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Taqwa Hariguna
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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 5 Documents
 1 
Search results for , issue "Vol 7, No 4: December 2024" : 5 Documents clear
A Comparative Study of Naive Bayes, SVM, and Decision Tree Algorithms for Diabetes Detection Based on Health Datasets Nurwicaksana, Satria; Oh, Lee Kyung; Sukmana, Husni Teja
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.v7i4.230

Abstract

Diabetes is a chronic, progressive condition whose global prevalence continues to rise, creating substantial public health and economic burdens. Early diagnosis and timely intervention are critical to preventing severe complications and improving long-term patient outcomes. In recent years, artificial intelligence (AI) particularly machine learning (ML) has emerged as a powerful tool in medical diagnostics, offering capabilities in automated pattern recognition and disease classification. This study aims to evaluate and compare the predictive performance of three supervised ML algorithms such as Naïve Bayes, Support Vector Machine (SVM), and Decision Tree for classifying and predicting diabetes based on two primary physiological indicators: glucose level and blood pressure. The dataset employed was sourced from Kaggle, comprising 995 patient records containing relevant clinical attributes. The research methodology involved several stages, including data preprocessing to ensure quality and consistency, data partitioning into training and testing subsets using an 80:20 split ratio, model training, and performance evaluation. Each algorithm’s effectiveness was measured using accuracy, precision, recall, and F1-score metrics. The experimental findings demonstrate that the Decision Tree algorithm achieved the highest classification accuracy (94.47%), outperforming SVM and Naïve Bayes, both of which recorded 92.96% accuracy. Moreover, the Decision Tree exhibited balanced precision and recall values, underscoring its robustness in identifying both diabetic and non-diabetic cases with minimal misclassification. These outcomes indicate that the Decision Tree model provides an optimal balance between predictive accuracy and interpretability, making it particularly suitable for clinical decision-support applications.
Optimizing Village-Level Quick Count Accuracy and Efficiency via a Stratified Systematic Cluster Random Sampling Approach Pratama, Rizki Yoga; Septiadi, Abednego Dwi; Prasetyo, Muhamad Awiet Wiedanto
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.v7i4.220

Abstract

Accurate and transparent election result reporting plays a vital role in preserving public confidence and reinforcing democratic legitimacy. This research evaluates the effectiveness of the Stratified Systematic Cluster Random Sampling (SSCRS) method in improving the accuracy and efficiency of village-level quick counts. Conducted in Panembangan Village, Cilongok District, Banyumas Regency, the study employs a quantitative descriptive approach to examine how the integration of stratification, clustering, and systematic selection techniques can generate statistically robust election estimates within limited operational constraints. The research population consisted of all valid ballots from the 2019 Village Head Election, distributed across ten polling stations (TPS). Applying the SSCRS design, five TPS were systematically selected following stratification, yielding a sample of 3,760 valid votes. Data were analyzed using statistical procedures to determine the Margin of Error (MoE) and the 95% Confidence Interval (CI). The findings show that Candidate Untung Sanyoto secured 59.16% of the votes, while Candidate Suprapto received 40.84%, with an MoE of ±0.69% and CI ranges of 58.47–59.84% and 40.16–41.53%, respectively. These outcomes demonstrate that the SSCRS method produces highly accurate and reliable estimates closely aligned with the official results, confirming both its statistical validity and field-level practicality. By combining three sampling techniques, the method ensures proportional representation, reduces sampling bias, and enhances data collection efficiency under constrained conditions. This research provides a methodological contribution to electoral statistics, presenting a replicable hybrid sampling model well-suited for small-scale electoral contexts. Future studies are encouraged to extend this framework to different regions and election types to further assess its flexibility and robustness across diverse demographic and logistical settings.
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.
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.
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.

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