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All Journal ComEngApp : Computer Engineering and Applications Journal Jusikom : Jurnal Sistem Komputer Musirawas Jurnal Sisfokom (Sistem Informasi dan Komputer) Reswara: Jurnal Pengabdian Kepada Masyarakat Jurnal Generic
Florensia, Yesinta
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Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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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

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.
Pemanfaatan Fitur Kecerdasan Buatan pada Aplikasi Multimedia bagi Guru SMA Alkautsar Bandar Lampung Putra, Bayu Wijaya; Sartika, Dewi; Putra, Apriansyah; Jambak, Muhammad Ihsan; Afif, Hasnan; Novianti, Hardini; Utari, Meylani; Florensia, Yesinta; Kurniati, Junia
Reswara: Jurnal Pengabdian Kepada Masyarakat Vol 6, No 1 (2025)
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/rjpkm.v6i1.5261

Abstract

Mitra pada Kegiatan Pengabdian Kepada Masyarakat (PKM) adalah SMA Al-Kautsar Bandar Lampung. Mitra merupakan salah satu sekolah yang telah memiliki fasilitas memadai serta guru-guru yang kompeten. Sebagai upaya meningkatkan kinerja guru dalam proses belajar mengajar, diharapkan semua guru dapat diberikan pengetahuan dan keterampilan dalam memanfaatkan teknologi untuk membuat media pembelajaran. Salah satu teknologi yang dapat dimanfaatkan dalam pembuatan media pembelajaran yaitu Canva. Canva merupakan platform berbasis web yang menyediakan fitur-fitur yang dapat digunakan untuk membuat berbagai jenis desain konten visual, seperti presentasi, poster, dan sebagainya. Sebanyak 82% guru SMA Al-Kautsar Bandar Lampung telah mengetahui Canva namun belum optimal dalam penggunaan fiturnya, khususnya fitur kecerdasan buatan atau lebih dikenal dengan istilah Artificial Intelligence (AI). Oleh karena itu, tim pelaksana PKM Universitas Sriwijaya memberikan pelatihan terkait pemanfaatan fitur AI pada Canva yang dapat digunakan untuk memudahkan dalam pembuatan media pembelajaran. Kegiatan telah terselenggara pada 19 September 2024 dengan metode penyuluhan dengan peserta sebanyak 32 orang guru. Tim pelaksana PKM memberikan tutorial praktis penggunaan fitur AI pada Canva yang langsung dipraktikkan oleh seluruh peserta. Berdasarkan hasil yang diperoleh menyatakan bahwa 89% menyatakan fitur AI pada Canva dianggap mudah digunakan, 81% menyatakan fitur AI pada Canva sesuai dengan kebutuhan, dan 97% menyatakan akan memanfaatkan fitur AI pada Canva secara berkelanjutan
Perancangan dan Implementasi Sistem Informasi Pencarian Kos Berbasis Web Florensia, Yesinta; Afif, Hasnan; Kirom, Miftahul; Sazaki, Yoppy; Kurniati, Junia; Efendi, Rusdi
Generic Vol 17 No 1 (2025): Vol 17, No 1 (2025)
Publisher : Fakultas Ilmu Komputer, Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/generic.v17i1.229

Abstract

Saat ini pencarian kos sering kali memakan waktu dan kurang efisien, baik bagi pencari kos maupun pemilik kos. Banyak pencari kos yang mendatangi tempat kost satu per satu untuk melihat kondisi dan menanyakan apakah masih tersedia kamar kosong. Seiring berkembangnya teknologi informasi, sistem berbasis digital telah menjadi solusi utama dalam berbagai masalah pencarian informasi. Penelitian ini bertujuan unutk merancang dan membangun sistem pencarian kos di Kabupaten Kendal berbasis web, sehingga dapat memudahkan masyarakat untuk mencari lokasi dan alamat kost. Penelitian yang dilakukan menggunakan metode RAD (Rapid Aplication Development). Metode yang di gunakan untuk pengumpulan data yaitu observasi, wawancara, studi pustaka, dan dokumentasi. Tahapan penelitian yaitu analisis, desain, pengkodean, pengujian, implementasi. Sistem ini dimodelkan menggunakan UML (Unfield Modeling Language) yang terdiri dari use case diagram, activity diagram, class diagram. Diagram ini digunakan untuk memberikan gambaran alur pembuatan dan penggunaan system informasi yang dapat dijadikan sebagai acuan bagi programmer dalam membuat Aplikasi Sistem Pencarian Tempat Kost di Kabupaten Kendal Berbasis Web, dan pembuatan aplikasi ini menggunakan PHP, Javascript, MySQL. Sistem Pencarian tempat Kos adalah sebuah sistem yang menampilkan informasi letak tempat kos dalam bentuk peta yang disertai dengan informasi rumah kos.
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.
PEMANFAATAN KECERDASAN BUATAN DALAM PERSONALISASI PEMBELAJARAN MAHASISWA Falah, Miftahul; Florensia, Yesinta
Jusikom : Jurnal Sistem Komputer Musirawas Vol 10 No 1 (2025): Jusikom : Jurnal Sistem Komputer Musirawas JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v10i1.2636

Abstract

The development of Artificial Intelligence technology has presented new opportunities in improving the quality of education, especially in creating adaptive and personalized learning systems. In higher education, the main challenge in the learning process is the differences in student characteristics such as learning styles, initial level of understanding, and learning motivation. This inequality often makes it difficult for lecturers to deliver material evenly and effectively. This study aims to examine the extent to which the use of AI technology can improve the personalization of student learning and its impact on learning outcomes. The research method used was a quasi-experiment with a pre-test and post-test approach to 62 students involved in AI-based learning systems. The instruments used included learning outcome tests and student perception questionnaires. The results of the study showed a significant increase in learning outcomes after the implementation of AI, with an average post-test score higher than the pre-test. These findings indicate that the application of AI in learning not only improves academic outcomes but also creates a more personalized and adaptive learning experience. The conclusion of this study emphasizes the importance of integrating AI into modern learning systems in order to address the challenges of differentiating student characteristics more effectively.
The Effect of the SMOTE Method on the Classification of Toddler Nutritional Status Using the Naïve Bayes Method Dewi Sartika; Florensia, Yesinta; Utari, Meylani
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 3 (2025): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i3.2381

Abstract

The first five years of life are a golden age for growth and development, so fulfilling nutritional intake during this period is very important to avoid stunting or growth failure. The problem of stunting is still the focus of the government because it is related to nutrition which is one of the key aspects for the development of qualified resources as well as in national development. According to the report of the Ministry of Health in 2023, it was stated that the results of the 2023 Indonesian Health Survey showed that there had been a decreasing in the prevalence of stunting over the past 10 years but it had not been able to meet the target of the 2020-2024 National Medium-Term Development Plan of 14% in 2024. This study will classify the toddler’s nutritional status using the Naive Bayes method. This method uses a probability technique with Bayes' theorem which is based on the assumption of mutually independent and equal conditions. The calculation of the Naive Bayes probability in this study uses the Multinomial distribution because the data used is discrete data. The total numbers of toddlers’ nutritional status data obtained was 245 data, with 4 invalid data. Based on the data set owned, the number of samples for each class label had an unbalanced number. One method could be used to handle this unbalanced data is the random oversampling method, Synthetic Minority Oversampling (SMOTE). SMOTE will create synthetic data randomly to balance minority data samples. The analysis and testing results showed that in Multinomial Naive Bayes with the 10-cross validation technique, the g-means value obtained on the original data set was 44.98% while in the balanced data set the g-means value was 80.06%. In Multinomial Naive Bayes with the split validation technique, the g-means value obtained on the original data set was 44.20% while in the balanced data set was 80.06%. This showed that there was an increase in the g-means value of 35%. It can be stated that the SMOTE method effectively improves the overall capability of the Multinomial Naive Bayes model.
Co-Authors Afif, Hasnan Apriansyah Putra Bayu Wijaya Putra DEWI SARTIKA Dewi Sartika Hardini Novianti Iman Saladin B. Azhar Jambak, Muhammad Ihsan Junia Kurniati, Junia Kirom, Miftahul Meylani Utari Miftahul Falah Rusdi Efendi Winda Kurnia Sari Yamani, Zaqqi