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All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal Penelitian Pendidikan Sainteknol : Jurnal Sains dan Teknologi Edu Elektrika Journal
Fathimah, Aisya
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Other

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Cron Job Implementation for Automated Data Processing and Transfer in Cloud Infrastructure Wibowo, Apriansyah; Fathimah, Aisya; Waskito, Deswal; Aprilianto, Rizky Ajie
Sainteknol : Jurnal Sains dan Teknologi Vol. 23 No. 1 (2025): June 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sainteknol.v23i1.22649

Abstract

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.
Deep Learning Approach for Pneumonia Prediction from X-Rays using A Pretrained Densenet Model Wafi, Ahmad Zein Al; Rochim, Febry Putra; Fathimah, Aisya
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 1 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i1.1457

Abstract

Pneumonia remains a major global health concern, particularly affecting young children and older adults, contributing to significant morbidity and mortality. Traditional diagnostic methods using chest CT scans are time-consuming and prone to errors due to the reliance on manual interpretation. This study investigates the application of DenseNet architectures DenseNet121, DenseNet169, and DenseNet201—for automated pneumonia detection from chest X-ray images. The dataset, obtained from the Guangzhou Women and Children’s Medical Center, consists of 5,216 training images and 624 testing images categorized into normal and pneumonia cases. Data augmentation techniques, including rotation, normalization, and shear, were applied to improve training efficiency. The DenseNet models were pre-trained on ImageNet and fine-tuned by adding fully connected layers with 256 neurons and sigmoid activation. The models were trained for 20 epochs using the Adam optimizer and binary cross-entropy loss function. Performance evaluation revealed that DenseNet201 outperformed the other models, achieving a precision of 0.99 and a recall of 0.61 for normal cases (F1-score of 0.75) and a precision of 0.81 with a recall of 0.99 for pneumonia cases (F1-score of 0.89). These findings demonstrate that DenseNet201 provides a reliable and effective solution for automated pneumonia detection, offering improved diagnostic efficiency and accuracy compared to traditional methods.
SCADA Learning Trainer Development for Vocational High School Electrical Engineering Teachers in Semarang Supraptono, Eko; Wibowo, Apriansyah; Andrasto, Tatyantoro; Arief, Ulfah Mediaty; Sukamta, Sri; Ekarini, Fitria; Baskoro, Aldo Luhung; Qunefi, Fuat; Mubarak, Raihan Fa'iq; Fathimah, Aisya
Jurnal Penelitian Pendidikan Vol. 42 No. 1 (2025): April 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/aa21hb05

Abstract

One of the most important things that support an effective learning process is the ability of educators to create learning media. The ability to make trainers or teaching aids that support theoretical and practical learning is very important, especially for Vocational High Schools (SMK) teachers in electrical engineering. The purpose of this community service project is to prepare electrical engineering teachers to make Supervisory Control and Data Acquisition (SCADA) trainers suitable for use in the Solar Power Plant (PLTS) curriculum. Fifty teachers from Nurul Baqi Vocational High Schools in Semarang participated in the training for 40 lesson hours delivered with a Project Based Learning (PjBL) approach. The training results showed an increased understanding and application of the SCADA system by the participants, especially in terms of measuring current, voltage, solar radiation, temperature, as well as.
Integrasi Cron Job untuk Otomatisasi Pengolahan dan Transfer Data pada Sistem Cloud-Fog Wibowo, Apriansyah; Fathimah, Aisya; Aprilianto, Rizky Ajie; Waskito, Deswal
Edu Elektrika Journal Vol. 12 No. 2 (2024)
Publisher : LPPM Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/2k6j4w06

Abstract

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
Co-Authors Aprilianto, Rizky Ajie Baskoro, Aldo Luhung Ekarini, Fitria Eko Supraptono Mubarak, Raihan Fa'iq Qunefi, Fuat Rochim, Febry Putra Sri Sukamta Tatyantoro Andrasto Ulfah Mediaty Arief Wafi, Ahmad Zein Al Waskito, Deswal Wibowo, Apriansyah