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All Journal Sinergi Journal of Smart Agriculture and Environmental Technology
Supli, Ahmad Affandi
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Veterinary

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Development of Land Suitability Assessment Applications for Sorghum, Sweet Potato and Sugarcane Rahim, Supli; Muchsiri, Mukhtarudin; Supli, Ahmad Affandi; Damiri, Nurhayati; Supli, Nur Aslamiah; Aminah, Iin Siti; Djazuli, Abid; Rosmiah, Rosmiah
Journal of Smart Agriculture and Environmental Technology Vol. 2 No. 2 (2024): August 2024: Published, 2024-08-10
Publisher : Indonesian Soil Science Society of South Sumatra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60105/josaet.2024.2.2.61-66

Abstract

Cultivation of food crops should be done on suitable land based on the results of the soil suitability assessment. The Soil Suitability Assessment Framework was initiated by the Food and Agriculture Organization (FAO). Today, smartphones have become a ubiquitous technology for solving problems in most environments, including: Assessment of soil suitability for food crops, plantations and many others. This white paper aims to address these benefits by transforming the framework into a mobile app. This solution aims to help land users conduct land valuations more effectively and efficiently. A rule-based system (RBS) algorithm is used to build the framework into a set of rules that are interconnected to draw land suitability conclusions. Regulations relate to annual rainfall, land topography, drainage, soil type, pH, flood risk, soil fertility, soil depth, etc. Only three of his food crops, sorghum, sweet potato, and sugarcane, will be evaluated in this study. Agroclimatic data governing crop suitability have evolved into generic and crop-specific criteria. An application that assesses land suitability for three food crops will be available on the Play Store for smartphones. Usability models were surveyed by 35 respondents who used the app. The user-friendliness of the app was evaluated as "very good."
Optimizing intrusion detection with data balancing and feature selection techniques Elsi, Zulhipni Reno Saputra; Supli, Ahmad Affandi; Jimmie, Jimmie; Al-Faris, Muhammad Ghozi; Rapel, David Agustianto
SINERGI Vol 29, No 3 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.3.019

Abstract

The rapid growth of IoT devices has brought significant security challenges, particularly in detecting various types of attacks within heterogeneous network environments. This study explores the effectiveness of data balancing techniques, including Random Undersampling (RUS), Cost-Sensitive Learning (CSL), Synthetic Minority Oversampling Technique (SMOTE), and Randomized Combination Sampling (RCS). Feature selection methods, namely correlation (threshold 0.8) and mutual information (top 15 features), were employed to optimize feature sets. The Decision Tree (DT) and Linear Discriminant Analysis (LDA) classifiers were used to evaluate the performance of balanced datasets. The evaluation metrics included accuracy, precision, recall, F1-score, G-mean, and ROC curves. The results revealed that SMOTE and RCS outperformed other balancing methods, with SMOTE achieving the highest accuracy (98.7%) and RCS demonstrating robust G-mean values across both feature selection techniques. DT consistently showed better performance compared to LDA across all metrics, while feature selection significantly improved the classification results, particularly under mutual information criteria. However, the analysis highlighted limitations of LDA in handling imbalanced datasets and high-dimensional features. This study concludes that a combination of advanced data balancing and effective feature selection significantly enhances the accuracy of intrusion detection in IoT networks. Future work will focus on integrating real-time detection systems and exploring hybrid models to further improve the detection of complex attacks in dynamic IoT environments. 
Co-Authors Abid Djazuli, Abid Al-Faris, Muhammad Ghozi Iin Siti Aminah, Iin Siti Jimmie, Jimmie Muchsiri, Mukhtarudin Nurhayati Damiri Rahim, Supli Rapel, David Agustianto Rosmiah Rosmiah, Rosmiah Supli, Nur Aslamiah Zulhipni Reno Saputra Els