Article Per Year (5 Year)
p-Index From 2021 - 2026
0.817
P-Index
This Author published in this journals
All Journal Prestise: Jurnal Pengabdian Kepada Masyarakat Bidang Ekonomi dan Bisnis International Journal of Mechanical, Industrial and Control Systems Engineering Green Engineering: Journal of Engineering and Applied Science
Muhamad Furqon
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Social Sciences

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Pemberdayaan Masyarakat Desa Sungi Kulon Melalui Pembuatan Produk Baru Dengan Pemasaran Modern Putri Ayu Anisatus Shalikha; Nadia Mustika Dewi; Dewi Anggita Milenia; Aqiella Rachmawati; Arisa Putri Yuliani; Muhamad Furqon
Prestise: Jurnal Pengabdian Kepada Masyarakat Bidang Ekonomi dan Bisnis Vol 2, No 1 (2022): Jurnal Pengabdian Kepada Masyarakat Bidang Ekonomi dan Bisnis
Publisher : Fakultas Ekonomi dan Bisnis Islam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/prestise.v2i1.24195

Abstract

Dengan adanya program ini kami memiliki tujuan yang hendak dicapai yakni Pemberdayaan masyarakat di Desa Sungi Kulon melalui pembuatan produk baru dengan menggunakan pemasaran modern. Pemberdayaan ini ditujukan untuk melatih para ibu PKK (Pembinaan Kesejahteraan Keluarga) agar dapat membuat produk baru dengan menggunakan pemasaran modern yaitu dengan melalui pemasaran di media sosial dengan menggunakan teknologi agar bisa memperoleh pendapatan yang lebih unggul untuk meningkatkan perekonomian masyarakat Desa Sungi Kulon, Kecamatan Pohjentrek, Kabupaten Pasuruan. Teknik pendekatan yang dilakukan menggunakan metode langsung dengan mengadakan program pelatihan pada ibu-ibu PKK yang didampingi langsung oleh Mahasiswa KKN Uniwara. Dengan pengabdian ini dapat membuka wawasan para ibu-ibu PKK Desa Sungi Kulon, Kecamatan Pohjentrek, Kabupaten Pasuruan, untuk dapat mengembangkan diri melalui pemberdayaan masyarakat untuk pembuatan produk baru demi meningkatkan keadaan perekonomian yang lebih baik kedepannya.Kata kunci: Pemberdayaan; Produk Baru; Pemasaran Modern
Development of a Digital Twin Based Smart Green Building Energy Management Model Integrating IoT Sensors and Predictive Sustainability Analytics Asro Asro; Solihin Solihin; John Chaidir; Febri Adi Prasetya; Tuti Susilawati; Muhamad Furqon; Bentar Priyopradono
Green Engineering: International Journal of Engineering and Applied Science Vol. 2 No. 2 (2025): April : Green Engineering: International Journal of Engineering and Applied Sci
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v2i2.287

Abstract

Introduction: The integration of Digital Twin (DT) technology and the Internet of Things (IoT) into Building Energy Management Systems (BEMS) offers a transformative approach to optimizing energy consumption in buildings. This study explores the development of a Digital Twin based BEMS prototype, which leverages real time data collection, predictive analytics, and machine learning to enhance energy efficiency, reduce costs, and support sustainability goals in modern buildings. The research also addresses key gaps in current energy management systems, including real time adaptive control and integration with smart grid platforms. Literature Review: Previous research highlights the limitations of traditional BEMS, which often rely on static control strategies and lack real time adaptability. Recent advancements, including predictive maintenance and machine learning integration, have improved energy optimization. However, challenges such as data interoperability, scalability, and cybersecurity remain. This review consolidates current approaches and identifies opportunities for enhancing BEMS through the integration of DT technology, IoT, and machine learning. Materials and Method: The methodology employed involves the design of a Digital Twin based BEMS prototype, incorporating IoT sensors for real time data collection on variables such as HVAC load, occupancy, and environmental factors. The system uses time series forecasting and adaptive control strategies to optimize energy consumption. A case study building is used for validation, with performance metrics such as energy savings, CO₂ footprint reduction, and peak load reduction assessed to evaluate the system's effectiveness. Results and Discussion: The results demonstrate a significant reduction in energy consumption (up to 50%) compared to traditional BEMS, along with improved forecasting accuracy and sustainability performance. The prototype achieved a high R² score in predicting energy usage, validated through real world application in the case study building. The economic feasibility analysis showed substantial cost savings and a strong return on investment, making the system a financially viable solution for energy efficient building management.
Explainable Artificial Intelligence Framework for Interpretable Fault Diagnosis and Remaining Useful Life Prediction in Smart Industrial Rotating Machinery Suyahman Suyahman; Deny Prasetyo; Ahmad Budi Trisnawan; Ardy Wicaksono; Muhamad Furqon
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 1 No. 1 (2024): March: IJMICSE: International Journal of Mechanical, Industrial and Control Sys
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v1i1.402

Abstract

Predictive maintenance (PdM) plays a crucial role in modern industrial systems by minimizing downtime, reducing maintenance costs, and optimizing asset performance. However, many predictive models operate as “black box” systems, limiting transparency and making it difficult for operators to interpret their outputs. This study aims to integrate Explainable Artificial Intelligence (XAI) techniques with Remaining Useful Life (RUL) prediction models to improve both accuracy and interpretability. Various machine learning and deep learning approaches, including Support Vector Machines (SVM), Random Forest (RF), XGBoost, Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN), are employed to predict RUL using real-time sensor data from rotating machinery. XAI methods such as SHAP, LIME, and attention mechanisms are applied to provide human-understandable explanations of model predictions. The models are evaluated based on accuracy, Root Mean Square Error (RMSE), and interpretability scores. The results show that XAI-enhanced models outperform traditional approaches in predictive performance while offering greater transparency. These explanations help maintenance engineers better understand the factors influencing predictions, thereby improving decision-making and trust in the system. Nevertheless, the integration of XAI introduces additional computational complexity, which may pose challenges for large-scale industrial implementation. Overall, this study highlights the potential of combining XAI with RUL prediction to develop more reliable, transparent, and effective predictive maintenance solutions.
Co-Authors Aqiella Rachmawati Ardy Wicaksono Arisa Putri Yuliani Asro Asro Bentar Priyopradono Daffa Fauzul Hakim Deny Prasetyo Dewi Anggita Milenia Febri Adi Prasetya John Chaidir Nadia Mustika Dewi Nova Indrayana Yusman Putri Ayu Anisatus Shalikha Solihin Solihin Suyahman Suyahman Trisnawan, Ahmad Budi TUTI SUSILAWATI