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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Jurnal Masyarakat Informatika Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Transformatika Seminar Nasional Informatika (SEMNASIF) JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Jurnal Ilmiah Dinamika Rekayasa (DINAREK) SISFOTENIKA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) JUDIMAS (Jurnal Inovasi Pengabdian Kepada Masyarakat) Jurnal Pendidikan dan Teknologi Indonesia Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Algoritma Prosiding Seminar Nasional Hasil-hasil Penelitian dan Pengabdian Pada Masyarakat Moneter : Jurnal Keuangan dan Perbankan Seminar Nasional Teknologi dan Multidisiplin Ilmu Scientific Journal of Informatics Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) INOVTEK Polbeng - Seri Informatika
L. Budi Handoko
Universitas Dian Nuswantoro
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Public Health Social Sciences Other

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Performance Analysis of Support Vector Classification and Random Forest in Phishing Email Classification Umam, Chaerul; Handoko, Lekso Budi; Isinkaye, Folasade Olubusola
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.3301

Abstract

Purpose: This study aims to conduct a performance analysis of phishing email classification system using machine learning algorithms, specifically Random Forest and Support Vector Classification (SVC). Methods/Study design/approach: The study employed a systematic approach to develop a phishing email classification system utilizing machine learning algorithms. Implementation of the system was conducted within the Jupyter Notebook IDE using the Python programming language. The dataset, sourced from kaggle.com, comprised 18,650 email samples categorized into secure and phishing emails. Prior to model training, the dataset was divided into training and testing sets using three distinct split percentages: 60:40, 70:30, and 80:20. Subsequently, parameters for both the Random Forest and Support Vector Classification models were carefully selected to optimize performance. The TF-IDF Vectorizer method was employed to convert text data into vector form, facilitating structured data processing. Result/Findings: The study's findings reveal notable performance accuracies for both the Random Forest model and Support Vector Classification across varying data split percentages. Specifically, the Support Vector Classification consistently outperforms the Random Forest model, achieving higher accuracy rates. At a 70:30 split percentage, the Support Vector Classification attains the highest accuracy of 97.52%, followed closely by 97.37% at a 60:40 split percentage. Novelty/Originality/Value: Comparisons with previous studies underscored the superiority of the Support Vector Classification model. Therefore, this research contributes novel insights into the effectiveness of this machine learning algorithms in phishing email classification, emphasizing its potential in enhancing cybersecurity measures.
The Implementation of AWS Cloud Technology to Enhance the Performance and Security of the Pharmacy Cashier Management System Hendy Kurniawan; L. Budi Handoko; Valentino Aldo
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/x0rctv54

Abstract

 This study examines the implementation of Amazon Web Services (AWS) in the MEKATEK pharmacy cashier management system to address the limitations of traditional systems, such as slow transaction processing, data loss risks, and challenges in handling transaction surges. The prototyping method was employed, involving user requirements analysis through interviews and observations, followed by iterative development of core features like inventory management, transactions, reporting, and data backups. Black box testing demonstrated a 100% success rate for core functionalities. Performance analysis recorded stable CPU utilisation below 5% under normal workloads and the ability to handle throughput up to 2532 packets/minute. System optimisation reduced AWS operational costs to IDR 150,000–160,000 per month. AWS implementation improved operational efficiency, strengthened data security through encryption and role-based access control, and minimised human errors. Initial user feedback indicated faster workflows, although adjustments are needed for users with limited technical backgrounds. This study recommends further development, including AI-based analytics and digital payment integration, to enhance MEKATEK’s functionality and competitiveness in the future.
Decision Tree Classification for Reducing Alert Fatigue in Patient Monitoring Systems Herfiani, Kheisya Talitha; Nurhindarto, Aris; Alzami, Farrikh; Budi, Setyo; Megantara, Rama Aria; Soeleman, M Arief; Handoko, L Budi; Rofiani, Rofiani
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8414

Abstract

The development of information technology in healthcare opens new opportunities to improve continuous patient monitoring. A major challenge is alert fatigue, where medical personnel are overwhelmed by excessive notifications, reducing concentration, work efficiency, and potentially compromising patient safety. This study presents a proof-of-concept application of the Decision Tree algorithm to analyze alert triggering factors in patient monitoring systems. The dataset is a synthetic health monitoring dataset from Kaggle, containing 10,000 entries with vital parameters including blood pressure, heart rate, oxygen saturation, and glucose levels, designed with deterministic logical relationships between threshold indicators and alert outcomes. The imbalanced dataset (73.67% alert triggered, 26.33% no alert) was intentionally not processed using imbalanced learning techniques to demonstrate Decision Tree's capability in processing structured health data and producing interpretable classifications. The research methodology included data preprocessing, exploratory data analysis, data splitting (90% training, 10% testing), GridSearchCV optimization, and performance evaluation. Results showed perfect metrics (100% accuracy, precision, recall, F1-score), reflecting the deterministic nature of the synthetic dataset rather than real-world clinical complexity. Feature importance analysis identified blood pressure as the most dominant variable, followed by heart rate and glucose levels. This study demonstrates Decision Tree's interpretability and feature importance analysis capabilities in health data contexts, establishing a methodological framework that requires validation on real clinical Electronic Health Record (EHR) data for practical application in reducing alert fatigue and supporting informed clinical decisions.
Co-Authors ., Muslih Abdus Salam, Abdus Abdussalam Abdussalam Abu Salam Abu Salam Acun Kardianawati Ade Surya Ramadhan Adelia Syifa Anindita Aisyah, Ade Nurul Aisyatul Karima Aisyatul Karima Ajib Susanto Al zami, Farrikh Alzami, Farrikh Andi Danang Krismawan Ardytha Luthfiarta Ari Saputro Ari Saputro, Ari ARIANTO, EKO Ariya Pramana Putra Ariyanto, Noval Budi Harjo Budi, Setyo Cahaya Jatmoko Chaerul Umam Chaerul Umam Chaerul Umam Chaerul Umam Chaerul Umam Christy Atika Sari De Rosal Ignatius Moses Setiadi Eko Hari Rachmawanto Elkaf Rahmawan Pramudya Erwin Yudi Hidayat Erwin Yudi Hidayat Etika Kartikadarma Fauzi Adi Rafrastara Fikri Firdaus Tananto Fikri Firdaus Tananto Filmada Ocky Saputra Firman Wahyudi, Firman Ghulam Maulana Rizqi Guruh Fajar Shidik Hafiidh Akbar Sya'bani Hanif Setia Nusantara Hanny Haryanto Hasan Aminda Syafrudin Hendy Kurniawan Herfiani, Kheisya Talitha Irfannandhy, Rony Irwan, Rhedy Isinkaye, Folasade Olubusola Izza Khaerani Ja'far, Luthfi Junta Zeniarja Karima, Nida Aulia Khafiizh Hastuti Khafiizh Hastuti Lucky Arif Rahman Hakim Maulana Ikhsan Megantara, Rama Aria Mira Nabila Mira Nabila Muhammad Jamhari Muslih Muslih Muslih Muslih Nurhindarto, Aris Ocky Saputra, Filmada Oki Setiono Pulung Nurtantio Andono Raihan Yusuf Rama Aria Megantara Ramadhan Rakhmat Sani Reza Pahlevi, Mohammad Rizky Rizqy, Aditya Rofiani, Rofiani Saputra, Filmada Ocky Saputri, Pungky Nabella Sarker, Md. Kamruzzaman Sendi Novianto Silla, Hercio Venceslau Soeleman, M Arief Sya'bani, Hafiidh Akbar Umi Rosyidah Valentino Aldo Wellia Shinta Sari Wildanil Ghozi