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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Teknik Informatika (JUTIF)
Kusnawi , Kusnawi
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Computer Science & IT

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Analyzing Public Sentiment Regarding the Qatar 2023 World Cup Debate Using TF-IDF and K-Nearest Neighbor Weighting Olajuwon, Sayyid Muh. Raziq; Kusrini, Kusrini; Kusnawi , Kusnawi
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13275

Abstract

This research aims to uncover the sentiment of Twitter users regarding the polemics surrounding the 2023 Qatar World Cup using a text-based sentiment analysis approach. The research methodology involves collecting data from Twitter posts, encompassing discussions, opinions, and responses related to the Qatar World Cup 2023. The TF-IDF weighting is applied to identify significant keywords in each post, while the K-Nearest Neighbor algorithm is employed to classify sentiments as positive, negative, or neutral. The findings reveal a comprehensive picture of how the public perceives the Qatar World Cup 2023 on the Twitter platform. The results not only cover positive and negative aspects of online discussions but also identify trends and patterns of sentiment that emerge during specific periods.The application of these methods provides valuable insights into understanding the dynamics of public opinion related to international sports events through the lens of social media. The results of the analysis demonstrate that a majority of Twitter users express positive sentiments towards the Qatar World Cup 2023, highlighting excitement and anticipation. However, some negative sentiments also arise, primarily related to controversies and concerns about the event. The research further identifies temporal variations in sentiment, reflecting changing public perceptions over time.This research contributes to the development of sentiment analysis methods by using a combination of TF-IDF weighting and the K-Nearest Neighbor algorithm to delve into Twitter users' perspectives. Consequently, the findings have practical applicability for further research and implementation in managing the social impact and public perception of major sporting events like the World Cup. .
A Comparative Analysis of Hyperparameter-Tuned XGBoost and LightGBM for Multiclass Rainfall Classification in Jakarta Pringandana, Cokorda Gde Lanang; Kusnawi , Kusnawi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4965

Abstract

The increasing frequency of extreme weather events in Jakarta has disrupted daily life and critical infrastructure, highlighting the urgent need for accurate rainfall prediction models to support disaster mitigation and early warning systems. This study aims to evaluate and compare the performance of two machine learning algorithms Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) for multiclass rainfall classification using historical meteorological data. The dataset, which includes features such as temperature, humidity, wind speed, and rainfall, was preprocessed through mean imputation, oversampling to address class imbalance, one-hot encoding, and feature engineering. Both models were trained and tuned using RandomizedSearchCV and assessed through cross-validation and independent testing. The results show that XGBoost consistently outperformed LightGBM, achieving 94% accuracy compared to 91%. Furthermore, XGBoost demonstrated higher precision, recall, F1-score, and specificity across all rainfall categories, resulting in fewer misclassifications and more stable predictions. Confusion matrices confirmed its superior ability to distinguish between similar weather conditions such as cloudy and rainy classes. These findings indicate that XGBoost is more effective in capturing nonlinear interactions between weather features and is therefore better suited for use in complex tropical climates. The study concludes that XGBoost is the more reliable model and recommends its integration into real-time early warning systems to improve climate resilience and disaster preparedness in urban areas like Jakarta that are increasingly affected by climate variability.
Co-Authors Kusrini, Kusrini Olajuwon, Sayyid Muh. Raziq Pringandana, Cokorda Gde Lanang