The Toolkit of Success Rate Calculation of Broiler Harvest

Sanmorino, Ahmad; Gustriansyah, Rendra; Terttiaavini, Terttiaavini; Isabella, Isabella

doi:10.12928/telkomnika.v15i4.6744

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…The NSL-KDD dataset has been preprocessed and contains several types of attacks that can be used to train and evaluate the performance of intrusion detection systems. One example of the type of attack found in the NSL-KDD dataset is the U2R attack [34]- [36]. Using ResNet50 as a pre-trained model for fine-tuning in DDoS detection is not a common or conventional approach, as ResNet50 is designed for image-related tasks, and DDoS detection typically involves network traffic data.…”

Section: Methodsmentioning

confidence: 99%

Fine-tuning a pre-trained ResNet50 model to detect distributed denial of service attack

Sanmorino,

Kesuma

2024

Bulletin EEI

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Distributed denial-of-service (DDoS) attacks pose a significant risk to the dependability and consistency of network services. The utilization of deep learning (DL) models has displayed encouraging outcomes in the identification of DDoS attacks. Nevertheless, crafting a precise DL model necessitates an extensive volume of labeled data and substantial computational capabilities. Within this piece, we introduce a technique to enhance a pre-trained DL model for the identification of DDoS attacks. Our strategy’s efficacy is showcased on an openly accessible dataset, revealing that the fine-tuned model we propose surpasses both the initial pre-trained model and other cutting-edge approaches in performance. The suggested fine-tuned model attained 95.1% accuracy, surpassing the initial pre-trained model as well as other leading-edge techniques. Please note that the specific evaluation metrics and their values may vary depending on the implementation, hyperparameter settings, number of datasets, or dataset characteristics. The proposed approach has several advantages, including reducing the amount of labeled data required and accelerating the training process. Initiating with a pre-existing ResNet50 model can also enhance the eventual model’s accuracy, given that the pre-trained model has already acquired the ability to extract significant features from unprocessed data.

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Section: Methodsmentioning

confidence: 99%

Fine-tuning a pre-trained ResNet50 model to detect distributed denial of service attack

Sanmorino,

Kesuma

2024

Bulletin EEI

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“…This is important to understand because if there is a failure, everything must be repeated from the beginning and will cause a lot of losses. Planning and calculation of the implementation of a system must be done correctly, to avoid failure, as we have done in other studies [13].…”

Section: Architecture Designmentioning

confidence: 98%

The Design of Notification System on Android Smartphone for Academic Announcement

Sanmorino

Fajri

2018

Int. J. Interact. Mob. Technol.

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Abstract-Through this article, we try to propose the design of notification system that runs on the Android operating system. This notification will be used for the academic announcements in the campus environment. The current announcement system only depends on the web portal, phones, groups in social media or short message system. The use of web portals and phones still has many limitations. Our proposed notification system can meet the needs of academic announcement broadcast quickly and massively. In the end, this will cut the cost that stakeholders need to operate the academic announcement system in the campus environment. The design of smart notification system that we proposed in the future will be integrated with the academic information system.

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“…After the competency value of each contract employee is obtained from the head of the HR department of a bottled drinking water production and distribution company in the city of Palembang, an evaluation of the performance of contract employees will be carried out using the SAW method so that a contract employee rank will be obtained which will be recommended for the extension of the work contract period. This contract employee performance evaluation system will be designed using UML [33] and is expected to be a tool for recommending contract employees who are eligible for the extension of their work contracts [34].…”

Section: A Research Designmentioning

confidence: 99%

Performance Evaluation of Contract Employees Using the Best-Worst and Simple Additive Weighting Methods

2021

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All companies need qualified employees to ensure the continuity and progress of the company. Therefore, some companies are very selective in evaluating employee performance based on the criteria set by the company. However, because of the many criteria used in the assessment, a large number of contract employees to be evaluated, and the evaluation that must be proportional, the Human Resources department has difficulty evaluating the performance of contract employees. Therefore, this study aimed to develop a system for evaluating the performance of contract employees by integrating the Best-Worst (BW) and Simple Additive Weight (SAW) methods. The BW method is used to determine the weight of each criterion related to the performance appraisal of contract employees, and the SAW method is used to evaluate the performance of contract employees. The results showed that the system developed can provide a more proportional evaluation. So, this study contributes as a recommendation system for HR managers in determining eligible contract employees to have their work contracts extended based on criteria determined by the company.

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The Toolkit of Success Rate Calculation of Broiler Harvest

Abstract: Abstract

Cited by 7 publications

References 8 publications

Fine-tuning a pre-trained ResNet50 model to detect distributed denial of service attack

Fine-tuning a pre-trained ResNet50 model to detect distributed denial of service attack

The Design of Notification System on Android Smartphone for Academic Announcement

Performance Evaluation of Contract Employees Using the Best-Worst and Simple Additive Weighting Methods

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