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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Data and Science
Fadia Irsania Putri
Universitas Negeri Malang
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Mathematics

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IoT-Based Water Quality Monitoring and Sustainable Modeling for Smart Campuses Fachrul Kurniawan; Miladina Rizka Aziza; Novrindah Alvi Hasanah; Fadia Irsania Putri; Aji Prasetya Wibawa; Jehad Hammad; Yuhefizar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7360

Abstract

This study proposes an IoT-based water quality monitoring framework integrated with a continuous sustainable modeling approach for smart campus applications. A total of 404 sensor observations were collected, including pH, turbidity, temperature, and Total Dissolved Solids (TDS). A continuous water suitability score ranging from 0 to 1 was constructed based on WHO drinking water standards, and Multiple Linear Regression was employed to model the relationship between water quality parameters and the suitability score. The main contribution of this study lies in the development of a lightweight analytical framework that combines continuous regression modeling with threshold-based classification to support real-time decision-making in resource-constrained environments. The dataset was divided into 90% training and 10% testing data. The results show that the proposed framework achieved a classification accuracy of 88.5% based on threshold mapping of regression outputs, with a misclassification rate of 11.5%. These findings demonstrate the effectiveness of integrating IoT-based monitoring with interpretable and computationally efficient analytical models for sustainable campus water management.
The Performance of the XGBOOST-LSTM and CNN-LSTM Algorithms in the Analysis of Stock Price Prediction Models for the Indonesian Banking Sector M. Zainal Arifin; Filbert Chaitra Bessel Kristianto; Fadia Irsania Putri; Agung Bella Putra Utama
Indonesian Journal of Data and Science Vol. 7 No. 2 (2026): Indonesian Journal of Data and Science
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v7i2.459

Abstract

Introduction: Accurate stock price forecasting is important for investors and financial institutions because stock movements reflect dynamic market conditions and influence investment decision-making. This study compares CNN-LSTM and XGBoost-LSTM models for predicting stock prices in the Indonesian banking sector. Method: Historical daily closing prices of PT Bank Central Asia Tbk (BBCA), PT Bank Rakyat Indonesia (Persero) Tbk (BBRI), and PT Bank Mandiri (Persero) Tbk (BMRI) were collected from Yahoo Finance, with 1,000 observations for each stock. Data were normalized using Min-Max scaling, transformed using a four-day sliding window to predict the following day, and chronologically divided into 80% training and 20% testing sets. Both models were evaluated using RMSE, MAE, R², and MAPE, with each experiment repeated ten times. Results and Discussion: CNN-LSTM consistently outperformed XGBoost-LSTM on all three test datasets. For BBCA, BBRI, and BMRI, CNN-LSTM achieved R² values of 0.8589, 0.7746, and 0.7409, respectively, compared with 0.8048, 0.7509, and 0.6260 for XGBoost-LSTM. CNN-LSTM also produced lower RMSE, MAE, and MAPE values across all test sets, indicating stronger and more stable generalization. Conclusion: CNN-LSTM provides more reliable predictive performance than XGBoost-LSTM for the evaluated Indonesian banking stocks, demonstrating the effectiveness of combining local feature extraction with temporal dependency learning for stock price forecasting
Co-Authors Agung Bella Putra Utama Aji Prasetya Wibawa Fachrul Kurniawan Filbert Chaitra Bessel Kristianto Jehad Hammad M. Zainal Arifin Miladina Rizka Aziza Novrindah Alvi Hasanah Yuhefizar Yuhefizar