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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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Skin Cancer Detection Approach Using Convolutional Neural Network Artificial Intelligence Hayat, Sabda Norman; Watef, Lulu'ul; Indraswari, Rarasmaya
International Journal of Informatics and Information Systems Vol 7, No 2: March 2024
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v7i2.196

Abstract

Skin cancer is a type of cancer that can cause death, where skin cancer is included in the 15 common cancers that occur in Indonesia. The number of skin cancer sufferers was around 6,170 cases of non-melanoma skin cancer and 1,392 cases of melanoma skin cancer in 2018 in Indonesia. Therefore, research related to skin cancer classification is increasing. This is done as an initial step in detecting whether a lesion can be said to be cancerous or not. The deep learning approach has certainly shown promising results in carrying out classification, so this research proposes a deep learning-based method used for skin cancer classification. The proposed approach involves Convolutional Neural Networks with the ISIC 2017 dataset. The models used for skin cancer classification are InceptionV3, EfficientNetB0, ResNet50, MobileNetV2, and NASNetMobile. The highest accuracy of the single model produced reached 69.3% using the MobileNetV2 model. An ensemble model combining the five models was also tested and produced the highest accuracy compared to other single models with an accuracy result of 80.6%.
Strategies for Fault Propagation and Recovery in Urban Rail Transit Systems Using Complex Network Analysis Yang, Wei; Mu, Liping
International Journal of Informatics and Information Systems Vol 6, No 4: December 2023
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v6i4.178

Abstract

Urban rail transit is a vital part of the transportation infrastructure in cities, becoming a preferred choice for an increasing number of commuters. Nevertheless, the occurrence of station failures due to emergencies poses a significant challenge that can disrupt the entire transit system. This situation underscores the necessity for improved resilience and effective recovery strategies within urban rail systems. This research addresses the critical issue of how to effectively control fault spread and swiftly restore system operations after a failure. Utilizing metro network data from Gaode maps and leveraging complex network theory, we constructed a network topology model to analyze these dynamics. Our investigation focused on the patterns and mechanisms of fault propagation within the metro network, both in the absence and presence of recovery interventions. The primary objective was to uncover the fundamental principles governing fault spread in urban rail systems. Through detailed data analysis and the development of recovery models, we aimed to offer valuable insights into reducing the impact of failures and enhancing the overall reliability of urban rail transit. The study concludes with a simulation of four distinct recovery strategies, providing a comparative assessment of their effectiveness. These findings are essential for urban planners, transit authorities, and policymakers in crafting strategies to mitigate the effects of emergencies on urban rail transit. Ultimately, the research contributes to ensuring a resilient and efficient transportation system that can support the demands of a growing urban population.
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.
A Multiple Linear Regression Approach to Predicting AI Professionals’ Salaries from Location and Skill Data Maidin, Siti Sarah; Yi, Ding; Ayyasy, Yahya
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.v7i3.213

Abstract

The rapid growth of Artificial Intelligence (AI) industries worldwide has increased the demand for skilled professionals and highlighted the need to understand salary determinants in this sector. This study aims to analyze the factors influencing the compensation of AI professionals globally, with a particular focus on the effects of company location, experience level, and required technical skills. Using a dataset of 15,000 AI job postings collected from multiple countries, a Multiple Linear Regression (MLR) model was developed to identify predictive relationships between independent variables—location, experience, and skills—and the dependent variable, annual salary in U.S. dollars. Data preprocessing included one-hot encoding for categorical variables, standardization of numerical attributes, and vectorization of text-based skill descriptions. Model evaluation produced strong predictive results, with an R² of 0.82, a Mean Absolute Error (MAE) of 18,677 USD, and a Root Mean Squared Error (RMSE) of 25,704 USD. Statistical tests confirmed that company location and experience level significantly affected salary outcomes (p 0.05), while technical skills contributed only marginally. These findings suggest that structural factors such as geography and seniority play a more decisive role in determining AI salaries than specific technical competencies. The study concludes that MLR offers a transparent and interpretable analytical framework for exploring salary disparities in the global AI workforce. The results provide practical implications for organizations designing fair compensation policies, professionals assessing market value, and educators aligning training programs with evolving industry demands.
The Role of Media Literacy in Shaping Public Opinion and Political Participation in the Digital Era El Emary, Ibrahiem M. M.; Alhebbi, Mohammed Ahmed
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.v8i3.271

Abstract

The rapid development of digital technology has transformed how people access, interpret, and respond to political information. This study explores the role of media literacy in shaping public opinion and political participation in the digital era. Using a descriptive qualitative approach, data were collected through in-depth interviews with ten respondents from diverse educational and social backgrounds. The findings reveal that individuals with higher media literacy demonstrate stronger critical thinking, higher awareness of information credibility, and greater involvement in political discourse and civic engagement. Conversely, those with lower media literacy are more susceptible to misinformation, hoaxes, and emotional manipulation, which can distort political perceptions and reduce participation. The study highlights that media literacy not only enhances citizens’ ability to filter and evaluate political content but also strengthens democratic quality by encouraging informed and responsible participation. It recommends that media literacy education be integrated into both formal and informal learning environments to foster critical, active, and digitally responsible citizens.
A Gaussian Naive Bayes and SMOTE-Based Approach for Predicting Breast Cancer Aggressiveness in Imbalanced Datasets Dewi, Deshinta Arrowa; Kurniawan, Tri Basuki
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.v8i1.250

Abstract

Breast cancer remains one of the leading causes of death among women worldwide, making early and accurate detection essential to improving patient outcomes. This study aims to develop a predictive model for breast cancer aggressiveness using the Gaussian Naive Bayes algorithm on the Breast Cancer Wisconsin Diagnostic Dataset. The dataset contains 569 instances with 30 numerical features representing various cell characteristics. Preprocessing steps included data cleaning, label encoding, and Min-Max normalization. The model was evaluated using accuracy, precision, recall, F1-score, and a confusion matrix. Initially, the model achieved an accuracy of 78.88%; however, the recall for malignant cases was relatively low at 45.5%, highlighting a critical limitation in detecting aggressive cancer. To address class imbalance and improve model sensitivity, the Synthetic Minority Oversampling Technique (SMOTE) was applied. While detailed post-SMOTE metrics were not reported in this version, the approach is expected to enhance recall and F1-score for the malignant class. This research demonstrates the potential of Gaussian Naive Bayes, combined with data balancing techniques, as a fast and interpretable tool for early breast cancer diagnosis. Future work will focus on model comparison, cross-validation, and statistical evaluation to improve robustness and reliability.
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.
Examining Risk and Trust in Student Mobile Banking Adoption: An Extended Technology Acceptance Model Perspective Rahma, Felinda Aprilia; Ulfah, Siti Zayyana
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.v8i2.261

Abstract

This study examines the influence of trust, compatibility, satisfaction, perceived risk, and risk acceptance on students’ intention to use mobile banking applications. As mobile banking becomes increasingly popular for its convenience and efficiency, especially among tech-savvy youth, understanding the behavioral factors influencing adoption is crucial. Utilizing an extended Technology Acceptance Model (TAM), this study employs a quantitative approach with multiple linear regression analysis. The dataset, comprising responses from 219 Indonesian university students, was drawn from the "cogent_adoption_m_banking_pls" survey. The results reveal that satisfaction, compatibility, and trust significantly influence students’ intention to use mobile banking, while perceived risk and risk acceptance do not show a significant effect. The model explains approximately 57% of the variance in adoption intention (R² = 0.570), indicating substantial explanatory power. These findings offer valuable insights for mobile banking developers and financial institutions to enhance user satisfaction, align services with user lifestyles, and strengthen trust—key factors for increasing adoption among student users.
Identifying Adolescent Behavioral Profiles Through K-Means Clustering Based on Smartphone Usage, Mental Health, and Academic Performance Aristo, Dominic Dinand; Srinivasan, Bhavana
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.v8i1.231

Abstract

The pervasive integration of digital devices into students’ daily lives has profoundly shaped their learning habits and psychological well-being. As technology becomes increasingly embedded in academic and personal routines, understanding the relationship between digital engagement, mental health, and academic outcomes is vital for developing effective student-support and intervention frameworks in higher education. This study seeks to uncover behavioral patterns among college students by examining the interconnections between smartphone usage, mental health indicators, and academic performance through a data-driven machine learning approach. Utilizing the K-Means clustering algorithm, students were categorized into distinct behavioral profiles derived from eight core features: daily screen time, sleep duration, grade performance, exercise frequency, anxiety level, depression level, self-confidence, and screen exposure before sleep. A dataset comprising 3,000 entries was preprocessed through normalization and analyzed within the Knowledge Discovery in Databases (KDD) framework to ensure structured and reliable data processing. The Elbow Method identified four optimal clusters, each reflecting unique behavioral characteristics. Cluster 1 represented well-balanced students with stable academic and emotional states; Cluster 2 included high-achieving yet anxious individuals; Cluster 3 captured those exhibiting excessive digital engagement and psychological distress; and Cluster 4 comprised moderately engaged students with lower self-confidence. Visual representations, including bar and radar charts, were generated to illustrate inter-cluster variations and enhance interpretability of behavioral distinctions. The findings reveal that digital usage patterns are closely linked to mental health and academic performance, suggesting that excessive or unregulated device use can heighten emotional strain and academic inconsistency. These insights highlight the necessity of personalized mental health initiatives and targeted digital literacy programs grounded in behavioral segmentation. Overall, the study demonstrates the applicability of unsupervised machine learning for behavioral profiling and provides evidence-based recommendations for educators, mental health practitioners, and policymakers seeking to foster balanced and healthy digital habits among students.
Analyzing the Impact of Company Location, Size, and Remote Work on Entry-Level Salaries a Linear Regression Study Using Global Salary Data Khosa, Joe; Mashao, Daniel; Subekti, Fajar
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.v7i3.215

Abstract

This research explores the key factors influencing entry-level salaries in the global labor market of 2024, emphasizing the roles of company location, organizational size, and the extent of remote work in shaping compensation levels. Drawing on the Global Salary 2024 dataset from Kaggle, which comprises over 5,600 observations across multiple industries and geographic regions, the study applies a multiple linear regression model executed in Python via Google Colab to quantitatively examine salary disparities. The results indicate that company location and size significantly affect entry-level earnings, underscoring how regional economic contexts, cost-of-living variations, and organizational capacity continue to drive wage formation. Conversely, the remote work ratio exhibits a negligible and statistically insignificant effect, implying that flexibility in work arrangements has yet to translate into measurable financial value for early-career professionals. Furthermore, introducing job title as a control variable enhances the model’s explanatory power, reaffirming the influence of individual skill specialization and job function in determining compensation outcomes. These findings reinforce human capital theory while extending it by incorporating contextual and organizational dimensions relevant to the digital labor economy. For job seekers, the study offers data-driven insights to guide career decisions and salary expectations across regions, while employers may utilize the results to formulate fair and competitive pay strategies in an increasingly interconnected workforce. Ultimately, this study provides a comprehensive understanding of how structural and individual factors interact to shape entry-level salary dynamics in the modern digital era.

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