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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

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

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.

Analisis Segmentasi Pelanggan Menggunakan RFM dan K-Means Clustering sebagai Dasar Penyusunan Aturan Pendukung Keputusan Andini, Meisya Dwi; Catra, Rafa Nadira; Homausyah, Weli Ratri; Aurelia, Haaniyah; Meiriza, Allsela; Tania, Ken Ditha; Yamani, Zaqqi
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9511

One of the important methods in supporting data-driven Customer Relationship Management (CRM) initiatives is customer segmentation. However, in practice, segmentation results are often limited to descriptive analysis and are not further utilized in decision-support processes. This study aims to utilize customer segmentation results based on the Recency, Frequency, Monetary (RFM) approach and the K-Means algorithm as a basis for developing decision-support recommendations. The research stages include data preprocessing, RFM value calculation, normalization using the Min-Max Scaling method, and determining the optimal number of clusters using the Elbow Method and Silhouette Score. The evaluation results indicate that the optimal number of clusters is four, with a Silhouette Score of 0.61, which reflects a moderately good level of cluster separation. The segmentation results classify customers into four categories: High Value/VIP Customers, Loyal Customers, Potential Customers, and Low Value/Dormant Customers, each exhibiting distinct transactional behavior characteristics. These characteristics are then interpreted into decision rules using IF–THEN logic; for example, customers with low Recency, high Frequency, and high Monetary values are recommended strategies such as loyalty rewards and upselling. The findings suggest that customer segmentation can be extended beyond descriptive analysis and utilized as a practical basis for marketing decision-making, although the approach remains relatively simple and heuristic-based. The contribution of this study is to integrate RFM-KMeans segmentation results with IF–THEN decision rules to generate more applicable marketing strategy recommendations in supporting data-driven decision making.

Perancangan Knowledge Management System Berbasis Website Menggunakan Model SECI untuk Mendukung Knowledge Sharing Guru pada SMP Bina Karya Dirgantara, Muhammad Ihsan; Sepriansyah, Fakhri; Izzatul Maula, Nurly; Daffazka, Farhan; Ditha Tania, Ken; Yamani, Zaqqi
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp112-126

The design of a website-based Knowledge management system (KMS) using the SECI model at SMP Bina Karya is motivated by several problems, including knowledge that remains stored individually with each teacher, the unavailability of centralized learning materials and lesson plans (RPP), and the difficulty faced by substitute teachers in delivering lessons when replacing the main teacher who is absent. The Knowledge management system serves as a solution to document, distribute, and prevent the loss of knowledge, while also acting as a medium to enhance the culture of knowledge sharing among teachers. The design method used is a qualitative approach consisting of data collection through observation, interviews, and literature studies, identification of knowledge management using the SECI model, system requirements analysis, system design, and testing using Focus Group Discussion (FGD). This study produces a website-based KMS equipped with features such as user account management, substitute teacher schedule management, learning material management, lesson plan management, and discussion forums. The results of the FGD testing show an average acceptance rate of 94.2% for all developed features, with the substitute teacher schedule management feature serving as the main differentiator that successfully addresses the specific problems at SMP Bina Karya.

Deep learning for mental health analysis: long short-term memory approach to text-based condition classification Yamani, Zaqqi; Lestarini, Dinda; Raflesia, Sarifah Putri; Sari, Purwita; Athalina, Ghita
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1762-1770

The increasing prevalence of mental health disorders highlights the need for scalable and automated approaches to early detection. This study proposes a deep learning–based text classification framework using a long short-term memory (LSTM) network to identify mental health conditions from user generated textual data. A corpus of 103,488 labeled texts representing anxiety, stress, bipolar disorder, depression, personality disorder, suicidal ideation, and normal states was preprocessed through tokenization, padding, and word embedding. The proposed LSTM model achieved overall accuracy of 87% on test set, with strong class-wise performance reflected by precision, recall, and F1-scores, particularly for anxiety, personality disorder, and normal classes. Comparative error analysis using a confusion matrix revealed challenges in distinguishing depression from suicidal ideation, indicating semantic overlap between these conditions. The results demonstrate that LSTM-based models can effectively capture sequential linguistic patterns relevant to mental health classification. This framework shows potential as a decision-support tool for early screening and digital mental health applications, complementing clinical assessment rather than replacing it.

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