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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Edukasi dan Penelitian Informatika (JEPIN) JOIV : International Journal on Informatics Visualization Jurnal Ilmiah Binary STMIK Bina Nusantara Jaya JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Desain dan Analisis Teknologi Computer & Science Industrial Engineering Journal Prosiding Seminar Nasional Ilmu Sosial dan Teknologi (SNISTEK)
Maslan, Andi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Social Sciences Other

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ANALISIS PERFORMA INTRUSION DETECTION SYSTEM SNORT DAN SURICATA TERHADAP SERANGAN SQL INJECTION Ramot Argenta Pasaribu, Fabian; Maslan, Andi
Computer Science and Industrial Engineering Vol 13 No 2 (2025): Comasie Vol 13 No 2
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/comasiejournal.v13i2.10403

Abstract

Web application security is becoming increasingly important due to the rise of threats such as SQL Injection, which exploits vulnerabilities to access sensitive data. As one of the most severe types of attacks, SQL Injection compromises the confidentiality, integrity, and access control of a system. Intrusion Detection Systems such as Snort and Suricata are used to detect and mitigate this. This study compares the effectiveness of Snort and Suricata in detecting SQL Injection using an experimental setup. The vulnerable web application (DVWA) was installed on Ubuntu, while attacks were launched from Kali Linux. Both IDS tools were configured to monitor network traffic and detect intrusions based on predefined rules. Performance was evaluated using accuracy, precision, recall, and F1 score. Suricata outperformed Snort in all metrics, Suricata also demonstrated faster detection. These results indicate that Suricata is more accurate and efficient at detecting SQL injection attacks in the test environment.
DETEKSI SERANGAN MALWARE MENGGUNAKAN METODE SUPPORT VECTOR MACHINE Simbolon, Hery Sanjaya; Maslan, Andi
Computer Science and Industrial Engineering Vol 13 No 2 (2025): Comasie Vol 13 No 2
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/comasiejournal.v13i2.10478

Abstract

The rapid development of information technology has increased the potential for threats to system security, one of which is malware attacks. Malware is malicious software that has the ability to disrupt, damage, or steal computer system data without user knowledge. To prevent further damage to the system, malware activity detection is very important. The purpose of this study is to create a classification model that can identify malware attacks based on the behavior of operating system processes when using the Support Vector Machine (SVM) method. The dataset used has 100,000 data entries that have 33 attributes that indicate process activity such as CPU usage, memory, and context shifts. Data is divided into training data and test data, exploratory data analysis (EDA) to understand data characteristics, data preprocessing to clean and standardize attributes, feature selection based on correlation to reduce model complexity, and development and training of a classification model using SVM with a linear kernel. Using a confusion matrix and evaluation metrics such as accuracy, precision, recall, and F1 score, the model is evaluated. Test results show that the developed SVM model performed very well, with an accuracy of 99.57%, a precision of 99.76%, a recall of 99.38%, and an F1 score of 99.57%. This model also distinguished malware processes from normal processes with a very small number of misclassifications. The results indicate that SVM can perform malware detection based on the behavior of system processes quite well. This research can contribute to the development of automated security systems that can detect threats in real time and help strengthen system defenses against cyberattacks.
PENERAPAN FUZZY LOGIC UNTUK MEMPREDIKSI PENJUALAN MAKANAN DI USAHA SHAKEEL KEBAB MENGGUNAKAN METODE MAMDANI Nasaruddin, Andira; Maslan, Andi
Computer Science and Industrial Engineering Vol 13 No 4 (2025): Comasie Vol 13 No 4
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/comasiejournal.v13i4.10581

Abstract

Particularly in the food industry, like Shakeel Kebab MSME, sales are a crucial performance metric. The lack of a data-driven method to predict future sales trends is one of the main issues. Using historical sales data from 2022 to 2024, this analysis predicts food sales using the Mamdani fuzzy logic method. Fuzzification, rule formation, inference, and defuzzification are all steps in the research process, and MATLAB software is used for implementation. The outcomes show that the fuzzy system can correctly identify sales trends. For example, the system generated a defuzzification value of 91.67 in September 2024, and consistently 80 in November and December. These outcomes demonstrate that the Mamdani fuzzy method is effective in supporting predictive decision-making for food sales, especially for small business owners.
Hybrid N-gram-based framework for payload distributed denial of service detection and classification Maslan, Andi; Mohd Foozy, Cik Feresa; Bin Mohamad, Kamaruddin Malik; Hamid, Abdul; Fitriawan, Dedy; Hasugian, Joni
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4763-4774

Abstract

There are three primary approaches to DDoS detection: anomaly-based, pattern-based, and heuristic-based. The heuristic-based method integrates both anomaly- and pattern-based techniques. However, existing DDoS detection systems face challenges in performing HTTP payload-level analysis, mainly due to high false positive rates and insufficient granularity in current datasets. To address this, the study introduces a novel heuristic approach based on a hybrid N-Gram model. This hybrid combines two components: CSDPayload+N-Gram and CSPayload+N-Gram. CSDPayload represents the gap (measured via Chi-Square Distance) between a given payload and normal traffic payloads, while CSPayload reflects the similarity (measured via Cosine Similarity) between them. These metrics form a new feature set evaluated using three datasets: CIC2019, MIB2016, and H2N-Payload. The methodology begins with packet extraction and conversion of TCP/IP traffic—specifically HTTP traffic—into hexadecimal payloads. N-Gram analysis (from 1-Gram to 6-Gram) is then applied to these payloads. For each N-Gram, frequency counts are computed, followed by calculations of Chi-Square Distance (CSD), Cosine Similarity (CS), and Pearson’s Chi-Square test to classify payloads as either benign or malicious. Subsequently, feature selection is performed using weight correlation, and the resulting features are fed into three machine learning classifiers: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Neural Network. Experimental results demonstrate high detection accuracy, particularly in the 4-Gram feature category: Neural Network achieves 99.65%, KNN 95.14%, and SVM 99.73% accuracy on average.
Feature Selection to Enhance DDoS Detection Using Hybrid N-Gram Heuristic Techniques Maslan, Andi; Mohamad, Kamaruddin Malik Bin; Hamid, Abdul; Pangaribuan, Hotma; Sitohang, Sunarsan
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1533

Abstract

Various forms of distributed denial of service (DDoS) assault systems and servers, including traffic overload, request overload, and website breakdowns. Heuristic-based DDoS attack detection is a combination of anomaly-based and pattern-based methods, and it is one of three DDoS attack detection techniques available. The pattern-based method compares a sequence of data packets sent across a computer network using a set of criteria. However, it cannot identify modern assault types, and anomaly-based methods take advantage of the habits that occur in a system. However, this method is difficult to apply because the accuracy is still low, and the false positives are relatively high. Therefore, this study proposes feature selection based on Hybrid N-Gram Heuristic Techniques. The research starts with the conversion process, package extract, and hex payload analysis, focusing on the HTTP protocol. The results show the Hybrid N-Gram Heuristic-based feature selection for the CIC-2017 dataset with the SVM algorithm on the CSDPayload+N-Gram feature with a 4-Gram accuracy rate of 99.86%, MIB- Dataset 2016 with the 2016 algorithm. SVM and CSPayload feature +N-Gram with 100% accuracy for 4-Gram, H2N-Payload Dataset with SVM Algorithm, and CSDPayload+N-Gram feature with 100% accuracy for 4-Gram. As a comparison, the KNN algorithm for 4-Gram has an accuracy rate of 99.44%, and the Neural Network Algorithm has an accuracy rate of 100% for 4-Gram. Thus, the best algorithm for DDoS detection is SVM with Hybrid N-Gram (4-Gram).
Implementasi Data Intelligence Pada Proses Pengambilan Keputusan Bisnis: (Studi Kasus: Rekomendasi Kontrak Kerja PT.BATM) Saragih, Saut Pintubipar; Husein, Alice Erni; Arnomo, Sasa Ani; Maslan, Andi
Jurnal Desain Dan Analisis Teknologi Vol. 5 No. 1 (2026): Januari
Publisher : Aptikom Kepri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58520/jddat.v5i1.97

Abstract

Penelitian ini bertujuan untuk menganalisis data karyawan IT dalam rangka mendukung pengambilan keputusan terkait perpanjangan kontrak kerja. Dataset yang digunakan mencakup data karyawan IT selama periode enam tahun dengan 19 atribut utama, termasuk latar belakang pendidikan, jabatan, durasi kontrak, dan status kepegawaian. Metode penelitian dilakukan melalui tahapan analisis data intelligence yang meliputi proses filterisasi, pembersihan data, serta analisis deskriptif dan korelasional. Hasil penelitian menunjukkan bahwa mayoritas karyawan IT memiliki latar belakang pendidikan sarjana (S1), yang mencerminkan standar rekrutmen yang relatif tinggi. Distribusi durasi kontrak didominasi oleh rentang 7–12 bulan, dengan tingkat keberhasilan probation yang dapat diidentifikasi melalui perbandingan status lulus dan diperpanjang terhadap tidak lulus. Korelasi positif yang kuat (0,65) antara kesesuaian pendidikan IT dan durasi kontrak mengindikasikan bahwa latar belakang pendidikan berpengaruh terhadap retensi karyawan. Dari sisi jabatan, peran senior seperti Project Manager memiliki tingkat retensi tertinggi, sementara peran developer menunjukkan durasi kontrak yang konsisten. Penelitian ini juga menemukan bahwa sekitar 60% resign terjadi dalam enam bulan pertama masa kerja, sehingga bulan ke-3 dan ke-6 diidentifikasi sebagai waktu optimal untuk intervensi retensi.
Co-Authors A.S, Darmawan Abdul Hamid Arnomo, Sasa Ani Bin Mohamad, Kamaruddin Malik Cik Feresa Mohd Foozy, Cik Feresa Dedy Fitriawan Elisa, Erlin Fadzri, Alief Achmad fajrin, alfannisa annurrallah Fajrin, Alfannisa Annurullah Gary, Gary Hasugian, Joni Heri Gunawan Husein, Alice Erni ihsana, angga novenzo Ihwan Ghazali Imam Budi Utomo, Imam Budi Kremer, Hendri Mariyanto Mariyanto Mohamad, Kamaruddin Malik Bin Nasaruddin, Andira Nofedrinata, Windri Nopri Hidayat Pangaribuan, Hotma Puan, Loretta Anak Putri, M.Kom, Anggia Dasa Rafsyanzani s, Firman Firmansyah Rahman, Azan Ramot Argenta Pasaribu, Fabian Saragih, Saut Pintubipar Simanjuntak, Pastima Simbolon, Hery Sanjaya Sitohang, Sunarsan Tamin, Norfauzi Tampubolon, Piala Wiyono, Pieter Yonanta, Rhesty Dian Ziliwu, Krisma Budi