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All Journal International Journal of Advances in Applied Sciences Prosiding SNATIF Jurnal Pekommas INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Ilmiah Matrik Journal Information System Development International Journal of New Media Technology Technomedia Journal Jurnal ULTIMA InfoSys Jurnal ULTIMATICS SENSITEK Journal of Applied Data Sciences IAIC Transactions on Sustainable Digital Innovation (ITSDI) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Proceeding National Conference Business, Management, and Accounting (NCBMA) Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi GIAT: Teknologi untuk Masyarakat International Transactions on Education Technology (ITEE) International Transactions on Artificial Intelligence (ITALIC)
Widjaja, Andree E.
Universitas Pelita Harapan
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

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Journal : Journal of Applied Data Sciences

 1 
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.
Unsupervised Learning for MNIST with Exploratory Data Analysis for Digit Recognition Hery, Hery; Haryani, Calandra A.; Widjaja, Andree E.; Tarigan, Riswan E.; Aribowo, Arnold
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.v5i2.184

Abstract

This research investigates the application of unsupervised learning techniques for digit recognition using the MNIST dataset. Through a comparative analysis, various dimensionality reduction methods, including ISOmap, PCA, and tSNE, were evaluated for their effectiveness in visualizing and processing the MNIST data. The findings reveal that tSNE consistently outperforms ISOmap and PCA in terms of accuracy, F1- score, precision, and recall, showcasing its superior capability in preserving relevant information within the dataset. Utilizing tSNE for visualizing and clustering digits provides valuable insights into the underlying structure of the dataset, uncovering complex patterns in digit relationships. These results contribute to the advancement of digit recognition systems, offering potential improvements in classification accuracy and model reliability. The success of tSNE highlights the importance of nonlinear dimensionality reduction techniques in handling complex datasets, such as MNIST. This research underscores the significance of unsupervised learning approaches, particularly tSNE, in enhancing digit recognition systems' performance, with implications extending across various application domains. Continued research is recommended to explore further applications and potentials of unsupervised learning techniques and to deepen our understanding of the MNIST dataset's structure and complexity.
Text Mining Application With K-Means Clustering to Identify Sentiments and Popular Topics: A Case Study of The Three Largest Online Marketplaces in Indonesia Widjaja, Andree E; Fransisko, Andy; Haryani, Calandra Alencia; Hery, Hery
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.v4i4.134

Abstract

The number of internets and social media users, which continues to increase at a very fast rate, has resulted in the emergence of new business opportunities in Indonesia. One of those indications is the emergence of marketplace companies in Indonesia. The presence of these online marketplaces provides people with more online marketplace choices according to their preferences. One of the factors that became the basis for this election was reading comments or reviews from consumers on the marketplace posted on social media. This research was conducted using text mining method with k-means clustering algorithm to systematically identify the sentiments and topics that are widely discussed by online marketplace consumers in Indonesia. The data was processed by the k-means algorithm in the form of comments or reviews from three online marketplaces (Tokopedia, Shopee and Bukalapak) on Twitter. The amount of data for each marketplace referred to was 1500 data tweets. The results showed that the three online marketplaces were associated to different topics, even though they are in the same industry. These differences arise due to the fact that most consumers discuss the topics of programs held by their respective online marketplaces. The main topics related to Tokopedia are “belanja” (“shopping”) and “terimakasih” (“thank you”); while for Shopee “pilih” (“choose”) and “jongho”, and for Bukalapak “pra-kerja” (“pre-employment”). In addition, the sentiment analysis carried out shows that the sentiment of the three online marketplaces is predominantly neutral.
Co-Authors Aditya R. Mitra Aditya R. Mitra Alencia Haryani, Calandra Alvira Putri Yudini Alya M. Amalia Amalia, Alya M. Amelia Magdalena Kaheja Amelinda Chendra Arnold Aribowo Arnold Aribowo Arnon M Sugiarto Azim Ashar Calandra A. Haryani Calandra Alencia Haryani Calandra Alencia Haryani Carolyn Feiby Supit Christian Marsel Wijaya2 David Habsara Hareva Debora Kathrin Yuwono Debora Margareta Efendi Tarigan, Riswan Feliks Victor Parningotan Samosir Ferdinand, Ferry Vincenttius Filbert Chan Fransisko, Andy Gabrielle Florencia Gennady, Erick Goestjahjanti, Francisca Sestri Harjono, Nathanael Joshua Haryani, Calandra A. Haryani, Calandra Alencia Hery Hery Hery Hery Hery Hery Hery Hery Hery Hery Juan Situmorang Hikam, Ihsan Nuril Husni Teja Sukmana Irene Eka Sri Saraswati Joshua Nathanael Justin A. Haratua Karnawi Kamar, Karnawi Kristina G. Simanjuntak Kusno Prasetya Kusno Prasetya Laurentia Anggun P Lisia, Vanella Maya Avinda Mayumi Utama Michelle Angelica Mitra, Aditya R. Mitra, Aditya Rama Mouw, Christ Wibowo Mulyati Mulyati Nadya Rosanna Nathalie, Julia Nathanael, Joshua Prasetya, Kusno Raphael Christopher Renaldi, Ary Renaldo Luih, Joshua Ririn Ikana Desanti Riswan E Tarigan Riswan E. Tarigan Riswan E. Tarigan Riswan Efendi Tarigan Sugiarto, Arnon M Supriyanti, Dedeh Suryasari Suryasari Suryasari Suryasari Suryasari Suryasari Suryasari Suryasari Suryasari Tania Jovita Wibowo Tarigan, Riswan E. Vanella Lisia Veronica, Winnie Vincent Cahyadi Vivi Melinda Wijaya, Yoana Sonia Willy Darmawan Yumna, Saidah ‪Alfa Satya Putra