Article Per Year (5 Year)
p-Index From 2021 - 2026
1.261
P-Index
This Author published in this journals
All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Seminar Nasional Teknologi Informasi Komunikasi dan Industri Journal Information System Development Jurnal Pengabdian Masyarakat Asia
I Made Murwantara
Universitas Pelita Harapan
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Other

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search

 1   2 
Comparative analysis of ARIMA and LSTM for predicting fluctuating time series data Taslim, Deddy Gunawan; Murwantara, I Made
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.6034

Abstract

The investigation of time series data forecasting is a critical topic within the realms of economics and business. The autoregressive integrated moving average (ARIMA) model has been prevalently utilized, notwithstanding its limitations, which include the necessity for a substantial quantity of data points and the presumption of data linearity. However, with recent developments, the long short-term memory (LSTM) network has emerged as a promising alternative, potentially overcoming these limitations. The objective of this study is to determine an effective approach for managing time series data characterized by volatility and missing values. Evaluation was conducted using RMSE for accuracy assessment, and the execution time measured using the Python Timeit library. The findings indicates that in a dataset comprising 60 data points, the LSTM model (RMSE 0.037618) surpasses the ARIMA model (RMSE 0.062667) in terms of accuracy. However, this trend reverses in a larger dataset of 228 data points, where the ARIMA model demonstrates superior accuracy (RMSE 0.006949) compared to the LSTM model (RMSE 0.036025). In scenarios with missing data, the LSTM model consistently outperforms the ARIMA model, although the accuracy of both models diminishes with an increase in the number of missing values. The ARIMA model significantly outpaces the LSTM model.
Community Video Profiling using Generative AI: A Scenario-Based Practical Experience to Catholic Community in Serpong Utara Murwantara, I Made; Tjahyadi, Hendra; Yugopsupito, Pujianto; Andriyani
Jurnal Pengabdian Masyarakat Vol. 6 No. 2 (2025): Jurnal Pengabdian Masyarakat
Publisher : Institut Teknologi dan Bisnis Asia Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jpm.v6i2.2769

Abstract

Purpose: The aim of this community service is to have a scenario-based practical use of Artificial Intelligence to enhance video profiling which ultimately to increase people engagement to their religious life. Method: The activity making use of a practical approach to design and develop a profiling video via short training and demonstration to encourage religious activity in providing tutorial own video using available AI tools. It uses children scenario to develop the narration. Practical Applications: Participants using the Generative AI tools to help them in enhancing the existing scenario into video narration. The time efficient video preparation with benefit and less experience is achieved by configuring the setting of the tool and engaging to the scenario. Conclusion: Generative AI has potential to enhance video generation for religious stewardship by providing reliable, less time and more creative in providing a way to achieve goals. In adopting AI tools to support video creation generation with scenario-based has convey into its portion to religious community.
Co-Authors Alessandro Luiz Kartika Andriyani Arnold Aribowo Fanny Sinthia Ferdinand, Ferry Vincenttius Friska Natalia Ferdinand Hendra Tjahyadi Irene A. Lazarusli Johan Julianto Roestam Kartika, Alessandro Luiz Rickhen Hermawan Taslim, Deddy Gunawan Yugopsupito, Pujianto Yugopuspito, Pujianto Yustinus Soelistio