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All Journal Trikonomika: Jurnal Ekonomi Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer
Noor, Mohd
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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SYNERGY OF INTELLECTUAL CAPITAL AND SHARIA FINANCIAL CAPITAL IN IMPROVING FINANCIAL PERFORMANCE Albahi, Muhammad; Yuslem, Nawir; Soemitra, Andri; Noor, Mohd
TRIKONOMIKA Vol 23 No 2 (2024): December Edition
Publisher : Faculty of Economics and Business, University of Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/trikonomika.v23i2.18574

Abstract

This study aims to analyze the influence of Intellectual Capital and Sharia Financial Capital on the financial performance of companies listed in the Jakarta Islamic Index (JII) and to evaluate the synergy between the two capitals in increasing competitiveness and business sustainability. The data used are from the annual reports of JII companies during the period 2020–2024. Intellectual Capital is measured using the Value-Added Intellectual Coefficient (VAIC) model, and Sharia Financial Capital, which includes the zakat ratio, profit-sharing ratio, and Sharia debt ratio. Financial performance is measured using Return on Equity (ROE), Return on Assets (ROA), and Price-to-Book Value (PBV). The results show that Intellectual Capital and Sharia Financial Capital have a positive influence on financial performance. The synergy between Intellectual Capital and Sharia Financial Capital has been proven to have a significant impact, creating a substantial competitive advantage in the context of Maqashid Sharia.
K-Nearest Neighbor Algorithm for Intelligent Monitoring and Control System Integration in Renewable Energy Applications Junus, Mochammad; Fa‘izah, Laily Nur; Noor, Mohd; Putra, Indra Lukmana
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 2 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i2.1565

Abstract

A real-time biogas monitoring and control system was developed by integrating the K-Nearest Neighbor (KNN) algorithm into an IoT-based framework for methane pressure prediction and automated control. The system uses an ESP32 microcontroller connected to temperature, gas, and pressure sensors (DHT22, MQ-4, MPX5700) to continuously collect data, with cloud connectivity provided through Firebase and Blynk platforms. The predictive model operates within a live feedback loop, allowing immediate actuation based on forecasted methane conditions. With an optimal parameter of k=4, the KNN model achieved 93.33% accuracy, supported by a mean absolute error (MAE) of 0.18 and a root mean square error (RMSE) of 0.21. A comparative evaluation with Random Forest and Gradient Boosting algorithms showed that, although these models yielded slightly higher accuracy, KNN provided superior computational effi-ciency for embedded deployment. The system maintained stable operation during tests involving sensor anomalies, network interruptions, and data noise. However, redundancy mechanisms and improved vali-dation strategies are recommended to enhance robustness. The findings demonstrate that methane pro-duction can be effectively predicted using temperature and pressure data, with further accuracy im-provements possible through additional process variables such as pH and fermentation age.
Co-Authors Albahi, Muhammad Andri Soemitra Fa‘izah, Laily Nur INDRA LUKMANA PUTRA Mochammad Junus Nawir Yuslem, Nawir