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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Annual Research Seminar Sistemasi: Jurnal Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Mantik Indonesian Journal of Electrical Engineering and Computer Science Sriwijaya Journal of Informatics and Applications
Ahmad Heryanto
Universitas Sriwijaya
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Languange, Linguistic, Communication & Media

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Monitoring Jaringan Server Menggunakan SNMP dan ICMP Pada Server ERP Fakultas Ilmu Komputer Universitas Sriwijaya Ahmad Heryanto
Annual Research Seminar (ARS) Vol 2, No 2 (2016): Special Issue : Penelitian, Pengabdian Masyarakat
Publisher : Annual Research Seminar (ARS)

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

Abstract

Server adalah Sebuah komputer yang terhubung dengan jaringan komputer dan menyediakan berbagai jenis layanan yang dapat diakses oleh komputer lainnya (client). Server harus didukung dengan oleh hardware dan software yang handal. Komputer Server harus selalu aktif supaya client ERP tetap bisa mengaksesnya setiap saat. Sebaliknya Jika server ERP down maka aplikasi web tidak bisa diakses sama sekali oleh client. Oleh karena itu, dibutuhkan mekanisme monitoring server ERP untuk mengetahui status dari server tempat aplikasi ERP tersedia. Sistem monitoring menggunakan ICMP dan SNMP untuk mendapatkan status dari setiap server yang digunakan. Berdasarkan data percobaan, SNMP dan ICMP mampu memberikan informasi yang akurat dan cepat untuk mendapatkan status aktif atau down dari server yang menjadi objek penelitian.
Implementasi Sistem Database Terdistribusi Dengan Metode Multi-Master Database Replication Ahmad Heryanto; Albert Albert
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 3, No 1 (2019): Januari 2019
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v3i1.1098

Abstract

Databases are the main need for every computer application to store, process and modify data. One important problem faced in databases is the availability of adequate information technology infrastructure in managing and securing data contained in the database. Data stored on the database must have protection against threats and disturbances. Threats and disruptions can result from a variety of things, such as maintenance, data damage, and natural disasters. To anticipate data loss and damage, replication of the database system needs to be done. The replication mechanism used by researchers is multi-master replication. The replication technique is able to form a database cluster with replication time of fewer than 0.2 seconds.
HIGH AVAILABILITY IN SERVER CLUSTERS BY USING BACKPROPAGATION NEURAL NETWORK METHOD Ahmad Heryanto; Aditya Gunanta
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol 4 No 1 (2021): Jurnal Teknologi dan Open Source, June 2021
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v4i1.936

Abstract

Server is a host device applications to serve every request in finding information needs. The server must fully support the services used for the organization's digital needs 24 hours in a day, 7 days in a week, and 365 days in a year. The concept of High Availability is needed to maintain the quality of server services. The algorithm used to build HA can use both classical and modern algorithms. The algorithm used in this research is using backpropagation neural network. In this study, the parameter values to obtain optimal accuracy are learning rate 0.1, training data 80 and test data 20, the number of nodes in hidden layer 4, minimum error 0.0001, and the number of iterations 2500.The best accuracy value using these parameters is 93.79% .
Cross-Site Scripting Attack Detection using Rule-Based Signature deris Stiawan; Gonewaje gonewaje; Ahmad Heryanto; Rahmat Budiarto
Sriwijaya Journal of Informatics and Applications Vol 2, No 1 (2021)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v2i1.20

Abstract

Rule-Based Signature or also known as Misuse Detection is IDS which rely on matching data captured on retrieval of attack pattern which in system that allow attacks. If the attack activity detected according to existing signature, then it will be read by system and called as attack. The advantage of this Signature-Based IDS is the accuracy of detecting matched attack which in the system with low false-positive result and high true-positive. Cross-Site Scripting is type of attack which is perform by injecting code (usually) JavaScript to a site. XSS is very often utilized by attacker to steal web browser resource such as cookie, credentials, etc. Dataset which used in this research is dataset which created by injecting script into a website. Once obtained the dataset, then feature extraction is performed to separate the attribute which used. XSS attack pattern can be easily recognized from URI, and then detected using engine which has been created. Detection result of algorithm which used is evaluated using confusion matrix to determine detection accuracy value which performed. Obtained accuracy detection of research result reached 99.4% with TPR 98.8% and FPR 0%.
A malicious URLs detection system using optimization and machine learning classifiers Ong Vienna Lee; Ahmad Heryanto; Mohd Faizal Ab Razak; Anis Farihan Mat Raffei; Danakorn Nincarean Eh Phon; Shahreen Kasim; Tole Sutikno
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 3: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i3.pp1210-1214

Abstract

The openness of the World Wide Web (Web) has become more exposed to cyber-attacks. An attacker performs the cyber-attacks on Web using malware Uniform Resource Locators (URLs) since it widely used by internet users. Therefore, a significant approach is required to detect malicious URLs and identify their nature attack. This study aims to assess the efficiency of the machine learning approach to detect and identify malicious URLs. In this study, we applied features optimization approaches by using a bio-inspired algorithm for selecting significant URL features which able to detect malicious URLs applications. By using machine learning approach with static analysis technique is used for detecting malicious URLs applications. Based on this combination as well as significant features, this paper shows promising results with higher detection accuracy.  The bio-inspired algorithm: particle swarm optimization (PSO) is used to optimized URLs features. In detecting malicious URLs, it shows that naïve Bayes and support vector machine (SVM) are able to achieve high detection accuracy with rate value of 99%, using URL as a feature.
Utilizing neural networks with CICIDS2018 dataset for detecting brute force attack anomalies in intrusion detection systems Ahmad Heryanto; Adi Hermansyah; Triwanda Septian; Ali Bardadi
Jurnal Mantik Vol. 7 No. 4 (2024): February: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v7i4.4919

Abstract

In this study, the effectiveness of neural networks in Intrusion Detection Systems (IDS) has been tested using the CICIDS2018 dataset to achieve accurate intrusion detection results. The research findings reveal that several neural network parameters will reach optimal results with a learning rate of 0.1, a training and testing data proportion of 80:20, and an optimal number of nodes in the hidden layer of 4. Other parameters such as a minimum error of 0.0001 and 2500 iterations also play a crucial role in improving IDS capability. Based on the research, it is shown that neural network models can provide optimal results in detecting intrusion patterns. This study can assist in the development of reliable and efficient neural network-based IDS to address the challenges of intrusion detection
Co-Authors Adi Hermansyah Adi Hermansyah Aditya Gunanta Aditya Putra Perdana Prasetyo Ahmad Fali Oklilas Albert Albert Ali Bardadi Ali Bardadi Ali Djamhuri anggoro prasetyo Anis Farihan Mat Raffei candra adi winanto Danakorn Nincarean Eh Phon Darmawijoyo, Darmawijoyo Deris Stiawan Dwi Isdestian Erwin, Erwin Gonewaje gonewaje Kurniabudi, Kurniabudi M Nizar Mohd Faizal Ab Razak Ong Vienna Lee Osvari Arsalan Purnama, Benni Reza Firsandaya Malik Rizki Kurniati Rossi Passarella Samsuryadi Samsuryadi Sarmayanta Sembiring Shahreen Kasim Sharipuddin, Sharipuddin Sutarno Sutarno Tole Sutikno Triwanda Septian Yundari, Yundari Yuyun Hartati