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All Journal Prestise: Jurnal Pengabdian Kepada Masyarakat Bidang Ekonomi dan Bisnis International Journal of Mechanical, Industrial and Control Systems Engineering
Muhamad Furqon
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Social Sciences

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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
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 Deny Prasetyo Dewi Anggita Milenia Nadia Mustika Dewi Putri Ayu Anisatus Shalikha Suyahman Suyahman Trisnawan, Ahmad Budi