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Firdaus Annas
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Knowbase : International Journal of Knowledge in Database
Published by Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi
ISSN : 27980758     EISSN : 27977501     DOI : https://www.doi.org/10.30983/knowbase
Core Subject : Science,
Computer Science & IT
Knowbase : International Journal of Knowledge in Database is a peer-reviewed journal that publishes articles which contribute new results in all areas of the database management systems & its applications. The goal of this journal is to bring together researchers and practitioners from academia to focus on understanding Modern developments in this field, and establishing new collaborations in these areas. Authors are solicited to contribute to the journal by submitting articles that illustrate research results that describe significant advances in the areas of Database management systems.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 153 Documents
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Design of an Expert System for Determining Learning Styles (SIPATUGABEL) for Students at The Madrasah Tarbiyah Islamiyah (MTI) in Canduang Sadar Martua Haholongan Sir; Supriadi
Knowbase : International Journal of Knowledge in Database Vol. 6 No. 1 (2026): June 2026
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v6i1.11107

Abstract

Humans possess diverse potentials that are inherent from birth and must be nurtured to develop fully. These potentials can be developed in various ways, one of which is through learning. The learning process can help individuals develop their potential in accordance with their unique characteristics. Since every individual has different characteristics, students have diverse learning styles. It is crucial to consider learning styles in the learning process to ensure meaningful learning. The Merdeka Curriculum also includes several assessments, one of which is diagnostic assessment, which serves as the initial step a teacher takes to determine students’ baseline conditions both cognitive and non-cognitive. However, in implementing this diagnostic assessment at MTI Canduang, a challenge arises: the difficulty in accurately and quickly identifying students’ learning styles. Based on this problem, the researcher aims to design a web-based expert system for determining students’ learning styles using the Certainty Factor method, which can identify students’ learning styles in a valid, practical, and effective manner. The researcher employed the Research and Development (R&D) methodology to produce an expert system product, utilizing the Systems Development Life Cycle (SDLC) model with the Waterfall method, to develop an expert system for determining the learning styles of students at Madrasah Tarbiyah Islamiyah (MTI) Canduang. After the system was successfully designed, product testing was conducted. The results of the product testing, such as the system validity test, indicated that the system was valid, with an average score of 0.96 from the validator. The designed system was then deemed practical, achieving an average score of 0.93 from the practicality evaluator, and the designed system was deemed effective, achieving an average score of 0.56 from all respondents the fourth grade students of the MTI Canduang special class. Based on these test results, the designed expert system for determining learning styles was deemed valid, practical, and effective in determining learning styles quickly and accurately.
Lightweight Hybrid SVM-LSTM Edge AI for Real-Time Smart Greenhouse Anomaly Detection Irfan Fahmi Ahmadi; Seno Adi Putra; Atam Rifa'i Sujiwanto
Knowbase : International Journal of Knowledge in Database Vol. 6 No. 1 (2026): June 2026
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v6i1.11389

Abstract

Smart greenhouse systems require an artificial intelligence model that can transform sensor records into real-time abnormal-condition decisions while remaining practical for edge computing devices. This paper proposes a lightweight Hybrid SVM-LSTM Edge AI model for real-time smart greenhouse anomaly detection. The model integrates packet validation, deterministic feature fusion, SVM-based discriminative scoring, and LSTM temporal sequence learning to classify greenhouse states as normal or abnormal. The feature representation combines current sensor values, short-term sensor changes, sampling-gap information, and daily periodic patterns so that the model can capture both instantaneous conditions and recent environmental transitions. Using the IoT Agriculture 2024 smart greenhouse dataset, the evaluation compares several Hybrid SVM-LSTM and LSTM-only configurations under proxy labels for actuator-relevant abnormal states. The refined water-related proxy achieved 0.979 accuracy, 0.763 precision, 0.690 recall, and a 0.725 F1-score with 1.142 ms P95 inference time. The broader NPK-gap proxy achieved 0.983 accuracy, 0.886 precision, 0.549 recall, and a 0.678 F1-score with 1.448 ms P95 inference time. These results indicate that the proposed Hybrid SVM-LSTM model is sufficiently compact and accurate for real-time anomaly detection at the edge, especially when proxy-label design and threshold selection are aligned with the target greenhouse condition.
Application of Artificial Intelligence and Deep Learning in Remote Sensing Image Analysis for Natural Resource Monitoring and Management Mohammad Eisa Sediqi; Musawer Hakimi
Knowbase : International Journal of Knowledge in Database Vol. 6 No. 1 (2026): June 2026
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v6i1.11559

Abstract

Natural resource monitoring increasingly relies on satellite and airborne remote sensing to observe land cover change, forest loss, agricultural conditions, and water dynamics over large and often inaccessible areas. Conventional pixel-based and shallow machine-learning classifiers struggle to exploit the spatial, spectral, and temporal complexity of modern multi-sensor archives. This paper reviews and synthesizes recent developments in artificial intelligence (AI) and deep learning (DL) for remote sensing image analysis, focusing on four application domains central to natural resource management: land use/land cover classification, forest and deforestation monitoring, agricultural and crop monitoring, and water resource and flood mapping. Literature spanning convolutional neural networks, encoder-decoder segmentation architectures, and attention-based transformer models is compared in terms of methodology, sensor modality, and reported performance. A generalized processing workflow and an integrated cross-domain conceptual framework are proposed to guide the design of operational AI-based monitoring systems; unlike prior single-domain reviews, this synthesis is explicitly cross-domain, linking architecture choice to operational constraints shared across land cover, forest, agriculture, and water monitoring rather than treating each application silo in isolation. The review finds that transformer and hybrid CNN-transformer architectures tend to outperform purely convolutional baselines on scene-level classification tasks, although this advantage depends on application domain, dataset characteristics, and evaluation protocol and is less consistently observed for dense pixel-level segmentation, where U-Net-family encoder-decoders remain the dominant choice due to their balance of accuracy and computational cost. Persistent challenges include scarcity of high-quality labeled data, limited cross-region generalization, high computational demand, and limited interpretability of deep models for policy-relevant decision making. The paper concludes that multimodal data fusion, self-supervised pretraining, and explainable AI represent the most promising directions for advancing AI-driven natural resource monitoring toward operational, trustworthy deployment.

Page 16 of 16 | Total Record : 153

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