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Journal of Soft Computing Exploration
Published by shm publisher
ISSN : 27467686     EISSN : 27460991     DOI : -
Core Subject : Science,
Computer Science & IT
Journal of Soft Computing Exploration is a journal that publishes manuscripts of scientific research papers related to soft computing. The scope of research can be from the theory and scientific applications as well as the novelty of related knowledge insights. Soft Computing: Artificial Intelligence Applied Algebra Neuro Computing Fuzzy Logic Rough Sets Probabilistic Techniques Machine Learning Metaheuristics And Many Other Soft-Computing Approaches Area Of Applications: Data Mining Text Mining Pattern Recognition Image Processing Medical Science Mechanical Engineering Electronic And Electrical Engineering Supply Chain Management, Resource Management, Strategic Planning Scheduling Transportation Operational Research Robotics
Arjuna Subject : Ilmu Komputer (semua kategori) - Perangkat Lunak
Articles 146 Documents
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Performance measurement implementation on the smart fisheries village website using pagespeed insight Panduwika, Panduwika; Solehatin, Solehatin
Journal of Soft Computing Exploration Vol. 5 No. 2 (2024): June 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i2.363

Abstract

Websites have become the primary way organizations and individuals to communicate, provide information, and offer daily services. The purpose of creating the Smart Fisheries Village (SFV) website was to enhance the performance and quality of the user experience by measuring and optimizing image sizes using Google's tools, specifically Google PageSpeed Insight. We monitored and analyzed the implementation performance to ensure faster loading times without compromising visual quality. The implementation results showed significant improvements in the SFV loading speed, leading to a more satisfactory user experience. To identify images that slow website loading, we used data from PageSpeed Insight. After implementing improvements, we distributed a questionnaire to users to evaluate the development results. The results of the questionnaire revealed a significant increase in user satisfaction with the loading speed and quality of the user experience of the Bangsring Smart Fisheries Village (SFV) website. These findings provide valuable information for the continued development and optimization of website performance in the future. Therefore, this research makes a valuable contribution to improving the performance and user experience of the Bangsring Smart Fisheries Village (SFV) website.
Classification of residual hearing of deaf students based on audiometer using google data studio visualization method Samosir, Amril; Sulistiyanto, Sulistiyanto; Oktapriandi , Sony; Muhammad, M
Journal of Soft Computing Exploration Vol. 5 No. 2 (2024): June 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i2.364

Abstract

Classification of hearing loss is necessary because it provides treatment or learning methods for students which are certainly not the same. This classification is displayed in a graphical form because graphics are able to provide information quickly. The results of this writing are information in the form of visualization of the residual hearing which is grouped according to the decibels or residual hearing they have. Patterns that will be applied in learning will later be adjusted based on classification, so that students can comfortably follow the learning process. When creating this visualization, use Google Data Studio because it can be used to represent complex data sets in an interesting and clear way. The data used are data on deaf students for 2014-2021, with a total of 357 data and 14 attributes. The results of data processing are in the form of graphs of students for each generation, distribution of student demographics, and classification of student hearing measurement results. From the visualization results, 3 categories were obtained, with the results being 9 light categories,, 129 medium categories and 219 heavy categories. The mild category will receive oral treatment, while the moderate and severe categories will be given sign language and written treatment
Performance analysis of amd ryzen 5 4600h mobile processor undervolting using AMD APU tuning utility on cinebench R23 Sulistiyono, Mulia; Ariadi, Muhammad Vicri; Kharisma, Rizqi Sukma; Saputro, Uyock Anggoro
Journal of Soft Computing Exploration Vol. 5 No. 2 (2024): June 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i2.369

Abstract

In an effort to optimize laptop performance for gaming and high-demand applications without costly hardware upgrades, this research investigates the impact of CPU undervoltage using the AMD Ryzen Mobile 4600H processor. Undervolting, the process of reducing the CPU's voltage supply, is proposed as a strategy to enhance performance by lowering operational temperatures, potentially allowing for more efficient processing. This study uses the AMD APU Tuning Utility to adjust voltage settings and assesses performance changes using a series of benchmarks. Initial findings indicate that undervoltage can indeed have beneficial effects. The most significant data point from the research is the comparison of Cinebench R23 scores before and after applying undervolting settings. From a baseline score of 6835 points, system performance increased to 7880 points in the optimal undervolting scenario, an improvement of 1045 points. This shows a noticeable enhancement in processing efficiency. However, the study also reveals some complexities in undervolting, such as an initial drop in performance in the first configuration before gains are realized in subsequent adjustments. Efficiency values varied across different settings, starting with a decrease (-0.41) and culminating in a substantial gain (+1.54) by the fourth configuration. These results suggest that while undervolting can improve performance, the outcomes depend significantly on finding the right voltage balance, highlighting the nuanced nature of CPU voltage manipulation for performance optimization.
Developing a classification system for brain tumors using the ResNet152V2 CNN model architecture Rhomadhon, Syahruu Siyammu; Ningtias, Diah Rahayu
Journal of Soft Computing Exploration Vol. 5 No. 2 (2024): June 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i2.372

Abstract

According to The American Cancer Society, in 2021 there were 24,530 cases of brain and nervous system tumors. The National Cancer Institute reports that there are approximately 4.4 new cases of brain tumors per 100,000 men and women per year. Brain tumors can be detected using magnetic resonance imaging (MRI), a scanning tool that uses a magnetic field and a computer to record brain images and is able to provide clear visualization of differences in soft tissue such as white matter and gray matter. However, this cannot be done optimally because it still relies on manual analysis, so it cannot classify brain tumor types on larger datasets with the potential for error and a low level of accuracy. To accurately determine the type of brain tumor, a better classification method is needed. The aim of this study is to determine the accuracy of brain tumor calcification using the deep learning model. In this study, the classification of brain tumor types was carried out using the ResNet152V2 convolutional neural network (CNN) model which has a depth of 152 layers. The dataset used in this study was 7,023 MRI images of brain tumors consisting of 1,645 meningiomas, 1,621 gliomas, 1,757 pituitary and 2,000 normal. Research results show an accuracy value of 94.44%, so it can be concluded that the ResNet152V2 model performs well in classifying brain tumor images and can be used as a medium for physicians to more accurately diagnose brain tumor patients more accurately.
Breast tumor classification using adam and optuna model optimization based on CNN architecture Sari, Christy Atika; Rachmawanto, Eko Hari; Daniati, Erna; Setiawan, Fachruddin Ari; Hyperastuty, Agoes Santika; Mintorini, Ery
Journal of Soft Computing Exploration Vol. 5 No. 2 (2024): June 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i2.373

Abstract

Breast cancer presents a significant challenge due to its complexity and the urgency of the intervention required to prevent metastasis and potential fatality. This article highlights the innovative application of Convolutional Neural Networks (CNN) in breast tumor classification, marking substantial progress in the field. The key to this advancement is the collaboration among medical professionals, scientists, and artificial intelligence experts, which maximizes the potential of technology. The research involved three phases of training with varying proportions of training data. The first training phase achieved the highest accuracy rate of 99.72%, with an average accuracy of 99.05% in all three phases. Metrics such as precision, recall, and F1 score were also highly satisfactory, underscoring the model's efficacy in accurately classifying breast tumors. Future research aims to develop more complex and precise predictive models by incorporating larger and more representative datasets. This progression promises to improve understanding, prevention, and management of breast cancer, offering hope for significant advances in 2024 and beyond.
Development and usability testing of diabetes risk calculator (diacal): a health education application Febriani, Dita Hanna; Aryu, Scholastica Fina
Journal of Soft Computing Exploration Vol. 5 No. 2 (2024): June 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i2.375

Abstract

Despite the growing popularity of the mobile health application, the application that addressed to calculated diabetes risk is still limited. DiaCal was developed to prevent type 2 diabetes mellitus by screening and early detection approach. This study aimed to develop and examine the usability of DiaCal smartphone application-based education. This study was conducted with a cross-sectional approach. The framework of this application is based on the American Diabetes Association diabetes risk screening instrument. The development of the DiaCal was divided into three phases: preparation, design, and piloting. System Usability Testing (SUS) instruments used to examine the user-level acceptance of this application. DiaCal app developed in android platform core modules: a) data entry, b) conversion and calculation, c) output of the risk assessment, d) education. Twenty respondents were recruited in this study to evaluate DiaCal through SUS instrument. The average adjective range score is 85.25 which indicates that the DiaCal application is in the “excellent” category and the grade level scale is in “A+”. This study showed a significant usability and acceptability of DiaCal in terms of effectiveness, efficiency, and satisfaction.
Improved convolutional neural network model for leukemia classification using EfficientNetV2M and bayesian optimization Wibowo, Kevyn Alifian Hernanda; Rianto, Nur Azis Kurnia; Unjung, Jumanto
Journal of Soft Computing Exploration Vol. 5 No. 2 (2024): June 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i2.378

Abstract

Leukemia is a health condition in which the body produces too many abnormal white blood cells or leukocytes. Leukemia can affect both children and adults. Early diagnosis of leukemia faces significant challenges, as diagnostic methods are time consuming, require experienced medical experts, and are expensive. Previous studies have been conducted using deep learning approaches, but it is still rare to find a model that shows the best performance and uses optimization methods to classify leukemia diseases. Therefore, a Convolutional Neural Network (CNN) model with EfficientNetV2M architecture and Bayesian Optimization is proposed as the main method assisted by ImageDataGenerator in preprocessing. This study shows a significant impact of Bayesian optimization with good Accuracy, Precision, Recall and F1-Score results of 91.37%, 93.00%, 87.00%, 89.00%, respectively, which are expected to improve the performance of the model in previous studies in classifying leukemia diseases.
Quadrotor height control system using LQR and recurrent artificial neural networks Rahani, Faisal Fajri; Rosyida, Miftahurrahma
Journal of Soft Computing Exploration Vol. 5 No. 2 (2024): June 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i2.379

Abstract

The quadorotor is a type of unmanned flying vehicle known as Unmanned Aerial Vehicle (UAV). In recent years, quadrotors have attracted much attention from researchers around the world due to their excellent maneuverability. A good control system in this quadrotor system is needed for ease of use of this quadrotor. One control system that is often used is the Linear Quadratic Regulator (LQR) control system. This control system has challenges for dynamic system disturbances in quadrotor control. Researchers proposed a recurrent artificial neural network (RNN) system to address these challenges.RRN is used to change the value of the feedback component in the LQR control system. The nature of the feedback component in LQR, which is static, is changed based on the system error value based on changes in the error value entered into the RNN. The result of this RNN is a change in the value of the LQR feedback component based on the input of the system. The results of this research show that LQR control with RNN produces a faster system response of 0.075 seconds and a faster settling time of 0.221 seconds. Compensation for the system response speed produces a higher overshot value.
Design and building system analysis on the smart fisheries village (SFV) website at the banyuwangi fisheries training and counseling center Khotijah, Adinda Inne; Solehatin, Solehatin
Journal of Soft Computing Exploration Vol. 5 No. 3 (2024): September 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i3.360

Abstract

This research aims to analyze and design a smart fisheries village-based website system to facilitate back-end, front-end, and UI/UX designers in the application of website creation according to the needs desired by the agency and with an organized database so that the creation of data reports will be faster. The early stages of the research began with the identification of the the specific needs of fishing villages and involved an in-depth analysis of the system needs that supported the vision of the Smart Fisheries Village. The design began with data collection consisting of observation methods and interviews, where researchers interviewed the authorities. In this method, the author gives 5 questions to the user, data analysis, and design of the Unified Modeling Language (UML). The design of this SFV web system uses a Unified Modeling Language (UML), which involves the use of diagrams and UML notation to describe various aspects of the system visually. The results of this study include UML diagrams, which encompass activity diagrams (for users and admins), flow diagrams (for users and admins), use case diagrams (for users and admins), and class diagrams that have undergone 4-5 iterations. The design of the Smart Fisheries Village website system is necessary to improve the welfare of the fishing village. Contributions of this research include the standardization of modeling, increased productivity, improved analysis and planning, and improved understanding. Previous research might have concentrated on a single type of system or domain. However, research should be expanded to various types of systems and industries.
Classification of water quality based on dissolved solids and turbidity parameters with the utilization of total dissolved solids sensor and turbidity sensor Hidayana, Elmi; Setiawan, Edy; Juniani, Anda Iviana
Journal of Soft Computing Exploration Vol. 5 No. 3 (2024): September 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i3.376

Abstract

Clean water quality is essential for public health, but its scarcity is increasing amid population growth and industrialization. Monitoring turbidity and total dissolved solids (TDS) is essential to determine the quality of clean water. This study addresses the urgent need for accurate and reliable water quality monitoring to test the applicability of TDS and turbidity sensors in taking measurements, aiming to develop efficient monitoring solutions for public health and sustainable water management. The TDS sensor operates according to the principle of electrical conductivity, with a range of 0 to 1000 ppm and an accuracy of ±10%. The turbidity sensor detects water turbidity by determining the level of turbidity particles. The ESP32 microcontroller integrates Wi-Fi and USB capabilities. The hardware and software design ensures accurate sensor readings, which are critical to successful water quality measurement and monitoring. The test results show satisfactory accuracy of the TDS sensor with an average error of 0.09% and good accuracy of the turbidity sensor with an average error of about 1.536%. Concerning the above two parameters, in this study, among 15 water samples, seven were clean, meeting the standard, while eight water samples were dirty, exceeding the limit, making them unsafe for human consumption.

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