“…The K-Nearest Neighbor (kNN) method is a very simple but effective method for predicting or grouping data based on similarities to data that already exists in the dataset (Pattnaik & Parvathi, 2022) (Kurniadi, Mulyani, & Muliana, 2021) (Nugraha & Herlina, 2021). The K-Nearest Neighbor (kNN) method is a method with a classification model that is easy to understand and realize (Prasetio, 2020) (Supriyadi, Safitri, Amriza, & Kristiyanto, 2022). The K-Nearest Neighbor (kNN) method is a good classification method and has good accuracy.…”

Section: Metode K-nearest Neighbor (Knn)mentioning

confidence: 99%

Analysis of the Level of Public Satisfaction on the Tiktok Application as an E-Commerce

Irmayani,

Sinaga,

Masrizal

2023

SinkrOn

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Online shopping has become one of the alternatives used by society today. This happens because doing online shopping saves a lot of time. There are many online shops in Indonesia and are used by many people, such as Shopee, Lazada and Tokopedia. But now there is an application that initially only acted as a social media platform, but now also doubles as an E-commerce application, namely TikTok. TikTok has now become an E-commerce application. The prices given are also very cheap and there are lots of promotions given to customers. But there are still some people who don't want to shop online on TikTok on the grounds that the goods are not good. So from these 2 things, research needs to be made to determine the level of public satisfaction with the TikTok application as an E-commerce. The aim of this research is to see how many people are satisfied shopping on TikTok. This research was carried out using a classification model in data mining using the K-Nearest Neighbor (kNN) method and the Decision Tree method. The classification results obtained were 119 community data (for representation of 96.74%) and for people who were dissatisfied with the TikTok application as an E-commerce it was 4 community data (for representation of 3.25%). These results provide the conclusion that many people are satisfied shopping on the TikTok application as E-commerce

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Klasifikasi Loyalitas Pengguna Sistem E-Learning Menggunakan Net Promoter Score dan Machine Learning

Cited by 2 publications

References 15 publications

Analysis of Attack Detection on Log Access Servers Using Machine Learning Classification: Integrating Expert Labeling and Optimal Model Selection

Analysis of Attack Detection on Log Access Servers Using Machine Learning Classification: Integrating Expert Labeling and Optimal Model Selection

Analysis of the Level of Public Satisfaction on the Tiktok Application as an E-Commerce

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