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All Journal Sistemasi: Jurnal Sistem Informasi Jurnal Telematics and Information Technology (TELEFORTECH)
Putra, Rulyansyah Permata
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Computer Science & IT Electrical & Electronics Engineering

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Implementasi Framework CodeIgniter untuk Pengembangan Sistem Bimbingan Konseling pada SMK Amal Bakti Jatimulyo, Lampung Romadhoni, Randi; Setiawansyah, Setiawansyah; Ahdan, Syaiful; Putra, Rulyansyah Permata
TELEFORTECH : Journal of Telematics and Information Technology Vol 4, No 2 (2023)
Publisher : Fakultas Teknik dan Ilmu Komputer, Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/tft.v4i2.4555

Abstract

Bidang bimbingan dan konseling di SMK Amal Bakti Jatimulyo membutuhkan sistem data berbasis komputerisasi untuk mempercepat penyampaian informasi. Saat ini, sekolah belum menggunakan sistem komputer dalam aktivitasnya, sehingga penulis merancang sistem bimbingan konseling berbasis web menggunakan Framework CodeIgniter. Tujuan penelitian ini adalah untuk merancang sistem informasi yang mencakup pengolahan data siswa, absensi, kasus, dan konseling, serta menghasilkan laporan. Metode yang digunakan adalah penelitian kualitatif melalui observasi dan wawancara. Pengujian black box dilakukan untuk menguji fungsionalitas sistem, dan hasil pengujian menunjukkan tingkat kelayakan 100%, yang berarti sistem ini layak digunakan. Perancangan sistem ini mengikuti enam tahapan: Understand, Research, Sketch, Design, Implementation, dan Evaluate. Sistem ini dibuat dengan PHP dan MySQL, dan berhasil mengatasi masalah pendataan konseling siswa di SMK Amal Bakti Jatimulyo.
A Comparative Study of Machine Learning Algorithms for Intrusion Detection Systems using the NSL-KDD Dataset Putra, Rulyansyah Permata; Amarudin, Amarudin
Sistemasi: Jurnal Sistem Informasi Vol 14, No 4 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i4.5246

Abstract

In today’s digital era, cyberattacks are becoming increasingly complex, rendering traditional rule-based Intrusion Detection Systems (IDS) often ineffective in recognizing new attack patterns. The primary objective of this study is to design and implement a machine learning model for detecting network intrusions efficiently while minimizing latency, through a comparative analysis of several algorithms: Decision Tree, Random Forest, Support Vector Machine (SVM), and Boosting. The research methodology includes the collection of the NSL-KDD dataset, followed by data transformation, cleaning, normalization, and partitioning into training and testing sets. Each algorithm was trained using tuned parameters, and performance was evaluated using metrics such as accuracy, precision, recall, F1-score, and an analysis of training and prediction time. The results indicate that the Boosting algorithm stands out, achieving an accuracy rate of 99.36%. Boosting also demonstrated greater reliability in detecting minority classes, despite requiring longer training times. The application of machine learning methods—particularly Boosting—proves to be an effective approach to enhancing intrusion detection and can serve as a foundation for developing more adaptive and reliable cybersecurity systems.
Co-Authors Ahdan, Syaiful Amarudin, Amarudin Romadhoni, Randi Setiawansyah Setiawansyah