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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Agustriono, Agustriono
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Computer Science & IT Engineering

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Enhancing Multiple Linear Regression with Stacking Ensemble for Dissolved Oxygen Estimation Rahmaddeni, Rahmaddeni; Wicaksono, M. Teguh; Wulandari, Denok; Agustriono, Agustriono; Ibrahim, Sang Adji
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 24 No 1 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i1.4280

Abstract

Maintaining optimal dissolved oxygen levels is essential for aquatic ecosystems, yet industrial and domestic waste has led to a global decline in dissolved oxygen. Traditional measurement methods, such as oxygen meters and Winkler titration, are often costly or time-consuming. This study aims to improve the Root Mean Square Error, Mean Absolute Error, and R2 values for estimating dissolved oxygen levels. The research method uses Multiple Linear Regression with various training and testing data splits, both before and after applying polynomial features. The model is further optimized using a stacking technique, with Random Forest Regressor and Gradient Booster Regressor as base models.The results show that the best model was achieved using the stacking ensemble technique with a 90:10 data split and polynomial features, yielding a Root Mean Square Error of 1.206, Mean Absolute Error of 0.990, and R2 of 0.670. This model has also met the assumptions of linear regression, such as residual normality, homoscedasticity, and no autocorrelation of residuals. This study concluded that the ensemble stacking technique and the addition of polynomial features could improve the model in estimating dissolved oxygen values and also contribute by providing an accessible user interface using the Gradio Framework, allowing users to estimate dissolved oxygen levels effectively.
Enhancing Multiple Linear Regression with Stacking Ensemble for Dissolved Oxygen Estimation Rahmaddeni, Rahmaddeni; Wicaksono, M. Teguh; Wulandari, Denok; Agustriono, Agustriono; Ibrahim, Sang Adji
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i1.4280

Abstract

Maintaining optimal dissolved oxygen levels is essential for aquatic ecosystems, yet industrial and domestic waste has led to a global decline in dissolved oxygen. Traditional measurement methods, such as oxygen meters and Winkler titration, are often costly or time-consuming. This study aims to improve the Root Mean Square Error, Mean Absolute Error, and R2 values for estimating dissolved oxygen levels. The research method uses Multiple Linear Regression with various training and testing data splits, both before and after applying polynomial features. The model is further optimized using a stacking technique, with Random Forest Regressor and Gradient Booster Regressor as base models.The results show that the best model was achieved using the stacking ensemble technique with a 90:10 data split and polynomial features, yielding a Root Mean Square Error of 1.206, Mean Absolute Error of 0.990, and R2 of 0.670. This model has also met the assumptions of linear regression, such as residual normality, homoscedasticity, and no autocorrelation of residuals. This study concluded that the ensemble stacking technique and the addition of polynomial features could improve the model in estimating dissolved oxygen values and also contribute by providing an accessible user interface using the Gradio Framework, allowing users to estimate dissolved oxygen levels effectively.
Modelling the Hatching Success of Sea Turtle Eggs Using Long Short-Term Memory (LSTM) for Conservation Oriented Ecotourism Agustriono, Agustriono; Susanti, Susanti; Lusiana, Lusiana; Mardainis, Mardainis; Irfansyah, Rahmat
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 16 No. 2 (2025): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v16i2.28856

Abstract

This study proposes a Long Short-Term Memory (LSTM) model to predict the hatching success of sea turtle eggs in the Anambas Islands Marine Conservation Area, Indonesia. Leveraging nesting data (2022–2024) provided by LKKPN Pekanbaru and associated environmental variables, the model’s performance was assessed across various configurations of time steps (2, 5, 7, 30, and 45 days) and data splits (ranging from 60:40 to 90:10). The optimal configuration—7-day time step with a 60:40 train-test split—yielded RMSE = 17.90, MAE = 8.67, and R² = 0.34. Results revealed strong seasonal nesting trends and statistically significant interspecies differences in incubation periods (p < 0.05). While the model demonstrated high predictive accuracy for standard incubation durations (30–45 days), performance declined in extreme cases, highlighting the need for location-specific environmental data. This research illustrates the practical application of LSTM for ecological time series forecasting and provides a machine learning framework to support decision-making in ecotourism scheduling and marine conservation planning in island-based coastal ecosystems
Co-Authors Ibrahim, Sang Adji Irfansyah, Rahmat Lusiana Lusiana Mardainis Rahmaddeni Rahmaddeni Susanti Susanti Wicaksono, M. Teguh Wulandari, Denok