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All Journal Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering)
Subhan Arif Budiman
Universitas Jember
Agriculture, Biological Sciences & Forestry Engineering

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Soil Damage Potential Index Based on Weighting Scoring Analysis and Utilization of Geographical Information Systems Basuki Basuki; Subhan Arif Budiman; Laily Mutmainnah; Muhammad Ghufron Rosyady
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol 11, No 4 (2022): Desember 2022
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtep-l.v11i4.601-616

Abstract

Indonesia's population in 2021 will increase by 0.92% from 2020. The increasing population demands the fulfillment of food. Land changes and their consequences indicate land damage. The purpose of the study was to assess the soil damage potential index (SDPI) on the slopes of Mount Argopura through terrain analysis and the use of geographic information system technology. The research was carried out on the slopes of Mount Argopura in 2022. The tools used included a clinometer, a GPS, spectrophotometer, AAS, arc GIS 13 and minitab. The materials included administrative maps, soil maps, slope maps, RBI maps, land use maps, rainfall maps. This research is descriptive exploratory with field survey method. The activity is divided into 3 stages, namely pre-survey, field survey, and post-survey. Research parameters include texture, soil thickness, soil type, soil pH, CEC, base saturation, soil drainage, parent material, landform, relief, rainfall, and land use. Based on the results of the study, the SDPI with an area of 22,148.75 ha was in the heavy category 44.12% (9772.28 ha), the medium category was 53.11% (11762.84 ha), and the light category was 2.77% (613.63 ha). Keywords:  Geographic Information System, Land Degradation, Mount Argopura, Soil Damage
Kinerja Convolutional Neural Network untuk Klasifikasi Level Kadar Air Tanah Berdasarkan Gambar Permukaan Tanah Hasbi Mubarak Suud; Subhan Arif Budiman; Ebban Bagus Kuntadi; Dwi Erwin Kusbianto; Ika Purnamasari
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 15 No. 2 (2026): April 2026
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtepl.v15i2.571-579

Abstract

Computer vision offers a promising method for soil moisture assessment especially for real-time field monitoring where sensor-based measurements are limited. This study evaluates the performance of a traditional Convolutional Neural Network (CNN) and ResNet-50 architecture for classifying soil moisture levels directly from in-situ surface images. The research involved 200 field-captured images and corresponding moisture data from a rainfed agricultural area. The models were trained with datasets grouped into two, three, and four moisture categories to test performance under varying complexity. The results showed poor model performance, characterized by high instability and severe overfitting across all experiments. Model accuracy for the traditional CNN significantly decreased from 0.513 to 0.256 as the number of classification categories increased and from 0.487 to 0.205 for ResNet-50. High RMSE values from 0.433 to 0.507 further confirmed substantial prediction errors. This finding highlights the limitation of RGB-based in-situ imagery for soil moisture classification, where environmental variability dominates the visual signal. It also suggests that soil moisture-related features are not sufficiently distinguishable under uncontrolled field conditions. The study concludes that the high variability of direct field images due to factors like inconsistent lighting, illumination, and the presence of non-soil objects is a primary obstacle to accurate classification. Future studies should implement advanced pre-processing techniques such as segmentation to reduce illumination noise.
Co-Authors Basuki, Basuki Dwi Erwin Kusbianto Ebban Bagus Kuntadi Hasbi Mubarak Suud Ika Purnamasari Laily Mutmainnah Rosyady, Muhammad Gufron