Scientific Journal of Information System

Utilization Of Artificial Intelligence In Mobile Applications For Customer Satisfaction At PT. Tirta Mas

Toto Hermanto — 2025-10-30

The internet has a very positive impact on human life, with the internet people can provide useful
knowledge. In this era of globalization, the development of applications is increasing every year, in
technology in the modern digital era like this, business competition is increasing with the presence of
Artificial Intelligence, a technology that has developed very rapidly in recent years. Artificial
Intelligence has the potential to change various aspects of people's lives ranging from the industrial
world, education to public services. Artificial intelligence is expected to be developed so that
applications in companies can run well. Artificial intelligence is one of the innovations related to the
manufacture of computers and machines that can involve data on a large scale or can be called big
data, every year the development of artificial intelligence is increasing and has a very positive impact
on companies. The data used for one month of research, customer satisfaction data for consumers is
used as something significant to solve problems, so researchers provide a form of appreciation for the
company's sustainability in the future and a form of gratitude. The company continues to develop a
product, guaranteed quality and attractive services so that consumers can shop again. Based on the
quality of service service distance and quality1 is 97% greater than other products, so with this
customer satisfaction is the most important.

The Impact of Knowledge Management Systems in Enhancing the Competitiveness of Retail Companies

Fajar Muttaqi — 2025-10-30

This study investigates the role of Knowledge Management System (KMS) implementation in
enhancing the competitiveness of retail companies, with a specific focus on Lotte Mart Indonesia.
Using a qualitative exploratory case study approach, the research collected data through in-depth
interviews, field observations, and company document analysis. The findings demonstrate that KMS
accelerates the flow of information, reduces duplication, and improves operational efficiency, thereby
enabling better coordination among departments. Furthermore, KMS facilitates knowledge sharing
and collaboration, which supports the development of service innovations and responsive marketing
strategies. Employees reported that the system allows faster access to documents, real-time inventory
checking, and more structured workflows. Beyond operational benefits, KMS contributes to
strengthening customer satisfaction through improved responsiveness and accurate information
delivery. Additionally, KMS supports the company’s digital transformation by integrating internal
systems such as ERP, CRM, and e-commerce platforms. Overall, KMS functions not only as a
knowledge repository but as a strategic enabler of sustainable competitive advantage in the retail
sector.

Implementation and Analysis of Multiple Interface Policies through System Feature Visibility on Fortigate FG-60F

Moh Alfaujianto — 2025-10-30

Fortigate FG-60F is one of the popular firewall appliances utilized by small and medium-scale
networks in managing security. However, some of the needed features such as multiple interface
policies are not displayed by default on the user interface. This study explores the functionality and
effectiveness of enabling system-feature visibility for easier management of inter-interface policies.
Employing an experimental approach, the Fortigate FG-60F device was configured to activate the
hidden feature, and subsequently, a set of policy rule scenarios with multiple interfaces were
established and tested. The results indicate that supporting system-feature visibility enhances
significantly the administrator's ability to implement more specific traffic policies that are
commensurate with network topology requirements. Moreover, performance analysis showed no
negative impact on device performance after the implementation of multi-interface policy. The
findings are expected to serve as a valuable reference for network administrators in optimizing
Fortigate FG-60F security capabilities by leveraging advanced, previously hidden features

Automated Financial Report Summarization Using Python: A PDF-Based Approach

Fahmi Rizky Nugraha — 2025-10-30

Financial reports are often lengthy, complex, and filled with domain-specific jargon, making it
difficult for analysts and stakeholders to extract key insights efficiently. This study proposes an
automated summarization system using Natural Language Processing (NLP) techniques to generate
concise and coherent summaries of financial reports. The system employs a two-stage summarization
architecture combining extractive and abstractive methods based on Transformer models such as
BART, PEGASUS, and T5. Evaluation on simulated financial document datasets demonstrates that
the hybrid two-stage model achieves the highest ROUGE scores and information retention rates
compared to single-model baselines. The results indicate that NLP-driven summarization can
significantly reduce analysts’ workload and improve financial decision-making speed

Implementation of Regression CART Decision Tree for Best Cycling Time Recommendation Based on Weather Data

Nurul Badriah — 2025-10-30

Cycling requires careful time planning to ensure safety and comfort, especially when considering
weather conditions such as temperature, wind speed, and overall weather status. However, cyclists
often struggle to determine the optimal time to ride due to the lack of accurate and easily accessible
recommendations. This study aims to design and implement a mobile application that recommends
the best cycling time based on real-time weather data. The system applies the Regression CART
Decision Tree method, trained using hourly temperature, wind speed, and weather condition
parameters. Unlike classification approaches, Regression CART Decision Tree produces a
continuous percentage score indicating the suitability level of each hour for cycling. Real-time data
is obtained via the OpenWeatherMap API to maintain accuracy. The developed prototype displays
hourly weather data along with the recommendation percentage, helping users plan their rides more
effectively. Model evaluation shows that the Regression CART Decision Tree achieved high accuracy
with a low Mean Absolute Error (MAE) and strong correlation between predicted and actual
suitability scores. The results confirm that the model performs consistently in various weather
scenarios. Overall, the system successfully delivers reliable, data-driven recommendations, assisting
cyclists in selecting the safest and most comfortable cycling times.