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All Journal Bulletin of Electrical Engineering and Informatics Register: Jurnal Ilmiah Teknologi Sistem Informasi Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer International Journal of Artificial Intelligence Research JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Knowledge Engineering and Data Science JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sisfokom (Sistem Informasi dan Komputer) ILKOM Jurnal Ilmiah Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Infotekmesin Journal of Innovation Information Technology and Application (JINITA) Madani : Indonesian Journal of Civil Society Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Sistem Informasi Galuh Journal of Mechatronics and Artificial Intelligence
Arif Wirawan Muhammad
Fakultas Informatika, Universitas Telkom Purwokerto
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Mechanical Engineering Social Sciences

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ANALISIS STATISTIK LOG JARINGAN UNTUK DETEKSI SERANGAN DDOS BERBASIS NEURAL NETWORK Muhammad, Arif Wirawan; Riadi, Imam; Sunardi, Sunardi
ILKOM Jurnal Ilmiah Vol 8, No 3 (2016)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1103.013 KB) | DOI: 10.33096/ilkom.v8i3.76.220-225

Abstract

Distributed denial-of-service (DDoS) merupakan jenis serangan dengan volume, intensitas, dan biaya mitigasi yang terus meningkat seiring berkembangnya skala organisasi. Penelitian ini memiliki tujuan untuk mengembangkan sebuah pendekatan baru untuk mendeteksi serangan DDoS, berdasarkan log jaringan yang dianalisis secara statistik dengan fungsi neural network sebagai metode deteksi. Data pelatihan dan pengujian diambil dari CAIDA DDoS Attack 2007 dan simulasi mandiri. Pengujian terhadap metode analisis statistik terhadap log jaringan dengan fungsi neural network sebagai metode deteksi menghasilkan prosentase rata-rata pengenalan terhadap tiga kondisi jaringan (normal, slow DDoS, dan DDoS) sebesar 90,52%. Adanya pendekatan baru dalam mendeteksi serangan DDoS, diharapkan bisa menjadi sebuah komplemen terhadap sistem Intrusion Detection System (IDS) dalam meramalkan terjadinya serangan DDoS.
MULTISCHEME FEEDFORWARD ARTIFICIAL NEURAL NETWORK ARCHITECTURE FOR DDOS ATTACK DETECTION Muhammad, Arif Wirawan; Feresa Mohd Foozy, Cik; Malik, Kamaruddin
Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i1.2671

Abstract

Distributed denial of service attack classified as a structured attack to deplete server, sourced from various bot computers to form a massive data flow. Distributed denial of service (DDoS) data flows behave as regular data packet flows, so it is challenging to distinguish between the two. Data packet classification to detect DDoS attacks is one solution to prevent DDoS attacks and to maintain server resources maintained. The machine learning method especially artificial neural network (ANN), is one of the effective ways to detect the flow of data packets in a computer network. Based on the research that has carried out, it concluded that ANN with hidden layer architecture that contains neuron twice as neuron on the input layer (2n) produces a stable detection accuracy value on Quasi-Newton, Scaled-Conjugate and Resilient-Propagation training functions. Based on the studies conducted, it concluded that ANN Architecture sufficiently affected the Scaled-Conjugate and Resilient-Propagation training functions, otherwise the Quasi-Newton training function. The best detection accuracy achieved from the experiment is 99.60%, 1.000 recall, 0.988 precision, and 0.993 f-measure using the Quasi-Newton training function with 6-(12)-2 neural network architecture
Analysis of e-learning readiness level of public and private universities in Central Java, Indonesia Saintika, Yudha; Astiti, Sarah; Kusuma, Dwi Januarita Ardianing; Muhammad, Arif Wirawan
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 7, No 1 (2021): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v7i1.2042

Abstract

The development of information technology has reached into various fields, such as education. The emergence of e-learning is one manifestation of information and communication technology (ICT) in education. Until recently, only a few universities (6%) have implemented e-learning in Indonesia. Those that have implemented e-learning are still not optimally utilized. Some experts have also warned all organizations that will adopt e-learning to be concerned with thorough preparation to avoid overruns in costs. There is a method that consists of factors to measure the level of readiness of tertiary institutions towards the implementation of e-learning. The level of readiness is obtained through the distribution of questionnaires using 5 Likert scales. This research proposed a framework that produces four factors from the university, which covers the lecturer’s characteristics, e-learning facilities, learning environment, learning management, and four factors from the student’s side, namely, self-learning, motivation, learner’s control, student’s characteristic. The measurement results show the level of readiness for e-learning implementation in tertiary institutions in Central Java Province reaches level 3 or ready but needs a few improvements. Improvements that must be made includes (1) Designing exciting learning content through interactive multimedia; (2) Increasing the frequency of e-workshops or e-training related to technological developments, especially to e-learning; (3) encouraging students to be more active in discussions and giving opinions; (4) Developing plans related to infrastructure such as servers related to their capacities; (5) strengthening the role of IT units in serving e-learning users.
Block-hash of blockchain framework against man-in-the-middle attacks Riadi, Imam; Umar, Rusydi; Busthomi, Iqbal; Muhammad, Arif Wirawan
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 8, No 1 (2022): In progress (January)
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v8i1.2190

Abstract

Payload authentication is vulnerable to Man-in-the-middle (MITM) attack. Blockchain technology offers methods such as peer to peer, block hash, and proof-of-work to secure the payload of authentication process. The implementation uses block hash and proof-of-work methods on blockchain technology and testing is using White-box-testing and security tests distributed to system security practitioners who are competent in MITM attacks. The analyisis results before implementing Blockchain technology show that the authentication payload is still in plain text, so the data confidentiality has not minimize passive voice. After implementing Blockchain technology to the system, white-box testing using the Wireshark gives the result that the authentication payload sent has been well encrypted and safe enough. The percentage of security test results gets 95% which shows that securing the system from MITM attacks is relatively high. Although it has succeeded in securing the system from MITM attacks, it still has a vulnerability from other cyber attacks, so implementation of the Blockchain needs security improvisation.
Multischeme feedforward artificial neural network architecture for DDoS attack detection Arif Wirawan Muhammad; Cik Feresa Mohd Foozy; Kamaruddin Malik bin Mohammed
Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i1.2383

Abstract

Distributed denial of service attack classified as a structured attack to deplete server, sourced from various bot computers to form a massive data flow. Distributed denial of service (DDoS) data flows behave as regular data packet flows, so it is challenging to distinguish between the two. Data packet classification to detect DDoS attacks is one solution to prevent DDoS attacks and to maintain server resources maintained. The machine learning method especially artificial neural network (ANN), is one of the effective ways to detect the flow of data packets in a computer network. Based on the research that has carried out, it concluded that ANN with hidden layer architecture that contains neuron twice as neuron on the input layer (2n) produces a stable detection accuracy value on Quasi-Newton, scaled-conjugate and resilientpropagation training functions. Based on the studies conducted, it concluded that ANN architecture sufficiently affected the scaled-conjugate and resilient-propagation training functions, otherwise the Quasi-Newton training function. The best detection accuracy achieved from the experiment is 99.60%, 1.000 recall, 0.988 precision, and 0.993 f-measure using the quasinewton training function with 6-(12)-2 neural network architecture.
Impact of Feature Selection Methods on Machine Learning-based for Detecting DDoS Attacks : Literature Review Muhammad Nur Faiz; Oman Somantri; Abdul Rohman Supriyono; Arif Wirawan Muhammad
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 5, No 2 (2022): Issues January 2022
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v5i2.6112

Abstract

Cybersecurity attacks are becoming increasingly sophisticated and increasing with the development of technology so that they present threats to both the private and public sectors, especially Denial of Service (DoS) attacks and their variants which are often known as Distributed Denial of Service (DDoS). One way to minimize this attack is by using traditional mitigation solutions such as human-assisted network traffic analysis techniques but experiencing some limitations and performance problems. To overcome these limitations, Machine Learning (ML) has become one of the main techniques to enrich, complement and enhance the traditional security experience. The way ML works are based on the process of data collection, training and output. ML is influenced by several factors, one of which is feature engineering. In this study, we focus on the literature review of several recent studies which show that the feature selection process greatly impacts the level of accuracy of this ML. Datasets such as KDD, UNSW-NB15 and others also affect the level of accuracy of ML. Based on this literature review, this study can observe several feature engineering strategies with relevant impacts that can be chosen to improve ML solutions on DDoS attacks.
Penguatan Ekonomi Lokal Pada Pelaku UMKM Berbasis Digital Di Desa Winduaji Kabupaten Brebes Achmad Zaki Yamani; Arif Wirawan Muhammad; Muhammad Nur Faiz
Madani : Indonesian Journal of Civil Society Vol. 1 No. 1 (2019): MADANI, Agustus 2019
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/madani.v1i1.29

Abstract

In the industrial era 4.0, technological knowledge, especially information technology is very important. UMKM are micro-enterprises that should have used the information access for the economic welfare of a region, but vice versa. Current problems with UMKM include lack of capital and knowledge of information technology. Winduaji village is one of the villages with UMKM actors with minimal information technology knowledge. The method of implementation is the method of discussion with the format of Training regarding identifying problems to the use of technology media. This training activity was attended by 56 participants consisting of village officials, UMKM actors, and tourism conscious reservoirs. As a result, all participants showed great interest in using social media marketing continuously.
Implementasi Intrusion Prevention System (IPS) OSSEC dan Honeypot Cowrie Risa Eri Susanti; Arif Wirawan Muhammad; Wahyu Adi Prabowo
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 11, No 1 (2022): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v11i1.1246

Abstract

Perkembangan teknologi yang semakin canggih ini banyak digunakan sebagai tindak kejahatan, seperti pencurian data, pemalsuan data hingga merusak sistem maupun jaringan. Dengan adanya permasalahan tersebut, dibutuhkan sistem pengamanan berlapis untuk menjaga integritas data maupun sistem agar tetap utuh. Pengamanan sistem OSSEC yang diintegrasikan dengan honeypot cowrie ini bertujuan untuk menekan waktu penyerangan, dimana pada sistem ini saling bekerja sama untuk memberikan log untuk melakukan tindakan terhadap penyerang. OSSEC bekerja layaknya firewall yang dapat melakukan allow maupun block. Sedangkan honeypot cowrie ini bekerja layaknya server asli untuk menjebak penyerang seolah-olah berhasil melakukan penyerangan. Dalam penelitian ini, sistem yang telah dirancang agar dapat menangani adanya serangan seperti Port Scanning, SSH brute force, Man in The Middle (MITM) attack, dan Distributed Denial of Service (DDoS). Dari hasil perbandingan serangan dengan confusion matrix ini OSSEC yang diintegrasikan dengan honeypot cowrie memiliki tingkat akurasi yang besar terhadap serangan DDoS, Berdasarkan log, akurasi deteksi dapat mencapai persentase 100%.
Human Intestinal Condition Identification based-on Blended Spatial and Morphological Feature using Artificial Neural Network Classifier Ummi Athiyah; Arif Wirawan Muhammad; Ahmad Azhari
Knowledge Engineering and Data Science Vol 3, No 1 (2020)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v3i12020p19-27

Abstract

Colon cancer is a type of disease that attacks the intestinal walls cell of humans. Colorectal endoscopic screening technique is a common step carried out by the health expert/gynecologist to determine the condition of the human intestine. Manual interpretation requires quite a long time to reach a result. Along with the development of increasingly advanced digital computing techniques, then some of the weaknesses of the manually endoscopic image interpretation analysis model can be corrected by automating the detection process of the presence or absence of cancerous cells in the gut. Identification of human intestinal conditions using an artificial neural network method with the blended input feature produces a higher accuracy value compared to the artificial neural network with the non-blended input feature. The difference in classifier performance produced between the two is quite significant, that is equal to 0.065 (6.5%) for accuracy; 0.074 (7.4%) for recall; 0.05 (5.0%) for precision; and 0.063 (6.3%) for f-measure.
Analisis Sentimen Evaluasi Terhadap Pengajaran Dosen di Perguruan Tinggi Menggunakan Metode LSTM Muhammad Afrizal Amrustian; Widi Widayat; Arif Muhammad Wirawan
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 1 (2022): Januari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i1.3527

Abstract

Education in Indonesia is divided into several levels, from elementary education to university education. At the university education level, lecturers are asked to not only teach material but also emphasize to students that students have an important role for the future.  Due the students are considered as adults to make the decisions and take a responsibility for those decisions. During a pandemic, teaching activities are carried out online, in order the teaching activities run well, the evaluation from students is needed. Considering that students are one of the important elements in university education. In this study, sentiment analysis was carried out on the evaluation of teaching by students. The data used in this study amounted to 2280 data with the number of words in the evaluation text ranging from 3 to 50 words. The LSTM method is the method used in this study, and the results of the accuracy of using the LSTM method are 91.08%. With the analysis carried out, lecturers can improve their teaching methods based on the results of the evaluation analysis.
Co-Authors Abdul Fadlil Abdul Rohman Supriyono Achmad Zaki Yamani Ahmad Azhari Alameka, Faza Ali Diko Putra, Muhammad Alika, Shintia Dwi Amalia, Hasna Shafa Arsita Pinandita Atika Ratna Dewi Azhari, Ahmad Bita Parga Zen Busthomi, Iqbal Cik Feresa Mohd Foozy Davito Rasendriya Rizqullah Putra Dwi Januarita Ardianing Kusuma Feresa Mohd Foozy, Cik Firda Millennianita Foozy, Cik Feresa Mohd Imam Riadi Kamaruddin Malik bin Mohammed Laura Sari Maharrani, Ratih Hafsarah Malik, Kamaruddin Muhammad Afrizal Amrustian Muhammad Nur Faiz Muhammad Nur Faiz, Muhammad Nur Muhammad Quthb Habiburrahman Nofrizaldi Oktavia Jazilatus Sa’adah Oman Somantri Pangestu, Happy Gery Ratri Ayunita Kinasih Risa Eri Susanti Rusydi Umar Santi Purwaningrum Sarah Astiti S.Kom., M.MT Sari, Laura Sinaga, Rifaldo Yohannes Sukma Aji Sunardi Sunardi Supriyono, Abdul Rohman Tuti Rohayati Ulya, Fadilla Zundina Ummi Athiyah Wahyu Adi Prabowo Widi Widayat Yudha Saintika Yunita Kisworini, Rianti