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All Journal Pixel : Jurnal Ilmiah Komputer Grafis Jurnal Mahasiswa Fakultas Hukum Infotek : Jurnal Informatika dan Teknologi Magistrorum et Scholarium: Jurnal Pengabdian Masyarakat Lokawati: Jurnal Penelitian Manajemen dan Inovasi Riset Science Technology and Management Journal (STMJ) Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi International Journal of Mechanical, Industrial and Control Systems Engineering
Ahmad Nugroho
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice

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Diagram Unified Modelling Language (UML) untuk Perancangan Sistem Informasi Manajemen Penelitian dan Pengabdian Masyarakat (SIMLITABMAS) Siska Narulita; Ahmad Nugroho; M. Zakki Abdillah
Bridge : Jurnal Publikasi Sistem Informasi dan Telekomunikasi Vol. 2 No. 1 (2024): Bridge: Jurnal publikasi Sistem Informasi dan Telekomunikasi
Publisher : Asosiasi Profesi Telekomunikasi Dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/bridge.v2i3.174

Abstract

The management of data collection of research activities, community service, and publications that occur at the National University of Karangturi Semarang is still done manually using Microsoft Excel, which still has various problems related to collecting, storing, searching, processing, and presenting data when needed for the accreditation process. Based on these problems, an information system is needed that can accommodate all the problems experienced by users. Therefore, an information system will be developed to manage all data related to research activities, community service, and publications carried out by lecturers and improve the efficiency of data management. System design is one of the stages of developing an information system. Simlitabmas design has two main objectives, namely to fulfill the needs of users and to provide a clear picture to all personnel involved in system development. System design is one of the important steps in a system development in which a visual representation of a series of processes or activities in an institution or organization is made. One of the modeling or design tools that can be used is the Unified Modeling Language (UML). UML helps describe and design systems, especially in object-oriented programming. In this research, UML diagrams made consist of use case diagrams, activity diagrams, and sequence diagrams.
A Fault Diagnosis and Intelligent Monitoring Framework Using Explainable Artificial Intelligence for Smart Industrial Machinery Siska Nar; Ahmad Nugroho; Ahmad Subhan Yazid; Helmi Wibowo; Alyauma Hajjah
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 2 No. 4 (2025): December :IJMICSE: International Journal of Mechanical, Industrial and Control
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v2i4.405

Abstract

Background: The development of industrial technology in the Industry 4.0 era has encouraged the implementation of intelligent monitoring systems to improve machine reliability and operational efficiency. However, machine fault diagnosis systems based on artificial intelligence often face limitations in terms of interpretability because the models used are complex and difficult to explain. Objective: This study aims to develop a deep learning-based industrial machine fault diagnosis system integrated with an Explainable Artificial Intelligence (XAI) approach to improve diagnostic accuracy while providing interpretable insights for users. Method: The research method involves collecting data from industrial machine sensors consisting of vibration signals, temperature measurements, and acoustic signals, followed by data preprocessing and feature extraction processes. The processed data are then used to train a deep learning-based diagnostic model, after which explainability methods such as SHAP or LIME are applied to analyze the contribution of each feature to the model’s prediction results. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Results: The results indicate that the proposed deep learning model achieves better performance compared to conventional machine learning methods such as Support Vector Machine and Random Forest. Furthermore, the explainability analysis reveals that vibration amplitude, increases in machine component temperature, and anomalies in acoustic signals are the main factors influencing machine fault detection. Therefore, the proposed system not only improves the accuracy of machine fault diagnosis but also provides transparency in the decision-making process, thereby supporting the implementation of predictive maintenance in smart manufacturing environments.
Co-Authors Ahmad Subhan Yazid Alyauma Hajjah Aurelio Akeno Adiwijaya Cicilia Yolisday Cristeddy Asa Bakti Edwin Prayoga H Erba Lutfina Gati Zulfikar Helmi Wibowo Hermawan Honggo Widagdo Ignatius Marcheivan Sunjaya Irene Adriana Putri Andini M. Zakki Abdillah Maria Yuliana Belaon Paulus Damar Bayu Murti Sekarlangit Siska Nar Siska Narulita Tranhuji Gumbara