Perkembangan pesat perangkat Internet of Things (IoT) telah menghasilkan peningkatan signifikan dalam volume data, yang menciptakan tantangan dalam pemrosesan data secara real-time.  Meskipun sistem cloud-fog dapat mengurangi latensi dengan memproses data lebih dekat ke sumbernya, pengelolaan dan transfer data tetap bergantung pada tugas terjadwal yang efisien. Penelitian ini bertujuan untuk mengurangi intervensi manual dan meningkatkan efisiensi sistem melalui penggunaan cron job yang dikonfigurasi dengan logika skrip PHP dan diimplementasikan di Cloud Panel. Sistem ini dirancang untuk menjadwalkan transfer data secara otomatis dengan interval yang disesuaikan dan memastikan pembaruan data yang tepat waktu dalam lingkungan cloud-fog. Hasil penelitian menunjukkan bahwa penerapan otomatisasi transfer data berhasil mengurangi kesalahan, meningkatkan efisiensi, dan memastikan pembaruan data yang lebih tepat waktu dalam sistem cloud-fog

The rapid growth of cloud computing has necessitated efficient and automated solutions for data management in cloud infrastructure. Manual data processing and transfer methods are prone to delays, errors, and inefficiencies, particularly in real-time applications. This study proposes a systematic approach to automate data processing and transfer using cron job, PHP script logic, and Cloud Panel integration. A MySQL database was designed with two tables: data_now for real-time data and data_one_hour for scheduled transfers at one-hour intervals. Php script were developed to automate data transfer logic, while cron job were configured on the Cloud Panel to execute these scripts at predefined intervals. The system was tested in a cloud environment, demonstrating error-free, hourly data transfers with significant improvements in efficiency, accuracy, and timeliness. Results showed that the automated system reduced manual workload, ensured real-time data consistency, and optimized resource utilization. This study provides a scalable and reliable framework for automating data workflows in cloud-based systems, offering practical solutions for industries such as healthcare, finance, and IoT. The findings contribute to the field of cloud automation by presenting a robust approach that can be readily implemented across various organizational infrastructures.

The rise of renewable energy has led to growing interest among researchers in optimizing renewable energy systems to harvest the highest possible energy output, especially in photovoltaic (PV) systems. Photovoltaic systems generate energy from sunlight, but the intermittent nature of sunlight poses a challenge for providing constant power. Another problem is that the most affordable conventional Pulse-Width Modulation (PWM) controllers on the market are unable to provide a current monitoring system. This issue is critical when users require detailed data to perform diagnostics on their PV systems. In this study, a monitoring system using microcontrollers and the ACS712 current sensor was developed and implemented to observe the behavior and performance of a photovoltaic (PV) charging system based on the current output from a PWM controller. The primary objective of this system is to develop a solution that can identify PV modules operating under suboptimal conditions, which can be caused by various factors such as increased solar cell temperature, cloud cover effects, PV module degradation, and the performance of the Pulse-Width Modulation (PWM) solar charge controller (SCC). This is crucial because PV modules can be susceptible to various environmental and technical factors that may impact their efficiency and power output. By closely monitoring the performance of each PV module using the ACS712-based current sensing system, the researchers aim to promptly detect and address any issues that may arise, ensuring the overall optimal operation of the PV system.