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All Journal Jurnal EMT KITA International Journal of Social Science and Business International Journal Software Engineering and Computer Science (IJSECS) Jurnal Manajemen dan Teknologi
Sulistyawati, Upik Sri
Institut Teknologi Dan Bisnis Muhammadiyah Bali
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences Other

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Journal : International Journal Software Engineering and Computer Science (IJSECS)

 1 
Billing Barber Shop (An Implementation of GoodBarber App Builder) Upik Sri Sulistyawati; Bahruni; Afrizal; Alfina
International Journal Software Engineering and Computer Science (IJSECS) Vol. 2 No. 2 (2022): OCTOBER 2022
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v2i2.798

Abstract

The research objective is to develop a Billing Barbershop application by implementing GoodBarber App Builder in application development. The research method used in this study consists of 5 parts, namely the literature study method, data collection method, analysis method, design method, and the development method used is extreme programming. From the research results, a Billing Barber Shop application has been developed by implementing the GoodBarber App Builder which is designed to consist of login, home, billing, orders, product, and users forms. The test results are known in Usability testing, Compatibility testing, Interface testing, Low level resource, Performance testing, Operational testing, Installation testing, and Security testing stating very good with an average percentage above 80%. But in Service testing, choosing poor because Billing Barber Shop still uses the internet as a connection to data using Zapier Add-ons and Google Sheets.
Analyzing Customers in E-Commerce Using Dempster-Shafer Method Nazaruddin, Erizal; Caroline; Andrijanni; Sulistyawati, Upik Sri
International Journal Software Engineering and Computer Science (IJSECS) Vol. 3 No. 2 (2023): AUGUST 2023
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v3i2.1497

Abstract

This research explores the analysis of consumer sentiment in the context of e-commerce by applying the sophisticated Dempster-Shafer method. We started with the collection of more than 20,000 consumer reviews from various leading e-commerce platforms and continued with a detailed data pre-processing stage to obtain a clean and structured dataset. Next, we leverage the Dempster-Shafer method to represent and combine information from multiple sources, addressing uncertainty in diverse consumer opinions. The results of the sentiment analysis show that the Dempster-Shafer method achieves an accuracy of around 85%, with good evaluation metrics. Additionally, this research provides insight into the factors that influence consumers' views of products or services in the growing e-commerce context. The literature review also reveals the potential application of the Dempster-Shafer method in other aspects of e-commerce business, such as risk management and consumer trust. This research highlights the contribution of the Dempster-Shafer method in addressing uncertainty and complexity in consumer sentiment analysis, yielding a deep understanding of consumer perceptions, and enabling more accurate decision making in a dynamic e-commerce context. This research also provides a foundation for further development in consumer sentiment analysis and the application of the Dempster-Shafer method in e-commerce.
Stock Portfolio Analysis with Machine Learning Algorithmic Approach for Smart Investment Decisions Munawir; Sulistyawati, Upik Sri
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 3 (2024): DECEMBER 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i3.2606

Abstract

This study investigates the application of machine learning algorithms in stock portfolio analysis within the Indonesia Stock Exchange (IDX) and their impact on investment decision-making. By engaging 500 respondents from diverse market segments, including retail investors, institutional investors, and stock traders, the research provides a comprehensive overview of adopting and utilising machine learning technologies in the Indonesian stock market. The findings reveal that over 80% of respondents have integrated machine learning algorithms into their investment strategies. The algorithms are applied in various capacities: 45% of respondents use them for portfolio risk analysis, 30% for stock price prediction, and 25% for identifying new investment opportunities. Preferences for specific algorithms vary, with regression, Support Vector Machines (SVM), and Random Forest emerging as the most used tools. The integration of machine learning was strongly associated with improved investment decisions, as more than 60% of respondents reported enhanced portfolio performance and greater accuracy in their decision-making. These results highlight the transformative potential of machine learning algorithms in enabling more innovative and more adaptive investment strategies.
Billing Barber Shop (An Implementation of GoodBarber App Builder) Sulistyawati, Upik Sri; Bahruni; Afrizal; Alfina
International Journal Software Engineering and Computer Science (IJSECS) Vol. 2 No. 2 (2022): OCTOBER 2022
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v2i2.798

Abstract

The research objective is to develop a Billing Barbershop application by implementing GoodBarber App Builder in application development. The research method used in this study consists of 5 parts, namely the literature study method, data collection method, analysis method, design method, and the development method used is extreme programming. From the research results, a Billing Barber Shop application has been developed by implementing the GoodBarber App Builder which is designed to consist of login, home, billing, orders, product, and users forms. The test results are known in Usability testing, Compatibility testing, Interface testing, Low level resource, Performance testing, Operational testing, Installation testing, and Security testing stating very good with an average percentage above 80%. But in Service testing, choosing poor because Billing Barber Shop still uses the internet as a connection to data using Zapier Add-ons and Google Sheets.
Co-Authors Afrizal Afrizal Alfina Alfina Andrijanni Bahruni Bahruni . Bahruni Bahruni Caroline Dedi Sufriadi Edi Susilo Muhammad Jamil Munawir Munawir Munawir Munawir Nazaruddin, Erizal Purwoko, Agus Sufriadi, Dedi Syamsul Rizal