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Applied Information System and Management
Published by Universitas Islam Negeri Syarif Hidayatullah Jakarta
ISSN : 26212536     EISSN : 26212544     DOI : 10.15408/aism
Core Subject : Education,
Languange, Linguistic, Communication & Media
Arjuna Subject : -
Articles 227 Documents
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Enhancing Repeat Buyer Classification with Multi Feature Engineering in Logistic Regression Mauludiah, Siska Farizah; Crysdian, Cahyo; Arif, Yunifa Miftachul
Applied Information System and Management (AISM) Vol. 8 No. 1 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i1.45025

Abstract

This study presents a novel approach to improving repeat buyer classification on e-commerce platforms by integrating Kullback-Leibler (KL) divergence with logistic regression and focused feature engineering techniques. Repeat buyers are a critical segment for driving long-term revenue and customer retention, yet identifying them accurately poses challenges due to class imbalance and the complexity of consumer behavior. This research uses KL divergence in a new way to help choose important features and evaluate the model, making it easier to understand and more effective at classifying repeat buyers, unlike traditional methods. Using a real-world dataset from Indonesian e-commerce with 1,000 records, divided into 80% for training and 20% for testing, the study uses logistic regression along with techniques like SMOTE for oversampling, class weighting, and regularization to fix issues with data imbalance and overfitting. Model performance is assessed using accuracy, precision, recall, F1-score, and KL divergence. Experimental results indicate that the KL-enhanced logistic regression model significantly outperforms the baseline, especially in balancing precision and recall for the minority class of repeat buyers. The unique contribution of this work lies in its synergistic use of KL divergence in both the feature engineering and evaluation phases, offering a robust, interpreted, and data-efficient solution. For e-commerce businesses, the findings translate into improved targeting of high-value customers, better personalization of marketing efforts, and more strategic allocation of resources. This research offers practical tips for enhancing predictive customer analytics and supports data-driven decision-making in digital commerce environments.
Digital Transformation at Bandung Wholesale Center: The Impact of Information Technology on Sales Performance and Experience Syaifuddin, Syaifuddin; Puad, Noor Aimi Mohamad
Applied Information System and Management (AISM) Vol. 8 No. 1 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i1.45191

Abstract

This study examines the impact of digital transformation on sales performance and customer experience in wholesale centers in Bandung, specifically focusing on the adoption of digital inventory management systems, electronic payment platforms, and mobile applications, including the use of TikTok and Instagram for digital marketing. The study aims to analyze how these technologies improve operational efficiency, transaction speed, and customer satisfaction in the wholesale sector. A mixed-method approach was used, combining qualitative and quantitative data. Qualitative data was obtained through in-depth interviews with 10 wholesale center owners and managers, while quantitative data was collected through a survey distributed to 200 customers. Purposive sampling was used to select wholesale centers that had adopted digital technology. The findings show significant improvements in operational efficiency, with inventory checking time reduced by 66.67% (from 12 hours to 4 hours), stock errors reduced by 66.67% (from 15% to 5%), and order fulfillment speed increased by 50% (from 2 days to 1 day). The use of TikTok and Instagram in digital marketing has expanded market reach, increased customer engagement, and raised brand awareness. In addition, electronic payment systems and mobile applications have accelerated transactions, reduced queues, and increased sales volumes. Customers reported higher satisfaction, with ease of transaction increasing by 40.63% and overall satisfaction increasing by 31.43%. This study supports existing literature on the benefits of digitalization in retail and highlights challenges such as investment costs and staff training. These findings offer important insights for the wholesale sector to embrace digital transformation to increase competitiveness in an increasingly digital marketplace.
Implementation of Bidirectional Long Short-Term Memory and Convolutional Neural Network in Detecting Hoax Content on Social Media Lantang, Oktavian A.; Sendow, Raphael Edber Christopher; Kambey, Feisy Diane
Applied Information System and Management (AISM) Vol. 8 No. 1 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i1.45222

Abstract

The advancement of internet technology has facilitated the spread of information, including false information or fake news. The dissemination of hoaxes on social media, such as Twitter, can cause confusion and negatively impact society. This study aims to implement a hybrid model that combines Bidirectional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Network (CNN) for hoax detection. The dataset used consists of English tweets containing both real and fake news, collected between 2020 and 2022, as provided by the TruthSeeker dataset. The model utilizes an embedding layer with word2vec, a Conv1D layer, and a BiLSTM layer to effectively capture temporal and spatial patterns in text data. Additionally, experiments were conducted by varying the number of BiLSTM units and CNN filters to analyze their impact on model performance. After conducting parameter experiments, the best results were achieved using a Conv1D layer with 64 filters and a BiLSTM layer with 64 neurons/units. The evaluation results on the test data indicate an accuracy of 96.14%, a precision of 96%, a recall of 96.25%, and an F1-score of 96%. These results demonstrate the model's high capability in accurately detecting hoaxes, which is significant for combating misinformation on social media. With its strong performance, the model has potential applications in real-time content moderation systems, early hoax detection tools, and digital literacy platforms to help reduce the spread of false information.
Web-Based Decision Support System for Selecting Exemplary Teachers usingTOPSIS Method Sumardiono, Sumardiono; Ismail, Norhafizah; Shadiq, Jafar; Nida, Zahra Qotrun; Solikin, Solikin; Suryani, Riska
Applied Information System and Management (AISM) Vol. 8 No. 1 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i1.45488

Abstract

This study creates an online Decision Support System (DSS) using the TOPSIS algorithm to fairly choose outstanding teachers from vocational schools in Bekasi City, which has 87 state and private vocational secondary schools with about 62,000 students. To tackle the current biased selection process, our research uses a multi-criteria approach that looks at discipline (25%), travel costs (20%), personality (20%), teaching administration (15%), and learning achievements (20%). Targeting this substantial educational population, our research addresses the current subjective selection process by implementing a multi-criteria approach evaluating discipline (25%), travel costs (20%), personality (20%), teaching administration (15%), and learning achievements (20%). The TOPSIS method was selected for its proven effectiveness in ranking alternatives based on geometric distance from ideal solutions, particularly valuable in large-scale educational contexts. Analysis of 14 teacher candidates from SMK Bina Karya Mandiri demonstrated the system's precision, with Didi Saputra, S.Pdi, emerging as top-ranked (preference value: 0.63). When extrapolated to Bekasi's 87 SMKs, the model shows potential to standardize teacher assessment citywide, reducing regional disparities in recognition practices. The web-based platform enhances accessibility, allowing principals across 21 sub-districts to input localized data while maintaining centralized benchmarking. Key findings reveal (1) discipline and personality collectively account for 45% of exemplary status determination, (2) cost-related factors show inverse correlation with remote school nominations, and (3) system implementation could reduce selection time by ≈68% compared to manual methods. This study contributes both a scalable framework for educational DSS and empirical data on vocational teacher excellence criteria in urban Indonesia.
E-Government Service for Driving Digital Creative Economy in Developing Region: Perspective of Technology Readiness Index and PLS-SEM Rahmawati, Nanes Fitri; Inan, Dedi I.; Juita, Ratna; Indra, Muhamad
Applied Information System and Management (AISM) Vol. 8 No. 1 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i1.45514

Abstract

E-government plays an important role in digital economic transformation by improving access to public services and driving economic growth. One implementation in West Papua Province is Rumahekraf, a digital marketing platform for creative economy players in the local region. However, the adoption of this platform is still low, and not many studies have examined factors that influence public acceptance of this service. Therefore, this study aims to identify factors that contribute to and inhibit users' intentions to adopt this platform in West Papua. We combine the Technology Readiness Index (TRI) and Technology Acceptance Model (TAM) to understand this phenomenon. Data was gathered via an online survey utilizing Google Forms, consisting of 157 merchants/business owners. The information was examined employing Partial Least Squares Structural Equation Modeling (PLS-SEM). The research indicates that innovativeness, perceived ease of use (PEOU), and facilitating conditions play an important role in increasing users’ intention to adopt Rumahekraf (R² = 59.4%). Additionally, PEOU also contributes to increased perceived usefulness (PU) (R² = 41.1%), which in turn strengthens the benefits perceived by users (R² = 55.7%). Optimism significantly affects PEOU but not usefulness. Meanwhile, insecurity and discomfort were not found to be major barriers to the adoption of this platform.   This research provides insights for local government and platform development in improving the effectiveness of e-government implementation in West Papua by providing more adequate infrastructure and increasing public trust in the platform.
A Comparative Study of Machine Learning Models for Fashion Product Demand Prediction: Exploring Algorithms, Data Splitting, and Feature Engineering Mardiah, Reviana Siti; Fitrianingsih, Fitrianingsih
Applied Information System and Management (AISM) Vol. 8 No. 1 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i1.45600

Abstract

The fashion industry faces challenges in accurately predicting demand due to inherent uncertainty, leading to suboptimal inventory and financial losses. Machine learning (ML) offers a robust solution by analyzing large and complex data, identifying non-linear patterns, and providing more accurate predictions than conventional methods that rely on limited factors.  This research aims to compare and evaluate the performance of six different ML models—XGBoost, SVM, RF, GBM, KNN, and NN, considering the influence of feature engineering and various data split ratios on predicting fashion product demand. KNN and NN were included due to distinct modeling approaches and competitive capabilities in identifying local and non-linear patterns across numerical, categorical, and time series data.  Techniques such as feature extraction and selection and various data split ratios (70:30, 80:20, 90:10) were used.  Using Adidas sales data, the models were evaluated based on Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results indicate that the XGBoost-based model with feature engineering consistently outperforms the other models across all data split ratios. Particularly, XGBoost with feature engineering at a data split ratio of 90:10 achieved the best performance with an RMSE of 4.46 and an MAE of 1.51. Analyzing model performance shows that the predictive ability of ML models is influenced by the implementation of feature engineering and the selection of the data split ratio. These results demonstrate the potential of using feature-engineered XGBoost models and optimized data ratios to mitigate the risk of stockouts or overstocks, and reduce financial losses and environmental waste.
Effect of System, Information, and Service Quality and Green IT Attitude Towards User Satisfaction on Clientele ITSM Application Bank XYZ Adzhani, Muhamad Hafidh; Sfenrianto, Sfenrianto
Applied Information System and Management (AISM) Vol. 8 No. 1 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i1.45669

Abstract

The banking industry is increasingly shifting toward the advanced use of information technology (IT) to enhance customer service. It is important for the audience to be able to get satisfaction when using banking services. In this case, there are several factors that will impact user satisfaction when using the application. The study's objectives are to examine how system quality affects client satisfaction with the ITSM application at bank XYZ; how user satisfaction with the ITSM application for clients at bank XYZ is impacted by the quality of the information; how client satisfaction with the ITSM application at bank XYZ is impacted by service quality; and how client ITSM application at bank XYZ is impacted by a green IT attitude toward satisfaction. The research employs a quantitative approach, utilizing SEM PLS analysis. The result shows that the system quality has a positive and substantial impact on user satisfaction with a p-value of of 0.021 < 0.05; the information quality has a positive and significant impact on user satisfaction with a p-value of 0.000 < 0.05; the service quality has a negative and significant impact on user satisfaction with a p-value of 0.044 < 0.05; and green attitude has a positive impact on user satisfaction with a p-value of 0.000 < 0.05. The research results indicate that system quality, information quality, and green attitude positively influence user satisfaction, whereas service quality negatively influences it.
Evaluation of E-Learning Usability Based on ISO 25010 with Hofstede's Cultural Dimensions as Moderation: A PLS-SEM Study in Higher Education Januhari, Ni Nyoman Utami; Setyanto, Arief; Kusrini, Kusrini; Utami, Ema; Béjar, Rodrigo Martínez
Applied Information System and Management (AISM) Vol. 8 No. 1 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i1.45738

Abstract

Although e-learning has rapidly advanced in higher education, many platforms still fall short of meeting user needs due to a lack of integration between usability and cultural dimensions. This study explores how usability influences user satisfaction with e-learning platforms, with cultural dimensions based on Hofstede’s model examined as moderating variables. Usability Quality (QiU) is assessed using the ISO/IEC 25010 framework, which includes five key elements: effectiveness, efficiency, user satisfaction, risk avoidance, and contextual relevance. A total of 384 students from private universities in Bali participated in the study, representing a diverse range of academic disciplines. Using SmartPLS and Partial Least Squares Structural Equation Modeling (PLS-SEM), the analysis revealed that usability has a significant effect on user satisfaction (T=7.528, β=0.270), and cultural variables also play a substantial role (T=21.094, β=0.704). Although the moderating effect of culture was statistically significant (T=2.379, β=0.042), its impact was relatively modest compared to the direct effect of usability. Among the usability components, efficiency emerged as the most influential factor. Regarding cultural dimensions, individualism versus collectivism was found to have the strongest effect. These findings emphasize the importance of designing e-learning systems that are both usability-driven and culturally sensitive, ensuring alignment with user expectations and the educational context.  
Enhancing MSME Sales Performance on E-commerce Platforms: Exploring the Interplay of Digital Skills, Product Innovation, and User Experience Titin, Titin; Ausat, Abu Muna Almaududi; Wanof, M. Indre; Syamsuri, Syamsuri; Kraugusteeliana, Kraugusteeliana
Applied Information System and Management (AISM) Vol. 8 No. 1 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i1.45854

Abstract

This study aims to analyze the relationship between digital skills, product innovation, and user experience with the sales performance of MSMEs (Micro, Small, and Medium Enterprises) on e-commerce platforms in Semarang. Based on the Resource-Based View (RBV) and Diffusion of Innovations (DOI) theories, this study uses quantitative methods with Structural Equation Modeling-Partial Least Squares (SEM-PLS) techniques to analyze data from 100 MSMEs active on digital platforms. The results showed that digital skills have a positive and significant impact on sales performance, as they are able to assist MSMEs in optimizing digital marketing and online transaction processes efficiently. In addition, product innovation also makes an important contribution by aligning products with consumer needs and preferences, thereby driving customer loyalty and expanding market share. User experience (UX) also plays a crucial role in influencing customer satisfaction levels and the likelihood of repeat purchases, which directly impacts sales. The findings confirm that MSMEs need to prioritize digital skills development, continuous product innovation, and e-commerce platform optimization to remain competitive in the growing digital economy. This research contributes to the literature by offering an integrated framework of how digital capabilities can help MSMEs to stay competitive in the digital economy.
Deep Learning Model for Automated Tire Crack Detection Using Convolutional Neural Networks Hilabi, Shofa Shofiah; Fauzi, Ahmad; Savina, Savina
Applied Information System and Management (AISM) Vol. 8 No. 1 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i1.46226

Abstract

Tire cracks pose a significant safety risk, as undetected defects can lead to severe accidents. Traditional inspection methods rely on manual visual assessments, which are prone to human error. This study proposes an automated tire crack detection system using Convolutional Neural Networks (CNN), leveraging transfer learning techniques to improve accuracy and generalization. A dataset of 600 tire images was collected and preprocessed, including augmentation techniques such as rotation, flipping, and brightness adjustments. The CNN model was trained with different optimizers, including Adam and Stochastic Gradient Descent (SGD), to compare their performance. Experimental results indicate that Adam achieved the highest test accuracy of 78.3% with the lowest test loss of 53%, while SGD required more epochs to reach optimal performance. This study demonstrates the feasibility of deep learning-based automated tire inspection, providing a scalable alternative to traditional methods. Future research should focus on optimizing model architectures, expanding datasets, and integrating real-time detection for industrial applications.

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