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All Journal JOURNAL OF INFORMATION TECHNOLOGY, INFORMATION SYSTEMS AND COMMUNICATIONS RAGAM: Journal of Statistics and Its Application
Naufalarizqa Ramadha Meisa Putra
Universitas Satya Negara Indonesia
Humanities Computer Science & IT Economics, Econometrics & Finance Mathematics Public Health

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Evaluasi Simulasi Phishing Sebagai Upaya Peningkatan Kesadaran Keamanan Informasi Naufalarizqa Ramadha Meisa Putra
JOURNAL OF INFORMATION TECHNOLOGY, INFORMATION SYSTEMS AND COMMUNICATIONS Vol. 4 No. 1 (2026): Mei
Publisher : Department of System Information

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/jistic.v4i1.14066

Abstract

Phishing attacks remain one of the most prevalent information security threats in organizations due to their reliance on social engineering techniques that exploit human behavior rather than technical vulnerabilities. In the financial services sector, the increasing use of corporate email and digital applications further amplifies the potential impact of phishing incidents. This study evaluates the effectiveness of phishing simulation as an instrument to assess user behavior and information security awareness within an organizational environment. The research employs a quantitative descriptive approach based on a controlled phishing drill simulation delivered via organizational email. Interaction data were collected from 17,062 successfully delivered simulation emails and analyzed using behavioral indicators, including open rate, click rate, data submission rate, and response time. The results show that while most users did not engage in risky actions, a small proportion proceeded to critical interaction stages, such as clicking malicious links and submitting credentials. Notably, interactions involving users with critical access accounts, although limited in number, represent a disproportionate risk due to their potential impact on organizational security. The analysis of response time indicates that a significant portion of clicks occurred shortly after email receipt, suggesting a tendency toward rapid decision-making without sufficient verification, particularly in messages emphasizing operational urgency. The findings highlight the importance of risk-based mitigation strategies and demonstrate that phishing simulations should be positioned not only as measurement tools but also as part of a continuous improvement cycle integrating targeted security awareness interventions, user segmentation, and scenario variation to strengthen organizational resilience against phishing threats.
PEMODELAN KUALITAS UDARA JAKARTA BERBASIS DATA MINING DENGAN ALGORITMA RANDOM FOREST, KNN, DAN NAIVE BAYES Naufalarizqa Ramadha Meisa Putra
RAGAM: Journal of Statistics & Its Application Vol 5, No 1 (2026): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v5i1.18196

Abstract

Air quality prediction plays an important role in supporting public health monitoring in highly urbanized regions such as DKI Jakarta. This study aims to predict the Air Pollutant Standard Index (ISPU) category using three supervised learning algorithms, namely Random Forest, k Nearest Neighbors (kNN), and Naive Bayes, based on five pollutant parameters: PM10, SO2, CO, O3, and NO2. The dataset used in this study consists of validated daily air‑quality records that have undergone preprocessing steps including handling missing values and applying min max normalization. Model evaluation is conducted using the Test and Score feature in the Orange Data Mining software, which provides a visual programming environment for machine learning analysis. The results show that Random Forest achieves the highest performance with an accuracy of 97 percent, followed by kNN with 94 percent and Naive Bayes with 88 percent. Feature ranking using the Chi Square test indicates that PM10 is the most dominant factor influencing ISPU category with a value of 870.174, followed by O3 and NO2. These findings highlight that ensemble-based models are well suited for multiclass air quality classification and confirm that particulate matter remains a key determinant of air quality conditions in Jakarta.
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