Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Journal of Electronics, Electromedical Engineering, and Medical Informatics
Mushthofa
Department Of Computer Science, Faculty Of Mathematic And Natural Science, IPB University, Bogor, West Java, Indonesia
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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

Found 2 Documents
Search

 1 
Network-Based Molecular Features Selection to Predict the Drug Synergy in Cancer Cells Syarifah Aini; Wisnu Ananta Kusuma; Medria Kusuma Dewi Hardhienata; Mushthofa
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 3 (2023): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeemi.v5i3.307

Abstract

Identifying synergistic drug combinations in cancer treatment is challenging due to the complex molecular circuitry of cancer and the exponentially increasing number of drugs. Therefore, computational approaches for predicting drug synergy are crucial in guiding experimental efforts toward finding rational combination therapies. This research selects the molecular features of cancer cells with a diffusion network-based approach. Additionally, a model is developed using non-linear regression algorithms, namely Random Forest, Extremely Randomized Tree, and XGBoost, to predict the synergy score of drug combinations against the selected cancer cell features. The data used are drug combination screening data and cancer cell molecules provided by AstraZeneca-Sanger DREAM Challenge. The feature selection results demonstrate the relevance of cancer cell molecular features selected by the diffusion network. The prediction results indicate that the Random Forest algorithm shows a good correlation value of 0.570 in the model with a small dataset. In contrast, for the model with an instance or row size larger than the number of features or columns, the XGBoost algorithm achieves a good correlation value of 0.932. INDEX TERMS cancer, drug combination, drug synergy, network diffusion kernel, non-linear regression.
SOIL MOISTURE PREDICTION USING LSTM AND GRU: UNIVARIATE AND MULTIVARIATE DEEP LEARNING APPROACHES Jemsri Stenli Batlajery; Agus Buono; Mushthofa -Mushthofa
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1659-1674

Abstract

Soil moisture is an important indicator in the management of water resources, precision agriculture, and disaster mitigation, such as drought and land fires. Fluctuations in soil moisture are influenced by various climate variables, requiring a reliable predictive approach essential. This research develops a daily soil moisture prediction model using Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms with univariate and multivariate approaches. Soil moisture data were obtained from Google Earth Engine, while climate data were collected from 10 BMKG stations in East Java for the period 2019–2024. Data preprocessing includes cubic spline interpolation to handle missing values and Min-Max normalization to achieve uniform feature scaling. Models were built using a direct forecasting approach for horizons to and five evaluation metrics: MAE, MSE, RMSE, MAPE, and R². The results show that the multivariate GRU model performs best at horizon with MAE = 0.05455, MSE = 0.00604, RMSE = 0.07539, MAPE = 0.19280, and R² = starting from 0.9626 on day 1 (t), then decreasing to 0.8075 on day 10 The univariate LSTM model excelled in training time efficiency (<400 seconds) at most stations. The multivariate GRU model demonstrates the highest accuracy and stability, making it suitable for medium- to long-term forecasting, while the univariate LSTM excels in training speed, making it effective for daily predictions. The model’s performance remains limited to the dataset's spatial and temporal scope. Therefore, future research should test the model in other regions and under extreme climate conditions, as well as apply transfer learning in data-scarce areas. The novelty of this study lies in comparing LSTM and GRU performance for daily soil moisture prediction in both univariate and multivariate scenarios, using complete climate variables from multiple stations.
Co-Authors Agus Buono Hardhienata, Medria Kusuma Dewi Jemsri Stenli Batlajery Syarifah Aini Wisnu Ananta Kusuma