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All Journal Jurnas Nasional Teknologi dan Sistem Informasi Literasi: Jurnal Pengabdian Masyarakat dan Inovasi Jurnal Ticom: Technology of Information and Communication
Muhammad Kamil Suryadewiansyah
Universitas Budi Luhur
Computer Science & IT Decision Sciences, Operations Research & Management Other

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Naïve Bayes dan Confusion Matrix untuk Efisiensi Analisa Intrusion Detection System Alert Muhammad Kamil Suryadewiansyah; Teja Endra Eng Tju
Jurnal Nasional Teknologi dan Sistem Informasi Vol 8, No 2 (2022): Agustus 2022
Publisher : Jurusan Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v8i2.2022.81-88

Abstract

Banyaknya malware menyebabkan IDS (Intrusion Detection System) dituntut menyesuaikan diri semakin kompleks sehingga mahal dan membebani perusahaan yang menggunakannya. Sistem yang berbasis teknologi Host-based IDS dan Signatured-based IDS sudah banyak digunakan namun hanya mampu mendeteksi serangan yang sudah diketahui sebelumnya, untuk memperbaiki kinerjanya perlu dilakukan analisa pada data log berdasarkan alert yang diberikan. Teknik klasifikasi Naïve Bayes digunakan untuk membantu meningkatkan efisisensi dan efektifitas analisa tersebut. Penelitian ini dilakukan dengan mengambil empat langkah bagian dari metodologi SKKNI (Standar Kompetensi Kerja Nasional Indonesia) No.299 tahun 2020, Artificial Intelligence, sub bidang Data Science, yaitu data understanding, data preparation, modeling, dan model evaluation. Dataset dari penyedia layanan IDS sebanyak 575 data yang dibagi menjadi 515 data latih dan 60 data uji. Hasil evaluasi data uji dengan confusion matrix diperoleh pengukuran metrik accuracy 0,87, recall 0,89, precision 0,83, dan F-Measure 0,86. Adanya FP (False Positive) dan FN (False Negatif), keduanya sangat penting bagi penguna IDS untuk meningkatkan kualitas layanan kepada pelanggan dan mengurangi resiko akibat adanya intrusi. FP dan FN menjadi fokus dalam melakukan analisa log alert dari IDS sehingga tidak perlu menganalisa keseluruhan data, berdampak memberikan hasil 85% lebih efektif dan berkontribusi pada efisiensi tenaga dan waktu bagi tim keamanan suatu peruasahaan pengguna IDS. Selain itu didapat bahwa sekitar 50% data IDS adalah intrusi atau pengganggu lainnya.
Diseminasi Teknik Klasifikasi Naïve Bayes pada Instrusion Detection System di Perusahaan Artajasa Teja Endra Eng Tju; Dolly Virgian Shaka Yudha Sakti; Muhammad Kamil Suryadewiansyah
Literasi: Jurnal Pengabdian Masyarakat dan Inovasi Vol 2 No 2 (2022)
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (366.651 KB) | DOI: 10.58466/literasi.v2i2.513

Abstract

Artajasa as a real-time online transaction service provider faces the challenge of monitoring transaction traffic and identifying transactions that are allowed or not. This community service activity aims to create a web-based application to help analyze alerts from the detection system that was previously carried out manually. The implementation of activities consists of interviews and observations, data collection and processing, application analysis and design, application testing and implementation. With the Naïve Bayes algorithm, the prediction results from the historical data obtained an accuracy of 0.88 and the results of the User Acceptance Test showed that all functions had been tried and worked well. With the application made, the work of artajasa's security team becomes effective and efficient because there is no need to check every alert data anymore, but simply focus on the results of the difference between the results of predictions and alerts.
Penerapan Metode Certainty Factor dan Interpolasi Untuk Diagnosa Penyakit Kolik Abdomen Pada Rumah Sakit Qadr Tangerang Muhammad Kamil Suryadewiansyah; Hari Soetanto
Jurnal Ticom: Technology of Information and Communication Vol 12 No 1 (2023): Jurnal Ticom-September 2023
Publisher : Asosiasi Pendidikan Tinggi Informatika dan Komputer Provinsi DKI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Abdominal colic is pain in the stomach caused by enlargement, blockage, or inflammation of organs in the body. Frequently, abdominal pain is mistaken for common gastritis. This has led to 259 million undiagnosed cases of appendicitis in men. Most cases of abdominal colic require surgical intervention, as it encompasses several diseases with similar symptoms. During surgery, doctors sometimes discover other types of diseases in patients, which adds to the time and effort required. The urgency of this research can impact the concentration and performance of doctors during surgical procedures, increasing the risk of complications that may result in fatalities for the patients. The proposed solution for this research is the implementation of an expert system based on a web application model. The research stages include data collection, data processing, interpolation method, certainty factor method, and testing. This research combines the certainty factor and interpolation methods for diagnosing abdominal colic diseases using 29 symptoms and 14 diseases. It also incorporates user belief values customized to the user's consultation form. The interpolation method is used for laboratory results, while the certainty factor method is applied to the anamnesis and physical examination. The research findings show an accuracy of 96%, with 96 patients accurately diagnosed by the system compared to the original data from 100 test patients at the Qadr Tangerang hospital.
Co-Authors Dolly Virgian Shaka Yudha Sakti Hari Soetanto Tju, Teja Endra Eng