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All Journal Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Journal of Electrical Engineering and Informatics (JEEI)
Anton Sukamto
Institut Bisnis dan Informatika Kesatuan Bogor
Agriculture, Biological Sciences & Forestry Economics, Econometrics & Finance Engineering Public Health Social Sciences

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Collaborative Digital Marketing for Community Service to Support Promotion and Sales of Bogor City MSME Products with IBIK and UTHM: Pemasaran Digital Kolaborasi Pengabdian kepada Masyarakat untuk Mendukung Promosi dan Penjualan Produk UMKM Kota Bogor Bersama IBIK dan UTHM Aang Munawar; Ani Mekaniwati; Budi Setiawan; Yulia Nurendah; Bambang H. Rainanto; Tarida Marlin; Anton Sukamto; Muanas; Adil Fadilah; Tri Marlina; Bambang Pamungkas; Sutarti
JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Vol. 7 No. 3 (2023): Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Publisher : DPD Jatim Perkumpulan Dosen Indonesia Semesta

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Digital marketing not only makes it easier for MSMEs to display their products to consumers but also expands their customer base. The limited knowledge of business actors, generally adults to old age, causes many businesses to experience setbacks and are forced to stop their business because they cannot compete in marketing. Therefore, training and assistance related to digital marketing need to be carried out to provide solutions for business actors so they can survive. This activity is a collaborative community service program between IBI Kesatuan (IBIK) with Universiti Tun Hussein Onn Malaysia (UTHM) and Bogor City MSMEs. The result of this activity is increased insight from business people regarding the use and use of digital platforms and other knowledge related to the requirements for selling products overseas.
Predicting Employee Burnout Risk Using Machine Learning and Workplace Well-Being Indicators Feti Arman; Lidia Olga; Anton Sukamto
Journal of Electrical Engineering and Informatics (JEEI) Vol. 1 No. 2 (2026): JUNE (II)
Publisher : LPPM Sekolah Tinggi Teknologi Nusantara Lampung

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Employee burnout has become a critical concern in human resource management, particularly in flexible and work-from-home environments where workload intensity, digital exposure, and work-life boundaries are increasingly difficult to monitor. This study aims to develop a machine learning-based model for predicting employee burnout risk using daily work-from-home behavioral patterns and workplace well-being indicators. The dataset consisted of 1,800 daily records collected from 180 users, including work hours, screen time, meeting frequency, breaks, after-hours work, sleep duration, task completion rate, burnout score, and burnout risk category. To prevent target leakage, user identity and burnout score were excluded from the predictor set. The original burnout risk label was transformed into a binary classification target by grouping Medium and High categories into an At-Risk class, while Low was retained as Low Risk. Several supervised machine learning algorithms were evaluated using 10-fold stratified cross-validation, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosted Trees, Support Vector Machine, Naïve Bayes, and k-Nearest Neighbors. The results showed that Random Forest achieved the best overall performance, with 97.11% accuracy, 94.37% macro-F1, 94.24% balanced accuracy, and 90.11% recall for the At-Risk class. Feature importance analysis indicated that task completion rate was the most influential predictor, followed by work hours, sleep hours, and screen time. These findings demonstrate that machine learning can support HR analytics-based early warning systems for employee burnout risk detection in remote work settings.
Co-Authors Aang Munawar Adil Fadilah Ani Mekaniwati Bambang H. Rainanto Bambang Pamungkas Budi Setiawan Feti Arman Lidia Olga Muanas Sutarti Tarida Marlin Tri Marlina Yulia Nurendah