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All Journal Applied Information Technology and Computer Science (AICOMS)
Irsyad , Hafiz
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Computer Science & IT

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Pemrosesan Query dan Pemeringkatan Judul Berita Terkait Gubernur Jawa Barat Menggunakan TF-IDF dan Cosine Similarity Saputra, Chandra; Wilcent , Wilcent; Irsyad , Hafiz; Rahman , Abdul
Applied Information Technology and Computer Science (AICOMS) Vol 4 No 1 (2025)
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/aicoms.v4i1.1799

Abstract

Increasing efficiency and relevance in searching for news information is a pressing need in the digital era. This study aims to develop a news title ranking system based on keywords (queries) by combining the Term Frequency-Inverse Document Frequency (TF-IDF) and cosine similarity methods. The data used are 2,507 news titles from four of the most popular news sites in Indonesia, namely Kompas.com, Detik.com, CNNIndonesia.com, and Tempo.com in the last one year. The stages carried out include web scraping, pre-processing (case folding, tokenizing, stopwords removal, and stemming), word weighting using TF-IDF, similarity calculation using cosine similarity, to system performance evaluation with accuracy, precision, recall, and f1-score metrics. The test results on three different queries show that the system is able to provide very good results with an average accuracy of 99.75%, precision 96.67%, recall 100%, and f1-score 98.33%. This study shows that the combination of TF-IDF and cosine similarity is effective in optimizing the search for news titles that are relevant to the entered query.
Pemrosesan Query dan Pemeringkatan Judul Berita Terkait Gubernur Jawa Barat Menggunakan TF-IDF dan Cosine Similarity Saputra, Chandra; Wilcent , Wilcent; Irsyad , Hafiz; Rahman , Abdul
Applied Information Technology and Computer Science (AICOMS) Vol 4 No 1 (2025)
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/aicoms.v4i1.1799

Abstract

Increasing efficiency and relevance in searching for news information is a pressing need in the digital era. This study aims to develop a news title ranking system based on keywords (queries) by combining the Term Frequency-Inverse Document Frequency (TF-IDF) and cosine similarity methods. The data used are 2,507 news titles from four of the most popular news sites in Indonesia, namely Kompas.com, Detik.com, CNNIndonesia.com, and Tempo.com in the last one year. The stages carried out include web scraping, pre-processing (case folding, tokenizing, stopwords removal, and stemming), word weighting using TF-IDF, similarity calculation using cosine similarity, to system performance evaluation with accuracy, precision, recall, and f1-score metrics. The test results on three different queries show that the system is able to provide very good results with an average accuracy of 99.75%, precision 96.67%, recall 100%, and f1-score 98.33%. This study shows that the combination of TF-IDF and cosine similarity is effective in optimizing the search for news titles that are relevant to the entered query.
Opini Publik terhadap Isu Pengoplosan Pertamax di Youtube Menggunakan Metode Naive Bayes Adikara Alif Nurrahman; Moza , Earlando; Md, Ramanda; Rizvi Roshan , Muhamad; Rizky , Ahmad; Irsyad , Hafiz
Applied Information Technology and Computer Science (AICOMS) Vol 4 No 2 (2025)
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/aicoms.v4i2.1990

Abstract

This study aims to explore public perceptions regarding the issue of Pertamax fuel adulteration, a topic that has sparked widespread discussion on YouTube, by employing sentiment analysis techniques based on the Naive Bayes algorithm. This issue has attracted significant public attention and become a trending topic on social media, particularly on the YouTube platform. The data analyzed in this research consist of user comments responding to the issue. The Naive Bayes algorithm is used to classify sentiments in the comments into three categories: positive, negative, and neutral. To address the imbalanced distribution of data, the Synthetic Minority Over-sampling Technique (SMOTE) is applied. The results show that before applying SMOTE, the model achieved an accuracy of only 48%, with a precision of 0.48, recall of 0.36, and an F1-score of 0.41 for the negative category, as well as a precision of 0.48, recall of 0.56, and an F1-score of 0.52 for the positive category. After implementing SMOTE, the model's accuracy increased significantly to 88%, with a precision of 0.91, recall of 0.93, and an F1-score of 0.92 for the negative category. For the positive category, precision improved to 0.80, although recall decreased to 0.75, yielding an F1-score of 0.77. The average precision, recall, and F1-score (macro average) after applying SMOTE reached 0.85, 0.84, and 0.85, respectively, representing a substantial improvement compared to the results before SMOTE. This study highlights the importance of using SMOTE to enhance sentiment analysis accuracy, particularly in addressing class imbalance issues within the dataset.
Co-Authors Adikara Alif Nurrahman Chandra Saputra Md, Ramanda Moza , Earlando Rahman , Abdul Rizky , Ahmad Rizvi Roshan , Muhamad Wilcent , Wilcent