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News classification using light gradient boosted machine algorithm Muhammad Hatta Rahmatul Kholiq; Wiranto Wiranto; Sari Widya Sihwi
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp206-213

Abstract

News classification is a complex issue as people are easily convinced of misleading information and lack control over the spread of fake news. However, we ca n break the problem of spreading fake news with artificial intelligence (AI), which has developed rapidly. This study proposes a news classification model using a light gradient boosted machine (LightGBM) algorithm. The model is analyzed using two feature extraction techniques, count vectorizer and Tfidf vectorize r and compared with a deep learning model using long - short term memory (LSTM). The experimental evaluation showed that all LightGBM models outperform LSTM. The best model is the count vectorizer Li ghtGBM, which achieves an accuracy value of 0.9933 and an area under curve (AUC) score of 0.9999.
Speech emotion recognition using 2D-convolutional neural network Fauzivy Reggiswarashari; Sari Widya Sihwi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6594-6601

Abstract

This research proposes a speech emotion recognition model to predict human emotions using the convolutional neural network (CNN) by learning segmented audio of specific emotions. Speech emotion recognition utilizes the extracted features of audio waves to learn speech emotion characteristics; one of them is mel frequency cepstral coefficient (MFCC). Dataset takes a vital role to obtain valuable results in model learning. Hence this research provides the leverage of dataset combination implementation. The model learns a combined dataset with audio segmentation and zero padding using 2D-CNN. Audio segmentation and zero padding equalize the extracted audio features to learn the characteristics. The model results in 83.69% accuracy to predict seven emotions: neutral, happy, sad, angry, fear, disgust, and surprise from the combined dataset with the segmentation of the audio files.
PENINGKATAN LITERASI DIGITAL MANAJEMEN CONTENT MANAGEMENT SYSTEM BERBASIS WORDPRESS UNTUK MENCEGAH SERANGAN JUDI GACOR PADA WEBSITE PORTAL ORGANISASI PERANGKAT DAERAH Winarno Winarno; Bambang Harjito; Wiranto Wiranto; Heri Prasetyo; Sari Widya Sihwi
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 6, No 6 (2023): martabe : jurnal pengabdian kepada masyarakat
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v6i6.2252-2257

Abstract

Maraknya aksi peretasan di Indonesia saat ini terjadi tidak hanya di Lembaga-lembaga besar, namun juga lembaga pemerintah daerah. Salah satu bentuk peretasan yang sangat marak akhir-akhir ini adalah judi gacor pada Content Managemen System(CMS) berbasis Wordpress. Di Kabupaten Karanganyar masih ada 6.180 konten judi gacor yang terlihat di Google. Untuk menanggulangi masalah tersebut diperlukan Langkah praktik baik bagaimana mencegah dan mengelola sebuah CMS berbasis Wordpress agar terhindar dari serangan siber. Pelaksanaan pengabdian berupa workshop mengelola website mulai dari instalasi sampai implementasi. Hasil workshop menunjukkan peningkatan kemampuan peserta yang semula hanya 40% menjadi 80% paham bagaimana mengelola sebuah website yang aman.