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All Journal UNP Journal of Statistics and Data Science Indonesian Journal of Statistics and Its Applications
Fayyadh Ghaly
Universitas Negeri Padang
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Social Sciences Other

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Analisis Sentimen Pengguna Twitter Terhadap Serangan Moskow oleh ISIS dengan Algoritma Naive Bayes Cindy Pratiwi; Dodi Vionanda; Fayyadh Ghaly
UNP Journal of Statistics and Data Science Vol. 3 No. 1 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss1/349

Abstract

This study aims to analyze public sentiment towards the ISIS attack in Moscow, Russia on March 22, 2024 through twitter data using the Naive Bayes classification method. The attack had a significant impact on people's perceptions and reactions as reflected in the tweets of twitter social media users. To analyze this, 3005 English tweets from 22 March 2024 to 30 April 2024 relating to the event were collected using the crawling method with the phyton programming language. Preprocessing was done on the data to clean the data, then data labeling was done using phyton TextBlob. Naive Bayes algorithm is used to classify the sentiment of tweets into positive, and negative classes. The results of the research using Naive Bayes show that public sentiment tends to be negative towards the attacks that occurred. Naive Bayes classification results are quite good with an accuracy value of 70%, but there is an imbalance of data that tends to be biased towards negative sentiment. This research provides insight into how public opinion responds to events that occur and the performance of the Naive Bayes model in classification.
Classification of Rice Growth Phase Using Regression Logistic Multinomial Model and K-Nearest Neighbors Imputation on Satellite Data Fayyadh Ghaly; Yenni Kurniawati; Nonong Amalita; Dina Fitria
Indonesian Journal of Statistics and Applications Vol 9 No 1 (2025)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v9i1p1-9

Abstract

One of the efforts made by the government to maintain food security is to provide statistical data on rice production through accurate calculation of harvest areas using the area sampling framework approach. Although area sampling framework surveys produce accurate estimates, the costs required are quite high when applying this method. To overcome this problem, one solution that can be applied is to utilize satellite imagery to monitor the greenness index of plants using the enhanced vegetation index. However, in real conditions, the Landsat-8 optical satellite is susceptible to cloud cover, which results in missing data. This study aims to model the phase of rice plants using the regression logistic multinomial model by utilizing Landsat-8 satellites and k-nearest neighbors imputation handling to overcome missing data. The results showed that the model had varying performance in each phase, with an average balanced accuracy of 66.45%. This figure shows that the model can classify the area sampling framework data imputed using the k-nearest neighbors imputation method well. The model shows optimal performance in the late vegetative and generative phases but is less effective in detecting the harvest, puso, and non-rice paddy phases.
Co-Authors Cindy Pratiwi Dina Fitria Dodi Vionanda Nonong Amalita