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Science Information System and Technology
Published by Westscience Press
ISSN : 30261120     EISSN : 30255120     DOI : https://doi.org/10.58812/wsist.v1i02
Core Subject : Science,
Computer Science & IT
West Science Information System and Technology is a scholarly journal dedicated to the exploration and advancement of knowledge in the field of information systems and technology. The journal aims to publish high-quality research articles that contribute significantly to the understanding and development of information systems and technologies in the Western world. The journal covers a wide range of topics related to information system design, development, implementation, and management. It encompasses areas such as information systems development methodologies, database management systems, information technology infrastructure, enterprise systems, decision support systems, information systems security and privacy, human-computer interaction, and ethical and social implications of information systems.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 75 Documents
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User Interface and User Experience Design for Vidyanusa on Android Mobile Phones Using an Evaluation Framework Aditya Pratama
West Science Information System and Technology Vol. 2 No. 02 (2024): West Science Information System and Technology
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsist.v2i02.860

Abstract

This research will evaluate the User Interface and User Experience (UI/UX) of Vidyanusa for Android mobile phones. User Experience will be measured using sub-variables such as attractiveness, agility, efficiency, dependability, stimulation, and novelty. The object of this research is User Experience. The results of this research will be used to evaluate Vidyanusa's Android mobile product to achieve a product that is suitable for users at the junior high school level. User experience testing on the Vidyanusa mobile application uses evaluation methods with query techniques. Evaluation is a test of the level of system usability and functionality that is carried out in the laboratory, in the field, or in collaboration with users. What is evaluated is the design and implementation, and the technique used is the evaluation technique with the User Experience Questionnaire. The results of the user experience testing of the Vidyanusa mobile application tend to be negative on the points of attractiveness, perspicuity, efficiency, dependability, and novelty based on the User Experience Questionnaire in the benchmark table, but positive on the point of stimulation.
Optimizing Liver Disease Detection Through Combining Genetic Evolutionary Algorithm and Linear Discriminant Analysis (LDA) Dwi Ari Suryaningrum; Muhammad Romadhoni Indra Firmansyah
West Science Information System and Technology Vol. 2 No. 01 (2024): West Science Information System and Technology
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsist.v2i01.1019

Abstract

Liver diseases such as cirrhosis, hepatocarcinoma and fatty liver disease are global health problems with high morbidity and mortality. Early detection is crucial but is often hampered by the limitations of conventional methods in analyzing medical images and laboratory results. Machine learning and artificial intelligence technologies, particularly Genetic Evolutionary Algorithm (GA) and Linear Discriminant Analysis (LDA), offer opportunities to improve diagnosis accuracy. This research explores the combination of GA and LDA to improve liver disease detection using the ILPD (Indian Liver Patient Dataset) dataset from the UCI Machine Learning Repository. This study aims to optimize feature selection and classification to improve detection accuracy. The research method includes the use of GA for feature selection and LDA for dimensionality reduction and classification. Tests were conducted on various parameters such as the number of generations, population size, and the combination of crossover and mutation rates in the genetic algorithm. The test results show that the best parameter combination (generation 400, population size 40, crossover rate 0.9, and mutation rate 0.1) results in an Average Forecast Error Rate (AFER) value of 0.0345%, which indicates that the developed detection model is highly accurate. This study shows that the combination of GA and LDA can improve the effectiveness of liver disease detection compared to conventional methods, with potential practical applications in clinical diagnosis systems.
Housing Value Predicted Modelling using Random Forest Regression: Case study California Housing Dataset Firman Matiinu Sigit; Haniel Rangga Pramuditya Putra
West Science Information System and Technology Vol. 2 No. 01 (2024): West Science Information System and Technology
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsist.v2i01.1021

Abstract

Housing price comes from many factors which are location, population, style of house, age of house, and people income. Many real estate developer companies use this data to predict price of house and give amount of investment for potential housing prices. In this study, we try to help the developer companies to predict price of house based on dataset. We try to build machine learning that can predict for housing price. There are three machine learning models that are used for this study, namely Linier Regression Modelling, Decison Three Regression Modelling, and Random Forest Regression Modelling. Each of those machine learning is trained using California Housing Dataset (1990) which is split into training set and testing set that training set contains 16512 instances and testing set contains 4128 instances. Training dataset is trained into each of machine learning model (Linier Regression, Decison Tree Regression, and Random Forrest Regression) after finished the training followed by evaluting the error prediction using K-Folds Cross Validation and showed by using Root Mean Square Error (RMSE). In this study, Random Forest Regression gives a better performance than two others (Linier Regression and Decision Tree Regression models) with error RMSE =49642.12.
Information System for Procurement of Goods and Services of the Politeknik Harapan Bersama (SIPHARBER) Muhamad Bakhar; Evanita Evanita; Ulil Albab
West Science Information System and Technology Vol. 2 No. 02 (2024): West Science Information System and Technology
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsist.v2i02.1173

Abstract

The application of information technology in various fields, including higher education, is rapidly evolving and has extended to critical operational activities such as the procurement of goods and services. This research aims to design and implement a Procurement Information System at Politeknik Harapan Bersama (SIPHARBER), which is expected to enhance efficiency, transparency, and accountability in the procurement process. The system is designed to facilitate coordination between the procurement unit and vendors, reduce the time required for tender processes, and minimize errors in data processing. The development method used for this system is the Waterfall model, which involves stages from requirement analysis to system testing. The outcome of this research is a web-based information system accessible by all related units, enabling real-time monitoring of the procurement process by the leadership, and simplifying vendors' processes to offer their products or services. With the implementation of SIPHARBER, the procurement process at Politeknik Harapan Bersama is expected to be conducted more effectively and efficiently.
Advancing Animal Health: A Web-Based Expert System Utilizing Forward Chaining for Disease Diagnosis Sarmidi Sarmidi; Ade Bastian; Muhammad Taufiq; Volodymyr Rusyn; Adnan Arshad; Ristina Siti Sundari
West Science Information System and Technology Vol. 2 No. 02 (2024): West Science Information System and Technology
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsist.v2i02.1207

Abstract

The increasing prevalence of animal diseases, along with the increasing need for animal products, highlights the urgent need for efficient diagnostic tools in veterinary medicine. The aim of this research is to create an expert system that uses a forward chaining algorithm to diagnose animal diseases. The forward chaining algorithm is a deductive reasoning approach that starts with existing facts and uses expert tree rules for hypotheses. This process continues until the desired goal is achieved or no additional conclusions can be drawn. Even though there are developments in expert systems, there are still shortcomings in implementing the forward chain for rapid and precise diagnosis of livestock diseases. This work aims to fill this gap by developing an expert system that improves the accuracy and efficiency of disease diagnosis in the livestock industry. A database of animal diseases and symptoms was created by observing and interacting directly with farmers. The system architecture is specifically intended to optimize data processing and user engagement, enabling rapid diagnosis and treatment recommendations. This test shows a level of accuracy and precision, thereby reducing the possibility of misdiagnosis. The capacity of expert systems to provide fast and reliable diagnoses has the potential to improve livestock health management, thereby helping farmers maintain animal welfare and productivity. The results of this work advance the field of veterinary diagnostics and propose other uses of expert systems in animal health management.
The Effect of Enterprise Resource Planning (ERP) System Implementation, User Training, and Management Support on User Satisfaction in Manufacturing Companies Sudarmo Sudarmo; Arnes Yuli Vandika; Rival Fahrijal
West Science Information System and Technology Vol. 2 No. 02 (2024): West Science Information System and Technology
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsist.v2i02.1209

Abstract

This study examines the impact of Enterprise Resource Planning (ERP) system implementation, User Training, and Management Support on User Satisfaction within manufacturing companies. Using a quantitative approach, data were collected from 160 respondents and analyzed using Structural Equation Modeling-Partial Least Squares (SEM-PLS). The results indicate that all three factors—ERP system implementation, User Training, and Management Support—have a positive and significant effect on User Satisfaction. Among these, User Training emerged as the most influential factor, followed by Management Support and ERP system implementation. These findings underscore the critical role of human-centric factors in ensuring successful ERP adoption and highlight the importance of comprehensive training and strong management involvement. The study offers practical insights for organizations aiming to enhance user satisfaction with ERP systems, emphasizing the need for a holistic approach that integrates technical and human factors.
The Effect of Application Development, Data Security, and Infrastructure Availability on Cost Savings and Company Economic Performance Made Susilawati; Arnes Yuli Vandika; Tera Lesmana
West Science Information System and Technology Vol. 2 No. 02 (2024): West Science Information System and Technology
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsist.v2i02.1210

Abstract

This study examines the impact of application development, data security, and infrastructure availability on the economic performance and cost savings of companies in Indonesia. Utilizing a quantitative research design, data were collected from 180 companies across various industries using a structured questionnaire. The data were analyzed using Structural Equation Modeling-Partial Least Squares (SEM-PLS) 3. The results indicate that all three factors—application development, data security, and infrastructure availability—positively and significantly influence economic performance and cost savings. Infrastructure availability emerged as the most influential factor, underscoring its critical role in supporting technological advancements and efficient operations. These findings provide valuable insights for business leaders and policymakers, emphasizing the importance of strategic investments in technology to enhance financial outcomes and competitiveness in the Indonesian market.
Evolution of Cloud Computing Research in Information Systems Loso Judijanto; Arnes Yuli Vandika; M. Ammar Muhtadi
West Science Information System and Technology Vol. 2 No. 02 (2024): West Science Information System and Technology
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsist.v2i02.1211

Abstract

Cloud computing has become a cornerstone of modern information systems, driving significant advancements across various industries. This study conducts a bibliometric analysis to explore the evolution of cloud computing research within the field of information systems, focusing on key trends, influential publications, and author collaborations. The analysis, based on data from Google Scholar, reveals that security has consistently been a critical area of focus, reflecting the ongoing concern for safeguarding cloud environments. Additionally, the integration of cloud computing with emerging technologies like the Internet of Things (IoT) and big data is highlighted as a growing trend, emphasizing the expanding applications of cloud computing. The study also identifies distinct research clusters, suggesting opportunities for enhanced collaboration across different subfields. While the findings offer valuable insights for both researchers and practitioners, the study acknowledges its limitations, including the reliance on a single database and the potential oversimplification of complex research interactions. Future research should consider these limitations and explore new avenues to further understand and enhance cloud computing practices.
Implementation of Internet of Things (IoT) in Information System Loso Judijanto; Arnes Yuli Vandika; Yana Priyana
West Science Information System and Technology Vol. 2 No. 02 (2024): West Science Information System and Technology
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsist.v2i02.1212

Abstract

This study employs bibliometric analysis to map the network of research collaborations within a specific academic field, identifying key contributors and the structure of their interconnections. By analyzing data from prominent academic databases, the study visualizes clusters of authors and assesses their influence based on publication and citation metrics. The findings offer strategic insights into the core research communities, highlighting central authors and potential areas for collaboration. Practical implications are discussed for academic institutions, research networks, and funding strategies, emphasizing how these entities can utilize the analysis to enhance research output and innovation. Limitations of the study include potential database biases and a focus on quantitative measures, which may not fully capture the dynamic and qualitative aspects of individual contributions. Despite these challenges, the bibliometric analysis provides valuable guidance for strategic decision-making in research and academic communities.
Application of Artificial Intelligence to Improve Production Process Efficiency in Manufacturing Industry Lucky Mahesa Yahya; Suharni Suharni; Deddy Hidayat; Arnes Yuli Vandika
West Science Information System and Technology Vol. 2 No. 02 (2024): West Science Information System and Technology
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsist.v2i02.1221

Abstract

The rapid advancement of Artificial Intelligence (AI) has profoundly impacted the manufacturing industry, offering transformative potential to enhance production process efficiency. This paper presents a systematic literature review of AI applications in the manufacturing sector, focusing on key AI technologies such as machine learning, robotics, predictive analytics, and natural language processing. The review highlights how these technologies have improved quality control, resource management, and overall operational performance. However, the adoption of AI also presents challenges, including significant investment costs, the need for a skilled workforce, and concerns over data security and privacy. Despite these challenges, the integration of AI in manufacturing presents numerous opportunities for future research and innovation, particularly in the areas of sustainable manufacturing and the convergence of AI with other emerging technologies. This study concludes that while AI offers substantial benefits for production efficiency, its successful implementation requires careful strategic planning and investment in both technology and human resources.

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