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All Journal Scientific Journal of Informatics JOIN (Jurnal Online Informatika) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JISKa (Jurnal Informatika Sunan Kalijaga) Journal of Soft Computing Exploration Journal of Student Research Exploration Journal of Information System Exploration and Research Scientific Journal of Informatics
Pertiwi, Dwika Ananda Agustina
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Library & Information Science

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Journal : journal of information system exploration and research

 1 
The Effect of Digitalization on Business Performance in the MSME Industry Context Umar, Fadhil; Septian, M. Rivaldi Ali; Pertiwi, Dwika Ananda Agustina
Journal of Information System Exploration and Research Vol. 2 No. 1 (2024): January 2024
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v2i1.199

Abstract

The current digital era is increasingly developing in the use of new technology that creates value for companies and offers benefits. Digitalization is useful for increasing competitive advantage to improve business performance. The purpose of this study is to find out whether digitalization affects business performance and to find out whether competitive advantage can mediate digitalization on business performance. The sample of this research is 115 SMEs in Semarang. data were analyzed using the SEM approach with the smartPLS tool. The results of the study show that the digitalization variable has an influence on business performance, furthermore, competitive advantage also has a positive and significant effect on business performance. The results of the indirect effect test also show that competitive advantage can mediate the relationship between digitalization and business performance. The better the implementation of digitalization, the higher the competitive advantage MSMEs, consequently leading to an increase the business performance.
Breast Cancer Diagnosis Utilizing Artificial Neural Network (ANN) Algorithm for Integrating Multi-Omics Data and Clinical Features Rofik, Rofik; Artiyani, Fani; Pertiwi, Dwika Ananda Agustina
Journal of Information System Exploration and Research Vol. 2 No. 2 (2024): July 2024
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v2i2.249

Abstract

Breast cancer is one of the most common diseases affecting women worldwide, with a significant impact on patient's health and quality of life. Despite advances in medical technology and research, breast cancer diagnosis remains a challenge due to its complexity involving various biological and clinical factors. Several previous studies have focused on detecting this disease with optimal accuracy, but the selection of appropriate algorithms and methods is key to achieving this goal. This study aims to improve the accuracy of breast cancer diagnosis by using the ANN algorithm and data balancing method, SMOTE. This research uses Multi-Omic data and Clinical Features obtained in general from Kaggle. The research process is carried out in several stages, namely Data Collection, Preprocessing, Oversampling, Modeling, and Evaluation. This research successfully obtained an increase in accuracy, which was able to achieve an accuracy of 99.30%.  This research shows that early detection of breast cancer with ANN algorithm and data balancing using SMOTE can improve accuracy performance in early detection of breast cancer. Given the use of data in this study is not too large, it is recommended for further research to use a larger dataset to validate the strength of the model that has been built on more varied data.
Operational Supply Chain Risk Management on Apparel Industry Based on Supply Chain Operation Reference (SCOR) Pertiwi, Dwika Ananda Agustina; Yusuf, Muhammad; Efrilianda, Devi Ajeng
Journal of Information System Exploration and Research Vol. 1 No. 1 (2023): January 2023
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v1i1.103

Abstract

The occurrence of uncertainty requires proper handling to avoid the adverse effects called risk. Risk tends to arise in the supply chain process called supply chain risk. The purpose of this research is to identify the possible level of risk that occurs and has the potential to disrupt supply chain activities, determine priority risk sources based on Supply Chain Operation References (SCOR). The object of this research is the apparel industry, which is a company engaged in fashion and apparel production. This study uses a qualitative and quantitative approach, the value of the instrument is assessed based on the results of the Aggregate Risk Potential (ARP) calculation in the House of Risk method phase 1.  The results showed that there were 39 correlations between risk events and risk agents, with 22 correlations with a high scale and 1 correlation with a low scale, and 15 correlations on a medium scale.
Analyzing the Impact of Effort Expectancy and Cognitive Attitudes on The Willingness to Accept ChatGPT Saputra, Andri; Noraini, Oktafiyani Aisah; Pertiwi, Dwika Ananda Agustina
Journal of Information System Exploration and Research Vol. 3 No. 2 (2025): July 2025
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v3i2.599

Abstract

This study aims to analyze the impact of Effort Expectancy (EE) adapted from the Unified Theory of Acceptance and Use of Technology (UTAUT) and Cognitive Attitude (CA) from the Theory of Reasined Action (TRA) model on Willingness to Accept (WA) adapted from TAM on ChatGPT. By understanding the relationship between these factors, we can identify effective strategies to increase user acceptance of ChatGPT technology. The research method used is quantitative with multiple linear regression calculations in SPSS. This study obtained 50 respondents with a total of 10 variables but there were 3 main variables. With the final result, Effort Expectancy has no significant effect on Willingness to Accept while Cognitive Attitude has a significant effect on Willingness to Accept. This suggests that users’ perceptions of how easy or difficult it is to use ChatGPT do not influence their decision to accept and use the technology. In this context, users may feel that ease of use is not a major factor influencing their acceptance of ChatGPT. This means that users’ cognitive attitudes—including their beliefs, perceptions, and understanding of the technology—play an important role in their decision to accept and use ChatGPT.
Analysis and Visualization of Purchasing Pattern in Retail Product Transaction using Apriori Algorithm Febriani SM, N. Nelis; Setyoningrum, Nuk Ghurroh; Lodana, Mae; Pertiwi, Dwika Ananda Agustina; Muslim, Much Aziz
Journal of Information System Exploration and Research Vol. 4 No. 1 (2026): January 2026
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i1.650

Abstract

The rapid growth of the retail industry generates large volumes of transaction data that can be analyzed to support data-driven business decision making. This study aims to analyze and visualize purchasing patterns in retail product transactions by applying data mining techniques using the Apriori algorithm and business intelligence visualization through Microsoft Power BI. The dataset consists of 1 million retail transactions collected from an open retail transaction repository. The research stages include data collection, transaction data preprocessing, implementation of the Apriori algorithm with a minimum support threshold of 0.002 and a minimum confidence of 0.5, and visualization of the analysis results through interactive dashboards using Power BI and a Python-based application developed with the Streamlit framework. The results indicate that the Apriori algorithm successfully identifies frequent product associations and generates 12 association rules that meet the criteria of strong association rules. Power BI visualizations provide comprehensive insights into transaction trends based on customer categories, store types, payment methods, seasons, and transaction regions. These findings are expected to assist retail companies in formulating marketing strategies, developing product recommendations, and optimizing inventory management in a more effective and data-driven manner. This study contributes by integrating large-scale association rule mining with interactive business intelligence visualization for retail decision support.
Co-Authors Ahmad, Kamilah Al-Zahra, Gea Destadia Ali, Muazam Annegrat, Ahmed Mohamed Artiyani, Fani Aslamiyah, Sulaibatul Budi Prasetiyo, Budi Budiman, Kholiq Darmawan, Aditya Yoga Dianti, Erika Noor Djuniadi Djuniadi Dullah, Ahmad Ubai Efrilianda, Devi Ajeng Fattah, Ahmad Maulana Malik Hadiq, Hadiq Hidayah, Sayidah Rohmatul Jumanto Jumanto, Jumanto Jumanto Unjung Khan, Atta Ullah Kharisma, Sidiq Noor Khoirunnisa, Oktaria Lestari, Apri Dwi Lodana, Mae Much Aziz Muslim Muhammad Yusuf Muzayanah, Rini N. Nelis Febriani SM Ningsih, Maylinna Rahayu Noraini, Oktafiyani Aisah Nuk Ghurroh Setyoningrum Nurwijayanti Prabaswara, Ireneus Putri Utami, Putri Putro, Ari Nugroho Riliandini, Popy Rofik Rofik, Rofik Salahudin, Shahrul Nizam Santosa, Kevin Septian, M. Rivaldi Ali Solehatin, Solehatin Syahra, Syahra Audiyani Fitra Umar, Fadhil