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All Journal Jurnal Simetris Jurnal Teknik Elektro CESS (Journal of Computer Engineering, System and Science) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Techne : Jurnal Ilmiah Elektroteknika Cyber Security dan Forensik Digital (CSFD) Jurnal Elkolind : Jurnal Elektronika dan Otomasi Industri Jurnal Teknik Informatika (JUTIF) Jurnal Energi Baru dan Terbarukan Electron: Jurnal Ilmiah Teknik Elektro Sainteknol : Jurnal Sains dan Teknologi Edu Elektrika Journal Majalah Ilmiah Teknologi Elektro
Aprilianto, Rizky Ajie
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Environmental Science Other

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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
An Empirical Analysis and Benchmarking Framework for FreeRTOS on the Arduino Uno Al-Azhari, Abdurrakhman Hamid; Syah, Mario Norman; Aprilianto, Rizky Ajie
Jurnal Elektronika dan Otomasi Industri Vol. 12 No. 3 (2025): Jurnal Elkolind Vol 12 No 3 (September 2025)
Publisher : Program Studi Teknik Elektronika Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/elkolind.v12i3.8833

Abstract

The scalability of FreeRTOS makes it a popular choice for embedded systems, yet its behavior on processors with severe memory constraints remains poorly quantified. This study conducts a rigorous empirical evaluation of FreeRTOS on the Arduino Uno platform, based on the ATmega328P microcontroller with only 2KB of SRAM. We introduce a systematic benchmarking framework designed to probe the kernel's operational limits by measuring task capacity, stack depth requirements, and scheduler overhead under controlled stress conditions. Our findings reveal that heap fragmentation is the primary determinant of task capacity and precisely quantify the performance trade-offs associated with kernel configuration. The results provide a set of validated, practical guidelines for developers, enabling more reliable design choices for deploying RTOS-based solutions on resource-limited hardware commonly found in cost-sensitive IoT applications.
Design of ACS712-based Current Sensing Monitoring Evaluation for PWM Solar Charge Controller Wibowo, Apriansyah; Jayendra, I Gede Bagus; Utari, Dhea Yunita; Rasyiid, Muhammad; Aprilianto, Rizky Ajie
Sainteknol : Jurnal Sains dan Teknologi Vol. 22 No. 2 (2024): December 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sainteknol.v22i2.20604

Abstract

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.
Pengembangan Intelligent Electrocardiograph Portable untuk Pemantauan Detak Jantung: Systematic Literature Review Hardi, Septian Akbar Noor Wahyu; Aviando, Rizqi; Pribadi, Feddy Setio; Aprilianto, Rizky Ajie
CESS (Journal of Computer Engineering, System and Science) Vol. 9 No. 2 (2024): July 2024
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v9i2.59003

Abstract

Kesehatan jantung menjadi faktor penting yang harus diperhatikan, terutama pada orang yang melakukan aktivitas fisik tinggi, seperti atlet. Untuk meningkatkan identifikasi dini penyakit jantung dan mengurangi bahaya kematian mendadak, perangkat elektrokardiogram (EKG) cerdas portabel telah banyak diusulkan untuk mendeteksi aktivitas jantung secara real-time. Penelitian ini bertujuan untuk memberikan informasi tentang klasifikasi sinyal jantung dengan memanfaatkan Filter Infinite Impulse Response (IIR) untuk menghilangkan noise sinyal dan Random Forest yang berguna untuk mengkategorikan masalah jantung secara cepat dan akurat. Referensi yang dirujuk, dipetakan berdasarkan sistematic literature review menggunakan metode preferred reporting items for systematic reviews and meta-analyses (PRISMA). Berdasarkan hasil ulasan yang telah dilakukan, terbukti EKG portable dengan filter IIR terbukti mampu membersihkan sinyal yang didukung dengan algoritma Random Forest untuk klasifikasi sehingga menghasilkan tingkat akurasi yang baik.
Review on Impact of Artificial Intelligent on Efficiency and Productivity in Industrial Automation Fawwaz, Ega Nur; Setianingsih, Lita Dwi; Prabantara, Satria Krisna; Fawwaz, Fatahillah Nabil; Hidayat, Dwi Alvin; Aprilianto, Rizky Ajie; Pribadi, Feddy Setio
Majalah Ilmiah Teknologi Elektro Vol 24 No 1 (2025): ( Januari - Juni ) Majalah Ilmiah Teknologi Elektro
Publisher : Study Program of Magister Electrical Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.205.v24i01.P09

Abstract

As Industry 4.0 technologies evolve, the application of Artificial Intelligence (AI) in the manufacturing sector has become a major factor in improving operational efficiency, optimizing production processes, and reducing costs, enabling predictive analytics, data-driven maintenance, and automation of tasks that previously required human intervention. This study conducts a systematic literature review (SLR) on various AI methods applied in industrial automation, evaluates the effectiveness of their implementation, and identifies key challenges in their adoption. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Population,   Intervention, Comparison, Outcome, Context (PICOC) approaches are adopted. The sources used to search the literature included four electronic databases, comprising ScienceDirect, Taylor & Francis, Scopus, and Emerald Insight, resulting in 33 selected articles. The result shows that AI contributes significantly to improving production efficiency, but it still faces challenges in system integration, implementation costs, and workforce readiness. This study provides a comprehensive overview of the effectiveness of AI implementation in industrial automation and the challenges that need to be overcome to optimize competitiveness and production efficiency
THE ROLE OF BIG DATA ANALYTICS AND ARTIFICIAL INTELLIGENCE IN BUSINESS STRATEGY: A SYSTEMATIC REVIEW Rahmeisi, Nazli; Sachroni, Mu'alfi Fahrul Fanani; Andyanto, Yehezkiel Nesta; Aprilianto, Rizky Ajie; Pribadi, Feddy Setio
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 16 No. 2 (2025): JURNAL SIMETRIS VOLUME 16 NO 2 TAHUN 2025
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v16i2.14909

Abstract

The accelerating digital transformation across industries has intensified the need for datadriven approaches in strategic business management. This study conducts a Systematic Literature Review (SLR) to examine how Big Data Analytics (BDA) and Artificial Intelligence (AI) influence business strategy formulation, risk management, and organizational competitiveness. Guided by the PICOC framework and PRISMA 2020 protocol, 27 peer-reviewed journal articles published between 2020 and 2024 were analyzed through thematic synthesis and bibliometric visualization using VOSviewer. The results indicate that BDA and AI enhance strategic decision-making, operational efficiency, and risk mitigation through predictive insights and real-time analytics. However, their strategic integration remains limited due to socio-technical challenges such as inadequate analytical capability, weak data governance, and organizational resistance. The review highlights that the true strategic value of BDA and AI emerges when these technologies are embedded within long-term strategic planning, data governance, and sustainability frameworks, rather than treated merely as operational tools. This study contributes to strategic management literature by synthesizing cross-sectoral evidence and offering insights into how data-driven intelligence fosters long-term competitiveness and sustainable business transformation.
Co-Authors Adiastoro, Mahendra Al Kindi, Bilal Al-Azhari, Abdurrakhman Hamid Alkindi, Muhammad Faizal Ananda, Briska Putra Andyanto, Yehezkiel Nesta Anita, Nur Ariefianto, Rizki Mendung Arundaya, Adil Ati Zuhrotal Afifah, Ning Imas Aviando, Rizqi DZAKWAN, MUHAMAD AKMAL Fahreza, Rizqy Achmad Faqih, Faiq Mananul Fathimah, Aisya Fawwaz, Ega Nur Fawwaz, Fatahillah Nabil Feddy Setio Pribadi Hardi, Septian Akbar Noor Wahyu Heri Suryoatmojo Hidayat, Dwi Alvin Jayendra, I Gede Bagus Mario Norman Syah, Mario Norman Nurul'aini, Arvina Rizqi Pongoh, Arthur Gregorius Prabantara, Satria Krisna Rahmeisi, Nazli Rasyiid, Muhammad Sachroni, Mu'alfi Fahrul Fanani Setianingsih, Lita Dwi Suwito Suwito Syarifah, Dian Farah Utari, Dhea Yunita Waskito, Deswal Wibowo, Apriansyah