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All Journal JURNAL PASTI (PENELITIAN DAN APLIKASI SISTEM DAN TEKNIK INDUSTRI) Eastasouth Journal of Impactive Community Services IRA Jurnal Pengabdian Kepada Masyarakat
Jakfat Haekal
Universitas Esa Unggul
Control & Systems Engineering Decision Sciences, Operations Research & Management Education Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences Transportation Other

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Managing Workflow Time Overruns: A Workload-Aware Operational Management Approach Supported by Machine Learning Rizaldi Mu'min; Jakfat Haekal; Andrian Haro; Rhamdalia Fanny Gustaji; Joval Ifghaniyafi Farras
Jurnal PASTI (Penelitian dan Aplikasi Sistem dan Teknik Industri) Vol. 20 No. 1 (2026): Jurnal PASTI
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/pasti.2026.v20i1.004

Abstract

Workflow time overruns are recurring operational control problems rather than mere forecasting errors. When actual completion times exceed plan, managers face schedule instability, coordination losses, approval bottlenecks, and rising service costs. Using 2,500 task-level observations, this study examines how workload-aware analytics from routine workflow data can improve operational control over time overruns. The analysis treats time overrun as the main outcome and evaluates whether variables such as task type, department, priority, approval level, employee workload, estimated duration, and cost provide useful visibility into overrun risk. The results show that routine workflow data can indicate where overrun exposure tends to accumulate, especially around estimate quality, workload conditions, approval requirements, and task heterogeneity. However, the strongest managerial value of analytics lies less in replacing judgment than in improving planning discipline, estimate calibration, workload review, and exception monitoring. The study therefore reframes workflow overrun analysis as an operational control and process-governance issue.
Kajian Kualitas Air Rumah Tangga pada Kawasan Permukiman Padat Penduduk di Kembangan Utara, Jakarta Barat Adhista Triasa Renggananta; Christian Dwi Putra Widjaya; Rezayanti Novia Putrika Dewi; Siti Alpiah; SA Pratiwi; Desy Agung; Arif Rahman; Jakfat Haekal; Indra Putra Salim; Arys Andhikatama
Eastasouth Journal of Impactive Community Services Vol 4 No 03 (2026): Eastasouth Journal of Impactive Community Services (EJIMCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/ejimcs.v4i03.593

Abstract

Aktivitas manusia dan kepadatan penduduk yang tinggi berpengaruh terhadap penurunan kualitas air di kawasan permukiman padat. Penelitian ini bertujuan mengidentifikasi faktor penyebab penurunan kualitas air, menganalisis hubungan kepadatan penduduk dengan kualitas air, serta merumuskan upaya pengelolaan lingkungan. Penelitian menggunakan metode campuran (mixed methods) dengan teknik grab sampling, observasi, wawancara, dan penyebaran kuesioner kepada 70 responden. Hasil penelitian menunjukkan bahwa 92% responden menyatakan wilayahnya memiliki sanitasi dan drainase yang kurang memadai, sedangkan 85% responden menilai aktivitas domestik dan keterbatasan lahan memengaruhi kualitas air. Secara fisik, air tanah cenderung keruh, berbau, dan berwarna, sementara hasil pengukuran kimia menunjukkan nilai pH berada di bawah standar baku mutu air bersih. Sebaliknya, air PDAM memiliki kualitas lebih baik. Upaya perbaikan dapat dilakukan melalui peningkatan sanitasi, penerapan bio-septic tank komunal, filtrasi air sederhana, dan perluasan akses air bersih perpipaan.
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 Adhista Triasa Renggananta Andrian Haro Arif Rahman Arys Andhikatama Atep Atep Afia Hidayat Christian Dwi Putra Widjaya Desy Agung Hadi Pranoto Indra Almahdy Indra Putra Salim Joval Ifghaniyafi Farras Muhammad Isradi Muhammad Kholil Nanang Rukyat Rezayanti Novia Putrika Dewi Rhamdalia Fanny Gustaji Rizaldi Mu'min SA Pratiwi Selamet Riyadi Siti Alpiah Yudi Gunardi