Journal of Computer Science and Informatics Engineering (J-Cosine)

Comparative Analysis of the Performance of Linear Quadratic Regulator and State Feedback Control on DC-DC Shunt Boost Converter

I Ketut Wiryajati, Ade Safitra, Muh. Izzul Islam — Sun, 29 Dec 2024 02:39:03 +0800

In recent years, DC-DC converters, particularly shunt boost types, have become essential components in power systems, especially for applications requiring efficient voltage and current regulation. Proper control of these converters is crucial for ensuring optimal performance. This study analyzes two commonly used control methods: Linear Quadratic Regulator (LQR) and State Feedback Control for shunt boost DC-DC converters. LQR is known for its ability to minimize a cost function and optimize system performance, while State Feedback Control offers a simpler yet effective approach to system state control. Through MATLAB/Simulink-based simulations, this study evaluates the performance of both methods in terms of transient response, stability, and energy efficiency. The results show that LQR offers faster and more stable responses compared to State Feedback Control. However, the complexity of LQR in implementation makes it less practical than State Feedback Control, which is easier to apply in real-time applications

Server Performance Analysis Against Distributed Denial of Service (DDoS) Attacks (Case Study: Mataram University Academic Information System)

Rafli Gunawan Hadi, Ahmad Zafrullah Mardiansyah, Raphael Bianco Huwae — Sun, 29 Dec 2024 13:54:55 +0800

Information technology advancement has significantly impacted Indonesia's education sector, including the University of Mataram. The university relies on the Sistem Informasi Akademik (SIA) for various academic activities. However, increasing users has led to challenges such as excessive traffic and server downtime. This research analyzes the impact of DDoS attacks on the SIA server's performance and utilizes Zabbix for server monitoring. The results show that DDoS attacks mainly burden the network traffic component, with a significant increase compared to normal conditions. CPU and RAM resources also increased but less significantly. Monitoring with Zabbix improves the efficiency of performance management, enabling real-time monitoring and easy-to-understand visualization. The findings help administrators identify the need to increase server capacity and implement DDoS detection and mitigation systems to maintain the quality and availability of academic services.

Android Educational Game for Pancasila and Civic Education Learning

Haris Abdullah Fasha, Santi Ika Murpratiwi, Moh. Ali Albar, Ni Luh Putu Agetania — Sun, 29 Dec 2024 14:47:21 +0800

In the digital era, traditional teaching methods often fail to engage students, especially in Pancasila and Civic Education. Lack of understanding of Pancasila values leads to declines in religious, humanitarian, and unity values among students, evidenced by frequent fights and bullying. To address this, an interactive Android-based game, "Cerdas Pancasila," was developed with quizzes and puzzles to enhance students' understanding of Pancasila values. Using the Digital Game-Based Learning (DGBL) method, the study included five phases: analysis, design, development, quality assurance, and implementation. The analysis phase identified the need for engaging educational media for students aged 9 to 12. The design phase outlined instructional goals, and the development phase used Android Studio with Kotlin and Java to create the game prototype, which was distributed as a .apk file in the implementation phase. Quality assurance involved alpha and beta testing, resulting in a 90.2% acceptance rate, with 17 out of 18 students stating the game increased their knowledge about Pancasila.

Design and Development of a Website-Based “Agriplan” Information System to Help Plant Care

Wila Marviani BJ, Santi Ika Murpratiwi, Dwi Ratnasari, Herliana Rosika — Sun, 29 Dec 2024 15:45:50 +0800

Agriculture has been an important part of human life for a long time, with farmers as the main drivers. Farmers often face the challenge of increasing land conversion, especially in West Nusa Tenggara (NTB) with population growth and the need for land for housing, offices, and government facilities. As a result, every year around 10,000 hectares of agricultural land in NTB shrinks from a total of 270,000 hectares, causing an increase in raw material prices, economic decline and loss of farmer jobs. This research aims to develop a Website-Based “Agriplan” Information System to Assist Plant Care, to support the management of narrow agricultural land with access to agricultural information and solutions. Development using Personal Extreme Programming method, with a qualitative approach through observation and interviews, as well as Black Box Testing and User Acceptance Testing. The test results show that 90.04% of this system is effective in supporting sustainable agriculture.

Android-Based Porang Cultivation Application Design for North Lombok District Porang Farmers

Sun, 29 Dec 2024 02:18:35 +0800

Porang is a plant that has the potential to be developed as an export commodity as a food ingredient or industrial ingredient (i.e., food, cosmetics, medicine, or health). North lombok regency location is one of the districts in ntb that currently has potential for developing porang farming businesses. Porang farming is currently carried out in 2 subdistricts, namely gangga and bayan, and has 800 farmer members with a land area of 1000 ha. Having an android-based application will help with the management of farmer administration. Based on the results of black-box testing, all features were confirmed to function properly. Additionally, the System Usability Scale (SUS) evaluation involving 30 users yielded an average score of 74.16, placing it within the acceptable category).

Enhancing Robot Autonomy: Path Planning via Hand-Drawn Sketches and Computer Vision Integration

Mohamedalmogtaba Abdelrahman, Mietra Anggara — Sun, 29 Dec 2024 02:15:14 +0800

This investigation delves into an advanced method for enhancing robot autonomy by integrating hand-drawn sketches with computer vision systems to optimize route planning effectively. The procedure starts with the capture of image data, which is then processed through multiple stages until the sketches are turned into exact coordinates that guide a robot. Canny edge detection, a specific computer vision technique, is employed to detect the edges of the drawn lines, facilitating precise and dependable navigation for autonomous robot operations. Initially, the image is subjected to noise reduction and edge enhancement before converting the sketch lines into Cartesian coordinates. This step is succeeded by point filtering and ordering to ascertain accurate path adherence by the robot, with no coordinates overlooked. Scale adjustments are also made to match digital image coordinates with the real-world setting, thereby improving the robot’s interaction based on the received visual inputs. The program has been tested in Robotic Operating System 2 (ROS) using MoveIt2 and also in RoboDK software, where it was used to verify the results. It is important to note that path planning, which focuses on determining an optimal route from start to finish, is a subset of motion planning, which also considers the dynamics and kinematics of the robot's movement along that route.

Classification of Local Fruit Types using Convolutional Neural Network Method (Study Case: Lombok Island)

Moh. Azzam Al Husaini, Ario Yudo Husodo, Fitri Bimantoro — Sun, 29 Dec 2024 02:24:56 +0800

Indonesia, with its natural beauty and abundant resources, has significant potential for producing food and horticultural crops, particularly on Lombok Island, West Nusa Tenggara. This region is crucial in supplying tropical fruits such as Mangosteen, Pisang Kepok, and Rambutan Lebak Bulus. However, the agricultural sector in NTB faces challenges in post-harvest handling, especially in classifying fruit ripeness, impacting distribution and supply sustainability. To address this, researchers developed a fruit classification model using digital image processing with the Convolutional Neural Network (CNN) method. This model serves as a preliminary step before creating a fruit maturity classification model. Evaluation results showed that the RGB format model achieved 95% accuracy, while the HSV format reached 97%. Comparing three models in HSV format revealed: the proposed model (0.97), MobileNetV2 (0.96), and ResNet50 (0.97). These results indicate that implementing this model could enhance post-harvest efficiency in NTB, ensuring better fruit supply management.

Perancangan Mesin Klasifikasi Menggunakan Particle Swarm Optimization

Made Agus Dwiputra, I Gede Pasek Suta Wijaya, Ramaditia Dwiyansaputra — Sun, 29 Dec 2024 14:18:46 +0800

Designing an effective classification engine is very important in various pattern recognition and machine learning applications. In this research, the Particle Swarm Optimization (PSO) algorithm is applied for the development of classification engines on various datasets. PSO is a population-based optimization method inspired by the behavior of flocks of birds or fish, which is effectively used to find optimal solutions in large search spaces. This research aims to develop a classification model by using Particle Swarm Optimization (PSO) as a training element to determine weights and biases. To test the performance on several different datasets, namely on a dummy multi-class dataset, Sasak Aksara image dataset, and the well-known Iris dataset. In the Sasak Aksara data, Discrete Cosine Trasnform (DCT) is used as feature extraction with the aim of reducing computation time. The results show that PSO can be used in the implementation of several datasets used, in the classification of dummy data, iris data, and Sasak Aksara image data. The model achieved 100% accuracy, precision, recall, and F1-Score on dummy data and iris data. However, on the Sasak Aksara image dataset, the performance of the model decreased with accuracy only reaching 65%, precision 50%, recall 32%, and F1-Score 39%. This research contributes in demonstrating the effectiveness of PSO in optimizing Perceptron models on simpler datasets and highlights the need for further development to handle more complex datasets.

Comparison of ARIMA and Machine Learning Methods for Predicting Urban Land Surface Temperature in Jakarta

Sun, 29 Dec 2024 15:26:30 +0800

Climate change is a global challenge that requires serious attention from various parties, including the government. The existence of surface temperature and various other parameters is certainly closely related to climate change. In this context, this study was conducted to identify the best model in predicting urban land surface temperature in the Jakarta area, as one of the steps to understand and deal with the impacts of climate change. The research data used comes from MERRA-2, NASA, which provides datasets for various climate analyses. A comparison of ARIMA, SVR, LSTM, and ANN methods was conducted to evaluate the performance of each model in forecasting land surface temperature. The results show that the Long-Short Term Memory (LSTM) model provides the best performance with MAPE and values of 0.8381 and 0.8628. This model has an advantage over other models because it can remember various information that has been stored for a long period of time and can delete irrelevant information. This shows that LSTM is effective in capturing the pattern and variability of the Earth's surface temperature in the Jakarta area. Based on these findings, the government is expected to take concrete steps to address the impacts of climate change, especially issues related to increasing urban land temperature in Jakarta, such as reducing the use of private vehicles and switching to public transportation, expanding green open space, and relocating residents to reduce density.

Boosting Time Efficiency in Helmeted Face Recognition with Transfer Learning and k-NN

Sun, 29 Dec 2024 15:34:17 +0800

This research aims to optimize the retraining process of face recognition systems for motor vehicle parking areas using Transfer Learning and k-nearest Neighbors (k-NN). The system enhances efficiency by retraining only on new classes, identified by checking the encoding file, allowing it to skip pre-existing classes, thus significantly improving retraining speed. Face data from 100 individuals (2725 images) was collected and augmented, with the VGG16 architecture used to generate face embeddings through Transfer Learning. Achieving 95.62% accuracy, with a precision, recall, and F1-score of 0.96 at k=1, the system demonstrates high accuracy and speed in identifying registered faces while effectively rejecting unknown ones. These optimizations greatly increase the feasibility of face recognition in parking security systems, making them more responsive to new data. Future research should focus on increasing dataset diversity and exploring more complex conditions, such as varied lighting, to enhance system performance and robustness further.