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Hadi Pranoto
PT Anugerah Sarana Dinamika
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Pelatihan Penerapan AI untuk Analisis Beban Kerja di PT ASD Atep Atep Afia Hidayat; Yudi Gunardi; Hadi Pranoto; Muhammad Kholil; Jakfat Haekal
IRA Jurnal Pengabdian Kepada Masyarakat (IRAJPKM) Vol 3 No 3 (2025): Desember
Publisher : CV. IRA PUBLISHING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56862/irajpkm.v3i3.344

Abstract

Employee productivity is a key determinant of organizational competitiveness; however, manual workload analysis often leads to imbalances between employees’ capacity and assigned tasks, resulting in inefficiency and reduced well-being. This program aims to provide training on the application of Artificial Intelligence (AI), particularly Artificial Neural Networks, for workload analysis to optimize task distribution and enhance productivity at PT Anugerah Sarana Dinamika. The training consisted of a needs assessment, theoretical sessions on AI, hands-on practice in developing a prototype based on company workload data, mentoring, and workload scenario simulations. Evaluation using pre–post tests on a Likert scale showed a significant improvement in participants’ understanding of AI concepts (average score increased from 2.1 to 4.2), with 80% of participants able to operate the workload analysis prototype and an overall satisfaction rate of 88%. The program successfully improved participants’ digital literacy and technical skills and demonstrated the effectiveness of AI in supporting sustainable human resource management transformation.
Lokakarya Sistem Prediksi Pemeliharaan Mesin Menggunakan Algoritma Random Forest pada Perusahaan Layanan Perawatan Kendaraan Indra Almahdy; Muhammad Isradi; Hadi Pranoto; Muhammad Kholil; Jakfat Haekal
IRA Jurnal Pengabdian Kepada Masyarakat (IRAJPKM) Vol 3 No 3 (2025): Desember
Publisher : CV. IRA PUBLISHING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56862/irajpkm.v3i3.345

Abstract

This community service program was conducted to address the low efficiency of conventional machine maintenance systems and the high risk of unexpected breakdowns in vehicle service companies. The program aimed to introduce a predictive maintenance system based on the Random Forest algorithm, enabling partners to shift from reactive maintenance practices toward data-driven decision-making. The applied methods included a needs assessment, theoretical training on machine learning fundamentals for 10 participants, hands-on practice using Python and Google Colab, and guided case studies based on the partner’s historical machine data. Evaluation results indicated a 42% improvement in participants’ technical understanding, while the developed system demonstrated the capability to detect potential failures 2–3 days earlier than traditional methods. The implementation successfully reduced unscheduled downtime and operational costs. Overall, this program enhanced the partner’s capacity to implement predictive maintenance, strengthened technicians’ digital literacy, and supported sustainable digital transformation in the vehicle service sector.
Pelatihan Peramalan Permintaan Berbasis Machine Learning untuk Optimalisasi Produksi Mendukung SDG 8 Selamet Riyadi; Muhammad Kholil; Nanang Rukyat; Hadi Pranoto; Jakfat Haekal
IRA Jurnal Pengabdian Kepada Masyarakat (IRAJPKM) Vol 3 No 3 (2025): Desember
Publisher : CV. IRA PUBLISHING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56862/irajpkm.v3i3.346

Abstract

This community service program aims to enhance the capabilities of business actors and industry practitioners to apply machine learning-based demand forecasting to optimize production and support the achievement of SDG 8. The training was conducted through interactive workshops consisting of conceptual introduction, hands-on model development, and analysis of forecasting results using production datasets. Participants were guided in variable selection, model accuracy evaluation, and the use of prediction outputs for production decision-making. Evaluation results indicate an 85% improvement in participants' understanding of forecasting concepts and an 80% increase in software-use competence. This program contributes to improving technological literacy, enhancing production planning efficiency, and building digital capacity as part of sustainable economic development.
Co-Authors Atep Atep Afia Hidayat Indra Almahdy Jakfat Haekal Muhammad Isradi Muhammad Kholil Nanang Rukyat Selamet Riyadi Yudi Gunardi