A modelling approach for Mangrove carbon dynamics using integrated optical and SAR data in Balikpapan Bay, Indonesia: algorithm development and accuracy enhancement - A. A. Md. Ananda Putra Suardana; Nanin Anggraini; Muhammad Rizki Nandika; Devica Natalia Br Ginting; Nugraheni Setyaningrum; M. Amar Sajali; Azura Ulfa; Kholifatul Aziz; Kuncoro Teguh Setiawan; Ratih Dewanti Dimyati (Modeling Earth Systems and Environment, 2026)

Enhancement of antioxidant and cytotoxicity by nanoliposome formulation of pentagamavunon-6 in breast cancer cells - Muhammad Nizam Zulfi Zakaria; A’liyatur Rosyidah; Diah Anggraini Wulandari; Ritmaleni Ritmaleni; Muhammad Novrizal Abdi Sahid; Muhaimin Rifa’i; Sjaikhurrizal El Muttaqien; Damai Ria Setyawati; Fransiska Christydira Sekaringtyas; Arbi Dimyati; Amir Hamzah; Riyona Desvy Pratiwi (Naunyn-Schmiedeberg's Archives of Pharmacology, 2026)

Spatial modelling of regional drought severity index based on multiple criteria analysis using cloud-based remote sensing data in agriculture land - Khalifah Insan Nur Rahmi; Muhammad Dimyati; Mangapul Parlindungan Tambunan; Jalu Tejo Nugroho (Modeling Earth Systems and Environment, 2025)

Calibration interval scenario approach in spatial modeling of land cover change in East Kalimantan from 2016 to 2036 - I Wayan Gede Krisna Arimjaya; Ade Komara Mulyana; Muhammad Dimyati (Modeling Earth Systems and Environment, 2024)
Spatial modeling can be used to predict future land cover changes based on past and present conditions. However, it is not yet known to what extent this model can be used to predict the future with reliable accuracy. Therefore, by using multi-temporal land cover data, this study aims to build an optimal model based on the calibration interval scenario. The optimal model is then used to predict and analyze changes in land cover in East Kalimantan in 2016–2036. 11 classified multi-temporal land cover maps from the Landsat Time Series using Random Forest in Google Earth Engine are used to model 14 calibration interval scenarios. A land Change Modeler is used to model and predict land cover change with 14 driving variables. The results of the classification of multi-temporal land cover maps show a good level of accuracy, with an Overall Accuracy value of 71.43–85.14% and a Kappa value of 0.667–0.827. Then 2016–2021 is one of the best scenarios with 5-year intervals where the accuracy of future predictions can still be relied upon for up to three prediction iterations. The calibration interval scenario approach in spatial modeling in East Kalimantan can be relied upon to show a decrease in forest cover from 2016 to 2021, with a deforestation rate of 651 km 2 /year. The prediction of land cover in 2036 estimates that the remaining forest cover area in East Kalimantan is 69.203 km 2 . It is believed that topography is the most influential variable driving land cover change in East Kalimantan.

Spatio-temporal characteristics of urban heat Island of Jakarta metropolitan - Siswanto Siswanto; Danang Eko Nuryanto; Muhammad Rezza Ferdiansyah; Agita Devi Prastiwi; Ova Candra Dewi; Ahmad Gamal; Muhammad Dimyati (Remote Sensing Applications: Society and Environment, 2023)

Mangrove monitoring revealed by MDPrePost-Net using archived Landsat imageries - Muhammad Dimyati; Deha Agus Umarhadi; Ilham Jamaluddin; Disyacitta Awanda; Wirastuti Widyatmanti (Remote Sensing Applications: Society and Environment, 2023)

Development, implementation and validation of Sediment Transport and Erosion Prediction (STEP) model - Yanto; Muhammad Dimyati (Environmental Modelling & Software, 2023)