cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Much Aziz Muslim
Contact Email
a212muslim@yahoo.com
Phone
+628164243462
Journal Mail Official
shmpublisher@gmail.com
Editorial Address
J. Karanglo No. 64 Semarang
Location
Kota semarang,
Jawa tengah
INDONESIA
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 13 Documents
 1   2 
Search results for , issue "Vol. 5 No. 2 (2024): June 2024" : 13 Documents clear
Narrative literature review: Efficiency enhancement - user trust in chatbots as a tool for improving service quality by humans Margono, Hendro; Bastian, Devi; Faiza, Nova Auliatul
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.v5i2.286

Abstract

Chatbots have become efficient and reliable tools for instant and real-time information dissemination. Despite their effectiveness, user trust in chatbot systems remains relatively low. A holistic approach is necessary, integrating user emotional experiences, trust-building strategies, and continuous technological refinement to maximize chatbot benefits across various sectors. This research explores the potential for selective information dissemination based on user preferences using chatbots combined with artificial intelligence. Through a narrative approach, the study reviews literature and analyzes eight articles related to chatbots' application in information dissemination. The results indicate that chatbots are efficient in providing information and can be customized for various needs, such as population services, reminder notifications, and book processing. Chatbots have the potential to enhance services and can be integrated into information systems to improve service quality. However, challenges such as reliance on high-quality data and machine learning, difficulties in understanding non-formal language or slang, and limitations in handling complex questions need to be addressed for chatbots to reach their full potential.
Flood early warning system at Jakarta dam using internet of things (IoT)-based real-time fishbone method to support industrial revolution 4.0 Farabi, Muhammad Rizqi Al; Sintawati, Andini
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.v5i2.293

Abstract

This research aims to develop an effective flood early warning system to provide timely information to the public and support the government in disaster management. The Raspberry Pi mini-computer functions as the central control, collecting data from the Water Level Sensor to measure water height, the Ultrasonic Sensor for further monitoring, the DHT11 Sensor to monitor temperature and humidity, and a Buzzer as an audible warning device. The research method involves review of the literature and data acquisition from related journals. These data are utilized to design an Internet of Things (IoT)-based flood detection tool with the Raspberry Pi minicomputer as the main controller. The system can be implemented in vulnerable locations such as reservoirs, sluice gates, and rivers, as part of the Smart City and Smart Environment concepts. The test results indicate that the developed early warning system, integrating the Raspberry Pi minicomputer, the Water Level Sensor, the the Ultrasonic Sensor DHT11 Sensor, and Buzzer, approaches perfection. Real-time information is transmitted through the Twitter social media platform, which is shown to be more effective than manual notifications. The system can provide accurate early warnings, reduce flood-related damages, and positively contribute to flood prevention and disaster management efforts. This research is expected to make a significant contribution to improving the community and government preparedness for future flood disasters.
Enhancing soccer pass receiver prediction in broadcast images through advanced deep learning techniques: A comprehensive study on model optimization and performance evaluation Paneru, Biplov; Paneru, Bishwash; Poudyal, Ramhari; Poudyal, Khem
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.v5i2.301

Abstract

In this study, we present a graph neural network (GNN) model specifically designed for football pass receiver prediction in Broadcast Images is presented in this study. Important node properties, including ball possession indicators, hot-encoded team values, and normalized ground placements, are incorporated into the model along with a careful weighting of edges to account for player distances. With weighted BCE loss used to overcome class imbalance, its architecture consists of a linear layer, numerous GNN Message Passing layers, a SoftMax activation, and binary cross-entropy (BCE) loss for training. Of 206 examples, 101 valid predictions were made, indicating a predictive accuracy of 0.50 according to the evaluation data. Comparative analyzes show that GAT-V2 (0.85) and GAT (0.63) perform better in terms of optimization and accuracy, respectively. The effectiveness in recognizing football pass receivers is demonstrated in this paper, highlighting developments in computer vision applications for sports analytics.
Comparison of the performance of naive bayes and support vector machine in sirekap sentiment analysis with the lexicon-based approach Setiyawan, Ramadhana; Mustofa, Zaenal
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.v5i2.367

Abstract

The general public often uses the SiRekap application to see the progress of the election and to provide critical statements. Policies made by the government have good and bad outcomes, and users end up leaving their reviews and ratings on the Google Play Store, where the app can be downloaded. These reviews can be collected and processed into useful information such as sentiment analysis using Naïve Bayes and Support Vector Machine methods. Both methods have differences during training and during evaluation. The difference in results from the various scenarios tested was not much different. When training the Support Vector Machine model is able to process comment data labeled with a lexicon 10% better than the Naïve Bayes model by looking at the results of the accuracy of the two models. During the accuracy evaluation process, the two models produce the same accuracy of 72%. Although both models get the same accuracy during the evaluation process, there are differences in precision, recall, and f1 score. The difference is that the Support Vector Machine model is 5% better for precision, 8% for recall, and 3% for f1-score compared to the Naïve Bayes model. This research is limited to only knowing the performance of two machine learning models, namely the use of naive bayes and svm by using a label lexicon. The results obtained can be improved for the better. Improving the evaluation results can be done by adding data or using text data augmentation and there is creation from experts related to language sentiment.
Optimizing the implementation of the BFS and DFS algorithms using the web crawler method on the kumparan site Mustaqim, Amirul; Dinova, Dony Benaya; Fadhilah, Muhammad Syafiq; Seivany, Ravenia; Prasetiyo, Budi; Muslim, Much Aziz
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.v5i2.309

Abstract

Efficient access to timely information is critical in today's digital era. Web crawlers, automated programs that navigate the Internet, play an important role in collecting data from websites such as Kumparan, a leading news site in Indonesia. This research shows the effectiveness of the Breadth-First Search (BFS) and Depth-First Search (DFS) algorithms in indexing Kumparan content. The results of the research show that BFS consistently indexes more files comprehensively but with longer execution times compared to DFS, which provides faster initial results but with fewer files. For example, at depth 4 BFS indexed 949 files in 886.94 seconds, while DFS indexed 470 files in 233.02 seconds. These findings highlight the balance between precision and speed when selecting a crawling algorithm tailored to the needs of a particular website. This research provides insights into optimizing web crawler technology for complex websites such as Coil and suggests avenues for further research to improve permission efficiency and adaptability across a variety of crawling scenarios.
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.

Page 1 of 2 | Total Record : 13

 1   2 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol. 6 No. 4 (2025): December 2025 Vol. 6 No. 3 (2025): September 2025 Vol. 6 No. 2 (2025): June 2025 Vol. 6 No. 1 (2025): March 2025 Vol. 5 No. 4 (2024): December 2024 Vol. 5 No. 3 (2024): September 2024 Vol. 5 No. 2 (2024): June 2024 Vol. 5 No. 1 (2024): March 2024 Vol. 4 No. 4 (2023): December 2023 Vol. 4 No. 3 (2023): September 2023 Vol. 4 No. 2 (2023): June 2023 Vol. 4 No. 1 (2023): March 2023 Vol. 3 No. 2 (2022): September 2022 Vol. 3 No. 1 (2022): March 2022 Vol. 2 No. 2 (2021): September 2021 Vol. 2 No. 1 (2021): March 2021 Vol. 1 No. 1 (2020): September 2020 More Issue