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Prasetyo, Andhi
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Computer Science & IT Social Sciences

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Implementasi Algoritma C.45 dalam Klasifikasi Kondisi Ekonomi Warga Kabupaten Boyolali: (Studi Kasus Desa Sumbung Kecamatan Cepogo) Prasetyo, Andhi; Wahyono, Ari
Generation Journal Vol 8 No 2 (2024): Generation Journal
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/gj.v8i2.22827

Abstract

The economic impact following the Covid-19 pandemic has been felt by various countries. The Indonesian government is implementing economic recovery and providing social assistance based on economic conditions, but there are still residents who do not receive assistance but deserve help. The Sumbung Village Government is trying to anticipate by looking for indicators that influence economic conditions.The research was carried out using the Data Mining classification method. uses the C4.5 Algorithm because it produces decision tree visualizations that are easy to understand. The data used is Boyolali MCD data for Sumbung Village, Cepogo District. As a result of the analysis of 21 attributes, 8 criteria with the highest weight for indicators of economic conditions. The accuracy reached 94.47%, higher than Naïve Bayes (93.28%) and K-NN (91.70%), making it suitable for classifying the economic conditions of Boyolali Regency residents, especially Sumbung Village.
Deteksi Sarkasme Pada Dataset News Headlines Menggunakan Artificial Neural Network Berbasis TF-IDF Prasetyo, Andhi; Harjo, Budi
Journal of Creativity Student Vol. 8 No. 2 (2025)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jcs.v8i2.39855

Abstract

Sarcasm is a style of language frequently used in news headlines, especially in satirical media, thus posing a challenge for natural language processing systems in understanding the true meaning. The system's inability to recognize sarcasm can lead to misinterpretation in various tasks such as sentiment analysis and text classification. This study aims to detect sarcasm in news headlines using an Artificial Neural Network (ANN) with Term Frequency–Inverse Document Frequency (TF-IDF) feature representation. The dataset used is the News Headlines Dataset for Sarcasm Detection, which consists of approximately 28,000 news headlines from two sources, namely The Onion as sarcastic news and HuffPost as non-sarcastic news. The data is processed through a text pre-processing stage, then represented using TF-IDF with unigram and bigram schemes. The ANN model is trained using an 80:20 data split scheme and evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results show that the TF-IDF-based ANN model achieved an accuracy of 85.02% and an F1-score of 84.44% on the test data. These results demonstrate that the ANN approach with TF-IDF representation remains effective and efficient as a baseline method for detecting sarcasm in short texts such as news headlines.
Co-Authors Ari Wahyono Budi Harjo