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Taqwa, Rangga
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Computer Science & IT Library & Information Science Mathematics

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Analysis and design of an inset-feed microstrip antenna for a LEO satellite IoT ground station at 921 MHz Taqwa, Rangga; Rimbawa, H.A. Danang; Miptahudin, Apip; Hasibuan, Bayu Nuar Khadapi; Sastradinata, Aria Kusumah; Bangun, Abbas Madani
Jurnal Mandiri IT Vol. 14 No. 2 (2025): Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i2.464

Abstract

The evolution of the Internet of Things (IoT) demands global connectivity that terrestrial networks alone cannot provide1. Low Earth Orbit (LEO) satellites equipped with Long Range (LoRa) communication technology offer a promising solution to bridge this connectivity gap2. This paper presents a specific case study calculation for a LoRa-based IoT satellite mission, defining the system's operational constraints based on selected hardware3. This analysis is framed by the RFM95W LoRa transceiver for the ground station and the Satlab Polaris receiver for the satellite4. The datasheet specifications of these components establish the critical link parameters that dictate performance: a maximum Transmit Power (Pt) ) of 20 dBm from the RFM95W 5and a Receiver Sensitivity threshold of -130 dBm for the Satlab Polaris6. The objectives are: (1) to conduct a comprehensive link budget analysis to validate the communication viability between a LEO satellite and a ground station 77, and (2) to design and predict the performance of an inset-feed microstrip antenna operating in the 920-925 MHz Indonesian LoRa frequency band using an FR-4 substrate. The detailed link budget analysis, performed for an uplink to a 500 km orbit 9, reveals that these specific parameters create a stringent performance requirement: while a reliable link margin of $+7.8 \text{ dB}$ is achieved at a 90°  elevation (best case) 10101010, the system reaches its theoretical critical threshold (0.0 dB margin) at 19.1° and enters link failure with a -2.8 dB margin at the target 10°  elevation. This failure is directly linked to the preliminary simulation of the initial antenna design, which shows a suboptimal return loss (S11) of -9.41 dB. This paper concludes that the system's target for low-elevation communication has not been met. The performance gap, defined by the hardware constraints, confirms that the initial antenna design is insufficient15. Therefore, systematic optimization of the antenna design is identified as the crucial next step to achieve a positive link margin at the 10° target elevation and ensure a robust communication link across all operational scenarios.
Bayesian-Optimized XGBoost Model for Predicting Mushroom Toxicity Sastradinata, Aria Kusumah; Sunarta, Sunarta; Miptahudin, Rd. Apip; Abdurrahman, M. Daffa; Taqwa, Rangga
Jurnal Mandiri IT Vol. 14 No. 2 (2025): Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i2.465

Abstract

Mushroom poisoning remains a significant public health concern due to the morphological similarities between edible and poisonous species, making traditional identification unreliable. This study aims to develop an accurate and interpretable machine learning framework for mushroom toxicity prediction using a Bayesian-Optimized Extreme Gradient Boosting (XGBoost) model. The dataset consists of morphological and ecological features derived from the secondary mushroom dataset, which underwent preprocessing through imputation, standardization, and one-hot encoding. Bayesian Optimization, implemented via the Hyperopt Tree-structured Parzen Estimator (TPE) algorithm, was employed to automatically fine-tune the XGBoost hyperparameters, thereby improving convergence and reducing manual experimentation. The model’s performance was evaluated using 10-fold cross-validation and standard metrics, including accuracy, precision, recall, F1-score, and the Area Under the ROC Curve (AUC). Experimental results demonstrated that the proposed framework achieved an exceptionally high performance with an accuracy of 99.99% and an AUC of 1.0000, indicating near-perfect discrimination between edible and poisonous mushrooms. Feature importance analysis further revealed that habitat, veil color, and stem root were the most influential predictors of toxicity. The findings highlight the effectiveness of Bayesian-optimized ensemble learning in handling high-dimensional biological data, offering a reliable, transparent, and computationally efficient approach for biosafety assessment and ecological data analysis.
Co-Authors Abdurrahman, M. Daffa Bangun, Abbas Madani Hasibuan, Bayu Nuar Khadapi Miptahudin, Apip Miptahudin, Rd. Apip Rimbawa, H.A. Danang Sastradinata, Aria Kusumah Sunarta Sunarta