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All Journal Prosiding Konferensi Nasional PKM-CSR Journal of Digital Market and Digital Currency
Andy Firmansyah
Faculty Of Communication, Universitas Multimedia Nusantara, Indonesia
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Nursing Public Health Social Sciences

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Aplikasi Penjadwalan Pasien Online SIPURA pada Puskesmas Keranggan, Kabupaten Tangerang Selatan Florentina Kurniasari; Ika Yanuarti; Farica Perdana Putri; Purnamaningsih Purnamaningsih; Elissa Dwi Lestari; Andy Firmansyah
Prosiding Konferensi Nasional Pengabdian Kepada Masyarakat dan Corporate Social Responsibility (PKM-CSR) Vol 3 (2020): Peran Perguruan Tinggi dan Dunia Usaha Dalam Pemberdayaan Masyarakat Untuk Menyongsong
Publisher : Asosiasi Sinergi Pengabdi dan Pemberdaya Indonesia (ASPPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (363.244 KB) | DOI: 10.37695/pkmcsr.v3i0.722

Abstract

Kebutuhan masyarakat akan layanan kesehatan sangat tergantung pada keberadaan Puskesmas dikarenakan lokasinya yang mudah dijangkau, biaya yang murah atau bahkan gratis dengan pelayanan yang memadai meskipun terkadang Puskesmas juga memiliki keterbatasan dalam jumlah sumber daya yang dimiliki. Kegiatan PkM yang dilakukan oleh tim dosen UMN ini adalah merupakan solusi dalam merancang, mengimplementasikan sekaligus melakukan pendampingan bagi masyarakat Keranggan maupun bagi pihak internal Puskesmas Keranggan berupa penjadwalan pasien secara online. Sistem aplikasi ini dapat diunduh secara gratis melalui aplikasi Android sistem integrasi layanan kesehatan SIPURA. Aplikasi penjadwalan pasien online ini diharapkan dapat meningkatkan kualitas pelayanan kesehatan yang diberikan oleh Puskesmas Keranggan, dimana pasien harus melakukan reservasi secara online dan mengurangi waktu tunggu selaras dengan kebijakan Social Distancing di saat pandemic Covid 19 ini.
Prediction of Tesla Stock Prices Using Recurrent Neural Networks (RNN) within Financial Technology (FinTech) Framework Andy Firmansyah; Mochammad Fahlevi; Daniel Wisuda Purba
Journal of Digital Market and Digital Currency Vol. 3 No. 3 (2026): Regular Issue September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v3i3.88

Abstract

The integration of artificial intelligence (AI) into Financial Technology (FinTech) has transformed the landscape of financial data analysis and prediction. This research presents a predictive model for Tesla stock prices using a Recurrent Neural Network (RNN) as part of an AI-based FinTech framework. Historical Tesla stock data obtained from the Nasdaq market were normalized and structured using a 60-day time window to train the RNN model. The results show that the proposed model achieved a prediction accuracy of 93.45 percent, with a Mean Absolute Error (MAE) of 43.14, Mean Squared Error (MSE) of 3148.46, and Root Mean Squared Error (RMSE) of 56.11. The convergence of training and validation losses confirmed the model’s stability, while residual analysis indicated unbiased predictions centered around zero. These findings demonstrate that the RNN effectively captures temporal dependencies in stock price time-series data. Although the Long Short-Term Memory (LSTM) model achieved a slightly higher accuracy of 94.82 percent, the RNN remains advantageous due to its lower computational cost and faster training process. Overall, the RNN model offers a reliable and efficient approach for real-time FinTech applications such as automated trading systems, portfolio optimization, and investment risk analysis.
Co-Authors Daniel Wisuda Purba Elissa Dwi Lestari, Elissa Dwi Ika Yanuarti Kurniasari, Florentina Mochammad Fahlevi Purnamaningsih Purnamaningsih Putri, Farica Perdana