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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 149 Documents
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Decision support system for selecting outstanding students using simple addictive weighting (SAW) and rank order centroid (ROC) methods Sholikha, Hidayatin; Ardiansyah, Hery; Bianto, Mufti Ari
Journal of Soft Computing Exploration Vol. 6 No. 3 (2025): September 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v6i3.574

Abstract

Selecting outstanding students is essential in fostering appreciation and motivation within the school environment. Nevertheless, many educational institutions continue to use manual assessment methods, which are often subjective and inefficient. This research focuses on the development of a web-based decision support system designed to assist in the selection process at a in Indonesia. The system integrates the Simple Additive Weighting (SAW) technique to generate student rankings based on preference scores, while the Rank Order Centroid (ROC) method is applied to assign weight values to the evaluation criteria, including academic performance, attendance, behavior, and extracurricular involvement. Data for this study were collected through interviews, direct observation, and student records. The application was developed using PHP for the backend, MySQL for database handling, and Bootstrap for the user interface design. The system’s functionality was verified using black box testing, which confirmed that all features operated correctly. Additionally, the system was evaluated against the manual selection process conducted by the school, and the results showed an accuracy level of 80% in matching student rankings. This system proves to be a practical and structured solution for enhancing the transparency and objectivity of student achievement evaluations.
Corn sales analysis using linear regression and svm methods to improve production planning Saputra, Ahmad Hakiki; Priyanto, Dadang; Hammad, Rifqi
Journal of Soft Computing Exploration Vol. 6 No. 3 (2025): September 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v6i3.591

Abstract

This research aimed to analyze and predict corn sales at UD Muara Kasih to improve production planning accuracy. The study used historical corn sales data collected over a specific period, covering 42 data entries from January 2021 to December 2024. The dataset included variables such as sales date, quantity sold, selling price per ton, total sales value, weather conditions, market demand (in tons), and the number of laborers. Prior to model training, the data underwent comprehensive preprocessing involving data cleaning, feature extraction, and normalization to ensure its quality and readiness for analysis. Two predictive models were applied: Linear Regression and Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel. Simulation data for 2024 and 2025 were generated based on the monthly averages derived from the historical dataset. The results showed that the Linear Regression model produced more stable predictions with a lower Root Mean Squared Error (RMSE) of 255.84 compared to the SVM model’s RMSE of 256.42. While the SVM model showed greater responsiveness to seasonal variations, the Linear Regression model was identified as the most suitable for the given dataset. The predictive models developed in this study are expected to support UD Muara Kasih in making more accurate and informed production decisions in the future.
Identification of lung cancer using gray level co-occurrence matrix (GLCM) and artificial neural network with backpropagation algorithm Fauziah, Haniifah Hana; Ningtias, Diah Rahayu; Wahyudi, Bayu; Simanjuntak, Josepa ND
Journal of Soft Computing Exploration Vol. 6 No. 1 (2025): March 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v6i1.543

Abstract

Air pollution is a problem that occurs in various countries, including Indonesia. One of the consequences of poor air quality due to air pollution is health problems in the lungs, one of which is lung cancer. According to WHO data, lung cancer caused 1.80 million deaths in 2020. This is due to limited services to identify lung cancer early, resulting in delays in treatment. This study aims to identify lung cancer using CT-Scan image processing. The identification method uses a Backpropagation Artificial Neural Network (ANN BP) with Gray Level Co-occurrence Matrix (GLCM) feature extraction. Preprocessing is carried out to improve image quality by removing noise using a median filter. Segmentation of preprocessing results using Otsu threshold. Texture features from segmentation can be calculated from the resulting GLCM, such as Angular Second Moment (ASM)/energy, contrast, correlation, Inverse Different Moment (IDM)/homogeneity, and entropy. These values ​​are obtained from angles of 0°, 45°, 90°, and 135°, and a distance between pixels of 2 pixels. Identification using ANN with Backpropagation algorithm. This study used images of normal lungs and lung cancer with 160 training data images and 40 test data images. The best test results were obtained with the best accuracy level of 92.5%.
Design and construction of gym angkasa management website system in margoyoso village to improve customer service Muzaki, Muhammad; Khotimah, Tutik; Meimaharani, Rizkysari
Journal of Soft Computing Exploration Vol. 6 No. 2 (2025): June 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v6i2.583

Abstract

This study aims to design and implement a website-based gym management system at Angkasa Gym located in Margoyoso Village to improve customer service and operational efficiency. The current system is still manual, resulting in inefficiency and risk of errors in managing member registration, scheduling training sessions, and financial transactions. The proposed system is developed using the Waterfall model with stages of needs analysis, system design, implementation, testing, and maintenance. The Laravel framework was chosen because it supports the development of modern and secure web applications. The final system has key features such as user registration and login, trainer management, transaction processing, and review submission. Based on black box testing, all system functionality runs as expected. The results of the study indicate that the web-based system is able to significantly improve gym operational efficiency and improve user service experience. This study can be the basis for further development, such as automatic notifications, implemented in other gyms, and adaptation to the mobile application version.
Online reservation system development and digital payment integration in car wash business: Case study of car wash sniper Purwaningsih, Ratna; Murti, Alif Catur; Nindyasari, Ratih
Journal of Soft Computing Exploration Vol. 6 No. 2 (2025): June 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v6i2.586

Abstract

This study aims to develop a website-based online reservation system at Car Wash Sniper with digital payment integration to improve operational efficiency and customer convenience. The system design method uses a waterfall model approach that includes needs analysis, system design, implementation, testing, and maintenance. Black box testing is also carried out to test the suitability of the system with the design that has been developed. To support the payment process, this system is integrated with Midtrans as a payment gateway that provides various payment options such as bank transfers, e-wallets, and credit cards safely and in real-time. The results of the study show that this system is able to optimize the reservation process, reduce queues, and increase customer satisfaction. The novelty of this research by developing a system that is integrated with digital payments, making transactions more practical, efficient, and transparent. This system can be an innovative solution for business actors in the car wash industry to improve efficiency and service quality.
Tourism digital innovation geographic information system based web application for spatial information of tourism destinations Hidayat, Amrul Rais; Khotimah, Tutik; Meimaharani, Rizkysari
Journal of Soft Computing Exploration Vol. 6 No. 2 (2025): June 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v6i2.587

Abstract

This study aims to develop a web-based tourism application by utilizing Geographic Information System (GIS) to optimize the dissemination of tourism information in the Pati Karisidenan area. This application is designed to assist local communities and tourists in choosing tourist destinations by providing information such as location, description, route, facilities, and visitor reviews. In addition, the app is also equipped with search and filtering features to help users find tourist attractions based on category, distance, or available facilities. This application was developed using the PHP programming language and MySQL database. The development process adopts the Waterfall method, which consists of the requirements analysis stage, system design using ERD and DFD, implementation, testing, and maintenance. Application testing is carried out using the Black Box Testing method to ensure all functions run according to specifications. The test results from visitors showed satisfaction with the use of the application by 97% of 15 audiences. This application is expected to support the promotion, accessibility, and management of tourism potential digitally by local governments.
Web based IoT monitoring system for ultrasonic water flow measurement using ESP32-S3 and cloud database Nugroho, Waluyo; Arifianto, Mada Jimmy Fonda; Afianto, Afianto; Wicaksono, Andreadie; Nursim, Nursim
Journal of Soft Computing Exploration Vol. 6 No. 4 (2025): December 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v6i4.625

Abstract

Efficient water management is crucial for ensuring sustainable resource utilization and reducing water losses in both industrial and domestic applications. This study presents the design and implementation of a smart water monitoring system based on an ultrasonic flow meter, which enables accurate, real-time measurement of water flow without physical contact with the medium. The proposed system integrates ultrasonic sensors with a microcontroller-based data acquisition unit and wireless communication to transmit flow rate, volume, and consumption data to a cloud-based monitoring platform. The system was tested in various flow conditions to evaluate accuracy, stability, and energy efficiency. Experimental results demonstrate that the ultrasonic flow meter achieved a measurement accuracy of ±1% compared to a reference turbine flow meter, while maintaining minimal power consumption. Furthermore, the integration of Internet of Things (IoT) capabilities allows remote monitoring, anomaly detection, and data logging for long-term analysis. The results indicate that this ultrasonic-based monitoring system provides a reliable and non-invasive solution for smart water management, offering potential applications in household metering, agricultural irrigation, and industrial fluid monitoring.
Application of the TAM model for assesing the acceptance of IoT technology in a residential security application Ghaniy, Rajib; Wicaksana, Binanda; Arnes, Fahmi; Melati, Laras; Septiana, Helena
Journal of Soft Computing Exploration Vol. 6 No. 4 (2025): December 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v6i4.629

Abstract

Residential crime continues to be a significant concern, and traditional security systems relying oHousing is an area vulnerable to crime, especially if it is not supported by an adequate security system. Many housing complexes still rely on conventional security systems that only involve officers without technological support. Therefore, the application of technology, especially the Internet of Things (IoT), is needed to improve housing security systems. The success of a system is largely determined by the level of user acceptance, which can be measured using the Technology Acceptance Model (TAM). This study aims to measure user acceptance of an IoT-based housing security system using the TAM model. Data were obtained from 100 respondents and analyzed using the PLS-SEM method to test the research hypotheses. The results showed that four hypotheses had a significant relationship, namely the relationship between Subjective Norm (SN) and Perceived Usefulness (POU), Perceived Ease of Use (PEU) and POU, PEU and Attitude Toward Use (ATU), and POU and Behavioral Intention (BEI). Meanwhile, the other four hypotheses did not show a significant relationship.n manual monitoring are often insufficient in addressing modern security challenges. With the rapid development of Internet of Things (IoT) technology, digital security solutions offer new opportunities for improving surveillance and access control within housing environments. This study aims to assess user acceptance of an IoT-based residential security application by applying the Technology Acceptance Model (TAM). A quantitative survey method was used, involving 100 respondents who evaluated the prototype after testing it directly. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that Perceived Ease of Use significantly affects both Perceived Usefulness and Attitude Toward Using, while Perceived Usefulness strongly influences Behavioral Intention. However, Attitude Toward Using shows a marginal impact on Behavioral Intention, and Behavioral Intention does not significantly predict Actual Use. These findings reveal the dominant factors influencing acceptance and highlight areas for improvement in IoT-based security applications.
Topic modelling analysis of public policy narratives on prabowo-gibran in national news Hakim, Lukmanul; Aditya, Anggi Yudistira; Lubis, Muharman
Journal of Soft Computing Exploration Vol. 6 No. 4 (2025): December 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v6i4.632

Abstract

The rapid acceleration of digital transition has become an inevitable reality of the modern era. The proliferation of online communication platforms, news portals, and heterogeneous data formats has substantially increased big data volumes, leading to large-scale collections of unstructured data. This study aims to analyze dominant public policy–related topics concerning the Prabowo–Gibran administration by applying topic modeling techniques to national online news media. Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF) were employed as unsupervised learning approaches to extract latent semantic structure from a corpus of 200 credible news articles collected through URL fetching using Python 3. Data preprocessing included text cleaning, tokenization, bigram and trigram construction, and the development of a dictionary and corpus. Model performance was evaluated using topic coherence metrics, yielding scores of 0.3709 for LDA and 0.68 for NMF. To examine temporal dynamics, the dataset was divided based on the official inauguration date of the president and vice president, enabling a comparative analysis of dominant topics before and after the inauguration. Topic similarity across both periods was measured using cosine similarity, with the highest similarity score of 0.663 observed between Topic 4 in the pre-inauguration period and Topic 1 in the post-inauguration period. The findings provide insights into evolving media discourse and policy-related topic trends across the two periods, demonstrating the potentials of topic modeling in analyzing large-scale unstructured news data for diverse purposes to bridge computational science and empirical evidence of social science.

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