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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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Automation of aquaponics systems through integration of RTC modules, turbidity sensors, and water level sensors Jaya, Dicky Suman; Styawati, Styawati; Syahirul, Alim
Journal of Soft Computing Exploration Vol. 4 No. 4 (2023): December 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i4.267

Abstract

Automation of aquaponics systems is key in increasing agricultural efficiency and productivity. A system considered an innovative method of sustainable food production that combines fish farming with agriculture simultaneously. The problem that often occurs is crop failure, due to the lack of technology that can monitor automatically, so that farmers experience losses as a result of fish and plant growth does not thrive, and problems in urban areas that require land for planting and fish farming due to limited land in urban areas. There is another problem with the lack of accurate timing and monitoring of water quality in aquaponics. The purpose of this research is to implement an IoT system in aquaponics that is connected to various sensors, such as Turbidity sensors, Water Level sensors, and RTC Modules. To monitor water quality conditions in tilapia habitat and accurate time measurement to provide fish feed automatically so as to improve fish health and growth and support better and consistent yields. The findings of this study show that the implementation of IoT systems in aquaponics can overcome environmental monitoring and control problems effectively. Using the integration of RTC modules, turbidity sensors, and water level sensors effectively improves the automation of aquaponics systems. This optimized system provides better monitoring of environmental conditions, reduces reliance on manual maintenance, and increases overall productivity. It helps increase tilapia growth and plant productivity in a modern aquaponics system. This research demonstrates the great potential of IoT technology in increasing efficiency and productivity in aquaponics aquaculture, so that it can push the fisheries sector towards a more advanced and competitive direction. So the main conclusion is expected that this automation can increase the productivity of ecosystem balance, and can face food security challenges and move towards more environmentally friendly solutions, towards effective management in the future.
A text security evaluation based on advanced encryption standard algorithm Bima, Aristides; Irawan, Candra; Laksana, Deddy Award Widya; Krismawan, Andi Danang; Isinkaye, Folasade Olubusola
Journal of Soft Computing Exploration Vol. 4 No. 4 (2023): December 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i4.274

Abstract

This research approach analysis and examines a number of advanced encryption standard (AES) performance factors, including as encryption and decryption speed, processing resource, consumption, and resilience, to cryptanalysis attacks. The study’s findings demonstrate that AES is successful in providing high-level data security, particularly when used in the CBC (Cipher Block Chaining) operating mode. Performance is dependent on the length of the key that is utilized. Increasing the level of security through the use of longer keys may result in an increase in the amount of time needed for encryption. The experimental results show that the highest results from the data are as follows the length of the encryption time is 0.00005317 seconds, the length of the decryption time is 0.00000882 seconds, the results of BER and CER are 0, the results of entropy are 7.44237, and the results of avalanche influence are 54.86%.
Prediction of PTIK students' study success in the first year using the c4.5 algorithm Astuti, Asri; Maryono, Dwi; Liantoni, Febri
Journal of Soft Computing Exploration Vol. 5 No. 1 (2024): March 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i1.237

Abstract

The purpose of this study is to determine the factors that influence the success of student studies in the first year through data mining research using the C4.5 algorithm. This research is a type of quantitative research. This research uses student data of a study program as much as 85 data which will be processed using the Weka application. The data obtained will then be processed using the C4.5 data mining method to produce a decision tree containing rules to predict the success of student studies in the first year. The best result using percentage-split 80% obtained an accuracy of 82.35% as well as the rules contained in the decision tree. The most important factor in determining the success of studies in first-year students is the selection of college entrance pathways. Other factors that become other determinants are education before college, intensity of communication with friends, class year, intensity of off-campus organizations, and plans to change study programs.
Improved playstore review sentiment classification accuracy with stacking ensemble Santoso, Dwi Budi; Munna, Aliyatul; Untari Ningsih, Dewi Handayani
Journal of Soft Computing Exploration Vol. 5 No. 1 (2024): March 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i1.247

Abstract

In today's digital era, user reviews on the Playstore platform are an invaluable source of information for developers, offering insights that are critical for service improvement. Previous research has explored the application of stacking ensemble methods, such as in the context of predicting depression among university students, to enhance prediction accuracy. However, these studies often do not explicitly detail the data acquisition process, leaving a gap in understanding the applicability of these methods to different domains. This research aims to bridge this gap by applying the stacking ensemble approach to improve the accuracy of sentiment classification in Playstore reviews, with a clear exposition of the data collection method. Utilizing Logistic Regression as the meta classifier, this methodology is executed in several stages. Initially, data was collected from user reviews of online loan applications on Google Playstore, ensuring transparency in the data acquisition process. The data is then classified using three basic models: Random Forest, Naive Bayes, and SVM. The outputs of these models serve as inputs to the Logistic Regression meta model. A comparison of each base model output with the meta model was subsequently carried out. The test results on the Playstore review dataset demonstrated an increase in accuracy, precision, recall, and F1 score compared to using a single model, achieving an accuracy of 87.05%, which surpasses Random Forest (85.6%), Naive Bayes (85.55%), and SVM (86.5%). This indicates the effectiveness of the stacking ensemble method in providing deeper and more accurate insights into user sentiment, overcoming the limitations of single models and previous research by explicitly addressing data acquisition methods.
Activity-based function point complexity of use case diagrams for software effort estimation Jayadi, Puguh; Dewi, Renny Sari; Sussolaikah, Kelik
Journal of Soft Computing Exploration Vol. 5 No. 1 (2024): March 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i1.252

Abstract

This study proposes a Function Point Analysis (FPA) based software development effort estimation methodology integrated with Use Case Diagrams. These methods include identifying actor activities, classifying those activities into FPA categories, and calculating Unadjusted Function Points (UFP). Followed by the calculation of Technical Complexity Factors (TCF) and Adjusted Function Points (AFP), this study aims to produce more accurate man-hours estimates. Results show a UFP of 162 TCF of 11, AFP of 123.12, and an estimated effort of 1846.8 hours worked, while the actual effort is 1228 hours. Evaluation of estimates using the metrics Mean Magnitude of Relative Error (MMER) 0.34, Mean Magnitude of Relative Error (MMRE) 0.50, Mean Absolute Error (MAE) 618.80, Mean Balanced Relative Error (MBRE) 0.50, Mean Inverse Balanced Relative Error (MIBRE) 0.34, and Root Mean Squared Error (RMSE) 618.80, showed sufficient precision despite the overestimation. The study suggests the need for adjustments in TCF calculations and considering development environment factors in more detail to improve estimation accuracy. These findings are essential in improving the precision of effort estimation methodologies in software development, particularly in projects that use Use Case Diagrams as the primary framework.
A sentiment analysis of madura island tourism news using C4.5 algorithm Savitri, Vina Angelina; Sa’id, Moh.; Husni, Husni; Muntasa, Arif
Journal of Soft Computing Exploration Vol. 5 No. 1 (2024): March 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i1.258

Abstract

Over the past few years, the tourism sector has experienced significant growth in its contribution. The tourism potential on Madura Island is widespread across four regencies, namely Bangkalan, Sampang, Pamekasan, and Sumenep. This potential can be harnessed to support the local government's economy and the communities in the surrounding areas. This research aims to analyze the sentiment of Madura tourism news from online sources using the Decision Tree (C4.5) method. The data used in this study consist of 100 Madura tourism news articles collected from online news portals, which will be classified using the Decision Tree (C4.5) method. The test results show that this method has an average accuracy rate of 76.5% in 10 tests. The average accuracy results demonstrate that the use of the Decision Tree (C4.5) method in this research yields a sufficiently high accuracy level in the sentiment analysis of tourism news.
IoT-based implementation of rickshaws for real-time monitoring and measuring the weight of cattle Satrya, Alan; Styawati, Styawati; Ismail, Izudin; Alim, Syahirul
Journal of Soft Computing Exploration Vol. 5 No. 1 (2024): March 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i1.265

Abstract

In the era of modern agriculture that is increasingly dependent on technology, livestock management has become crucial to increasing efficiency and productivity. An important aspect in livestock management is providing appropriate feed to fattening cattle. Manual monitoring of feed weight is often complex and prone to errors, which can have a significant impact on operational efficiency and result in losses. Accuracy in monitoring feed weight is the key to maintaining optimal health and growth of cattle. Internet of Things (IoT) technology is emerging as an innovative solution to overcome these challenges. The use of Angkong load cells, a tool connected to IoT, allows automatic monitoring of feed weight with a high level of precision. The test results show an error rate close to zero, with a Mean Absolute Percentage Error (MAPE) of around 0.158%, making the Angkong load cell a reliable tool. With this capability, farmers can monitor cow feed weight in real-time with minimal error rates. This not only increases the operational efficiency of the farm but also optimizes the health and growth of livestock more efficiently, having a positive impact on overall farm productivity. The aim of this research is to monitor the amount of feed given to cows with an adequate level of accuracy. Rickshaw load cells can be well suited for this use due to their ability to handle relatively large weights with fairly good accuracy, but do not necessarily have the level of precision required in laboratory measurements or the pharmaceutical industry.
Implementation of a reinforcement learning system with deep q network algorithm in the amc dash mark i game Utomo, Wargijono
Journal of Soft Computing Exploration Vol. 5 No. 1 (2024): March 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i1.271

Abstract

Reinforcement learning is a branch of artificial intelligence that trains algorithms using a trial-and-error system. Reinforcement learning interacts with its environment and observes the consequences of its actions in response to rewards or punishments received. Reinforcement Learning uses information from every interaction with its environment to update its knowledge. The problem identified from this research is the lack of consistency, which is not always the same for Non-Player Characters (Agents) in the process of exploring an environment (Game environment). This research uses the Software Development Life Cycle (SDLC) Waterfall model method to train Non Player Characters (Agents) in the Amc Dash Mark I Game which uses the Deep Q Network (DQN) algorithm in several stages. Training results show improvements in model performance over time. The average duration of the episode and average reward episode showed an increase of 7.75 to 24.7, while the exploration rate decreased to 0.05. This indicates that the model has experienced learning and is improving to achieve better rewards by performing fewer actions. The lower loss also shows that the model has succeeded in reducing prediction errors and improving prediction capabilities.
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.
Air quality monitoring using multi node slave IoT Rahani, Faisal Fajri; Fathurrahman, Haris Imam Karim
Journal of Soft Computing Exploration Vol. 5 No. 1 (2024): March 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i1.292

Abstract

Jakarta is the city with the second poorest air quality in the world. IQAir data show that Jakarta's air quality is 159. In addition, the concentration of air particles in Jakarta is 14.2 times higher than the annual guidelines of the World Health Organization (WHO). According to the WHO, exposure to air pollution causes around 7 million premature deaths and millions of years of lost health time each year. Air pollution also stunts children's growth, impairs lung function, etc. Therefore, we need a system that can be used to combine air quality to determine how dangerous a place is with air quality. Knowing air quality, certain policies or actions being taken to overcome this danger. This research aims to build and test a prototype air quality monitoring system using multi-node slaves with the Internet of Things. The prototype development process was carried out by adapting the architectural framework of the air quality monitoring system with the Internet of Things. The testing of prototype results is carried out to sound sensor values and functional success. The results of the test show that the system can run well according to the design made. The DSM501A sensor device functions to detect particles of a size larger than one micrometer, which usually include cigarette smoke, house dust, ticks, spores, pollen, and mildew, and works well so that the controller can read the surrounding air conditions well.

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