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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 157 Documents
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Empirical Analysis of Social Media Interaction Metrics and Their Impact on Startup Engagement Wahid, Arif Mu'amar; Maulita, Ika
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.v8i3.272

Abstract

In the digital economy, social media serves as a crucial platform for startups to build relationships with audiences and strengthen brand presence. However, the specific effects of different types of user interactions—likes, comments, and shares—on startup engagement remain insufficiently quantified. This study provides an empirical analysis of how social media interaction metrics influence engagement using secondary data from the publicly available Social Media Engagement Metrics dataset on Kaggle. Employing a quantitative design, the study integrates descriptive statistics, Pearson correlation, Random Forest, and multiple linear regression to examine both linear and non-linear relationships. Results show that likes, comments, and shares collectively affect engagement rates, with Random Forest identifying likes as the most influential feature. However, regression results indicate that shares exert a statistically significant but negative effect on engagement, suggesting complex behavioral patterns behind user interactions. Visual analyses—including histograms, boxplots, and heatmaps—support data normality and highlight variation in post performance. The findings emphasize the importance of visually engaging content and interactive captions to enhance user engagement. This study contributes to digital marketing research by combining methodological rigor with actionable insights, offering data-driven recommendations for startups aiming to optimize their social media strategies.
Comparative Sentiment Analysis of Digital Wallet Applications in Indonesia Using Naïve Bayes Ghaffar, Soeltan Abdul; Setiawan, Wilbert Clarence
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.v8i2.251

Abstract

The rapid growth of financial technology in Indonesia has led to widespread use of digital wallet applications such as OVO, DANA, GoPay, and ShopeePay. User-generated reviews on platforms like the Google Play Store offer valuable insights into customer satisfaction and application performance. This study conducts a comparative sentiment analysis of user reviews for four major Indonesian e-wallets using the Multinomial Naïve Bayes algorithm. A total of 401 Indonesian-language reviews were collected and labeled based on user ratings, with sentiment classified as positive or negative. The TF-IDF method was applied for feature extraction, and the model was evaluated using accuracy, precision, and recall metrics. Results show that ShopeePay achieved the highest classification accuracy (89%), followed by DANA and GoPay (80%), while OVO recorded lower performance due to more informal and ambiguous language. The model demonstrated strong precision for positive sentiment but low recall for negative sentiment (28%), indicating challenges in detecting minority-class feedback. Word cloud visualizations were used to highlight common keywords in each sentiment category. This study confirms that Naïve Bayes is an effective approach for classifying user sentiment in Indonesian-language app reviews, while also emphasizing the need for improved handling of class imbalance in future research. The findings provide practical insights for developers to enhance user experience based on data-driven sentiment patterns.
A Quantitative Analysis of Artificial Intelligence’s Impact on Students’ Mindset and Critical Thinking in Higher Education Prambudi, Niko Lugas; Putawa, Rilliandi Arindra; Izumi, Calvina
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.v7i4.222

Abstract

The rapid advancement of Artificial Intelligence (AI) has significantly transformed higher education, redefining how students learn, reason, and engage with academic content. This study investigates the impact of AI utilization on students’ mindsets and critical thinking skills within university learning settings. Employing a quantitative research design, data were gathered through an online questionnaire administered to 28 students from various academic disciplines. The survey assessed students’ engagement with AI tools including ChatGPT, Gemini, and Perplexity in learning processes such as understanding course materials, completing assignments, and problem-solving activities. The results indicate that most participants perceive AI as highly beneficial for enhancing comprehension, efficiency, and creativity in academic work. Students report that AI applications help them approach problems from diverse perspectives and stimulate idea generation. Nevertheless, concerns about overdependence are evident, as 53.6% of respondents believe that excessive reliance on AI may diminish autonomy and critical reasoning capacity. While a majority of students claim to verify AI-generated responses, a minority remain unaware of biases and inaccuracies, emphasizing the need to strengthen AI literacy in academic contexts. Overall, the findings suggest that AI serves as both a catalyst for deeper learning and a potential risk to intellectual independence. Its integration into higher education must therefore be approached with pedagogical mindfulness, ensuring that AI acts not as a replacement for human thought but as a tool for reflection, creativity, and metacognitive growth. Educators are encouraged to design learning experiences that require students to analyze, compare, and critique AI outputs critically. In conclusion, AI represents a dual-edged innovation: when applied ethically and reflectively, it can foster a growth-oriented mindset and strengthen critical thinking, but without proper guidance, it may cultivate intellectual complacency and dependency.
Data-Driven SEO Strategy Optimization to Enhance MSME Sales Performance on Indonesian E-Commerce Platforms Sangsawang, Thosporn; Li, Shuang
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.v8i3.262

Abstract

The rapid growth of digital commerce in Indonesia has created both opportunities and challenges for Micro, Small, and Medium Enterprises (MSMEs) seeking to increase their online visibility and sales. This study presents a data-driven approach to Search Engine Optimization (SEO) strategy optimization aimed at enhancing MSME sales performance on leading Indonesian e-commerce platforms, including Tokopedia and Shopee. Using a quantitative design, the research integrates Microsoft Excel for preliminary data exploration and Google Colab (Python) for advanced analysis and predictive modeling. The dataset, comprising over 1,000 transaction entries, includes key SEO-related indicators such as keyword rank, website traffic, backlinks, social media engagement score, advertising spend, and monthly sales. Ensemble regression models—Random Forest and Gradient Boosting—were employed to evaluate the predictive relationship between SEO factors and sales outcomes, validated through RMSE and R² metrics. The findings indicate that advertising expenditure (r = +0.83), backlinks (+0.29), and social media engagement (+0.25) are the most influential predictors of sales performance, while website traffic shows a weaker positive correlation (+0.13). These results highlight the critical role of integrated SEO and digital advertising strategies in improving MSME competitiveness. The study demonstrates that accessible analytical tools can empower MSMEs to make data-driven marketing decisions. Future research should expand model generalization across industries and explore additional digital variables to improve predictive accuracy.
Classification and Prediction of Video Game Sales Levels Using the Naive Bayes Algorithm Based on Platform, Genre, and Regional Market Data Putra, Rafi Pratama; Ramadani, Nevita Cahaya; Nanjar, Agi
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.v8i1.242

Abstract

The exponential expansion of the video game industry has resulted in a vast accumulation of market data that can be leveraged to analyze and predict sales performance. This study aims to construct a classification model for video game sales levels by applying the Naïve Bayes algorithm, recognized for its simplicity, efficiency, and strong baseline performance in supervised learning tasks. The research employs a public dataset containing over 13,000 video game entries, encompassing key attributes such as genre, platform, publisher, release year, user and critic ratings, and global sales figures. The target variable global sales was discretized into three categories: Low (1 million units), Medium (1–5 million units), and High (5 million units) to represent distinct tiers of commercial success. Prior to modeling, the dataset underwent a comprehensive preprocessing pipeline involving duplicate removal, handling of missing data, normalization of numerical attributes, and feature selection to ensure optimal model performance. The Multinomial Naïve Bayes classifier was then implemented and assessed using standard evaluation metrics, including accuracy, precision, recall, and F1-score. Experimental results revealed an accuracy of 71.82% and an F1-score of 70.03%, signifying strong predictive capability for a probabilistic model of this simplicity. The classifier effectively identified low and medium sales categories, though slightly underperformed on the high sales group due to class imbalance within the dataset. Further analysis of conditional probabilities indicated that game genre, platform popularity (especially PS2 and Wii), and critic scores were the most influential determinants of higher sales outcomes. These findings affirm that the Naïve Bayes algorithm provides a reliable and interpretable foundation for video game sales prediction, serving as a benchmark model in market analytics. Future studies are encouraged to address data imbalance through oversampling or synthetic data generation, incorporate contextual variables such as marketing strategies and release schedules, and explore ensemble or deep learning approaches to enhance predictive accuracy and robustness.
Determinants of Consumption Behavior Among the Millennial Generation Saputra, Aina Aldi; Sarmini, Sarmini; Widiawati, Chyntia Raras Ajeng; Yunita, Ika Romadoni
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.v7i3.216

Abstract

This study examines the factors influencing consumption behavior among the millennial generation, emphasizing the effects of family size, education level, and income on food, non-food, and total household expenditure. As digitalization and demographic shifts continue to redefine modern lifestyles, understanding millennial consumption patterns offers valuable insights into changing welfare dynamics and economic structures. Employing a quantitative associative approach, data were collected from 120 millennial households through structured questionnaires and interviews, complemented by secondary data from the Central Statistics Agency (BPS). Multiple linear regression analysis was used to evaluate both simultaneous and partial relationships among variables, while descriptive statistics were applied to illustrate the respondents’ socioeconomic characteristics. The findings show that family size, education, and income collectively have a significant influence on consumption across all categories. Partially, family size and income significantly affect food-related spending, whereas education does not exhibit a notable impact in this segment. In contrast, for non-food and total consumption, all three variables display a positive and significant relationship, suggesting that higher income and education levels encourage more diversified expenditures. Moreover, non-food consumption (57.19%) surpasses food consumption (42.81%), supporting Engel’s Law and indicating improved living standards alongside a shift toward lifestyle diversification. Nonetheless, the proportion of non-food expenditure remains moderate, reflecting cautious financial behavior amid lingering post-pandemic income constraints. These findings align with Keynesian and Life-Cycle consumption theories, illustrating how income stability, education, and life-stage factors shape millennial consumption decisions. Overall, this study underscores the evolving nature of millennial households toward technology-driven, experience-based, yet financially mindful consumption patterns, providing implications for policymakers and businesses to enhance income resilience, digital literacy, and sustainable consumption growth in the digital economy.
Interpretable Product Recommendation through Association Rule Mining: An Apriori-Based Analysis on Retail Transaction Data Prasetio, Agung Budi; Aboobaider, Burhanuddin bin Mohd; Ahmad, Asmala bin
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.v8i2.252

Abstract

The rapid growth of e-commerce has generated vast amounts of transactional data, creating opportunities for data-driven decision-making in retail environments. This study presents an interpretable product recommendation approach based on association rule mining using the Apriori algorithm. Unlike complex black-box recommender models, the proposed method emphasizes transparency and explainability in identifying purchasing relationships. The Groceries dataset comprising 38,765 transactions was analyzed to discover frequent itemsets and generate actionable association rules. After applying minimum thresholds of 0.02 for support and 0.4 for confidence, a total of 67 frequent itemsets and 45 strong rules were obtained. The rule {whole milk, sausage, rolls/buns} → {yogurt} achieved the highest lift value of 1.66, revealing meaningful co-purchasing behavior. Visualization tools, including heatmaps and network graphs, were employed to illustrate rule strength and product interconnections, facilitating business interpretation. The findings demonstrate that interpretable rule-based recommendations can effectively support product bundling, cross-selling, and retail layout strategies. This study highlights the continuing relevance of Apriori in creating transparent, data-driven insights and proposes future integration with hybrid models to address personalization and scalability challenges in modern recommendation systems.
Enhancing Housing Price Prediction Accuracy Using Decision Tree Regression with Multivariate Real Estate Attributes Utomo, Ahmar Dwi; Hayadi, B Herawan; Priyanto, Eko
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.v7i4.226

Abstract

The real estate sector functions as a critical barometer of a nation’s economic performance; however, its inherent volatility and intricate pricing mechanisms often hinder precise valuation—particularly in developing urban markets. In the context of Indonesia, where the property industry contributes substantially to national GDP, deriving fair and data-driven housing price estimates remains a persistent challenge. Traditional appraisal methods, which rely predominantly on subjective human judgment, frequently fall short in reflecting market dynamics accurately. This research seeks to construct an interpretable machine learning framework for predicting residential housing prices by employing a Decision Tree Regression (DTR) model. The DTR method was chosen for its transparent and hierarchical structure, allowing for a clear understanding of how individual property characteristics affect price outcomes. The study utilizes a public dataset from Kaggle containing key housing attributes, including land area, building size, number of rooms, and location variables. The methodological steps encompass data preprocessing (cleaning and encoding using One-Hot Encoding), data partitioning into training and testing sets with an 80:20 ratio, and model performance evaluation using standard regression metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and the Coefficient of Determination (R²). The model attained an R² value of 0.385, suggesting that the selected features explain approximately 38.5% of the variance in housing prices. While this indicates moderate predictive capability, the DTR model offers valuable interpretive insights—particularly in identifying land area as the most influential predictor of price. The findings highlight that interpretable machine learning approaches can serve as effective analytical tools for property valuation in emerging markets, balancing predictive accuracy with transparency. Moreover, this study lays the groundwork for the future development of ensemble and hybrid predictive models, as well as the integration of AI-based analytics into decision-support systems for property valuation, investment forecasting, and urban development planning in Indonesia’s evolving real estate landscape.
Evaluating the Security of Electronic Medical Records in Indonesia’s SIMPUS Application Using the CIA Framework Durachman, Yusuf; Rahman, Abdul Wahab Abdul
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.v8i3.263

Abstract

Ensuring the security of electronic medical records (EMRs) is a critical challenge in the digital transformation of healthcare systems, particularly in developing countries. This study evaluates the security of Indonesia’s Community Health Center Information System (SIMPUS) based on the principles of confidentiality, integrity, and availability (CIA). A qualitative descriptive approach was employed, combining interviews and direct observation of SIMPUS implementation across multiple user roles. The findings reveal that while confidentiality is supported through user authentication, vulnerabilities remain due to shared account usage and the absence of automatic log-off features. Data integrity is maintained through restricted editing rights, but the lack of an audit trail limits the system’s ability to detect unauthorized changes. Data availability is generally sufficient; however, reliance on manual backup processes exposes the system to potential data loss. The study highlights the need for enhanced audit mechanisms, automated backup solutions, and staff training to strengthen data security compliance with national regulations and international standards such as ISO 27001 and HIPAA. Strengthening these measures will help ensure that SIMPUS can function as a secure and reliable platform for managing electronic medical records in Indonesia’s primary healthcare system.
An Efficient Sampling Approach for Village Elections: Quick Count Using Stratified Systematic Cluster Sampling Nugroho, Khabib Adi; Turino, Turino
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.v8i1.245

Abstract

Quick counts are widely used to estimate election outcomes before official results are announced. However, their accuracy depends on the quality of the sampling method used. This study applies the Stratified Systematic Cluster Random Sampling method in the 2019 village head election in Panembangan Village, Indonesia, to provide an efficient and statistically sound quick count process suitable for rural contexts with limited resources. The method integrates stratification (dividing voters by hamlet), clustering (grouping by polling stations), and systematic sampling (selecting polling stations at fixed intervals). Out of 10 polling stations, 5 were systematically selected after stratification. All valid votes from these polling stations were used for estimation. The results show that candidate Untung Sanyoto received 59.16% of the vote, while his opponent received 40.84%. The margin of error was ±0.69% at a 95% confidence level, and the estimates closely matched the official election results, demonstrating the method’s reliability. This study shows that combining statistical rigor with practical sampling strategies can produce fast, accurate, and cost-effective results. It provides a scalable model for local elections, especially in regions facing geographic or logistical challenges, and contributes to the development of transparent and trustworthy democratic practices.

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