Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JKIE (Journal Knowledge Industrial Engineering) Journal of Computer Networks, Architecture and High Performance Computing
Tiffany
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Industrial & Manufacturing Engineering Mechanical Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Sentiment Analysis On Twitter Posts About The Russia and Ukraine War With Long Short-Term Memory Simarmata, Allwin; Xu, Anthony; Tiffany; Phanie, Matthew Evan
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12235

Abstract

Sentiment analysis is one method for evaluating public opinion from the received text. In this study, we evaluate the performance of the LSTM model with Sastrawi in sentiment analysis in Indonesian using a Twitter dataset totaling 2537 data collected regarding the Russo-Ukrainian war. The purpose of this study is to determine the reliability of the LSTM model with Sastrawi in sentiment analysis in Indonesian and to evaluate the performance of the model with the collected Twitter dataset regarding the Russian-Ukrainian war. The method used in this study is data pre-processing, training and validation of the LSTM model with Literature, and model evaluation using the metrics of accuracy, precision, recall, and F1 score. In the dataset collected in this study, positive, neutral and negative sentiments were 54.7%, 35% and 10.2%. The results obtained from this study indicate that the LSTM model with Literature can provide good results in sentiment analysis with a prediction accuracy of 82%. The implication of the results of this study is that the LSTM model with Sastrawi can be used for sentiment analysis on Twitter and further research needs to be carried out with a wider and more diverse dataset, especially to produce even better accuracy.
Classification of diseases in snake plants using convolutional neural network Athalia, Kensa; Tiffany; Kevin Adhi Dhamma Setiawan; Bertrand Ferrari; Chairisni Lubis
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3201

Abstract

snake plant has an important role in human life, as well as in increasing the aesthetic value of the environment. Limited knowledge about diseases in snake plants has a crucial result in improper handling and control when the plant is attacked by disease. Advances in deep learning technology and Convolutional Neural Network (CNN) have presented high opportunities with their advantages in recognizing patterns and features from image data. This research will use a CNN model with VGG-19 architecture to classify diseases in the leaves of the snake plant. It is expected that by using the pre-trained VGG-19 model, the model can recognize complex visual patterns in snake plants. Diseases to be classified include several types of diseases that often attack snake plants such as anthracnose, rust, water soaked lesion, and healthy plants for comparison. The highest value of training accuracy reached a value of 98.08%, validation accuracy of 94.02%, and testing accuracy reached 94%.
Analysis of Boiler Machine Maintenance using The Reliability-Centered Maintenance Method Tiffany; Anita Christine Sembiring
Journal Knowledge Industrial Engineering (JKIE) Vol 11 No 1 (2024): JKIE (Journal Knowledge Industrial Engineering)
Publisher : Department of Industrial Engineering - Universitas Yudharta Pasuruan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35891/jkie.v11i1.5004

Abstract

Reliability Centered Maintenance is a structured approach to identifying maintenance needs based on the function and potential failure of a system. By conducting an in-depth analysis of the function and failure of boiler engine components, this research aims to formulate the most optimal maintenance strategy. This strategy is expected to increase the overall reliability of the boiler machine and at the same time reduce the maintenance costs required. This research begins with creating a function block diagram to map the main functions and sub-functions of the boiler machine. After that, a failure mode analysis is carried out to determine its impact using the Failure Mode and Effects Analysis method) which aims to identify potential failures in each component and the consequences they cause. So it is hoped that this research can determine appropriate maintenance actions to prevent or minimize the possibility of failure. The final result of this research is structured and effective maintenance recommendations for boiler engines. These recommendations include treatment strategies tailored to previously identified functions, failure modes, and impacts. By implementing the recommendations from this research, it is hoped that the performance and service life of the boiler machine can be improved significantly.
Co-Authors Anita Christine Sembiring Athalia, Kensa Bertrand Ferrari Chairisni Lubis Kevin Adhi Dhamma Setiawan Phanie, Matthew Evan Simarmata, Allwin Xu, Anthony