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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Seminar Nasional Teknologi Informasi Komunikasi dan Industri Journal Information System Development International Journal of New Media Technology Jurnal ULTIMA InfoSys Journal of Applied Data Sciences Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Instrumentasi dan Teknologi Informasi (JITI) GIAT: Teknologi untuk Masyarakat
Mitra, Aditya R.
Universitas Pelita Harapan
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics Social Sciences Other

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Journal : Journal of Applied Data Sciences

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Harnessing the Power of Prophet Algorithm for Advanced Predictive Modeling of Grab Holdings Stock Prices Hery, Hery; Haryani, Calandra A.; Widjaja, Andree E.; Mitra, Aditya Rama
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.181

Abstract

This study investigates the effectiveness of the Prophet algorithm in predicting Grab Holdings' stock prices dataset from Kaggle. By meticulously analyzing historical closing, high, low, and volume data, the research aims to uncover market patterns and gain insights into investor sentiment based on short-term forecasting. The findings reveal a dynamic trajectory for Grab Holdings' stock, characterized by significant fluctuations and evolving investor confidence. The stock reached a peak of $14 in early 2022, indicating optimism, but subsequently experienced a decline to $4 by late 2023, reflecting a shift in sentiment. Notably, 2023 witnessed heightened volatility compared to 2022, evident in more significant price swings and increased trading volume. The Prophet algorithm demonstrated promising potential for prediction better than traditional methods, which overlook the presence of seasonality or fail to adapt to evolving market conditions, leading to less accurate forecasts. The excellent performance of Prophet is indicated by a Mean Absolute Percentage Error (MAPE) of 10.45511%, a Mean Absolute Error (MAE) of 3.112026, and a Root Mean Squared Error (RMSE) of 3.516969. Compared to the traditional ARIMA, MAE and RMSE resulting from Prophet are much lower than their counterparts, which are 14.49675 and 16.079898, respectively. These widely used metrics suggest moderate accuracy in predicting future stock prices. This research offers valuable insights for investors that they can use to understand the trend of Grab Holdings' stock price and make more informed investment decisions regarding buying or selling opportunities. However, it is crucial to acknowledge the inherent limitations of such models and interpret results cautiously, considering the ever-changing dynamics of the financial market.
Automated Class Attendance Management System using Face Recognition: An Application of Viola-Jones Method Widjaja, Andree E; Harjono, Nathanael Joshua; Hery, Hery; Mitra, Aditya Rama; Haryani, Calandra Alencia
Journal of Applied Data Sciences Vol 4, No 4: DECEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i4.133

Abstract

Over the past few years, face recognition has been widely used to help human activities in various sectors, including the education sector. By using facial recognition, the class attendance system at universities can be significantly improved. For example, students are no longer asked to sign attendance sheets manually, but attendance can be taken, recorded, and managed automatically through an integrated class attendance management system using facial recognition. The recorded data can then be further analysed to produce useful information for instructors and administrators. In turn, this arrangement will assist them in making decisions about matters relating to student attendance. The main objective of this research is to develop an automatic class attendance management system using facial recognition. In particular, the system we propose was developed using a prototyping software development approach and was modelled using UML version 2.0. As a choice of methods and tools, we used the Viola-Jones method as a face detection algorithm, Python and PHP as programming languages, OpenCV as the computer vision library, and MySQL as the DBMS. The results obtained from a number of black box tests carried out were a fully functional automatic class attendance system prototype using facial recognition.
Co-Authors Arnold Aribowo Calandra A. Haryani Debora Kathrin Yuwono Debora Kathrin Yuwono Efanntyo Feliks Victor Parningotan Samosir Harjono, Nathanael Joshua Haryani, Calandra A. Haryani, Calandra Alencia Hery Hery Hery Hery Hery Hery Hudi, Robertus Kevin Arie Sandy Liana Tirtasendjaja Lisia, Vanella Putra, Alfa Satya Tarigan, Riswan Effendi Vanella Lisia Welly Kamarudin Widjaja, Andree E.