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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT) ComEngApp : Computer Engineering and Applications Journal Jurnal Pendidikan Karakter TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Bulletin of Electrical Engineering and Informatics Annual Research Seminar Bulletin of Electrical Engineering and Informatics Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Journal of Information Systems and Informatics Indonesian Journal of Electrical Engineering and Computer Science Computer Science and Information Technologies Sriwijaya Journal of Informatics and Applications Jurnal Generic Journal of Applied Data Sciences Journal of Innovation and Research in Primary Education Jurnal Indonesia Sosial Teknologi Jurnal Pendidikan Indonesia (Japendi)
Deris Stiawan
Universitas Sriwijaya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Review of the machine learning methods in the classification of phishing attack John Arthur Jupin; Tole Sutikno; Mohd Arfian Ismail; Mohd Saberi Mohamad; Shahreen Kasim; Deris Stiawan
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (723.905 KB) | DOI: 10.11591/eei.v8i4.1344

Abstract

The development of computer networks today has increased rapidly. This can be seen based on the trend of computer users around the world, whereby they need to connect their computer to the Internet. This shows that the use of Internet networks is very important, whether for work purposes or access to social media accounts. However, in widely using this computer network, the privacy of computer users is in danger, especially for computer users who do not install security systems in their computer. This problem will allow hackers to hack and commit network attacks. This is very dangerous, especially for Internet users because hackers can steal confidential information such as bank login account or social media login account. The attacks that can be made include phishing attacks. The goal of this study is to review the types of phishing attacks and current methods used in preventing them. Based on the literature, the machine learning method is widely used to prevent phishing attacks. There are several algorithms that can be used in the machine learning method to prevent these attacks. This study focused on an algorithm that was thoroughly made and the methods in implementing this algorithm are discussed in detail.
Interoperability framework for integrated e-health services M. Miftakul Amin; Adi Sutrisman; Deris Stiawan; Ermatita Ermatita; Mohammed Y. Alzahrani; Rahmat Budiarto
Bulletin of Electrical Engineering and Informatics Vol 9, No 1: February 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (342.68 KB) | DOI: 10.11591/eei.v9i1.1825

Abstract

As one of the country with largest population in the world, Indonesia is facing major challenge to serve people in various sectors, one of them is health sector. Utilization of Information and Communication Technology (ICT) has a strategic role in improving efficiency and expanding services access. The main challenge related to data interoperability is the ability to integrate and synchronize data sourced from health information (e-health) systems with different (heterogeneous) platforms. This research aims to build a framework to materialize data interoperability and information exchange among e-health systems. The interoperability is materialized by utilizing service oriented architecture (SOA) paradigm and is implemented using Web Service technology. Service oriented analysis and design (SOAD) is used as method in the system development at the analysis phase and designing phase to generate service portfolio which consisting of three levels: conceptual view, logical view, and physical view. This research intruduces Interoperability Matrix (IM) to describe the modules and entities that involved in the framework design. The framework resulted from this research can be used as reference in e-health systems development in variety of health care applications.
Deep Neural Network for Heart Disease Medical Prescription Expert System Majzoob K. Omer; Osama E. Sheta; Mohamed S. Adrees; Deris Stiawan; Munawar A Riyadi; Rahmat Budiarto
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 6, No 2: June 2018
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v6i2.456

Abstract

One of the most common causes of death is Ischaemic heart disease (IHD). Clinical decisions are often made based on doctors’ intuition and experience rather than on the knowledge-rich data hidden in the database, which leads to unwanted errors and excessive medical costs that affects the quality of service provided to patients. On the other hand, there is lack of cardiologist and IHD specialist in developing countries. Therefore, the development of an expert system that improves the diagnostic and therapeutic decision model of IHD creates a universal need. The expert system is developed based on the cardiologist expertises in diagnosing IHD symtomps and the given prescriptions. This work attempts to increase the accuracy and the effectiveness of the expert system to treat IHD patient by leveraging deep neural networks and adopting deep learning strategy for Retristic Boltzman Machine (RBM). The deep neural network in this work has 152 neurons in the input layer, 52 neurons in the output layer, and 4 hidden layer. Experimental results show that the proposed system achieves up to 0.00974 error level in the training sessions and average improvement of 0.7322% in term of accuracy compared to expert system with standard machine learning in the testing phase. Some results that have discrepancies are consulted to the cardiologist to confirm the results.
Network anomaly detection research: a survey Kurniabudi Kurniabudi; Benni Purnama; Sharipuddin Sharipuddin; Darmawijoyo Darmawijoyo; Deris Stiawan; Samsuryadi Samsuryadi; Ahmad Heryanto; Rahmat Budiarto
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 7, No 1: March 2019
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (416.184 KB) | DOI: 10.52549/ijeei.v7i1.773

Abstract

Data analysis to identifying attacks/anomalies is a crucial task in anomaly detection and network anomaly detection itself is an important issue in network security. Researchers have developed methods and algorithms for the improvement of the anomaly detection system. At the same time, survey papers on anomaly detection researches are available. Nevertheless, this paper attempts to analyze futher and to provide alternative taxonomy on anomaly detection researches focusing on methods, types of anomalies, data repositories, outlier identity and the most used data type. In addition, this paper summarizes information on application network categories of the existing studies.
Important Features of CICIDS-2017 Dataset For Anomaly Detection in High Dimension and Imbalanced Class Dataset Kurniabudi Kurniabudi; Deris Stiawan; Darmawijoyo Darmawijoyo; Mohd Yazid Bin Idris; Bedine Kerim; Rahmat Budiarto
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 2: June 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v9i2.3028

Abstract

The growth in internet traffic volume presents a new issue in anomaly detection, one of which is the high data dimension. The feature selection technique has been proven to be able to solve the problem of high data dimension by producing relevant features. On the other hand, high-class imbalance is a problem in feature selection. In this study, two feature selection approaches are proposed that are able to produce the most ideal features in the high-class imbalanced dataset. CICIDS-2017 is a reliable dataset that has a problem in high-class imbalance, therefore it is used in this study. Furthermore, this study performs experiments in Information Gain feature selection technique on the imbalance class datasaet. For validation, the Random Forest classification algorithm is used, because of its ability to handle multi-class data. The experimental results show that the proposed approaches have a very surprising performance, and surpass the state-of-the-art methods.
Enhanced Deep Learning Intrusion Detection in IoT Heterogeneous Network with Feature Extraction Sharipuddin Sharipuddin; Eko Arip Winanto; Benni Purnama; Kurniabudi Kurniabudi; Deris Stiawan; Darmawijoyo Hanapi; Mohd Yazid bin Idris; Bedine Kerim; Rahmat Budiarto
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 3: September 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/.v9i3.3134

Abstract

Heterogeneous network is one of the challenges that must be overcome in Internet of Thing Intrusion Detection System (IoT IDS). The difficulty of the IDS significantly is caused by various devices, protocols, and services, that make the network becomes complex and difficult to monitor. Deep learning is one algorithm for classifying data with high accuracy. This research work incorporated Deep Learning into IDS for IoT heterogeneous networks. There are two concerns on IDS with deep learning in heterogeneous IoT networks, i.e.: limited resources and excessive training time. Thus, this paper uses Principle Component Analysis (PCA) as features extraction method to deal with data dimensions so that resource usage and training time will be significantly reduced. The results of the evaluation show that PCA was successful reducing resource usage with less training time of the proposed IDS with deep learning in heterogeneous networks environment. Experiment results show the proposed IDS achieve overall accuracy above 99%.
Time Efficiency on Computational Performance of PCA, FA and TSVD on Ransomware Detection Benni Purnama; Deris Stiawan; Darmawijoyo Hanapi; Mohd. Yazid Idris; Sharipuddin Sharipuddin; Nurul Afifah; Rahmat Budiarto
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 1: March 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i1.3481

Abstract

Ransomware is able to attack and take over access of the targeted user'scomputer. Then the hackers demand a ransom to restore the user's accessrights. Ransomware detection process especially in big data has problems interm of computational processing time or detection speed. Thus, it requires adimensionality reduction method for computational process efficiency. Thisresearch work investigates the efficiency of three dimensionality reductionmethods, i.e.: Principal Component Analysis (PCA), Factor Analysis (FA) andTruncated Singular Value Decomposition (TSVD). Experimental results onCICAndMal2017 dataset show that PCA is the fastest and most significantmethod in the computational process with average detection time of 34.33s.Furthermore, result of accuracy, precision and recall also show that the PCAis superior compared to FA and TSVD.
Review of the machine learning methods in the classification of phishing attack John Arthur Jupin; Tole Sutikno; Mohd Arfian Ismail; Mohd Saberi Mohamad; Shahreen Kasim; Deris Stiawan
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (723.905 KB) | DOI: 10.11591/eei.v8i4.1344

Abstract

The development of computer networks today has increased rapidly. This can be seen based on the trend of computer users around the world, whereby they need to connect their computer to the Internet. This shows that the use of Internet networks is very important, whether for work purposes or access to social media accounts. However, in widely using this computer network, the privacy of computer users is in danger, especially for computer users who do not install security systems in their computer. This problem will allow hackers to hack and commit network attacks. This is very dangerous, especially for Internet users because hackers can steal confidential information such as bank login account or social media login account. The attacks that can be made include phishing attacks. The goal of this study is to review the types of phishing attacks and current methods used in preventing them. Based on the literature, the machine learning method is widely used to prevent phishing attacks. There are several algorithms that can be used in the machine learning method to prevent these attacks. This study focused on an algorithm that was thoroughly made and the methods in implementing this algorithm are discussed in detail.
A Study of Economic Value Estimation on Cryptocurrency Value back by Gold, Methods, Techniques, and Tools Ferdiansyah Ferdiansyah; Siti Hajar Othman; Raja Zahilah Md Radzi; Deris Stiawan
Journal of Information System and Informatics Vol 1 No 2 (2019): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33557/journalisi.v1i2.25

Abstract

After Bitcoin Introduced around the world, many Cryptocurrencies was created that followed the standard of bitcoin. The use of Bitcoin or other Cryptocurrency as a currency is also an interesting study from an Islamic economic perspective. They tried to use gold with value back by gold , which gold itself is famous for its exchange rate stability. From abu bakar There is a need for monitoring organization of the cryptocurrency, to controlling from Riba (Interest), Maysir (gambling) and ghahar (Uncertainty). To solve this problem there is a need a tool that can predict with certainty based on valid historical data, to produce accurate prediction results and produce Economic value estimations that are close to Gold real value. With the results we can monitoring day by day, see next day value and continuously based on Cryptocurrency with value back by gold, and see what other impact influences the value by looking the factor negative or positive with sentiment analysis. In the last section we discuss and provide method that we analyse from previous work to produce method to estimate value cryptocurrency value back by gold.
Network-on-Chip Paradigm for System-on-Chip Communication Rahmat Budiarto; Lelyzar Siregar; Deris Stiawan
Computer Engineering and Applications Journal Vol 6 No 1 (2017)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (36.543 KB) | DOI: 10.18495/comengapp.v6i1.186

Abstract

Developments of modern technologies in electronics, such as communication, Internet, pervasive and ubiquitous computing and ambient intelligence have figured largely our life. In our day micro-electronic products inspire the ways of learning, communication and entertainment. These products such as laptop computer, mobile phones, and personal handheld sets are becoming faster, lighter in weight, smaller in size, larger in capacity, lower in power consumptions, cheaper and functionally enhanced. This trend will persistently continue. Following this trend, we could integrate more and more complex applications and even systems onto a single chip. The System-on-Chip (SoC) technologies, where complex applications are integrated onto single ULSI chips became key driving force for the developments.
Co-Authors Abd Rahim, Mohd Rozaini Abdiansah, Abdiansah Abdul Hadi Fikri Abdul Hanan Abdullah Abdul Harris Adi Hermansyah, Adi Adi Sutrisman Aditya Putra Perdana Prasetyo Aditya Putra Perdana Prasetyo Adji Pratomo Agung Juli Anda Agus Eko Minarno Ahmad Fali Oklilas Ahmad Firdaus Ahmad Ghiffari Ahmad Heryanto Ahmad Heryanto Ahmad Heryanto Ahmad Heryanto, Ahmad Ahmad Zarkasi Ahmad Zarkasi Albertus Edward Mintaria Ali Bardadi Ali Firdaus Alshaflut, Ahmed Anto Saputra, Iwan Pahendra Bedine Kerim Bedine Kerim Bhakti Yudho Suprapto Bhakti Yudho Suprapto Bhakti Yudho Suprapto Bin Idris, Mohd Yazid Cahyani, Nyimas Sabilina Darmawijoyo, Darmawijoyo Dasuki, Massolehin Desak Putu Dewi Kasih Dewi Bunga Dian Palupi Rini Dwi Budi Santoso Edi Surya Negara Eko Arip Winanto Endang Lestari Ruskan Ermatita - Erwin, Erwin Fachrudin Abdau Fakhrurroja, Hanif Ferdiansyah Ferdiansyah Fikri, Abdul Hadi Firdaus Firdaus Firdaus, Firdaus Firnando, Rici Firsandaya Malik, Reza Gonewaje gonewaje Habibullah, Nik Mohd Hadipurnawan Satria Harris, Abdul Hermanto, Dedy Heru Saputra Huda Ubaya Huda Ubaya Huda Ubaya I Gede Yusa Idris, Mohd. Yazid Idris, Mohd. Yazid Imam Much Ibnu Subroto Iswari, Rosada Dwi John Arthur Jupin Juli Rejito Kemahyanto Exaudi Kurniabudi, Kurniabudi Latius Hermawan Lelyzar Siregar Lina Handayani M. Miftakul Amin M. Ridwan Zalbina Majzoob K. Omer Makmum Raharjo Mardhiyah, Sayang Ajeng Marisya Pratiwi Marita, Raini Massolehin Dasuki Mehdi Dadkhah Meilinda Meilinda Meilinda, Meilinda Mintaria, Albertus Edward Mohamed S. Adrees Mohamed Shenify Mohammad Davarpanah Jazi Mohammed Y. Alzahrani Mohd Arfian Ismail Mohd Azam Osman Mohd Faizal Ab Razak Mohd Rozaini Abd Rahim Mohd Saberi Mohamad Mohd Yazid Bin Idris Mohd Yazid bin Idris Mohd Yazid Idris Mohd Yazid Idris Mohd. Yazid Idris Mohd. Yazid Idris Mohd. Yazid Idris Muhammad Afif Muhammad Fahmi MUHAMMAD FAHMI Muhammad Fermi Pasha Muhammad Qurhanul Rizqie Muhammad Sulkhan Nurfatih Munawar A Riyadi Munawar Agus Riyadi Naufal Semendawai, Jaka Negara, Edi Surya Ni Ketut Supasti Dharmawan Nik Mohd Habibullah Nur Sholihah Zaini Nuzulastri, Sari Osama E. Sheta Osman, Mohd Azam Osvari Arsalan Pahendra, Iwan Permana, Dendi Renaldo Pertiwi, Hanna Prabowo, Christian Purnama, Benni Putra Perdana Prasetyo, Aditya Rahmat Budiarto Rahmat Budiarto Rahmat Budiarto Rahmat Budiarto Rahmat Budiarto Rahmat Budiarto Raja Zahilah Md Radzi Ramayanti, Indri Ramayanti, Indri Reza Firsandaya Malik Reza Maulana Riyadi, Munawar A Rizki Kurniati Rizma Adlia Syakurah Rizqie, Muhammad Qurhanul Rossi Passarella Samsuryadi Samsuryadi Saparudin Saparudin Saparudin, Saparudin Saputra, Muhammad Ajran Sari Sandra Sarmayanta Sembiring Sarmayanta Sembiring Sasut A Valianta Sasut Analar Valianta Semendawai, Jaka Naufal Shahreen Kasim Sharipuddin, Sharipuddin Sidabutar, Alex Onesimus Siti Hajar Othman Siti Nurmaini Sri Arttini Dwi Prasetyawati Sri Desy Siswanti Susanto Susanto Susanto Susanto Susanto, Susanto Sutarno Sutarno Syakurah, Rizma Adlia Syamsul Arifin, M. Agus Tasmi Salim tasmi salim Tole Sutikno Wan Isni Sofiah Wan Din Yaya Sudarya Triana Yazid Idris, Mohd. Yazid Idris, Mohd. Yesi Novaria Kunang Yoga Yuniadi Yudho Suprapto, Bhakti Yundari, Yundari Zulhipni Reno Saputra Els