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All Journal Sosiohumaniora InComTech: Jurnal Telekomunikasi dan Komputer Jurnal Sistem Cerdas JSAI (Journal Scientific and Applied Informatics) Jurnal Abdi Masyarakat (JAM) Jurnal Sistem Informasi dan Informatika (SIMIKA) Literatify : Trends in Library Development Jevief : Jurnal Vokasi International Journal of Community Service Implementation
Ida Nurhaida
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Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health Social Sciences Other

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Journal : jsai journal scientific and applied informatics

 1 
Klasifikasi Chest X-Ray Images Berdasarkan Kriteria Gejala Covid-19 Menggunakan Convolutional Neural Network Vina Ayumi; Ida Nurhaida
JSAI (Journal Scientific and Applied Informatics) Vol. 4 No. 2 (2021): Juni 2021
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v4i2.1513

Abstract

Deteksi dini terhadap adanya indikasi pasien dengan gejala COVID-19 perlu dilakukan untuk mengurangi penyebaran virus. Salah satu cara yang dapat dilakukan untuk mendeteksi virus COVID-19 adalah dengan cara mempelajari citra chest x-ray pasien dengan gejala Covid-19. Citra chest x-ray dianggap mampu menggambarkan kondisi paru-paru pasien COVID-19 sebagai alat bantu untuk diagnosa klinis. Penelitian ini mengusulkan pendekatan deep learning berbasis convolutional neural network (CNN) untuk klasifikasi gejala COVID-19 melalui citra chest X-Ray. Evaluasi performa metode yang diusulkan akan menggunakan perhitungan accuracy, precision, recall, f1-score, dan cohens kappa. Penelitian ini menggunakan model CNN dengan 2 lapis layer convolusi dan maxpoling serta fully-connected layer untuk output. Parameter-parameter yang digunakan diantaranya batch_size = 32, epoch = 50, learning_rate = 0.001, dengan optimizer yaitu Adam. Nilai akurasi validasi (val_acc) terbaik diperoleh pada epoch ke-49 dengan nilai 0.9606, nilai loss validasi (val_loss) 0.1471, akurasi training (acc) 0.9405, dan loss training (loss) 0.2558.
Optimasi Prediksi Cryptocurrency Menggunakan Pendekatan Deep Learning Ida Nurhaida; Mochamad Sobiri; Safitri Jaya
JSAI (Journal Scientific and Applied Informatics) Vol 6 No 2 (2023): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v6i2.5288

Abstract

Cryptocurrency is a decentralized digital currency that a central government regulates. Since cryptocurrencies are highly volatile, analysis is required before using cryptocurrencies to minimize losses. This research compares the Long Short Term Memory (LSTM) model and optimization algorithms such as Adam and Root Mean Square Propagation (RMSProp) to predict cryptocurrency values. The LSTM method was optimized using the Adam Optimizer and evaluated based on the Root Mean Square Error (RMSE). Thus the predicted RMSE value is 0.08217562639465784, which is a slight error value so that it is close to the actual value. While the RMSE value of 0.10699215580552895 using RMSProp gets a more significant value which impacts the accuracy of the prediction results. Thus the combination of the LSTM and Adam algorithms can accurately predict and optimize data.
Perancangan Aplikasi Web Untuk Deteksi Motif Batik Indonesia Berbasis Image Processing dan Machine Learning Vina Ayumi; Ida Nurhaida; Wachyu Hari Haji
JSAI (Journal Scientific and Applied Informatics) Vol 6 No 3 (2023): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v6i3.6240

Abstract

Machine learning batik motif detection is important because it helps identify, classify, and find batik by motif and area. The diversity of batik motifs in Indonesia poses a challenge to society, as many motifs have similarities in pattern or color, leading to errors in identification. Researchers have used machine learning techniques to address this problem. Machine learning models with image preocessing techniques such as torch techniques, log gabor, gray level co-occurrence matrix (GLCM) techniques have been used to identify batik motifs with high accuracy. This application will be developed using the web information system development methodology (WISDM) methodology. These advances in machine learning of batik motif detection contribute to preserving Indonesian culture and heritage. The best results were obtained from the combination of gabor, log gabor, GLCM features with retrieval rate quality reaching 84.54% in motif detection.
Co-Authors Ahmad Riza Faizal Andi Windah Andi Windah Anna Gustina Zainal Ayumi, Vina Bangun Suharti Cahyono Eko Sugiarto Devanna Alandra Khansa Fairo Mahaputranda Faisal Gregorius Rizcy Orlando Pradana Komang Putra Satria Negara Mochamad Sobiri Nina Yudha Aryanti Noprisson, Handrie Puspandari Setyowati Sugiyanto Ranida Pradita Safitri Jaya Wachyu Hari Haji