Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Tensor: Pure and Applied Mathematics Journal PENGAMATAN: Jurnal Pengabdian Masyarakat untuk Ilmu MIPA dan Terapannya
Waas, Devi V.
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Economics, Econometrics & Finance Mathematics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Optimization of LSTM Model for Rainfall Prediction in Ambon City: Comparison of Mean Imputation and Interpolation in Time Series Data Prediction Wattimena, Emanuella M. C.; Taihuttu, Pranaya D. M.; Waas, Devi V.; Palembang, Citra F; Pattiradjawane, Victor E.
Tensor: Pure and Applied Mathematics Journal Vol 6 No 1 (2025): Tensor: Pure and Applied Mathematics Journal
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Pattimura University, Ambon, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/tensorvol6iss1pp49-56

Abstract

Rainfall prediction is an essential aspect of meteorology, agriculture, and disaster management, particularly in regions like Ambon, where rainfall patterns significantly impact daily life. However, one of the major challenges in developing an accurate predictive model is handling missing values in the dataset. This study aims to optimize the Long Short-Term Memory (LSTM) model for rainfall prediction in Ambon by comparing two missing value handling techniques: mean imputation and interpolation. The dataset used in this study consists of daily rainfall data from 2021 to 2024, with approximately 26.89% missing values. Two experimental scenarios were conducted: the first using mean imputation to fill in missing values with the average rainfall, and the second using linear interpolation. Both scenarios utilized the same LSTM architecture to evaluate their impact on model performance. The evaluation metrics used in this study include Root Mean Square Error (RMSE) and R-squared (R²). The results show that the interpolation-based model achieved a lower RMSE and a slightly higher R² value than the mean imputation-based model, indicating better predictive performance. However, both models struggled to capture extreme values, necessitating further improvements. To address this limitation, a more complex LSTM architecture was implemented in the subsequent experiments, incorporating additional layers and optimized hyperparameters. The findings suggest that choosing an appropriate missing value handling method significantly influences the predictive accuracy of LSTM models for rainfall forecasting. This research contributes to the development of more reliable weather prediction models, which can aid in agricultural planning, flood risk assessment, and climate change adaptation in Ambon.
AI Starter Lab untuk Penguatan Kompetensi Kecerdasan Buatan Siswa SMA Negeri 1 Maluku Tengah Wattimena, Emanuella M. C.; Palembang, Citra F.; Radjawane, Jefri E. T.; Waas, Devi V.; Pattiradjawane, Victor E.; Saputri, Susan D.
PENGAMATAN: Jurnal Pengabdian Masyarakat untuk Ilmu MIPA dan Terapannya Vol 3 No 2 (2025): PENGAMATAN: Jurnal Pengabdian Masyarakat untuk Ilmu MIPA dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pengamatanv3i2p53-59

Abstract

Kegiatan Pengabdian kepada Masyarakat (PkM) ini bertujuan untuk meningkatkan literasi teknologi dan pemahaman dasar mengenai kecerdasan buatan (Artificial Intelligence/AI) di kalangan siswa SMA Negeri 1 Maluku Tengah. Program bertajuk AI Starter Lab ini memperkenalkan konsep, aplikasi, dan etika penggunaan AI melalui pendekatan pembelajaran interaktif dan berbasis proyek. Kegiatan dilaksanakan melalui tiga tahap utama, yaitu sosialisasi, pelatihan praktik (simulasi regresi linear sederhana dan pembuatan chatbot), serta pendampingan dan evaluasi. Hasil kegiatan menunjukkan peningkatan signifikan dalam pemahaman siswa terhadap konsep dasar AI dan penerapannya dalam kehidupan sehari-hari. Selain itu, siswa menunjukkan antusiasme tinggi dan kemampuan berpikir logis serta etis yang lebih baik dalam menggunakan teknologi digital. Kegiatan ini membuktikan bahwa literasi AI dapat diajarkan secara sederhana dan inklusif, bahkan di wilayah dengan keterbatasan infrastruktur digital seperti Maluku Tengah.
Co-Authors Palembang, Citra F Palembang, Citra F. Pattiradjawane, Victor E. Radjawane, Jefri E. T. Saputri, Susan D. Taihuttu, Pranaya D. M. Wattimena, Emanuella M C