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IJIIS: International Journal of Informatics and Information Systems
Published by Bright Publisher
ISSN : -     EISSN : 25797069     DOI : https://doi.org/10.47738/ijiis
Core Subject : Science,
Computer Science & IT
The IJIIS is an international journal that aims to encourage comprehensive, multi-specialty informatics and information systems. The Journal publishes original research articles and review articles. It is an open access journal, with free access for each visitor (ijiis.org/index.php/IJIIS/); meanwhile we have set up a robust online platform and use an online submission system to ensure the international visibility and the rigid peer review process. The journal staff is committed to a quick turnaround time both in regards to peer-review and time to publication.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 162 Documents
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A Quantitative Study on User Experience Dimensions and Their Impact on User Satisfaction in Indonesian Mobile E-Commerce Saputra, Afif Dwi; Tarigan, Riswan E.; Wijaya, Yoana Sonia
International Journal of Informatics and Information Systems Vol 7, No 3: September 2024
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v7i3.217

Abstract

This research examines how user experience (UX) dimensions influence user satisfaction in Indonesia’s mobile e-commerce ecosystem. As mobile shopping continues to dominate digital transactions, understanding the relationship between UX and user satisfaction becomes crucial for maintaining platform competitiveness. Adopting a quantitative explanatory approach, the study gathered data from 100 active users of leading e-commerce platforms such as Shopee, Tokopedia, and Lazada through an online questionnaire. The instrument was based on the User Experience Questionnaire (UEQ) framework, encompassing six dimensions—Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, and Novelty—with user satisfaction serving as the dependent variable measured via validated Likert-scale indicators. Analytical procedures included descriptive analysis, reliability and validity tests, and multiple linear regression using SPSS version 26. The findings reveal that five out of six UX dimensions significantly and positively affect user satisfaction (p 0.05). Among them, Perspicuity and Efficiency exert the strongest influence, underscoring the importance of intuitive interface design and smooth, error-free transaction processes. Dependability, Attractiveness, and Stimulation also play notable roles, indicating that both functional performance and emotional engagement contribute to favorable user experiences. Conversely, Novelty—though positively associated—does not reach statistical significance, implying that while users appreciate innovation, they prioritize clarity and reliability. The regression model yields an R² value of 0.742, suggesting that UX dimensions collectively account for 74.2% of the variance in user satisfaction. Overall, the study affirms that UX is a decisive factor in shaping user satisfaction and loyalty in mobile e-commerce environments. It enriches existing UX scholarship by providing empirical evidence from Indonesia’s fast-growing digital market. Practically, the results encourage developers to emphasize usability, dependability, and aesthetic design to maintain user engagement. Future studies are recommended to integrate trust, emotional attachment, and emerging technologies such as artificial intelligence and augmented reality to obtain a more comprehensive understanding of user satisfaction in digital commerce.
Enhancing Household Energy Consumption Forecasting Using the XGBoost Algorithm with Cross-Validation and Residual-Based Evaluation Sugianto, Dwi; Yulianto, Koko Edy
International Journal of Informatics and Information Systems Vol 8, No 2: March 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i2.253

Abstract

Accurate forecasting of household energy consumption plays a crucial role in optimizing energy efficiency, supporting sustainable policy decisions, and improving operational management in smart grid systems. This study enhances conventional XGBoost-based forecasting by integrating cross-validation and residual-based evaluation to ensure model robustness and interpretability. Using a dataset of over 90,000 daily household energy records that include temperature, humidity, and appliance-level usage, a systematic preprocessing pipeline was applied—comprising data cleaning, normalization, temporal feature transformation, and partitioning into training and testing subsets. The proposed model was trained using 10-fold cross-validation to minimize overfitting and validated through residual error analysis to assess stability and bias. Evaluation metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²), demonstrate superior predictive accuracy, achieving MAE = 0.48, RMSE = 0.64, and R² = 0.9864. Visualization of actual versus predicted consumption and symmetric residual distribution further confirm the model’s reliability. The findings highlight that the enhanced XGBoost model not only achieves high precision but also provides a robust foundation for real-time energy monitoring, anomaly detection, and sustainable household energy management. Future work will integrate SHAP-based interpretability and comparative benchmarking with deep learning approaches.
A Comparative Analysis of Linear Regression and XGBoost Algorithms for Predicting GPU Prices Using Technical Specifications Prakoso, Dendi Putra; Irfan, Muhammad; Siddique, Quba
International Journal of Informatics and Information Systems Vol 7, No 4: December 2024
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v7i4.228

Abstract

This study investigates and compares the predictive performance of Linear Regression and XGBoost algorithms in estimating Graphics Processing Unit (GPU) prices based on their technical specifications. GPU prices are known for their high volatility, influenced not only by hardware characteristics—such as memory capacity, clock speed, and bandwidth—but also by external market factors including demand from the gaming industry, machine learning applications, and cryptocurrency mining activities. The dataset used in this research comprises 475 GPU units from three leading manufacturers—NVIDIA, AMD, and Intel Arc—featuring 15 technical attributes obtained from publicly accessible data sources. Adopting an experimental quantitative approach, the dataset was divided into training and testing subsets using an 80:20 ratio. The data preprocessing phase involved handling missing values, detecting outliers through the Interquartile Range (IQR) method, performing data normalization, and encoding categorical features. The models were evaluated using four performance metrics: the Coefficient of Determination (R²), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results demonstrate that XGBoost outperforms Linear Regression, achieving an R² of 0.8129, MAE of 85.07 USD, RMSE of 122.03 USD, and MAPE of 35.23%. In comparison, the Linear Regression model recorded an R² of 0.7629, MAE of 106.59 USD, RMSE of 137.38 USD, and MAPE of 56.04%. The superior performance of XGBoost can be attributed to its ability to model non-linear relationships and capture complex feature interactions among GPU specifications.
An Android-Based Multimodal AI Application for Contextual Environmental Learning in Children Hananto, Andhika Rafi; Rahardian, Muhammad Izha
International Journal of Informatics and Information Systems Vol 8, No 3: September 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i3.264

Abstract

Children’s limited engagement with nature in the digital era poses a growing challenge for environmental education. This study presents the development of an Android-based educational application that leverages multimodal artificial intelligence (AI)—specifically the Google Gemini model—to facilitate contextual environmental learning for preschool and elementary-aged children. Using a prototyping methodology, the application integrates image capture, cloud-based processing through a FastAPI backend, and a Flutter-based interface designed for young learners. The system allows children to photograph plants and receive real-time, age-appropriate explanations about plant names, characteristics, and ecological functions in a narrative format. A limited usability trial involving children of varying age groups demonstrated positive engagement and curiosity, indicating the app’s potential as an interactive and enjoyable learning medium. Despite occasional inaccuracies in AI-generated descriptions and reliance on internet connectivity, user feedback suggested strong interest and educational value. Future enhancements will focus on developing localized plant databases, improving accuracy, and incorporating gamification elements. Overall, this study contributes to the growing field of AI-driven educational technology, demonstrating how multimodal AI can effectively bridge digital learning with real-world environmental experiences.
A Machine Learning Approach to Indonesian Climate Change Sentiment Analysis Using Naive Bayes Henderi, Henderi; Sofiana, Sofa
International Journal of Informatics and Information Systems Vol 8, No 1: January 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i1.246

Abstract

Climate change poses a significant global challenge, particularly for archipelagic nations such as Indonesia that are highly vulnerable to rising temperatures and extreme weather events. This study applies machine learning-based sentiment analysis to assess Indonesian public opinion on climate change using Twitter data. A total of 5,120 Indonesian-language tweets were collected through keyword-based scraping related to climate and weather conditions. Following text preprocessing (lowercasing, stopword removal, stemming, and cleaning), TF-IDF vectorization was used to extract the top 400 most significant terms. The dataset was divided into training (80%) and testing (20%) subsets, and a Multinomial Naïve Bayes classifier was trained to categorize sentiments into positive, neutral, and negative classes. The results show a dominance of negative sentiment (62%), primarily associated with extreme heat and storm-related events, while neutral (24%) and positive (14%) sentiments were linked to moderate weather conditions. Model evaluation achieved an F1-score of 0.95 for negative, 0.86 for neutral, and 0.83 for positive sentiment, yielding a macro-average F1-score of 0.88. The analysis also identified “panas (hot),” “hujan (rain),” and “banjir (flood)” as top lexical indicators influencing classification. Overall, the findings highlight that Indonesian public sentiment toward climate change is highly reactive to extreme weather. The study underscores the potential of Naïve Bayes as a baseline model for real-time environmental sentiment monitoring, offering valuable insights for institutions such as BMKG to enhance public communication and climate awareness strategies.
A Quantitative Study on Social Media Usage Patterns and Their Effects Among Internet Users Prasetya, Tegar Yudha; Hery, Hery; Haryani, Calandra
International Journal of Informatics and Information Systems Vol 7, No 3: September 2024
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v7i3.218

Abstract

This research conducts a quantitative analysis of social media usage habits and their effects among internet users, utilizing a secondary dataset of 999 respondents drawn from the Social Media Usage Survey available on Kaggle. Employing a descriptive–survey design, the study adopts a quantitative approach to examine behavioral tendencies, demographic variations, and relationships among variables such as usage duration, user motivation, privacy awareness, and intentions to reduce social media activity. Data analysis was performed using Python, incorporating descriptive statistics, crosstab analysis, and visual analytics through the Pandas, Matplotlib, and Seaborn libraries. The findings reveal that social media is deeply embedded in everyday routines, with users averaging 3.5 hours of screen time per day. Instagram, Facebook, and Twitter/X emerge as the most frequently used platforms, serving purposes that include entertainment, information access, and business promotion. Video-based content dominates user preferences, reflecting the broader global media consumption trend. Additionally, 69% of respondents acknowledge that social media influences their purchasing behavior, while 65% express moderate to high levels of privacy concern. Notably, about 68% of users report an intention to reduce their screen time, indicating a growing awareness of the need for digital balance. Correlation analysis shows that individuals with longer screen durations and heightened privacy concerns are more likely to intend reducing their usage, suggesting that excessive engagement may drive self-regulatory behavior. These insights illustrate the dual nature of social media—as a medium for empowerment and connectivity, yet simultaneously a potential source of psychological fatigue. Overall, this study contributes empirical evidence supporting efforts to foster healthy and responsible digital engagement, enriching the broader discourse on digital well-being, online literacy, and sustainable technology use in the modern digital landscape.
Analyzing Key Factors Influencing Employee Resignation Through Decision Tree Modeling and Class Balancing Techniques Saputra, Jeffri Prayitno Bangkit; Hidayat, Muhammad Taufik
International Journal of Informatics and Information Systems Vol 8, No 2: March 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i2.259

Abstract

Employee resignation poses a significant challenge to organizational stability and workforce planning. This study aims to analyze the key factors influencing employee resignation by developing an interpretable predictive model using the Decision Tree algorithm. The analysis is conducted on the IBM HR Analytics dataset, which includes 1,470 employee records with diverse demographic, behavioral, and job-related attributes. To address the issue of class imbalance—where resignation cases are underrepresented—the Synthetic Minority Over-sampling Technique (SMOTE) is applied to enhance model sensitivity and balance. After a comprehensive data preprocessing phase, including feature selection and label encoding, the Decision Tree model is trained with a limited depth to reduce overfitting and maintain interpretability. The model achieves an accuracy of 77%, with a recall of 0.80 and an F1-score of 0.77 for the resignation class. Feature importance analysis identifies stock option level, job satisfaction, monthly income, relationship satisfaction, and job involvement as the most influential predictors. These findings provide actionable insights for human resource practitioners seeking to implement targeted and data-driven employee retention strategies. The study highlights the practical value of interpretable machine learning models in human capital analytics.
Predicting Demand for MSME Products Using Artificial Neural Networks (ANN) Based on Historical Sales Data Endahti, Les; Faturahman, Muhammad Shihab
International Journal of Informatics and Information Systems Vol 8, No 4: Regular Issue: December 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i4.288

Abstract

Accurate demand forecasting plays a crucial role in supporting inventory and sales strategies, particularly for Micro, Small, and Medium Enterprises (MSMEs) that often face resource constraints. This study aims to develop a predictive model using Artificial Neural Networks (ANN) to forecast product demand based on historical sales data. The ANN model is trained and evaluated using a structured experimental approach, adjusting parameters such as the number of hidden layers, learning rate, and epochs to identify the best-performing architecture. Evaluation metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and the coefficient of determination (R²) are used to measure model performance. The results demonstrate that the ANN model is capable of capturing complex nonlinear relationships in multidimensional data and producing accurate demand forecasts. The model particularly performs well in predicting demand trends for products in the Electronics and Household categories. These findings provide valuable insights for MSME stakeholders in optimizing inventory planning and making data-driven business decisions.
An Empirical Study on the Impact of Feature Scaling and Encoding Strategies on Machine Learning Regression Pipelines Toer, Guevara Ananta; Kim, Gwanpil
International Journal of Informatics and Information Systems Vol 9, No 1: Regular Issue: January 2026
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v9i1.293

Abstract

Data preprocessing is a critical yet often underestimated component of Machine Learning (ML) regression pipelines. While prior studies have largely focused on algorithm selection and model architecture, the combined impact of feature scaling and categorical encoding strategies within end-to-end regression pipelines remains insufficiently explored. This study presents an empirical evaluation of how different preprocessing configurations influence regression model performance. Three regression algorithms, Linear Regression, Random Forest Regression, and Gradient Boosting Regression are evaluated in combination with multiple feature scaling methods (Min–Max, Standard, and Robust scaling) and categorical encoding techniques (One-Hot and Ordinal encoding). Experiments are conducted on a real-world car sales dataset comprising 50,000 records, using a k-fold cross-validation framework to ensure robust performance estimation. Model performance is assessed primarily using mean R², supported by RMSE and MAE as error-based metrics. The results demonstrate that ensemble-based models, particularly Gradient Boosting and Random Forest, consistently outperform Linear Regression across all preprocessing configurations. Feature scaling shows limited influence on ensemble model performance, whereas categorical encoding plays a more significant role, with One-Hot Encoding yielding higher predictive accuracy and lower error dispersion than Ordinal Encoding. Overall, the findings highlight that model choice is the dominant determinant of regression performance, followed by encoding strategy, while scaling has a comparatively minor effect. This study provides empirical guidance for designing robust and effective ML regression pipelines and underscores the importance of evaluating preprocessing techniques in conjunction with model selection.
Customer SCustomer Segmentation Using an Enhanced RFM–K-Means Framework on The Online Retail Datasetegmentation Using Enhanced K – Means Clustering Agus, Isnandar; Hasibuan, MS
International Journal of Informatics and Information Systems Vol 8, No 4: Regular Issue: December 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i4.289

Abstract

Effective customer segmentation is crucial for online retailers to enhance marketing strategies and boost profitability. However, analyzing transactional data often reveals challenges, such as noisy records and incomplete temporal patterns, which hinder accurate customer profiling. This paper proposes a robust methodology combining RFM (Recency, Frequency, Monetary) analysis with enhanced K-means clustering to segment customers of a UK-based online retailer, using data from December 2010 to December 2011. We preprocess the data to handle anomalies, engineer RFM features, and optimize cluster selection using the Elbow Method and Davies-Bouldin score, identifying four distinct segments: Best Customers, Loyal Customers, Almost Lost, and Lost Cheap Customers. Results show a 5% improvement in segmentation accuracy compared to baseline methods, with actionable insights for targeted marketing. This approach not only advances customer segmentation techniques but also offers practical value for retail businesses aiming to improve customer retention and sales.

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