Harco Leslie Hendric Spits Warnars

Bio: Harco Leslie Hendric Spits Warnars is an academic researcher from Binus University. The author has contributed to research in topics: Software & Computer science. The author has an hindex of 10, co-authored 80 publications receiving 432 citations.

Learning temporal representation of transaction amount for fraudulent transaction recognition using CNN, Stacked LSTM, and CNN-LSTM

Abstract: This paper aims to explore deep learning model to learn short-term and long-term patterns from imbalanced input dataset. Data for this study are imbalanced card transactions from an Indonesia bank in period 2016–2017 with binary labels (nonfraud or fraud). From 50 features of the dataset, 30 principal components of data contribute to 87 % of the cumulative Eigenvalues. This study explores the effect of nonfraud to fraud sample ratio from 1 to 4 and three models: Convolutional Neural Network (CNN), Stacked Long Short-term Memory (SLSTM), and Hybrid of CNN-LSTM. Using Area Under the ROC Curve (AUC) as model performance, CNN achieved the highest AUC for R=1,2,3,4 followed by SLSTM and CNN-LSTM.

Intelligent Traffic Monitoring System (ITMS) for Smart City Based on IoT Monitoring

Abstract: The internet network is the essential thing in life today, almost all devices are connected to the internet network, and many have been implemented in virtually all areas of life that exist in society today, with the concept of smart city internet system very, very play the most crucial role. This is because all have been connected to the internet network, and this system is expected to reduce many of the problems in the developing cities or developed cities. With an excellent precautionary method will lead to an orderly community system that passes traffic, can be monitored regarding vehicles, highways and traffic signs. Moreover, with intelligent monitoring, many help the government and officers work, with proper tracking the community can measure the distance traveled so that they can arrive quickly at the destination, and reduce accident in the road. The proposed Internet of Thing (IoT) monitoring which applied such as motion sensor monitoring, ultrasonic sensor monitoring, Passive Infra Red (PIR) sensor monitoring and speed sensor monitoring.

Software metrics for fault prediction using machine learning approaches: A literature review with PROMISE repository dataset

Abstract: Software testing is an important and critical phase of software development life cycle to find software faults or defects and then correct those faults. However, testing process is a time-consuming activity that requires good planning and a lot of resources. Therefore, technique and methodology for predicting the testing effort is important process prior the testing process to significantly increase efficiency of time, effort and cost usage. Correspond to software metric usage for measuring software quality, software metric can be used to identify the faulty modules in software. Furthermore, implementing machine learning technique will allow computer to “learn” and able to predict the fault prone modules. Research in this field has become a hot issue for more than ten years ago. However, considering the high importance of software quality with support of machine learning methods development, this research area is still being highlighted until this year. In this paper, a survey of various software metric used for predicting software fault by using machine learning algorithm is examined. According to our review, this is the first study of software fault prediction that focuses to PROMISE repository dataset usage. Some conducted experiments from PROMISE repository dataset are compared to contribute a consensus on what constitute effective software metrics and machine learning method in software fault prediction.

Gamification in the e-Learning Process for children with Attention Deficit Hyperactivity Disorder (ADHD)

Abstract: The technology that has developed at this time has been very advanced and very sophisticated, one industry that is growing with the times is the games industry, increasingly sophisticated games that make users of the game more spoiled with many things, such as graphics and technology, one of which is a part of the games industry is gamification. With gamification, a lot of people help people learn something natural, and gamification is usually inspired by everyday life, for example, the game The Sims, which combines daily life with a game in sports is one of the best examples of gamification implementation. Using gamification in a game makes learning more enjoyable than learning with truth, and without realizing it they have done learning. In this paper we proposed a gamification architecture for children with Attention Deficit Hyperactivity Disorder (ADHD) which include logic game such as brain, think, body, move, sport and logic. Moreover, there are five methods proposed for children with Attention Deficit Hyperactivity Disorder (ADHD) such as object game, content, and player, design, and framework, deploy and play, and finish structure.

A Proposed surveillance model in an Intelligent Transportation System (ITS)

Abstract: Progress in the complexity of large cities, highly complex systems, and intelligence science, in particular, smart city technology, has shown great ability in helping to reduce traffic congestion in developing cities. All ideas from the development of intelligent transportation to a town that wants to build and want to change into a smart city, especially in the field of ACP (system created, computing developed), based on parallel management and control system (PTMS). PTMS is considered to be enlarged to a new generation of an intelligent transportation system, and its essential component of architecture then make the hardware and software that will support a new architecture in a developing city to a smart city. The case in a lift is a communication system on a car that uses peer to peer networks and smart cards, with a communication system in a vehicle is expected to control congestion in a developing city through an original town with a connected system. This paper proposed four aspects of surveillance such as Traffic surveillance, vehicle Surveillance, passenger surveillance and driver surveillance. The combination of these surveillances will create best applied Intelligent Transportation Systems (ITS) or can be called as Smart Transportation Systems(STS).

Social Media and Fake News in the 2016 Election

Abstract: Following the 2016 U.S. presidential election, many have expressed concern about the effects of false stories (“fake news”), circulated largely through social media. We discuss the economics of fake news and present new data on its consumption prior to the election. Drawing on web browsing data, archives of fact-checking websites, and results from a new online survey, we find: (i) social media was an important but not dominant source of election news, with 14 percent of Americans calling social media their “most important” source; (ii) of the known false news stories that appeared in the three months before the election, those favoring Trump were shared a total of 30 million times on Facebook, while those favoring Clinton were shared 8 million times; (iii) the average American adult saw on the order of one or perhaps several fake news stories in the months around the election, with just over half of those who recalled seeing them believing them; and (iv) people are much more likely to believe stories that favor their preferred candidate, especially if they have ideologically segregated social media networks.

Deep Learning for Anomaly Detection: A Survey.

Abstract: Anomaly detection is an important problem that has been well-studied within diverse research areas and application domains. The aim of this survey is two-fold, firstly we present a structured and comprehensive overview of research methods in deep learning-based anomaly detection. Furthermore, we review the adoption of these methods for anomaly across various application domains and assess their effectiveness. We have grouped state-of-the-art research techniques into different categories based on the underlying assumptions and approach adopted. Within each category we outline the basic anomaly detection technique, along with its variants and present key assumptions, to differentiate between normal and anomalous behavior. For each category, we present we also present the advantages and limitations and discuss the computational complexity of the techniques in real application domains. Finally, we outline open issues in research and challenges faced while adopting these techniques.

DDoSNet: A Deep-Learning Model for Detecting Network Attacks

Abstract: Software-Defined Networking (SDN) is an emerging paradigm, which evolved in recent years to address the weaknesses in traditional networks. The significant feature of the SDN, which is achieved by disassociating the control plane from the data plane, facilitates network management and allows the network to be efficiently programmable. However, the new architecture can be susceptible to several attacks that lead to resource exhaustion and prevent the SDN controller from supporting legitimate users. One of these attacks, which nowadays is growing significantly, is the Distributed Denial of Service (DDoS) attack. DDoS attack has a high impact on crashing the network resources, making the target servers unable to support the valid users. The current methods deploy Machine Learning (ML) for intrusion detection against DDoS attacks in the SDN network using the standard datasets. However, these methods suffer several drawbacks, and the used datasets do not contain the most recent attack patterns - hence, lacking in attack diversity. In this paper, we propose DDoSNet, an intrusion detection system against DDoS attacks in SDN environments. Our method is based on Deep Learning (DL) technique, combining the Recurrent Neural Network (RNN) with autoencoder. We evaluate our model using the newly released dataset CICDDoS2019, which contains a comprehensive variety of DDoS attacks and addresses the gaps of the existing current datasets. We obtain a significant improvement in attack detection, as compared to other benchmarking methods. Hence, our model provides great confidence in securing these networks.