Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal ComEngApp : Computer Engineering and Applications Journal SOSIOEDUKASI : JURNAL ILMIAH ILMU PENDIDIKAN DAN SOSIAL
Yamani, Zaqqi
Unknown Affiliation
Computer Science & IT Education Engineering Social Sciences Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Fake News Detection Using Optimized Convolutional Neural Network and Bidirectional Long Short-Term Memory Sari, Winda Kurnia; Azhar, Iman Saladin B.; Yamani, Zaqqi; Florensia, Yesinta
Computer Engineering and Applications Journal Vol 13 No 03 (2024)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v13i03.492

Abstract

The spread of fake news in the digital age threatens the integrity of online information, influences public opinion, and creates confusion. This study developed and tested a fake news detection model using an enhanced CNN-BiLSTM architecture with GloVe word embedding techniques. The WELFake dataset comprising 72,000 samples was used, with training and testing data ratios of 90:10, 80:20, and 70:30. Preprocessing involved GloVe 100-dimensional word embedding, tokenization, and stopword removal. The CNN-BiLSTM model was optimized with hyperparameter tuning, achieving an accuracy of 96%. A larger training data ratio demonstrated better performance. Results indicate the effectiveness of this model in distinguishing fake news from real news. This study shows that the CNN-BiLSTM architecture with GloVe embedding can achieve high accuracy in fake news detection, with recommendations for further research to explore preprocessing techniques and alternative model architectures for further improvement.
Fake News Detection Using Optimized Convolutional Neural Network and Bidirectional Long Short-Term Memory Sari, Winda Kurnia; Azhar, Iman Saladin B.; Yamani, Zaqqi; Florensia, Yesinta
Computer Engineering and Applications Journal (ComEngApp) Vol. 13 No. 3 (2024)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The spread of fake news in the digital age threatens the integrity of online information, influences public opinion, and creates confusion. This study developed and tested a fake news detection model using an enhanced CNN-BiLSTM architecture with GloVe word embedding techniques. The WELFake dataset comprising 72,000 samples was used, with training and testing data ratios of 90:10, 80:20, and 70:30. Preprocessing involved GloVe 100-dimensional word embedding, tokenization, and stopword removal. The CNN-BiLSTM model was optimized with hyperparameter tuning, achieving an accuracy of 96%. A larger training data ratio demonstrated better performance. Results indicate the effectiveness of this model in distinguishing fake news from real news. This study shows that the CNN-BiLSTM architecture with GloVe embedding can achieve high accuracy in fake news detection, with recommendations for further research to explore preprocessing techniques and alternative model architectures for further improvement.
ANALYSIS OF DEMOGRAPHIC AND SOCIOECONOMIC FACTORS ON THE INCIDENCE OF DIABETES MELLITUS IN DKI JAKARTA USING LOGISTIC REGRESSION Fahlevi, M. Ilham; Manurung, Jackson Imanuel; Pratama, Mohd Rizky Putra; Hisyam , M Naufal; Meiriza, Allsela; Tania, Ken Ditha; Yamani, Zaqqi
SOSIOEDUKASI Vol 15 No 1 (2026): SOSIOEDUKASI : JURNAL ILMIAH ILMU PENDIDIKAN DAN SOSIAL
Publisher : Fakultas Keguruan Dan Ilmu Pendidikan Universaitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/sosioedukasi.v15i1.7722

Abstract

Diabetes mellitus (DM) is a non-communicable disease with a significant global impact and an increasing incidence rate. Indonesia records one of the highest diabetes rates, particularly in the province of DKI Jakarta, which shows the highest national prevalence. This observational study with a cross-sectional design aims to evaluate the factors influencing the onset of DM in the Jakarta area using data from the 2023 Indonesia National Health Survey (SKI). This research involves participants over the age of 15. Analysis was conducted using univariate, bivariate (chi-square test), and multivariate methods with the Logistic Regression method, while considering the complexity of the research design. Research findings indicate that age, education level, and comorbidities are factors that significantly influence the incidence of DM. Those below the productive age group are at a higher risk of experiencing DM (OR = 2.268). Secondary education lowers the risk compared to higher education (OR = 0.611). Comorbidity is the main risk factor, increasing the probability of DM incidence by 6.229 times. These findings emphasize the importance of managing comorbidities and implementing appropriate preventive measures for at-risk individuals in efforts to manage diabetes in major cities.
Co-Authors Allsela Meiriza, Allsela Fahlevi, M. Ilham Florensia, Yesinta Hisyam , M Naufal Iman Saladin B. Azhar Ken Ditha Tania Manurung, Jackson Imanuel Pratama, Mohd Rizky Putra Winda Kurnia Sari