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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences ComEngApp : Computer Engineering and Applications Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JSI: Jurnal Sistem Informasi (E-Journal) Scientific Journal of Informatics IJoICT (International Journal on Information and Communication Technology) Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF APPLIED INFORMATICS AND COMPUTING JOISIE (Journal Of Information Systems And Informatics Engineering) INFOMATEK: Jurnal Informatika, Manajemen dan Teknologi Jurnal Teknologi Dan Sistem Informasi Bisnis Sriwijaya Journal of Informatics and Applications Jurnal Generic REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Jurnal Kesehatan Komunitas Indonesia Buffer Informatika Sriwijaya Accounting Community Services
Winda Kurnia Sari
Universitas Sriwijaya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Other

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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.
Pendampingan Administrasi Perpajakan Bagi Anggota Organisasi Sosial di Desa Muara Penimbung Ulu Muhammad Hidayat; Sri Maryati; Ery Erman; Winda Kurnia Sari
Sriwijaya Accounting Community Services Vol. 3 No. 1 (2024): Sriwijaya Accounting Community Services
Publisher : Jurusan Akuntansi Fakultas Ekonomu Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29259/sacs.v3i1.26

Abstract

Taxes are the main source of state income. Taxpayer compliance is the key to the success of a country's development. Tax administration management assistance is needed not only by employees, but also by members of social organizations. The aim of this assistance is to increase public awareness of orderly tax administration, especially for the people in Muara Penimbung Ulu Village.
Hyperparameter optimization of convolutional neural network using particle swarm optimization for emotion recognition Rini, Dian Palupi; Sari, Tri Kurnia; Sari, Winda Kurnia; Yusliani, Novi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp547-560

Abstract

Emotion identification has been widely researched based on facial expressions, voice, and body movements. Several studies on emotion recognition have also been performed using electroencephalography (EEG) signals and the results also show that the technique has a high level of accuracy. EEG signals that detected by standart method using exclusive representations of time and frequency domains presented unefficient results. Some researchers using the convolutional neural network (CNN) method performed EEG signal for emotional recognition and obtained the best results in almost all benchmarks. Although CNN has shown fairly high accuracy, there is still a lot of room for improvement. CNN is sensitive to its hyperparameter value because it has considerable effect on the behavior and efficiency of the CNN architecture. So that the use of optimization algorithms is expected to provide an alternative selection of appropriate hyper parameter values on CNN. Particle swarm optimization (PSO) algorithm is a metaheuristic-based optimization algorithm with many advantages. This PSO algorithm was chosen to optimize the hyperparameter values on CNN. Based on the evaluation results in each model, hybrid CNN-PSO showed better results and achieved the best value in 80:20 split data which is 99.30% accuracy.
Pengembangan Fitur pada Aplikasi Penjualan Plafon PVC berbasis Website Firmansyah, M. Daffa; Hardiyanti, Dinna Yunika; Sari, Winda Kurnia; Afrina, Mira; Novianti, Hardini
Generic Vol 16 No 2 (2024): Vol 16, No 2 (2024)
Publisher : Fakultas Ilmu Komputer, Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/generic.v16i2.188

Abstract

Penelitian ini bertujuan mengembangkan sistem informasi penjualan plafon PVC berbasis web untuk meningkatkan efisiensi dan efektivitas proses bisnis pada PT. Eka Pertiwi Lestari. Metode pengembangan sistem yang digunakan adalah waterfall. Fitur-fitur utama yang dikembangkan meliputi katalog produk, keranjang belanja, dan pembayaran online. Pengujian blackbox dilakukan untuk memastikan fungsionalitas sistem. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan berhasil meningkatkan kecepatan transaksi, akurasi data, serta kepuasan pelanggan. Implementasi sistem informasi ini diharapkan dapat menjadi solusi bagi UMKM yang ingin meningkatkan daya saing di era digital
A Comparative Study of Deep Learning’s Performance Methods for News Article using Word Representations Azhar, Iman Saladin B.; Sari, Winda Kurnia; Gumay, Naretha Kawadha Pasemah
Sistemasi: Jurnal Sistem Informasi Vol 14, No 2 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i2.5090

Abstract

In natural language processing (NLP), text classification is a crucial task that involves analyzing textual data, which often has high dimensionality. A good word representation is essential to address this challenge, and the word representation using GloVe is one of the popular methods that provides pre-trained word representations in high-dimensional vectors. This research evaluates the effectiveness of three deep learning techniques Convolutional Neural Network (CNN), Deep Neural Network (DNN), and Long Short-Term Memory (LSTM) for online news classification using 300-dimensional GloVe word representations. The CNN model utilizes convolutional and pooling layers to extract local features, the DNN relies on dense layers to learn abstract representations, while the LSTM excels at capturing long-term dependencies between words. The results show that the LSTM model achieved the best accuracy at 93.45%, followed by CNN at 91.24%, and DNN at 90.67%. The superiority of LSTM is attributed to its ability to effectively capture temporal relationships and context, while CNN offers efficiency with faster training times. Although DNN produced solid performance, it is less optimal in understanding word sequences. These findings indicate that LSTM outperforms the other models in online news text classification tasks.
The Role of Image Generative Artificial Intelligence in Optimizing Digital Visual Assets for Animation and Motion Graphics Content B Azhar, Iman Saladin; Sari, Winda Kurnia; Exaudi, Kemahyanto; Prasetyo, Aditya Putra Perdana
REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Vol 6, No 2 (2025): Reka Elkomika
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/rekaelkomika.v6i2.162-172

Abstract

This Community Service Program (PKM) was conducted to introduce and promote the use of Motion Graphics and Image Generative AI technologies as innovative tools to support digital content creation, particularly in the context of industry and government institutions. Through a workshop format, participants were provided with an overview of how these technologies can improve the quality, clarity, and appeal of visual communication. The session included a conceptual explanation of Image Generative AI, such as Adobe Firefly, which allows users to generate visual assets through text prompts, and Adobe After Effects, a powerful tool for producing dynamic animations and motion graphics. Participants actively followed the material presented and showed interest in how these tools can be applied in real-world promotional or branding efforts. The workshop concluded that integrating these technologies holds great potential for enhancing institutional communication strategies. Future programs are expected to include more hands-on and practice-oriented sessions to further develop participants’ digital production skills.
Penerapan Metode K-Means Clustering untuk Segmentasi Performa Pembalap F1 Season 2024 Sahira, Mutia; Salsabila, Adella; Salsabila, Shofi; Putri, Aulia Najibah; Tania, Ken Ditha; Sari, Winda Kurnia
INFOMATEK Vol 27 No 1 (2025): Juni 2025
Publisher : Fakultas Teknik, Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/infomatek.v27i1.24297

Abstract

Performa pembalap Formula 1 tidak hanya ditentukan oleh hasil akhir balapan, tetapi juga oleh konsistensi catatan waktu dan lap tercepat. Penelitian ini menerapkan algoritma K-Means clustering untuk mengelompokkan pembalap berdasarkan performa mereka. Data yang digunakan mencakup hasil balapan resmi musim 2024 yang diterbitkan oleh FIA. Proses pengolahan data mencakup pengumpulan data, preprocessing, analisis eksploratori, penerapan algoritma clustering, serta evaluasi dan interpretasi hasil. Untuk menentukan jumlah cluster yang optimal, digunakan Metode Elbow dan skor Silhouette, yang menghasilkan empat kelompok pembalap dengan karakteristik performa yang berbeda. Hasil analisis menunjukkan bahwa metode ini berhasil mengidentifikasi pola performa yang relevan, memberikan wawasan bagi tim balap dalam menyusun strategi. Evaluasi menggunakan Silhouette Score menunjukkan bahwa segmentasi yang dihasilkan cukup baik dengan nilai sebesar 0.5735.
Deep learning with Bayesian Hyperparameter Optimization for Precise Electrocardiogram Signals Delineation Darmawahyuni, Annisa; Sari, Winda Kurnia; Afifah, Nurul; Siti Nurmaini; Jordan Marcelino; Rendy Isdwanta
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6171

Abstract

Electrocardiography (ECG) serves as an essential risk-stratification tool to observe further treatment for cardiac abnormalities. The cardiac abnormalities are indicated by the intervals and amplitude locations in the ECG waveform. ECG delineation plays a crucial role in identifying the critical points necessary for observing cardiac abnormalities based on the characteristics and features of the waveform. In this study, we propose a deep learning approach combined with Bayesian Hyperparameter Optimization (BHO) for hyperparameter tuning to delineate the ECG signal. BHO is an optimization method utilized to determine the optimal values of an objective function. BHO allows for efficient and faster parameter search compared to conventional tuning methods, such as grid search. This method focuses on the most promising search areas in the parameter space, iteratively builds a probability model of the objective function, and then uses that model to select new points to test. The used hyperparameters of BHO contain learning rate, batch size, epoch, and total of long short-term memory layers. The study resulted in the development of 40 models, with the best model achieving a 99.285 accuracy, 94.5% sensitivity, 99.6% specificity, and 94.05% precision. The ECG delineation-based deep learning with BHO shows its excellence for localization and position of the onset, peak, and offset of ECG waveforms. The proposed model can be applied in medical applications for ECG delineation.
A deep learning-based myocardial infarction classification based on single-lead electrocardiogram signal Darmawahyuni, Annisa; Sari, Winda Kurnia; Afifah, Nurul; Tutuko, Bambang; Nurmaini, Siti; Marcelino, Jordan; Isdwanta, Rendy; Khairunnisa, Cholidah Zuhroh
International Journal of Advances in Applied Sciences Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i2.pp352-360

Abstract

Acute myocardial infarction (AMI) carries a significant risk, emphasizing the critical need for precise diagnosis and prompt treatment of the responsible lesion. Consequently, we devised a neural network algorithm in this investigation to identify myocardial infarction (MI) from electrocardiograms (ECGs) autonomously. An ECG is a standard diagnostic tool for identifying acute MI due to its affordability, safety, and rapid reporting. Manual analysis of ECG results by cardiologists is both time-consuming and prone to errors. This paper proposes a deep learning algorithm that can capture and automatically classify multiple features of an ECG signal. We propose a hybrid convolutional neural network (CNN) and long short-term memory (LSTM) for automatically diagnosing MI. To generate the hybrid CNN-LSTM model, we proposed 39 models with hyperparameter tuning. As a result, the best model is model 35, with 86.86% accuracy, 75.28% sensitivity and specificity, and 83.56% precision. The algorithm based on a hybrid CNN-LSTM demonstrates notable efficacy in autonomously diagnosing AMI and determining the location of MI from ECGs.
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.
Co-Authors Abdillah Adipatria Budi Azhar Adella Salsabila Aditya Putra Perdana Prasetyo Ahmad Rifai Anissa, Cahya Rahmi Aspirani Utari Aulia Najibah Putri Ayuningtiyas, Pratiwi B Azhar, Iman Saladin B. Azhar, Iman Saladin Bambang Tutuko Cantika Aulia Darmawahyuni, Annisa Deris Stiawan Dian Palupi Rini Dian Palupi Rini Dian Palupi Rini Dian Palupi Rini Dinna Yunika Hardiyanti Ery Erman Firmansyah, M. Daffa Florensia, Yesinta Gumay, Naretha Kawadha Gumay, Naretha Kawadha Pasemah Hardini Novianti Iman Saladin B. Azhar Iman Saladin Budi Azhar Isdwanta, Rendy Jambak, Muhammad Ihsan Jordan Marcelino Kemahyanto Exaudi Ken Dhita Tania Ken Ditha Tania Khairunnisa, Cholidah Zuhroh Khalid Al Mas Ud M Tsabita Robani Marcelino, Jordan Marco Saputra Mira Afrina Muhammad Afif Muhammad Fakhri Nadrota Acta Muhammad Hidayat Mutia Sahira Novi Yusliani Pacu Putra Pangestu, Ridho Putri, Aulia Najibah Rachmamtullah, Muhammad Naufal Ramadhani, Indira Nailah Rendy Isdwanta Reza Firsandaya Malik Reza Firsandaya Malik Ricy Firnando Rizma Adlia Syakurah Sabrina Intan Zoraya Sahira, Mutia Salsabila, Adella Salsabila, Shofi Sari, Tri Kurnia Shofi Salsabila Siti Nurmaini Yamani, Zaqqi