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All Journal Jurnal Teknik Informatika (JUTIF) The Scientific Journal of Information Systems Fortech : Journal of Information Technology
Muttaqi, Fajar
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Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Environmental Science

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OPTIMIZATION OF INDOMARET'S BUSINESS STRATEGY IN JAKARTA THROUGH DATA MINING AND INFORMATION SYSTEM TECHNOLOGY Mohamad, Daffa Rafi Aldin; Alfaujianto, Moh; Kudmas, Mikhael; Muttaqi, Fajar; Lahagu, David
Scientific Journal of Information System Vol. 3 No. 1 (2025): Scientific Journal of Information System
Publisher : Universitas Utpadaka Swastika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/sjis.v3i1.170

Abstract

This study aims to analyze the number of Indomaret outlets in Jakarta by utilizing informationsystems technology and data mining techniques. Using quantitative data from 500 Indomaretlocations, the analysis was conducted to identify distribution patterns and the factors influencingoutlet growth. Clustering and linear regression methods were employed to evaluate the relationshipbetween the number of outlets and demographic and economic variables, such as population density,per capita income, and distance from the city center. The analysis results indicate a significantrelationship between population density and the number of Indomaret outlets, with a regressioncoefficient of 0.75 (p < 0.01), meaning that every increase of 1,000 people in population density isassociated with the addition of 3 Indomaret outlets. Clustering analysis also identified three strategiclocation groups with high growth potential. The main contribution of this research lies in integratingdata mining methods with spatial analysis to understand modern retail expansion in urban areas—anapproach that is still rarely explored in previous studies. These findings not only enrich the literatureon data-driven retail location analysis but also provide practical insights for industry players informulating data-based expansion strategies. This research offers valuable insights for Indomaret’smanagement in making strategic decisions regarding expansion and store placement, demonstratingthat the use of information systems and data mining is effective in supporting quantitative analysisfor business development in the retail sector.
PERANCANGAN FRAMEWORK GREEN IT UNTUK MENGURANGI DAMPAK NEGATIF DALAM ORGANISASI: Green IT, E-Waste, Energy Management, Organizations Muttaqi, Fajar; Alfaujianto, Moh.; Surahmat, Asep
FORTECH (Journal of Information Technology) Vol 9 No 1 (2025): Fortech (Journal Of Information Technology)
Publisher : LP2M Universitas Nurdin Hamzah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53564/fortech.v9i1.1561

Abstract

The adoption of technological advancements has strengthened the role of information technology (IT) in organizational operations. While IT provides various benefits, it also creates environmental challenges, such as high energy consumption and increasing electronic waste (e-waste). The International Energy Agency (IEA) reports that data centers and IT networks consume 1% of global energy, yet only 17.4% of e-waste is recycled. Therefore, a sustainable IT management approach is crucial. Green IT integrates sustainability principles into IT management to improve energy efficiency, waste reduction, and eco-friendly technology adoption. However, challenges such as unclear guidelines, infrastructure limitations, and low organizational awareness hinder its implementation. This study aims to design a Green IT framework suited to Indonesia’s context, using literature reviews, stakeholder interviews, and case studies. The findings will provide organizations with structured guidance to reduce environmental impact, improve efficiency, and enhance competitiveness in the digital era
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.
CCTV-Based River Waste Detection Using a Hybrid CNN–Graph Attention Network with Spatial–Contextual Feature Learning Surahmat, Asep; Zogara, Lukas Umbu; Muttaqi, Fajar
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5544

Abstract

River waste accumulation has become a serious environmental problem in urban areas, particularly in highly polluted rivers such as the Angke River in Tangerang, where floating waste disrupts ecological balance and increases flood risk. Conventional computer vision–based detection methods often fail under dynamic river conditions due to water surface reflections, turbulence, occlusion, and visually ambiguous debris. This study aims to improve the accuracy and robustness of river waste detection by proposing a hybrid deep learning framework that integrates convolutional and graph-based spatial–contextual reasoning. The proposed method utilizes a ResNet50 backbone for feature extraction from CCTV imagery, followed by spatial graph construction that models adjacency relationships between image regions. A Graph Attention Network (GAT) is then applied to capture contextual dependencies and refine feature representations prior to classification. Unlike conventional CNN-only or YOLO-based detectors that rely primarily on local visual cues and bounding-box representations, the proposed approach explicitly models spatial–contextual relationships between image regions through graph-based attention mechanisms. Experiments were conducted on 4,200 CCTV image frames collected from the Angke River under varying environmental conditions. The proposed model achieved an accuracy of 92.4%, precision of 91.1%, recall of 93.2%, F1-score of 91.9%, and a mean Average Precision (mAP) of 0.78, outperforming CNN-only and YOLO-based baseline models. These findings highlight the contribution of graph-enhanced visual reasoning to the fields of Computer Vision and Intelligent Surveillance, particularly for real-time environmental monitoring systems operating in complex and dynamic visual environments.
Co-Authors Alfaujianto, Moh Alfaujianto, Moh. Badriah, Nurul Fauziah, Helmi Yulianti Kudmas, Mikhael Lahagu, David Lukas Umbu Zogara Mohamad, Daffa Rafi Aldin Surahmat, Asep Veri Shandy, Sony