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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics CommIT (Communication & Information Technology) International Journal of Artificial Intelligence Research ComTech: Computer, Mathematics and Engineering Applications Jurnal Cahaya Mandalika
Nico Surantha, Nico
Bina Nusantara University
Humanities Computer Science & IT Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mathematics

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Hydroponic Nutrient Control System Based on Internet of Things Herman, Herman; Adidrana, Demi; Surantha, Nico; Suharjito, Suharjito
CommIT (Communication and Information Technology) Journal Vol 13, No 2 (2019): CommIT Vol. 13 No. 2 Tahun 2019
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v13i2.6016

Abstract

The human population significantly increases in crowded urban areas. It causes a reduction of available farming land. Therefore, a landless planting method is needed to supply the food for society. Hydroponics is one of the solutions for gardening methods without using soil. It uses nutrient-enriched mineral water as a nutrition solution for plant growth. Traditionally, hydroponic farming is conducted manually by monitoring the nutrition such as acidity or basicity (pH), the value of Total Dissolved Solids (TDS), Electrical Conductivity (EC), and nutrient temperature. In this research, the researchers propose a system that measures pH, TDS, and nutrient temperature values in the Nutrient Film Technique (NFT) technique using a couple of sensors. The researchers use lettuce as an object of experiment and apply the k-Nearest Neighbor (k-NN) algorithm to predict the classification of nutrient conditions. The result of prediction is used to provide a command to the microcontroller to turn on or off the nutrition controller actuators simultaneously at a time. The experiment result shows that the proposed k-NN algorithm achieves 93.3% accuracy when it sets to k = 5.
Portable smart attendance system on Jetson Nano Yose, Edward; Victor, Victor; Surantha, Nico
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.6061

Abstract

The masked face recognition-based attendance management system is an important biometric-based attendance tracking solution, especially in light of the COVID-19 pandemic. Despite the use of various methods and techniques for face detection and recognition, there currently needs to be a system that can accurately recognize individuals while they are wearing a mask. This system has been designed to overcome the challenges of widespread mask use, impacting the effectiveness of traditional face recognition-based attendance systems. The proposed system uses an innovative method that recognizes individuals even while wearing a mask without the need for removal. With its high compatibility and real-time operation, it can be easily integrated into schools and workplaces through an embedded system like the Jetson Nano or conventional computers executing attendance applications. This innovative approach and its compatibility make it a desirable solution for organizations looking to improve their attendance-tracking process. The Experimental results indicates using maximum resources possible the execution time needed on Jetson Nano is 15 to 22 seconds and 14 to 18 seconds respectively and the average frame capture if there are at least one face detected on Jetson Nano is 3-4 frames.
Smart Aquaculture Design for Vannamei Shrimp Farming Based on Quality Function Development Setiawan, Budi; Surantha, Nico
CommIT (Communication and Information Technology) Journal Vol. 18 No. 2 (2024): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v18i2.9466

Abstract

In the fishery industry, Indonesia’s large water area has the potential for developing and cultivating fisheries such as vannamei shrimp. For this reason, aquaculture, particularly vannamei shrimp farming, can play a crucial role in Indonesia’s economy and food supply. However, challenges such as fluctuating water quality, disease outbreaks, turbidity levels, and irregular shrimp feeding schedules in ponds can affect the productivity and sustainability of shrimp farming. The smart aquaculture system integrates technologies, such as IoT-based sensors, automated feeding mechanisms, and real-time water quality monitoring to optimize the farming process. The research proposes a smart aquaculture design for vannamei shrimp farming based on the Quality Function Development (QFD) method. It starts by creating questionnaires to identify stakeholders’ level of interest. The questionnaire results are used as a reference for system redesign using the QFD method to improve the quality and quantity of shrimp harvest, cultivating effectively and efficiently and helping and facilitating the supervision of pond managers on pond water quality, feeding, and feed availability. The result highlights the application of QFD in creating a tailored, technology-driven solution that supports better decision-making, resource optimization, and improved shrimp health. The system reduces human error, enhances farm management, and promotes higher yields by providing real-time data and automation. The evaluation results show that the proposed design can achieve high stakeholder satisfaction. It also achieves better scores compared to the other two competitor’s designs.
CNN-GRU for Drowsiness Detection from Electrocardiogram Signal Hendratno, Setiawan; Surantha, Nico
ComTech: Computer, Mathematics and Engineering Applications Vol. 16 No. 2 (2025): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v16i2.12755

Abstract

Drowsiness is a problem that needs to be addressed to improve road safety. To minimize this safety issue, driving-monitoring systems have been implemented in current car models, and electrocardiography (ECG) is one of the most commonly used driving monitoring techniques. ECG data are modeled using a deep neural network, including a Bidirectional Gated Recurrent Unit (Bi-GRU). However, the accuracy for classifying Wake-Sleep is under 80% and Wake-NREM-REM reaches less than 68%. To address this issue, ECG data from the MESA and SHHS datasets are modeled using a combination of a Convolutional Neural Network (CNN) and a Bi-GRU, referred to as CNN-GRU. This model incorporated Batch Normalization and RMSProp to achieve improved accuracy in classifying drivers' conditions. It operates in two computing sectors: cloud computing (Google Colaboratory, also known as Colab) and edge computing (utilizing an AMD Ryzen 5 4600H processor laptop). Those computing sectors focused on a case where no internet connectivity occurred to process the classification. Those classifications achieved accuracy rates of 82.88% and 81.78% for Wake-Sleep classification in cloud- and edge-computing, respectively. Additionally, it achieved 71.01% (Colab) and 68.85% (edge-computing) accuracy in Wake-NREM-REM classification. This result indicates that CNN-GRU achieved better performance, surpassing the previous Bi-GRU model, which only achieved 80.42% (Colab) and 76.2% (edge-computing) for Wake-Sleep, and 68.85% (Colab) and 66.43% for Wake-NREM-REM.
IoT Based for Monitoring Power Consumption of Electronic Home Appliances Wulandari, Iswahyuni; Surantha, Nico
International Journal of Artificial Intelligence Research Vol 8, No 1.1 (2024)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i1.1.1355

Abstract

In daily life in various activities, including in households and industry. The Ministry of Energy and Mineral Resources noted that electricity consumption per capita in Indonesia will be 1,173 kWh in 2022. This number has increased by 4.45% from the previous year which was 1,123 kWh. The use of fossil energy sources are increasing as demand increases, making reserves of fossil energy sources increasingly depleted. Nowadays, some measuring tools that sold commercially require wiring process to the MCB, so there is potential danger of electric shock and also the tools can’t store historical data that so the data can’t be analyze. Monitoring electricity usage can help users to alert about household appliances that have been accidentally left on. This is become the research topic by implemented monitoring electrical power consumption in household electronic equipment and storing historical data so users can analyze which electronic equipment with the largest power consumption based on real time and historical data displayed on a Web-based dashboard. Data was taken from testing using the SCT-013 5A 1V Current Transformer Sensor and Arduino Uno R3 microcontroller. Electronic equipment that will be monitored are air conditioner, water heater, washing machine, rice cooker, refrigerator, laptop, dispenser, iron, fan, and lamp. User able to monitor the amount of electrical power consumption used in various electronic equipment and control lamp via Web-based Dashboard.
DESIGN OF DISASTER RECOVERY PLAN BASED ON FRAMEWORK NIST 800-34 : CASE STUDY AT PT XYZ OF INDONESIA Waluya, Dimas Prihadi; Surantha, Nico
Jurnal Cahaya Mandalika ISSN 2721-4796 (online) Vol. 3 No. 3 (2022)
Publisher : Institut Penelitian Dan Pengambangan Mandalika Indonesia (IP2MI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/jcm.v3i3.1410

Abstract

The purpose of this study to identify Risk assessment in information systems at PT XYZ using the method Octave Allegro and develop Disaster Recovery Plan on the information system at PT XYZ using the NIST 800:34 framework. The downtime on information system that occurred resulted in the disruption of business processes and operations of PT XYZ which currently has 7 main information systems to support the company's business which if not addressed can result in a decrease in user confidence in the company. In this study, a risk assessment will be carried out on information assets owned by PT XYZ using the Octave Allegro method to identify threats to information assets along with the order of threat priority. Then the design of the Disaster Recovery Plan on the information system uses the NIST 800:34 framework containing recovery steps, backup strategies and RPO RTO which are used as priority parameters for information system recovery.
Enhancing cross-site scripting attack detection by using FastText as word embeddings and long-short term memory Mashuri, Muhammad Alkhairi; Surantha, Nico
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4923-4932

Abstract

Cross-site scripting (XSS) is one of the dangerous cyber-attacks and the number of attacks continues to increase. This study takes a new approach to detect attacks by utilizing FastText as word embedding, and long-short term memory (LSTM), which aims to improve the performance of deep learning. This method is proposed to capture the broader meaning and context of the data used, leading to better feature extraction and model performance. This study not only improves the detection of XSS attacks, but also highlights the potential for better text processing techniques. The results obtained showing this method achieves higher results than other methods, with an accuracy of 99.89%.
Co-Authors Budi Setiawan Demi Adidrana Hendratno, Setiawan Mashuri, Muhammad Alkhairi Suharjito Suharjito, Suharjito Victor Victor Waluya, Dimas Prihadi Wulandari, Iswahyuni Yose, Edward Yundari, Yundari