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Anggoro, Tri
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Efek lingkungan kerja terhadap kinerja pegawai kalurahan di Kapanewon Wates, Kulon Progo Burhanudin, Burhanudin; Anggoro, Tri
Journal of Management and Digital Business Vol. 6 No. 1 (2026): Journal of Management and Digital Business
Publisher : Nur Science Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53088/jmdb.v6i1.2461

Abstract

This study examines the effect of the work environment on the performance of village employees in Kapanewon Wates, Kulon Progo Regency, Special Region of Yogyakarta. Using a quantitative survey design, the study involved a population of 112 employees and a sample of 60 respondents. Primary data were collected through questionnaires and analyzed using simple linear regression. The findings show that the work environment has a positive and significant effect on employee performance. This result indicates that a more supportive and comfortable work environment is associated with higher employee performance in carrying out public service duties at the village administrative level. The study suggests that kalurahan leaders should enhance workplace quality to improve employee performance. Practical efforts may include providing ergonomic desks and chairs, ensuring adequate lighting and air circulation, reducing workplace noise, and fostering positive relationships between supervisors, subordinates, and coworkers. Overall, the work environment is an important organizational factor in improving employee performance and the quality of local public services.
Monitoring Kinerja Virtual Machine pada Lingkungan Google Cloud Platform dengan Notifikasi ke Media Sosial Widyaningsih, Irna; Haq, Abdul; Anggoro, Tri
IJAI (Indonesian Journal of Applied Informatics) Vol 10, No 1 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v10i1.109143

Abstract

Abstrak : Virtual Machine (VM) merupakan elemen penting dalam cloud computing karena mendukung fleksibilitas dan efisiensi pengelolaan sumber daya. Namun, lonjakan penggunaan VM dapat menurunkan kinerja jika tidak terdeteksi cepat. Penelitian ini mengembangkan sistem monitoring pada Google Cloud Platform (GCP) dengan Grafana yang terintegrasi Telegram untuk peringatan dini otomatis. Prometheus digunakan sebagai pengumpul metrik, sedangkan Grafana menampilkan visualisasi, berfokus pada pemantauan CPU secara real-time di Google Compute Engine (GCE). Notifikasi dikirim melalui Telegram ketika penggunaan CPU melewati ambang batas. Pengujian menunjukkan rata-rata keterlambatan notifikasi hanya 1 detik, kecuali satu anomali 11 detik. Pada skenario Threshold Validation, terjadi satu alert dengan CPU maksimum 37% dan rata-rata 29%, sedangkan Long Hold menghasilkan tiga alert dengan rata-rata 23,3% sesuai konfigurasi interval. Hasil ini membuktikan sistem mampu memberikan notifikasi hampir real-time, menjaga konsistensi, dan mendukung deteksi dini baik pada beban singkat maupun berkepanjangan di infrastruktur GCP.====================================================Abstract :Virtual Machines (VMs) are essential in cloud computing for flexibility and efficient resource management. However, sudden spikes in VM usage can degrade performance if not detected quickly. This study develops a monitoring system on Google Cloud Platform (GCP) using Grafana integrated with Telegram for automated early alerts. Prometheus collects metrics, while Grafana provides visualization, focusing on real-time CPU monitoring in Google Compute Engine (GCE). Alerts are sent via Telegram when CPU usage exceeds a set threshold. Testing shows an average notification delay of 1 second, except for a single 11-second anomaly. In the Threshold Validation scenario, one alert occurred with 37% maximum CPU and 29% average, while the Long Hold scenario produced three alerts with an average of 23.3%, following configured intervals. Results indicate the system delivers near real-time, consistent alerts and supports early detection under both short and sustained load conditions on GCP infrastructure.
Co-Authors Agustin, Ninik Azizaturrohmah, Nofriani Burhanudin Burhanudin Endang Wahyuningsih Endang Wahyuningsih Fachri, Fahmi Fadjeri, Akhmad Fersellia, Fersellia Haq, Abdul Hofsah, Trindi Puspita Lutfiyani, Anisa Muflih, Ghufron Zaida Nardi Riana, Septi Nur Arifin, Nur Riyan Jaya Sumantri Santoso, Rahmawan Septianisya, Rosy Rizabila Taufik Hidayat Widyaningsih, Irna Wijaya, Febri