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All Journal Applied Information Technology and Computer Science (AICOMS) Al'Adzkiya International of Computer Science and Information Technology Journal
Oris Krianto Sulaiman
Universitas Islam Negeri Ar-Raniry Banda Aceh
Computer Science & IT

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Analisis Komparatif Support Vector Machine dan Random Forest untuk Deteksi Email Phishing Indah Purnama Sari; Oris Krianto Sulaiman; Dicky Apdilah; Pastima Simanjuntak
Applied Information Technology and Computer Science (AICOMS) Vol 4 No 2 (2025)
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/aicoms.v4i2.1806

Abstract

Information and communication technology has rapidly advanced, bringing significant changes to daily life. With these advancements, access to information has become faster and easier; however, this convenience also introduces challenges, particularly concerning personal data security. One common cybercrime is email phishing, where attackers use malicious links to encrypt user data or devices and demand a ransom to restore access. Phishing emails often resemble official messages from trusted sources, making recipients unaware of the potential threat. To minimize such risks, technology can be utilized to automatically classify phishing emails. This study focuses on developing a machine learning model for automatic phishing email classification. The dataset used consists of 18,650 emails, including 11,322 non-phishing and 7,328 phishing emails. The proposed models employ two algorithms: Support Vector Machine (SVM) and Random Forest. To optimize performance, hyperparameter tuning was conducted using GridSearchCV. The experimental results demonstrate that the SVM algorithm achieved an accuracy of 97.27%, while the Random Forest algorithm achieved 96.51%. These findings indicate that the developed models can effectively support efforts to anticipate and mitigate phishing email threats..
Application of Data Mining in Determining the Performance of Family Planning Field Officers Using the C4.5 Algorithm Oris Krianto Sulaiman; Muhammad Zulfan Syuri Siambaton
Al'adzkiya International of Computer Science and Information Technology (AIoCSIT) Journal Vol 6, No 2 (2025)
Publisher : Al'Adzkiya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55311/aiocsit.v6i2.353

Abstract

The performance of Family Planning Field Officers plays a crucial role in the success of family planning programs. Accurate and objective performance evaluation is essential to support effective decision-making and policy formulation. This study applies data mining techniques to determine the performance of Family Planning Field Officers using the C4.5 decision tree algorithm. The dataset used in this research consists of officer performance indicators, including service coverage, counseling activities, reporting accuracy, and community participation. The C4.5 algorithm is employed to classify officer performance into predefined categories based on these attributes. The resulting decision tree provides interpretable classification rules that can support managerial decision-making. Experimental results show that the proposed model achieves satisfactory classification accuracy and demonstrates the effectiveness of the C4.5 algorithm in extracting meaningful patterns from performance data. This study highlights the potential of data mining approaches to enhance performance evaluation systems in public service institutions, particularly in the field of family planning management.
Co-Authors Dicky Apdilah Indah Purnama Sari Muhammad Zulfan Syuri Siambaton Simanjuntak, Pastima