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Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Published by Universitas Malikussaleh
ISSN : -     EISSN : 26567520     DOI : -
Core Subject : Science, Engineering,
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering
The "Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)" is a scientific publication that compiles innovative works from researchers, academics, and practitioners in the field of multidisciplinary engineering. This proceeding serves as a platform to present cutting-edge research, studies, and discoveries shared during the ICOMDEN forum, organized by the international engineering community. The proceedings cover a wide range of disciplines in engineering, including but not limited to: Mechanical Engineering, Civil Engineering, Electrical and Electronics Engineering, Computer Science and Software Engineering, Materials Engineering, Industrial Engineering, Environmental Engineering, and other related fields. Each paper published in this proceeding undergoes a rigorous peer-review process to ensure high scientific quality and impactful contributions. By integrating perspectives from various engineering disciplines, the proceedings aim to foster cross-disciplinary collaboration and provide innovative solutions to complex challenges in the field of engineering. The ICOMDEN Proceedings highlight research and technological advancements relevant to industry and society, promoting the application of sustainable engineering practices. This publication is intended to be a key reference for researchers, students, and engineering professionals to expand their knowledge and generate new ideas in addressing global challenges in engineering.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 119 Documents
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Analysis of Energy Harvesting Systems from RF to Dc Power Muhammad Fitra Zambak; Ar Ridho; Rohana Rohana; Suwarno4 Suwarno
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

Abstract Harvesting energy system from RF signals has great potential as an alternative energy source. This research focuses on the performance of an energy harvesting system that converts RF signals into DC Power using components such as Schottky BAT-17 diodes and MKM 100V 1nF capacitors. The main components used in this matching network are a 100 Ohm resistor, 100nH inductor, and 0.33pF capacitor. The 100 Ohm resistor was selected based on impedance analysis to achieve good matching, reduce signal reflections, and increase power transfer efficiency. A 100nH inductor is used to match impedance and filter out unwanted frequencies, chosen because this value provides appropriate inductance at the target frequency. Tests were carried out at distances of 1m, 3m and 5m with Vpp results of 30.4 mV at distances of 1m and 3m, and 45.6 mV at a distance of 5m. The obtained voltage decreases with increasing distance: 3.35 V at 1m, 3.19 V at 3m, and 2.85 V at 5m. Energy conversion efficiency also decreases with increasing distance. In the long term, performance shows good stability even though there is a decrease in voltage and efficiency. Technical obstacles such as signal degradation and efficiency can be overcome by network matching optimization.
Development of Portable IoT-Based Fish Pond to Enhance Freshwater Aquaculture Efficiency Rifkial Iqwal; M Ishlah Buana Angkasa; Nazwa Aulia; Subhan Hartanto; Tejas Shinde; Muhammad Fikry; Zara Yunizar
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

This paper presents the development of iPooL, a portable Internet of Things (IoT)-based fish pond system designed to optimize freshwater fish farming, particularly in resource-constrained and urban environments. By integrating real-time monitoring of essential water parameters—such as pH, temperature, dissolved oxygen, and ammonia levels—iPooL ensures that optimal environmental conditions are maintained for fish health and growth. The system employs IoT sensors connected to an ESP32 microcontroller, which processes and transmits data to a cloud platform, enabling farmers to receive real-time alerts and manage their ponds via a mobile app. Field trials demonstrated that the iPooL system reduces fish mortality by 20% and improves fish growth rates by maintaining stable water conditions. Additionally, the automation of feeding schedules and water management reduces operational costs, particularly in labor and feed, resulting in a 30% increase in profitability. With an estimated return on investment (ROI) within one year, iPooL offers a cost-effective solution for both small- and medium-scale fish farmers. The system also promotes environmental sustainability by optimizing water usage and reducing the need for chemical additives. Its portability allows fish farming in non-traditional environments, such as urban rooftops, contributing to decentralized food production and reducing the environmental impact of transporting fish to urban markets. iPooL’s scalability, combined with future integration of artificial intelligence and renewable energy sources, positions it as a transformative tool for the aquaculture industry, supporting both economic development and sustainable farming practices.
Home Assistant With IoT Smart Solution For Smart Home Sukma Rizki; Muhammad Fikry; M Ishlah Buana Angkasa; Fajar Rivaldi Chan
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

The advent of Internet of Things (IoT) technology has revolutionized various aspects of everyday life, particularly within the home environment. IoT-powered home assistants represent one of the primary implementations, offering intelligent automation and control solutions that enhance the modern home experience. This paper explores the implementation of IoT-based home assistants to improve convenience, security, and energy efficiency in smart homes. In addition, the challenges and future directions for the development of this technology are examined, with a focus on key areas such as device interoperability, data privacy and security, and user experience optimization. As demand for smart home solutions continues to rise, the integration of cloud computing, artificial intelligence (AI), and advanced communication protocols will further drive innovation in this field.
The Analysis of the Implementation of EMS ISO 14001:2015 Based on the Global Environmental Management Initiative at PT Pertamina Hulu Rokan Zone I Pangkalan Susu Field Fattahillah Harahap; Muhammad Zakaria
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

This research aims to analyze the implementation of the ISO 14001:2015 Environmental Management System at PT Pertamina Hulu Rokan Zone 1 Pangkalan Susu Field using the Global Environmental Management Initiative (GEMI) method. Evaluation was carried out using the GEMI checklist which includes the 7 main clauses of ISO 14001:2015 and GAP analysis. Data collection was carried out for 1 month through observation, interviews and assessments. The research results show that the implementation of the environmental management system in companies reached an average of 85.9%, with details: organizational context clause (85%), leadership (89.4%), planning (84.2%), support (78, 9%), operations (88.4%), performance evaluation (86.7%), and improvement (88.8%). Overall, the implementation of the environmental management system at PT Pertamina Hulu Rokan Zone 1 Pangkalan Susu Field is included in the good category, although there are still several aspects that require improvement, especially the support clause which received the lowest score.
Evaluation of the Effectiveness of Groin Buildings Against Erosion on Ujong Blang Beach Fran Cahya; Teuku Mudi Hafli; Fasdarsyah; Fadhliani; Nanda Savira Ersa; Yulia Prestika
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

Ujong Blang Beach is a dynamic area characterized by a variety of ecosystems. It serves a variety of purposes, including housing, tourism, and fishing. However, the coastline at Ujong Blang Beach, Lhokseumawe, is experiencing changes due to natural factors such as waves and sediment transport, causing erosion. This research aims to evaluate the erosivity and effectiveness of groyne structures in preventing erosion at Ujong Blang Beach. The methodology used involves 3D Delft numerical simulation using a grid size of 10 m x 10 m and a time period of 0.4 minutes. Input data for the Delft 3D simulation includes topography, bathymetry, tidal patterns, wave action, morphological factors, and sediment data. The simulation results show that Ujong Blang Beach experienced erosion, specifically, Region I Segment experienced erosion of around -40,277 meters, Segment II experienced erosion of around -29,122 meters, and Segment III experienced erosion of around -18,403 meters.
A Robust Approach to Student Attendance Using Web-Based Facial Recognition Irfan Sahputra; Muhammad Fikry; Kurniawati Kurniawati
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

In this paper, we introduce an innovative student attendance recording system that utilizes computer vision and machine learning to improve attendance management in educational settings. By employing YOLOv8 for real-time face detection and MobileNetV2 for face recognition, the system achieves high accuracy and efficiency across various classroom conditions. Rigorous testing in diverse lighting environments and varying student densities demonstrated a peak recognition accuracy of 98% in well-lit conditions, with an average face detection time of under one second. This system offers a more robust, efficient, and scalable solution than traditional manual attendance methods, addressing common limitations in accuracy and reliability. Future work will target optimization under low-light conditions, enhancing its applicability in real-world scenarios.
Application of the K-Nearest Neighbor Method for Classification of Leiomyoma (Myoma) Selly Alfika selly; Mukti Qamal; Zahratul Fitri
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

Information technology is very important in the process of human life. Along with the growth opens up opportunities for relatively large data growth, one of which is hospitals. Mioma is a disease that continues to increase and has a major impact on health female reproduction. Myoma is a benign tumor that grows in or around the uterus. Mioma is a medical condition experienced by women of all ages, but is often experienced by women who have entered pre-menopause, myoma is also the second benign tumor in Indonesia by age range sufferers 20-50 years old. Sufferers rarely cause specific symptoms so women are rarely aware of them the presence of myoma growth in their uterus. This research classifies patient data with a purpose to classify types of myoma disease using the K-Nearest Neighbor method. There are several The attributes used in this research are diastolic blood pressure, systolic blood pressure, hemoglobin, ever been pregnant, symptoms 1 and symptom 2. The data used for this research amounted to 288 myoma patient data which will be divided into 2, namely 70% training data and 30% testing data. Then it is divided into 3 classes, namely intramural myoma, submucosal myoma, and subserosal myoma. Results of myoma classification using the K-Nearest algorithm Neighbor at Aceh Tamiang Regional Hospital used 87 test data or patient data, indicating people with the disease Intramural myoma are more numerous with 48 data, subserosal myomas 15 data and for subserosal myomas there are 25 data with a high accuracy rate of 93%.
Full Automation and Control System Based on IoT in a Greenhouse (Case Study: Faculty of Agriculture, Malikussaleh University M Ishlah Buana Angkasa; Rizal Tjut Adek; Said Fadlan Anshari
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

This study aims to develop a full automation and control system based on the Internet of Things (IoT), implemented in a greenhouse to support real-time monitoring of temperature, soil moisture, and water levels in the tank. The system is designed using the ESP32-WROOM microcontroller as the core for data communication with various sensors, including the DHT22 sensor for air temperature and humidity, a soil moisture sensor for soil moisture, and a JSN-SR04T sensor for water level. The developed system connects to Firebase as a cloud data platform, enabling remote monitoring via a specially designed mobile application. Testing shows that the system works efficiently in supporting automated plant growth, reducing manual intervention, and increasing productivity. This system allows students and faculty in the Faculty of Agriculture at Malikussaleh University to more easily conduct research and teaching activities related to modern agricultural technology.
Face Recognition System For Student Identification Using VGG16 Convolutional Neural Network Chrisnata Manihuruk; Muhammad Fikry; Hafizh Al Kautsar Aidilof
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

In this paper, we present a robust facial recognition system designed to identify students at Department of Informatics in Universitas Malikussaleh using a Convolutional Neural Network (CNN) algorithm, specifically the VGG16 architecture. The advancement of information technology and machine learning has significantly improved facial recognition capabilities, establishing it as a reliable alternative to traditional identification methods such as fingerprinting and iris scanning. Our approach leverages a diverse dataset captured from five different angles, enhancing the representation of facial features and improving model training. The system development comprises several critical stages, including image acquisition, preprocessing, model training with training and validation data, and performance evaluation. Experimental results indicate that the CNN model achieves an impressive accuracy of 99.09% on training data and 100% on both validation and testing datasets. These findings affirm the model's high classification accuracy across the tested classes, underscoring the effectiveness of the VGG16-based CNN in facial recognition applications. The implications of this study suggest that the developed system can significantly enhance digital attendance and security systems, catering to the growing demand for reliable AI-driven security technologies in contemporary society. We anticipate that with its promising outcomes, this system can be implemented on a larger scale, contributing to the ongoing advancement of AI-based security solutions.
Algorithm Implementation C4.5 For Classification Food Menu to Prevent Stunting in Children Rizki Fadhilah Ramadhani; Bustami; Zahratul Fitri
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

Stunting in childhood is one of the most significant obstacles to human development and globally affects about 162 million children under five. One effort to prevent stunting is a program to increase the nutritional intake of the community, especially children under five, by providing supplementary food (PMT). Classification is one of the data processing techniques that can be used in this process. The results obtained from the study show that the designed system can input training data and data for classification so that the health centre and guardians can determine the good and bad food menus according to the existing data of toddlers. Based on the results of testing with training data and testing data with a ratio of 80:20 from a dataset of 200 data, namely 160 training data, and 40 test data using the C4.5 algorithm obtained in dataset 1 obtained an accuracy value of 82,5%, precision value of 0.96, recall value of 0,8 and F1-score of 0,87273, then in dataset 2 obtained an accuracy value of 72,5%, precision value 0,75, recall value 0,84 and F1-score value 0,79245.

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