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All Journal Prosiding National Conference for Community Service Project Journal of Information Systems and Technology Research
Jupiter Agustio Liu Siaw Ping
Universitas Internasional Batam
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering

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Menuju Pemilu Adil: Sosialisasi Pengawasan Pemilu bagi Calon Mahasiswa Baru UIB Juwita Sulputri; Vivi Chandrawati; Muhammad Hauzan Suhenal; Kellen Kellen; Jupiter Agustio Liu Siaw Ping; Noval Christanto; Wilson Wilson; M. Rashif Zabadi R; Mega Augustina Ng; Marcellina Marcellina; Kendrick Hartson
National Conference for Community Service Project (NaCosPro) Vol 5 No 1 (2023): The 5th National Conference for Community Service Project 2023
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Internasional Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37253/nacospro.v5i1.8411

Abstract

General Elections mean implementing people's sovereignty, carried out directly, publicly, freely, confidentially, honestly, and fairly within the Unitary State of Indonesia. They are based on Pancasila and the 1945 Constitution of the Republic of Indonesia. The collaboration between Bawaslu and Universitas International Batam students aims to provide socialization for the general election. This socialization is intended to give an understanding to prospective new students at Batam International University who will become beginner voters in the 2024 election. Beginner election voters are individuals aged 17 years and over or under 17 years and over who have been married, according to RI Constitution No. 7 of 2017, Article 348. By organizing this socialization activity, Batam International University students can fully comprehend the material provided and understand their rights as voters for the 2024 simultaneous election. They are also encouraged to participate in election supervision as first-time voters.
Designing a Hybrid Machine Learning Model for Weather Forecasting in Batam City Yefta Christian; Jupiter Agustio Liu Siaw Ping
Journal of Information Systems and Technology Research Vol. 5 No. 1 (2026): January 2026
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v5i1.1504

Abstract

Accurate weather forecasting in tropical regions such as Batam City is challenging due to high climate variability and frequent data gaps caused by unstable atmospheric conditions. This study aims to develop a reliable daily average temperature forecasting system using a hybrid approach that combines the Seasonal Autoregressive Integrated Moving Average (SARIMA) model and the Long Short-Term Memory (LSTM) neural network. The main novelty of this research lies in the residual hybridization method, where SARIMA is used to capture linear seasonal patterns and LSTM is applied to model the non-linear residual components, as well as the use of a multi-source data integration strategy to fill missing data. Historical temperature data from BMKG and other publicly available meteorological sources were merged to produce a continuous dataset covering the period from 2015 to 2021. The study evaluated several model architectures, including standalone statistical models, standalone machine learning models, and hybrid models, to identify the most effective approach. The experimental results show that the SARIMA–LSTM hybrid model outperformed the other models, achieving a high prediction accuracy with an R² value of 0.92 and a Root Mean Square Error (RMSE) of 1.73°C. These findings demonstrate that integrating linear and non-linear models can significantly improve temperature forecasting performance and provide a practical solution for weather monitoring in tropical environments
Co-Authors Christian, Yefta Juwita Sulputri Kellen Kellen Kendrick Hartson M. Rashif Zabadi R Marcellina Marcellina Mega Augustina Ng Muhammad Hauzan Suhenal Noval Christanto Vivi Chandrawati Wilson Wilson