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All Journal PROSIDING SEMINAR NASIONAL JSI: Jurnal Sistem Informasi (E-Journal) ICON-CSE PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Jurnal ULTIMATICS Jurnal Ilmiah Media Sisfo Jurnal Sistem Komputer & Kecerdasan Buatan Akademika International Journal of Mechanical, Industrial and Control Systems Engineering Green Engineering: Journal of Engineering and Applied Science
Erick Fernando
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Library & Information Science Other

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RANCANG MODEL FRAME MULTICOPTER: LITERATURE REVIEW Erick Fernando; Derist Touriono
Jurnal PROCESSOR Vol 11 No 2 (2016): Processor
Publisher : LPPM Universitas Dinamika Bangsa

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Abstract

Penelitian ini memaparkan Model-model multicopter yang dapat digunakan untuk perancangan multicopter. Penelitian ini menggunakan metode literature review. Literature didapat dengan melakukan penelusuran di berbagai artikel ilmiah yang terbit pada jurnal ilmiah bereputasi internasional, penelitian ini mereview dan menganalisis model-model multicopter. Artikel yang berkaitan dengan “model frame multicopter” dikumpulkan melalui database jurnal online seperti: Google Scholar, ProQuest, EBSCOhost, dan IEEEexplore. “model multicopter” dan “design multicopter” digunakan sebagai kata kunci dalam pencarian artikel. Artikel tersebut kemudian direview dan dianalisis untuk kemudian di paparkan berkaitan dengan model – model frame multicopter. Penelitian ini menemukan bahwa model multicopter untuk merancang pesawat multicopter ada 4 model multicopter yaitu: tricopter, quadcopter, octacopter, hexacopter. Multicopter tipe tricopter merupakan multicopter yang sangat menghemat daya arus dalam pengguna sedangkan Multicopter dengan tipe hexacopter dan octacopter merupakan multicopter yang memiliki stabilitas tinggi dalam penerbangan.
EXPERIMENTAL MODEL NAS DAN CLOUD DRIVE BERBASISKAN RASPBERRY-PI Erick Fernando; Derist Touriono
Jurnal PROCESSOR Vol 11 No 1 (2016): Processor
Publisher : LPPM Universitas Dinamika Bangsa

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Network Attached Storage (NAS) adalah tingkat penyimpanan data pada komputer yang terhubung ke jaringan komputer yang menyediakan akses data ke klien yang berbeda. Penelitian ini menghasilkan NAS yang dibangun dengan perangkat mini pc berbasis raspberry pi yang berfungsi sebagai server penyimpanan data terpusat (file server) yang akan diberikan kepada komputer klien pada lingkungan data yang memiliki kapasitas dalam jumlah besar. Sehingga dengan menggunakan NAS dapat memanajemen penyimpanan dan meningkatkan keandalan, kinerja dan efisiensi jaringan, serta meningkatkan produktivitas secara keseluruhan organisasi dalam kebutuhan data
PERANCANGAN APLIKASI KOMUNIKASI PENYANDANG TUNARUNGU BERBASIS ANDROID Wendy Liga; Erick Fernando; Hendri Hendri
Jurnal PROCESSOR Vol 12 No 1 (2017): Jurnal Processor
Publisher : LPPM Universitas Dinamika Bangsa

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Tunarungu merupakan kelainan fisik yang dialami individu berupa ketidak mampuan mendengar baik sebagian maupun seluruhnya dimana penyandang tunarungu akan mengalami gangguan komunikasi secara lisan dengan orang-orang disekitarnya sehingga untuk mengatasinya penyandang tunarungu memanfaatkan bahasa isyarat untuk berkomunikasi.Akan tetapi berdasarkan hasil survey sekitar 87% dari 69 penyandang tunarungu menyatakan masih kesulitan berkomunikasi dengan orang disekitar mereka dikarenakan ketidak mampuan orang disekitar mereka menggunakan dan memahami bahasa isyarat yang mereka gunakan. Guna memecahkan masalah tersebut, penulis merancang sebuah aplikasi yang memanfaatkan Speech Recognition agar dapat membantu penyandang tunarungu untuk berkomunikasi dengan orang sekitar atau sebaliknya.Penelitian ini berhasil menghasilkan aplikasi “Deaf Communicator” yang dapat membantu komunikasi antara penyandang tunarungu dan sebaliknya.
Perancangan E-Commerce Berbasis Website Pada Toko Mirabella Batik Jambi Andi Ridho Rachman; Beny Beny; Erick Fernando
Jurnal PROCESSOR Vol 12 No 2 (2017): Processor
Publisher : LPPM Universitas Dinamika Bangsa

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Abstract

Mirabella Batik Jambi is a business engaged in the sale of Jambi typical batik with various types of materials such as atbm, silk, semi silk, sanwos and cotton. The system runs now where consumers still have to come directly to the store to purchase the product. Constraints faced by the store today is difficult to market or sell its products outside the city or region and can only market it in jambi region alone, the lack of new product information updates, and it affects the lack of maximum income store. The purpose of this study to design ansystem e-commerce to support the sales process that can make reservations online and provide information up todate.The method used is a method of analysis that is done by literature study, data collection (observation).Development methodmethod waterfall and emphasis UML. Types of e-commerce that is used is Business to Customer (B2C). This research resulted in anapplication e-commerce for Mirabella Batik Jambi stores to facilitate costumer to see the product in detail and make a reservation without having to come to the store simply by accessing the web site e-commerce store Mirabella Batik Jambi
ARSITEKTUR TEKNOLOGI WEBSERVER BERBASIS MINI PC DENGAN RASPBERRY PI Erick Fernando
JURNAL AKADEMIKA 135-138
Publisher : LP2M Universitas Nurdin Hamzah Jambi

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Abstract

Development of a website in world of the Internet requires a webserver. The Development Webserver is something which is extremely important in providing services to clients that will access a website. Construction of this webserver using a mini pc technology architecture with Raspberry Pi device. Raspberry Pi is a Mini PC-based open source operating system as an application that uses Xampp webserver. This application is an application that is very powerful and widely used by users. Webserver architecture that uses a mini pc expected one of the alternativesin development webserver. With so can mengeffisienkan and mendinamiskan use hardware devices
Design of an Autonomous Solar Powered Smart Aquaculture Monitoring System for Energy Efficiency and Environmental Impact Reduction Lukman Medriavin Silalahi; Safrizal Safrizal; Erick Fernando; Hayadi Hamuda; Ribut Julianto; Yuanita Sinatrya
Green Engineering: International Journal of Engineering and Applied Science Vol. 2 No. 2 (2025): April : Green Engineering: International Journal of Engineering and Applied Sci
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v2i2.286

Abstract

Aquaculture is a vital sector in global food production, providing essential protein sources. However, the industry faces significant challenges, including high energy consumption and environmental impact. The integration of renewable energy, particularly solar power, with automation and IoT systems offers a promising solution to enhance energy efficiency, sustainability, and productivity in aquaculture operations. This study aims to evaluate the effectiveness of solar powered autonomous systems in reducing energy usage, improving operational efficiency, and promoting environmental sustainability in aquaculture. Literature Review: Recent research has explored various technologies, such as Digital Twins (DTs) and Precision Fish Farming (PFF), which integrate IoT sensors for real time monitoring and optimization of fish farming operations. The combination of Artificial Intelligence (AI) and the Internet of Things (IoT), known as AIoT, has further advanced the industry by enabling automated decision making and predictive analytics. Solar power integration with IoT systems has been shown to significantly reduce operational costs, minimize carbon emissions, and enhance the sustainability of aquaculture practices. These advancements have the potential to address the challenges of energy consumption and environmental degradation in the industry. Materials and Method: This research utilizes a hybrid solar powered IoT system for aquaculture, integrating solar panels, IoT sensors, and automated control systems. The system monitors key water quality parameters, such as pH, dissolved oxygen, turbidity, and temperature, to maintain optimal conditions for aquatic life. Data is collected through IoT sensors and analyzed through a cloud-based platform. A pilot study is conducted on a small scale aquaculture farm to evaluate the system's performance, including energy consumption, water quality management, and fish health. Energy savings, operational efficiency, and environmental impact are assessed. Results and Discussion: The integration of solar powered IoT systems significantly reduced energy consumption compared to traditional systems, with a notable decrease in grid electricity reliance. The system successfully maintained optimal water quality conditions, enhancing fish health and growth. Solar powered systems proved reliable, even in regions with variable sunlight, and demonstrated improvements in operational efficiency through automation. The environmental benefits were evident, with a reduction in carbon emissions and lower operational costs. The study highlights the feasibility of solar powered IoT systems as a sustainable solution for modern aquaculture operations.
AI driven Circular Waste to Energy Conversion System Using Smart Thermal Monitoring and Emission Optimization for Sustainable Urban Infrastructure Kiki Ahmad Baihaqi; Krisna Widi Nugraha; Rian Ardianto; Rosyid Ridlo Al-Hakim; Riza Phahlevi Marwanto; Erick Fernando
Green Engineering: International Journal of Engineering and Applied Science Vol. 2 No. 2 (2025): April : Green Engineering: International Journal of Engineering and Applied Sci
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v2i2.289

Abstract

This study explores the integration of Artificial Intelligence (AI) with thermal optimization in Waste-to-Energy (WtE) systems to enhance both energy recovery and emission control. Introduction: The growing need for sustainable urban waste management has highlighted the importance of optimizing WtE systems. AI technologies, including machine learning and deep learning, have shown potential in improving the efficiency of WtE processes, especially in reducing emissions and enhancing energy recovery. Literature Review: Previous research indicates that AI has been successfully applied to various WtE technologies such as pyrolysis, gasification, and incineration, yet the integration of AI specifically for thermal optimization remains underexplored. Most studies focus on predictive models for emission reduction rather than real time thermal optimization. Materials and Method: The study proposes the development of an AI-driven framework that integrates real time data collection from IoT sensors, predictive modeling, and real time control algorithms. The system optimizes key parameters such as combustion temperature and fuel flow to enhance energy recovery and minimize emissions. The method includes data collection from operational WtE plants, followed by model development using machine learning algorithms. Results and Discussion: Initial simulations and pilot testing showed significant improvements in energy efficiency and emission reduction. AI-driven systems outperformed conventional WtE systems by optimizing operational parameters in real time. The study identifies gaps in AI integration for thermal optimization and suggests future research directions, including the integration of AI with smart grids and carbon credit systems for more sustainable WtE operations.
Integrated Digital Twin and Physics Informed Machine Learning Model for Real Time Performance Prediction of Industrial Mechanical Systems Irlon Irlon; Siti Shofiah; Helmi Wibowo; Erick Fernando; Genrawan Hoendarto; Mursalim Mursalim
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 2 No. 2 (2025): June :IJMICSE: International Journal of Mechanical, Industrial and Control Syst
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v2i2.404

Abstract

Background: The rapid advancement of digital technologies in the Industry 4.0 era has transformed industrial mechanical systems into highly interconnected and data driven environments through the integration of sensors, the Internet of Things (IoT), data analytics, and cyber physical systems. This increasing complexity requires more adaptive and accurate monitoring and prediction methods than conventional simulation approaches, which often face limitations in capturing real time dynamic system behavior. Objective: This study aims to develop a predictive performance model for industrial mechanical systems by integrating Digital Twin technology with Physics Informed Machine Learning in order to improve monitoring accuracy and support predictive maintenance strategies. Methods: This research adopts a data driven modeling and simulation approach by developing a digital representation of an industrial mechanical system that is connected to real time sensor data. The prediction model is constructed using a Physics Informed Neural Network (PINN), which integrates operational data with physical principles governing system dynamics. The research process includes the development of a Digital Twin model, integration of sensor data, training of the PINN model, model validation using experimental data, and evaluation of prediction performance using statistical metrics. Results: The results indicate that the integration of Digital Twin technology and PINN significantly improves the prediction accuracy of industrial mechanical system performance compared with conventional simulation methods and purely data driven machine learning models. The proposed model is capable of representing system dynamics more consistently, accurately following sensor data patterns, and providing strong potential for supporting machine condition monitoring and predictive maintenance strategies in modern industrial environments.
Co-Authors Andi Ridho Rachman Andreas Condro Bimo Baihaqi, Kiki Ahmad Beny Beny Condrobimo, A. Raharto Deddy Ackbar Rianto Derist Touriano Derist Touriono Dina Fitria Murad Fachruddin, Fachruddin Genrawan Hoendarto Hayadi Hamuda Helmi Wibowo Hendri Hendri Hetty Rohayani Irlon Irlon Johan Budiman Krisna Widi Nugraha Lukman Medriavin Silalahi M. Riza Pahlevi B MARIA BINTANG Maryandi Maryandi Muhamad Irsan Mursalim Mursalim Pandapotan s Siagian Pandapotan Siagian Pandapotan Siagian Rian Ardianto Ribut Julianto Riza Phahlevi Marwanto Rosyid Ridlo Al-Hakim Safrizal Safrizal Sandy Anugrah Setiawan Assegaf Siti Shofiah Surjandy Surjandy Wendy Liga Yuanita Sinatrya