Science Information System and Technology
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.
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<![CDATA[Evaluation and Development of Building Material Sales Information System to Improve Inventory Management and Customer Service ]]>
<![CDATA[Loso Judijanto]]>
<![CDATA[ West Science Information System and Technology Vol. 2 No. 01 (2024): West Science Information System and Technology ]]>
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DOI: 10.58812/wsist.v2i01.830
<![CDATA[This research investigates the Evaluation and Development of Building Material Sales Information Systems (BMIS) to Enhance Inventory Management and Customer Service within the construction industry. Through a qualitative analysis approach, the study explores the challenges faced by building material suppliers, identifies the requirements for effective BMIS, and evaluates the impacts of BMIS implementation on inventory management practices and customer service. Data were collected through interviews, focus group discussions, and case studies with stakeholders representing diverse perspectives within the construction supply chain. The findings highlight the critical role of BMIS in addressing inventory management challenges, improving order accuracy, and enhancing communication with customers. The study contributes to the development of practical recommendations for building material suppliers seeking to optimize their operations and deliver superior service experiences to their clientele.]]>
<![CDATA[The Influence of Service Quality and Brand Image on Customer Satisfacation at BMR Tour And Travel Agent ]]>
<![CDATA[Rizki Nurul Nugraha]]>;
<![CDATA[Zumratul Meini]]>
<![CDATA[ West Science Information System and Technology Vol. 2 No. 01 (2024): West Science Information System and Technology ]]>
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DOI: 10.58812/wsist.v2i01.845
<![CDATA[Tourism development is increasingly rapid with many increasing tourism trends. The increasing development of tourism causes an increase in demand for tours usually carried out by Tour and Travel, with this increase the quality of service and brand image is needed to produce customer satisfaction. This research uses descriptive quantitative methods to determine the magnitude of the influence of variables (X), namely Service Quality and Brand Image and variable (Y), namely Customer Satisfaction, using a regression research design. The data obtained by 30 respondents was then tabulated and analyzed using multiple linear regression tests using the SPSS version 22.0 program. The results of the research on the influence of service quality and brand image on customer satisfaction showed that the F-test results obtained were an F count of 55,523, which states that all Service Quality and Brand Image variables simultaneously and significantly influence the dependent variable (Customer Satisfaction).]]>
<![CDATA[The Influence of Attractions, Accessibility, and Facilities on The Image of The Peak Batu Roti Ciampea Tourism Object ]]>
<![CDATA[Padri Achyarsyah]]>;
<![CDATA[Rizki Nurul Nugraha]]>;
<![CDATA[Dipa Teruna Awaloedin]]>
<![CDATA[ West Science Information System and Technology Vol. 2 No. 01 (2024): West Science Information System and Technology ]]>
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DOI: 10.58812/wsist.v2i01.846
<![CDATA[Puncak Batu Roti is a tourist attraction in Bogor Regency. The lack of diversity in tourist attractions causes fluctuations in the number of tourist visits. This title was chosen because Batu Roti Peak Ciampea is the specific location that wants to be researched, and this research tries to understand the factors that can influence the image of tourist attractions at that location. Batu Roti Peak is located on the Ciampea Limestone Mountain, Bogor. This peak is one of five peaks in the area, which also include Galau Peak, Lalana Peak, Arca Lima Peak, and Karang Gantung Peak. The location is about 17 km from the center of Bogor City. This research aims to see how tourist attractions (x) influence visiting interest (y) at the Puncak Batu Roti Tourist Attraction. Tourists who come to the Puncak Batu Roti Tourist Attraction are the objects of this research. Quantitative descriptive data analysis was used in this research. Data collected from the questionnaire was used for simple linear regression analysis. The research results show that the tourist attraction variable is in the good category with a percentage of 68.33% and the interest in visiting variable is also in the good category with a percentage of 2%. This result has a significant positive value, indicating that the more tourist attractions there are at the Puncak Batu Roti Tourist Attraction, the greater the interest in visiting. Researchers advise managers to develop new tourist attractions such as limestone educational tourism for elementary, middle and high school students by utilizing the property of local residents in the Puncak area. They also suggested building a Tourist Information Center (TIC) to provide information about all aspects of the Puncak Batu Roti Tourist Attraction for tourists.]]>
<![CDATA[Ciliwung River Resource Management And Ecotourism By The Ciliwung Community In Depok ]]>
<![CDATA[Ahmad Naisaburi Bintang Dhia]]>;
<![CDATA[Rizki Nurul Nugraha]]>;
<![CDATA[Rai Riya]]>
<![CDATA[ West Science Information System and Technology Vol. 2 No. 01 (2024): West Science Information System and Technology ]]>
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DOI: 10.58812/wsist.v2i01.847
<![CDATA[The research entitled “CILIWUNG RIVER RESOURCE MANAGEMENT AND ECOTOURISM BY THE CILIWING COMMUNITY IN DEPOK” aims to identify resource management and eco-ecosystems in the Ciliwung River, and to find out what obstacles to resource and ecosystems management in Ciliwing Depok River. The research uses qualitative methods, with descriptive qualitational emphasis and SWOT analysis. Data collection is carried out with field observations, interviews, and literature studies. The results of the research show that the river resource management strategy implemented has the potential to be an attractive ecosystem destination. Through the environmental conservation, recreation, and education methods carried out by KCD, the river can provide significant benefits to the surrounding community and its surrounding environment. Tourist components such as attractions, accessibility, and facilities around the river are important factors in attracting tourists and supporting sustainable ecosystem development.]]>
<![CDATA[Cultural Tourism at the Gelora Bung Karno Main Stadium ]]>
<![CDATA[Aditya Erlangga]]>;
<![CDATA[Rizki Nurul Nugraha]]>
<![CDATA[ West Science Information System and Technology Vol. 2 No. 01 (2024): West Science Information System and Technology ]]>
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DOI: 10.58812/wsist.v2i01.848
<![CDATA[Cultural tourism needs to be preserved as it can be a forum for future generations to continue to learn about local traditions and cultures amid rapid technological advances. Researchers use this title to identify the potential of cultural tourism at the Gelora Bung Karno Main Stadium (SUGBK). SUGBK was chosen as a locus because it is an important cultural landmark in Jakarta with various art and cultural activities. This research uses qualitative methods, with a qualitatively descriptive approach. Data collection is done through field surveys, interviews and literature studies. The need of this research is to explore the potential and impact of SUGBK cultural tourism on social, economic and cultural aspects. Socially, the stadium is a means of cross-cultural interaction and enriches people's insights. Economically, tourism creates business opportunities such as street sellers and souvenir sellers around the stadium. Meanwhile, from a cultural point of view, the various traditional art performances held at the stadium helped preserve the ancestral heritage.]]>
<![CDATA[Characteristics of Tourists Visiting Setu Babakan ]]>
<![CDATA[Dipa Awaloedin]]>;
<![CDATA[Padri Achyarsyah]]>;
<![CDATA[Rizki Nurul Nugraha]]>;
<![CDATA[Muhammad Surya Saleh]]>
<![CDATA[ West Science Information System and Technology Vol. 2 No. 01 (2024): West Science Information System and Technology ]]>
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DOI: 10.58812/wsist.v2i01.849
<![CDATA[The research with the title "Characteristics of Tourists Visiting Setu Babakan" was chosen to understand more deeply the characteristics of tourists visiting a tourist destination. The success and sustainability of a destination is highly dependent on a deep understanding of geography, demography, psychography and tourist behavior. The research location is Setu Babakan, Srengseng Sawah, Jagakarsa, South Jakarta. The urgency of this research is to improve the tourist experience and optimize the utilization of Setu Babakan's resources. By analyzing tourist characteristics, Setu Babakan can plan development strategies more effectively and can increase the number of visits. The data collection method is by distributing questionnaires to 100 respondents who have been determined by the slovin formula, interviews with informants who are purposely selected, and literature from previous studies. This type of research data is quantitative and the approach used is descriptive quantitative. The results showed that most Setu Babakan tourists came from within the city, dominated by Gen Z and Millennial age groups with preferences for cultural tourism activities. Setu Babakan tourists are relatively middle economic level, intend to visit again, and are willing to recommend this destination to friends and family. Setu Babakan needs to improve facilities in accordance with tourist expectations.]]>
<![CDATA[Self-Regulation Successfully Increases Employee Perceiving a Calling at PT United Tractors TBK Jakarta ]]>
<![CDATA[Lisa Sarinah]]>;
<![CDATA[Rizki Nurul Nugraha]]>
<![CDATA[ West Science Information System and Technology Vol. 2 No. 01 (2024): West Science Information System and Technology ]]>
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DOI: 10.58812/wsist.v2i01.850
<![CDATA[This study builds on Burnette’s SOMA model. The research was conducted to test the premise that a growth mindset of work predicts living a calling and to explore the mediating role of self-regulation in the influence of a growth mindset of work on living a calling. The process in Burnett’s SOMA model includes goal setting, goal operation, goal monitoring, and goal achievement. In this theoretical model, a growth mindset as a motivational construct predicts self-regulation. Data analysis uses the PLS method with the help of SmartPLS software. The research was conducted at PT United Tractors Tbk, located at Jl.Raya Bekasi KM 22 Jakarta, for three months. Research results: A growth mindset of work has a positive but insignificant effect on living a calling, with PValues of 0.244. A growth mindset of work positively and significantly affects self-regulation, PValues of 0.000. Self-regulation has a positive and significant effect on living a calling, with PValues of 0.002. It can mediate (full mediation) a growth mindset of work on living a calling on Employees of PT United Tractors Tbk Jakarta.]]>
<![CDATA[Implementation of Convolutional Neural Network (CNN) Method for Fish Processed Cuisine Image Identification Application with Google Maps Features ]]>
<![CDATA[Rachmad Saptono]]>;
<![CDATA[Abdul Rasyid]]>;
<![CDATA[Waluyo Waluyo]]>;
<![CDATA[Farida Arinie Soelistianto]]>
<![CDATA[ West Science Information System and Technology Vol. 2 No. 01 (2024): West Science Information System and Technology ]]>
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DOI: 10.58812/wsist.v2i01.863
<![CDATA[The rapid advancement of science and technology encourages dynamic transformation in various sectors, especially in the field of information and communication technology (ICT), especially with the existence of Android-based smartphones. This advancement revolutionizes the way we access information, especially about various processed fish dishes in Indonesia. However, despite the plethora of culinary offerings, travelers often find it difficult to discover traditional dishes through social media platforms. To bridge this gap, a new app that utilizes artificial neural networks has been developed. The app allows users to photograph and upload images of processed fish dishes to recognize and provide detailed descriptions and recipes. In addition, integrating Google Maps makes it easy for users to find nearby places that serve these dishes. Testing the app with a dataset consisting of 1577 images of six types of processed fish dishes yielded promising results, with accuracy reaching 97.57% over 120 epochs. This innovation not only preserves cultural heritage but also enhances the culinary experience for locals and tourists.]]>
<![CDATA[Optimizing Liver Disease Detection Through Combining Genetic Evolutionary Algorithm and Linear Discriminant Analysis (LDA) ]]>
<![CDATA[Dwi Ari Suryaningrum]]>;
<![CDATA[Muhammad Romadhoni Indra Firmansyah]]>
<![CDATA[ West Science Information System and Technology Vol. 2 No. 01 (2024): West Science Information System and Technology ]]>
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DOI: 10.58812/wsist.v2i01.1019
<![CDATA[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.]]>
<![CDATA[Housing Value Predicted Modelling using Random Forest Regression: Case study California Housing Dataset ]]>
<![CDATA[Firman Matiinu Sigit]]>;
<![CDATA[Haniel Rangga Pramuditya Putra]]>
<![CDATA[ West Science Information System and Technology Vol. 2 No. 01 (2024): West Science Information System and Technology ]]>
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DOI: 10.58812/wsist.v2i01.1021
<![CDATA[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.]]>
