Digital illustration of blue birds over Indonesia with news text overlay, symbolizing AI-driven tweet classification.

Decoding Indonesian News: How AI is Changing the Way We Understand Twitter

Lena Kashyap in Tech & Innovation September 2025 • 4 min read.

"Uncover how machine learning is revolutionizing tweet classification, making social media a powerful source for news and insights in Indonesia."

Social media platforms like Twitter have become vital for instant news and information sharing. With millions of tweets generated daily, the challenge lies in organizing this vast amount of data into meaningful categories. This has led to the development of automated tweet classifiers, particularly in regions like Indonesia, where social media penetration is high.

The need for efficient tweet classification arises from the overwhelming volume of information. Automatically categorizing tweets into relevant news topics helps users quickly find information of interest, reducing the time spent sifting through irrelevant content. This is especially useful in a diverse and dynamic information environment like Indonesia.

Recent research focuses on using machine learning to classify tweets based on news categories derived from mainstream Indonesian news portals. By applying algorithms like Naive Bayes Multinomial (NBM), Support Vector Machines (SVM), and Random Forests (RF), researchers aim to create systems that accurately categorize tweets into topics such as religion, business, entertainment, and more.

AI Revolutionizes Indonesian Tweet Classification

Digital illustration of blue birds over Indonesia with news text overlay, symbolizing AI-driven tweet classification.

A pioneering study delved into the automatic classification of Indonesian tweets, addressing the challenge of information overload on social media. The research aimed to categorize tweets into 11 distinct news categories, mirroring the structure of mainstream Indonesian news portals. This categorization facilitates easier searching and information retrieval for users interested in specific topics.

The study employed several machine learning algorithms to classify tweets, including ZeroR, Naive Bayes Multinomial (NBM), Support Vector Machine (SVM), Random Forest (RF), and Sequential Minimal Optimization (SMO). These algorithms were tested and compared to determine the most effective method for tweet classification. The performance of each algorithm was evaluated using 10-fold cross-validation, with accuracy as the primary performance metric.

Naive Bayes Multinomial (NBM): Achieved the highest accuracy at 77.47%.
Support Vector Machine (SVM): Demonstrated competitive performance, close to NBM.
Random Forest (RF): Provided a balance between accuracy and computational efficiency.
ZeroR: Served as a baseline, highlighting the improvements achieved by other algorithms.

The research demonstrated that the Naive Bayes Multinomial (NBM) algorithm outperformed other methods, achieving an accuracy of 77.47%. This indicates that NBM is particularly well-suited for classifying Indonesian tweets into news categories. The study also explored the impact of varying the maximum number of tweets and terms in each category, finding that optimal performance was achieved with 500 tweets and 1000 terms.

The Future of AI in Social Media Analysis

The successful application of machine learning in classifying Indonesian tweets opens new avenues for social media analysis. By automatically categorizing tweets, AI can help users quickly find relevant information, track trending topics, and gain insights into public sentiment. This technology has the potential to transform how we interact with and understand social media content.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1109/icoict.2018.8528788, Alternate LINK

Title: Automatic Tweet Classification Based On News Category In Indonesian Language

Journal: 2018 6th International Conference on Information and Communication Technology (ICoICT)

Publisher: IEEE

Authors: Jaka E. Sembodo, Erwin B. Setiawan, Moch Arif Bijaksana

Published: 2018-05-01

Everything You Need To Know

Why is efficient tweet classification important for understanding news and information in a country like Indonesia?

Social media platforms, specifically Twitter, generate an immense volume of tweets daily. Efficient tweet classification is crucial to organize this data into meaningful categories, enabling users to quickly find relevant news and information. This is particularly important in dynamic information environments such as Indonesia, where social media usage is high.

Which specific machine learning algorithms were used to classify Indonesian tweets into news categories, and what was the purpose of using these algorithms?

The study utilized machine learning algorithms such as Naive Bayes Multinomial (NBM), Support Vector Machine (SVM), Random Forest (RF) and others to classify tweets into 11 distinct news categories. These categories mirror those found in mainstream Indonesian news portals, which facilitates easier searching and information retrieval. The goal was to determine which algorithm could most accurately categorize tweets.

Which machine learning algorithm was found to be the most accurate in classifying Indonesian tweets, and what were the optimal parameters identified for achieving the best performance?

The Naive Bayes Multinomial (NBM) algorithm achieved the highest accuracy at 77.47% in classifying Indonesian tweets into news categories. While Support Vector Machine (SVM) and Random Forest (RF) also performed well, NBM proved to be the most effective in this specific application. The study also found that performance peaked when using 500 tweets and 1000 terms per category.

What machine learning algorithms were used to classify tweets, and how was the performance of each algorithm evaluated?

The study used machine learning algorithms, including ZeroR, Naive Bayes Multinomial (NBM), Support Vector Machine (SVM), Random Forest (RF), and Sequential Minimal Optimization (SMO), to classify tweets. Each algorithm was evaluated using 10-fold cross-validation, with accuracy as the primary metric. ZeroR served as a baseline, highlighting the improvements achieved by the other, more advanced algorithms.

How does the use of AI and algorithms such as Naive Bayes Multinomial (NBM) in classifying Indonesian tweets change the way we understand and interact with social media content?

By successfully applying machine learning for tweet classification, particularly using algorithms like Naive Bayes Multinomial (NBM), AI can significantly enhance social media analysis. It enables users to efficiently find relevant information, track trending topics, and understand public sentiment. This technology has the potential to transform how individuals interact with and comprehend content on social media platforms.