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The Scientific Journal of Information Systems
Published by Universitas Utpadaka Swastika
ISSN : -     EISSN : 3046711X     DOI : 10.70429
Core Subject : Science, Social,
Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Environmental Science
The Scientific Journal of Information Systems (JISI) aims to provide scientific literature specifically on studies of applied research in information systems (IS) and a public review of the development of theory, methods, and applied sciences related to the subject. The journal facilitates not only local researchers but also international researchers to publish their works exclusively in English.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 30 Documents
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Utilization Of Artificial Intelligence In Mobile Applications For Customer Satisfaction At PT. Tirta Mas Hermanto, Toto; Gulo, Monika
Scientific Journal of Information System Vol. 3 No. 2 (2025): Scientific Journal of Information System
Publisher : Universitas Utpadaka Swastika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/sjis.v3i2.222

Abstract

The internet has a very positive impact on human life, with the internet people can provide usefulknowledge. In this era of globalization, the development of applications is increasing every year, intechnology in the modern digital era like this, business competition is increasing with the presence ofArtificial Intelligence, a technology that has developed very rapidly in recent years. ArtificialIntelligence has the potential to change various aspects of people's lives ranging from the industrialworld, education to public services. Artificial intelligence is expected to be developed so thatapplications in companies can run well. Artificial intelligence is one of the innovations related to themanufacture of computers and machines that can involve data on a large scale or can be called bigdata, every year the development of artificial intelligence is increasing and has a very positive impacton companies. The data used for one month of research, customer satisfaction data for consumers isused as something significant to solve problems, so researchers provide a form of appreciation for thecompany's sustainability in the future and a form of gratitude. The company continues to develop aproduct, guaranteed quality and attractive services so that consumers can shop again. Based on thequality of service service distance and quality1 is 97% greater than other products, so with thiscustomer satisfaction is the most important.
The Impact of Knowledge Management Systems in Enhancing the Competitiveness of Retail Companies Muttaqi, Fajar; Zogara, Lukas Umbu; Alfaujianto, Moh.; Surahmat, Asep
Scientific Journal of Information System Vol. 3 No. 2 (2025): Scientific Journal of Information System
Publisher : Universitas Utpadaka Swastika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/sjis.v3i2.227

Abstract

This study investigates the role of Knowledge Management System (KMS) implementation inenhancing 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-depthinterviews, field observations, and company document analysis. The findings demonstrate that KMSaccelerates the flow of information, reduces duplication, and improves operational efficiency, therebyenabling better coordination among departments. Furthermore, KMS facilitates knowledge sharingand collaboration, which supports the development of service innovations and responsive marketingstrategies. Employees reported that the system allows faster access to documents, real-time inventorychecking, and more structured workflows. Beyond operational benefits, KMS contributes tostrengthening customer satisfaction through improved responsiveness and accurate informationdelivery. Additionally, KMS supports the company’s digital transformation by integrating internalsystems such as ERP, CRM, and e-commerce platforms. Overall, KMS functions not only as aknowledge repository but as a strategic enabler of sustainable competitive advantage in the retailsector.
Implementation and Analysis of Multiple Interface Policies through System Feature Visibility on Fortigate FG-60F Alfaujianto, Moh; Muttaqi, Fajar; Surahmat, Asep; Zogara, Lukas Umbu
Scientific Journal of Information System Vol. 3 No. 2 (2025): Scientific Journal of Information System
Publisher : Universitas Utpadaka Swastika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/sjis.v3i2.229

Abstract

Fortigate FG-60F is one of the popular firewall appliances utilized by small and medium-scalenetworks in managing security. However, some of the needed features such as multiple interfacepolicies are not displayed by default on the user interface. This study explores the functionality andeffectiveness 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 thehidden feature, and subsequently, a set of policy rule scenarios with multiple interfaces wereestablished and tested. The results indicate that supporting system-feature visibility enhancessignificantly the administrator's ability to implement more specific traffic policies that arecommensurate with network topology requirements. Moreover, performance analysis showed nonegative impact on device performance after the implementation of multi-interface policy. Thefindings are expected to serve as a valuable reference for network administrators in optimizingFortigate FG-60F security capabilities by leveraging advanced, previously hidden features
Implementation of Regression CART Decision Tree for Best Cycling Time Recommendation Based on Weather Data Badriah, Nurul; Muttaqi, Fajar; Veri Shandy, Sony; Alfaujianto, Moh
Scientific Journal of Information System Vol. 3 No. 2 (2025): Scientific Journal of Information System
Publisher : Universitas Utpadaka Swastika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/sjis.v3i2.233

Abstract

Cycling requires careful time planning to ensure safety and comfort, especially when consideringweather conditions such as temperature, wind speed, and overall weather status. However, cyclistsoften struggle to determine the optimal time to ride due to the lack of accurate and easily accessiblerecommendations. This study aims to design and implement a mobile application that recommendsthe best cycling time based on real-time weather data. The system applies the Regression CARTDecision Tree method, trained using hourly temperature, wind speed, and weather conditionparameters. Unlike classification approaches, Regression CART Decision Tree produces acontinuous percentage score indicating the suitability level of each hour for cycling. Real-time datais obtained via the OpenWeatherMap API to maintain accuracy. The developed prototype displayshourly weather data along with the recommendation percentage, helping users plan their rides moreeffectively. Model evaluation shows that the Regression CART Decision Tree achieved high accuracywith a low Mean Absolute Error (MAE) and strong correlation between predicted and actualsuitability scores. The results confirm that the model performs consistently in various weatherscenarios. Overall, the system successfully delivers reliable, data-driven recommendations, assistingcyclists in selecting the safest and most comfortable cycling times.
Automated Financial Report Summarization Using Python: A PDF-Based Approach Nugraha, Fahmi Rizky
Scientific Journal of Information System Vol. 3 No. 2 (2025): Scientific Journal of Information System
Publisher : Universitas Utpadaka Swastika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/sjis.v3i2.240

Abstract

Financial reports are often lengthy, complex, and filled with domain-specific jargon, making itdifficult for analysts and stakeholders to extract key insights efficiently. This study proposes anautomated summarization system using Natural Language Processing (NLP) techniques to generateconcise and coherent summaries of financial reports. The system employs a two-stage summarizationarchitecture combining extractive and abstractive methods based on Transformer models such asBART, PEGASUS, and T5. Evaluation on simulated financial document datasets demonstrates thatthe hybrid two-stage model achieves the highest ROUGE scores and information retention ratescompared to single-model baselines. The results indicate that NLP-driven summarization cansignificantly reduce analysts’ workload and improve financial decision-making speed
Comparative Analysis of Cloud Service Models for Professional Use: IaaS, PaaS, and SaaS Alfaujianto, Moh; Nugraha, Fahmi Rizky; Muttaqi, Fajar; Zogara, Lukas Umbu
Scientific Journal of Information System Vol. 4 No. 1 (2026): Scientific Journal of Information System
Publisher : Universitas Utpadaka Swastika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/sjis.v4i1.310

Abstract

This study aims to conduct a structured comparative analysis of cloud computing service models-Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS)-for professional use across multiple sectors. A quantitative comparative approach was employed using data collected from scientific literature and semi-structured interviews involving 15 professionals from education, business, and technology sectors. Each model was evaluated based on five parameters: flexibility, scalability, cost efficiency, user control, and sector relevance using a Likert scale (1–5). The results indicate that IaaS achieved the highest score in flexibility (5.0) and user control (5.0), PaaS showed balanced performance across development-related parameters (average score 4.2), while SaaS demonstrated the highest cost efficiency (5.0). These findings highlight that no single model is universally superior, and selection should be aligned with organizational priorities. This study contributes by providing a parameter-based quantitative comparison framework to support decision-making in cloud service adoption.
Machine Learning for Predicting Property Purchase Behavior: A Systematic Literature Review Lukas Umbu Zogara; Asep Surahmat
Scientific Journal of Information System Vol. 4 No. 1 (2026): Scientific Journal of Information System
Publisher : Universitas Utpadaka Swastika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/sjis.v4i1.329

Abstract

This study aims to examine the application of machine learning algorithms in predicting property purchase behavior based on consumer data. The main problem addressed is the limited use of intelligent data analysis in understanding consumer behavior in the Indonesian property sector, despite increasing market data availability. This research employs a systematic literature review approach by analyzing studies published in the last five years, focusing on classification algorithms such as Decision Tree, Random Forest, and Support Vector Machine (SVM). The analysis includes data collection, evaluation, and synthesis of selected studies. The results indicate that algorithm performance varies depending on data characteristics and application context. Random Forest tends to show strong performance in terms of accuracy and robustness, while Decision Tree and SVM also demonstrate competitive results in certain scenarios. These findings reflect general trends rather than definitive conclusions. Key factors influencing property purchase decisions include location, price, and developer reputation. In conclusion, machine learning has significant potential to support data-driven decision-making in the property sector. Future research should integrate real-time and more diverse data to improve predictive model accuracy
Machine Learning-Based Knowledge Trend Analysis Using LDA for Strategic Decision-Making Fajar Muttaqi; Alfaujianto, Moh; Atmaja, Pungky Hari Wira
Scientific Journal of Information System Vol. 4 No. 1 (2026): Scientific Journal of Information System
Publisher : Universitas Utpadaka Swastika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/sjis.v4i1.331

Abstract

In the digital economy, Knowledge Management Systems (KMS) often fail to provide actionable insights due to information overload, leaving valuable expertise fragmented and underutilized. This research aims to integrate Machine Learning (ML) to transform passive data into proactive strategic foresight by analyzing knowledge trends. Using a longitudinal dataset of search logs and document metadata, the study implements a text-mining pipeline centered on Latent Dirichlet Allocation (LDA) to extract thematic clusters. The model identified eight distinct knowledge domains, with "Advanced Data Analytics" emerging as a high-growth sector (TVI = +0.13), while a critical "Knowledge Gap" in cybersecurity was detected where search demand outpaced document supply by 58%. This study contributes by proposing a Trend Velocity Index (TVI) to quantify knowledge evolution and detect knowledge gaps, providing a robust framework for leaders to optimize resource allocation and ensure institutional agility.
Analysis of Priority-Based Communication Feature Using UI/UX Design Thinking Model: A Case Study of WhatsApp Badriah, Nurul; Veri Shandy, Sony; Muttaqi, Fajar; Alfaujianto , Moh
Scientific Journal of Information System Vol. 4 No. 1 (2026): Scientific Journal of Information System
Publisher : Universitas Utpadaka Swastika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/sjis.v4i1.333

Abstract

The rapid growth of instant messaging applications has significantly transformed the way individuals communicate in both personal and professional contexts. However, the increasing volume of incoming messages often leads to information overload, making it difficult for users to distinguish between important and less relevant conversations. This study aims to design and implement a priority-based communication feature using a Design Thinking approach, with WhatsApp as a case study. Unlike conventional chronological message ordering, the proposed system allows users to manually define priority contacts through a “High Priority Mode” feature, enabling important conversations to be automatically positioned at the top of the chat list. In addition, the system introduces visual differentiation in notifications to highlight messages from priority contacts. A prototype interface is developed to support intuitive configuration and improve usability. The results indicate that the proposed feature enhances message visibility, reduces the risk of overlooking important communications, and improves overall user efficiency. This study demonstrates that a user-centered, rule-based approach can provide a practical and effective solution for managing communication priorities in messaging applications.
Implementation of the Naive Bayes Algorithm for Classification of Public Service Complaints in E-Government at Kunciran Indah Tangerang Venequenn, Zjevassel; Surahmat, Asep
Scientific Journal of Information System Vol. 4 No. 1 (2026): Scientific Journal of Information System
Publisher : Universitas Utpadaka Swastika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/sjis.v4i1.334

Abstract

The implementation of e-government at the local government level is essential for improving the quality and efficiency of public services. However, the management of public service complaints at Kelurahan Kunciran Indah, Tangerang, is still conducted manually, leading to delays and inefficiencies in handling citizen reports. This study aims to implement the Naive Bayes algorithm to automatically classify public service complaints within an e-government system. A quantitative computational approach was employed using a dataset of 50 complaint records categorized into four classes: infrastructure, cleanliness, service, and administration. Data preprocessing techniques, including case folding, tokenization, and stopword removal, were applied prior to model training. The Naive Bayes classifier was used to build a classification model and evaluate its performance. The results show that the proposed model achieved an accuracy of 90%, demonstrating good performance in classifying text-based complaints across all categories. This indicates that the Naive Bayes algorithm is effective for supporting automated complaint classification in local government services. The implementation of this system can improve service efficiency, accelerate response time, and assist decision-making processes. Nevertheless, the study is limited by the relatively small dataset, and future research is recommended to utilize larger and more diverse data to enhance model performance.

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