This paper presents a three-dimensional computer vision-based object recognition on FLoW-Vision in RoISC (formerly ER2C) has entered its second phase. Previously, the robot had a basic vision that was used to replicate ‘human-like’ visual skills using 2D computer vision. As a result of the above discussion, we proposed the design and implementation of an object recognition and pose estimation system based on three-dimensional computer vision to handle object recognition and pose estimation tasks in real-world environments simultaneously. In the object recognition process, a point-cloud segmentation method is used to obtain possible object clusters before starting the calculation of feature description. Then, a keypoints-based two-stage matching process is performed to speed up the computation of finding correspondences between the object clusters of the current scene and a colored point cloud model of an object. Next, a Hough voting algorithm is employed to filter out matching errors in the correspondence set and estimate the initial 3D pose of the object. Last process process the pose estimation from clustered object using RANSAC to search the largest surface as Z surface. Experimental validate the object recognition can work correctly with percentage 100% and pose estimation accuracy of the proposed system can work correctly with percentage 60% in a complex real-world scene.

Spatial data could be sectoral data or city spatial planning data. Province government manage sectoral data, whereas local government manages city spatial planning. To management spatial data, local government can't access sectoral data in the provincial government. It has happened because there is no procedure to integrate their data. Each region/city also has their different map version so, that allows overlapping. This is because each local government hasn't reference in solving spatial, land, or development problems. Therefore a One Map Policy was formed, is becoming an important point that should be considered to be realized. This paper presents a new automatic WebGIS framework to integrate sectoral data and city spatial planning for support One Map Policy. This system using REST API Geoserver technology. This technology is carried out automatically with a web browser. In this case, Every local government can manage and access this system. In carrying out the process, each data must be in accordance with the Standard Operating Procedure (SOP) of the application system. Each local government will upload data using a web browser and the system will work automatically. After the data is successfully stored, the system also automatically processes the Geoserver until a layer is formed. The formed layer is ready to be displayed to WebGIS using Web Map Service. With REST API technology, it will help to manage the creation of a spatial and information system more efficient. The experiment result shows that this system can integrate the data between local government and provincial government. And it can use as a reference in the process of managing spatial data and information in the local government of East Java Province to support One Map Policy.

Utilization of wireless sensor network technology can improve the anticipation of the occurrence of fire hazards in the smart home; this is done by replacing the human task in monitoring the situation around the home by using multiple sensors which can directly interact with the environment. The goal of this paper is building the early fire detection systems on smart home-based wireless sensor network monitoring. Sensors are used to detect the level of fire danger include temperature sensors, humidity, carbon monoxide, and smoke. The system use fuzzy logic inference systems to process the data from the four sensors in order to improve the reliability and accuracy of the information provided for the system that will give warning to users. Based on the results obtained, the system has been able to give an alarm warning to users appropriately. The proposed system also implement sleep scheduling method in the system which can improve the delivery efficiency of data packets and can reduce battery resource usage.

To date, malware caused by botnet activities is one of the most serious cybersecurity threats faced by internet communities. Researchers have proposed data-mining-based IDS as an alternative solution to misuse-based IDS and anomaly-based IDS to detect botnet activities. In this paper, we propose a new method that improves IDS performance to detect botnets. Our method combines two statistical methods, namely low variance filter and Pearson correlation filter, in the feature-selection process. To prove our method can increase the performance of a data-mining-based IDS, we use accuracy and computational time as parameters. A benchmark intrusion dataset (ISCX2017) is used to evaluate our work. Thus, our method reduces the number of features to be processed by the IDS from 77 to 15. Although the number of features decreases, it does not significantly change the accuracy. The computational time is decreased from 71 seconds to 5.6 seconds.

Smart home is a concept in which all electronic equipment in a home can be monitored and controlled remotely. The main problem in the implementation of a smart home is a relatively expensive cost, the lack of centralized control system so that most nodes in a smart home will work independently and connectivity to the internet must always be there to activate a smart home because each node connected to the server on the internet. In this paper, we will present a smart home system using Arduino as node and raspberry pi as controller (local server) for smart home node, in this system also enable smart home can be accessed locally (intranet) and internet.

Fire is one of the few disasters that frequently occurred in Indonesia. Jakarta has archived 1139 fire incident with more than 20 casualties in 2016. By the rapid development of information technology and communication in Indonesia, it turns out that the number of incident and casualty still cannot be suppressed. This research is focused on early fire detection and home monitoring based on Wireless Sensor Network using 4 sensors such as temperature, air humidity, Carbon Monoxide, and smoke sensors. If the fire probability calculation categorized as high, system will perform an action of unlocking home door lock, turning on alarm, and notifying the user. Based on the trials and testing result, system is able to send notification to user precisely. However, error still can be encountered with up to 0.3% ratio on fire probability calculation.

The massive internet usage is followed by the rise of cyber-related crime such as information stealing, denial-of-service (DoS) attack, trojan and malware. To cope with the threats, one of most popular choice is using Intrusion Detection System (IDS). The logs produced by IDS in a day is huge and the limitation of computing power is the main problem to process that logs files. In this paper, we propose a big data analysis architecture of multi IDS sensors using in-memory data processing. Deployed IDS sensors are taking an extra role as computation slave to build scalable data analysis platform for network security analysis. So, adding more sensors means expanding computational resources. Adding to three sensors are helping data computation of clustering algorithm faster up to 27% comparing to the computation by using only one sensor. This research also introduces the use of memory-based processor, this system provides 7,9 times faster data processing than conservative MapReduce operation. And moreover, we also have performed botnets classification over Spark RDD that give high accuracy result to 99%.

This paper presents neuro-based push recovery controller applied in humanoid biped robot in order to keep the stability with minimum energy required. There are three motion patterns in human behavior when it gets external perturbation, those are ankle behavior, hip behavior, and step behavior. We propose the new model of modular recurrent neural network (MRNN) for performing online learning system in each motion behavior. MRNN consists of several recurrent neural networks (RNNs) working alternately depending on the condition. MRNN performs online learning process of each motion behavior controller independently. The aim of push recovery controller is to manage the motion behavior controller by minimizing the energy required for responding to the external perturbation. This controller selects the appropriate motion behavior and adjusts the gain that represent the influence of the motion behavior to certain push disturbance based on behavior graphs which is generated by adaptive regression spline. We applied the proposed controller to the humanoid robot that has small footprint in open dynamic engines (ODE). Experimental result shows the effectiveness of the push controller stabilizing the external perturbation with minimum energy required.

The development of microkernel has sharply increased. One of the most successful microkernel implementation is L4. L4Linux is L4 version that is able to run virtualized Linux. We have also built our microkernel named FLoW. In this paper we described about our achievement in developing virtualized Linux on top of our FLoW microkernel. We implemented unique design about virtualizing more than one Linux. The idea is about creating more than one Linux to handle the requests made by user application about Linux system call. So every user application that requests the feature of Linux system call will be handled by more than one Linux Service. The result shows that there is an acceleration of processing time. The total CPU cycle used by user application in working with user request are less when having such mechanism. That means our mechanism successfully accelerate around 17% processing time of user application that request Linux system call from within our Linux Service that run on top of FLoW Microkernel. Then the system endurance testing resulted that our system is able to handle around 96 Linux system calls request almost at the same time without break.

TOR (The Onion Router) TOR (The Onion Router) has been a very popular anonymous proxy service. Since its first usage, TOR has been improved and become a very big network consisting more than 7000 relays. Beside used by journalist, activist, and writer as a tool for their freedom of speech, its highly anonymous service is also used by bot-nets, malware, distributed denial of service attacks, hidden services that sells illegal things, spams, and many more. This paper will explain about a TOR usage detection system by using deep packet inspection to extract and analyze its network traffic. The result of the analysis will be used as a parameter for a proxy server to block TOR traffic in the network. Using this network, detection, and blocking design, we can block the Onion Router Traffic originated from TOR browser.

Nowadays, the use of Electrocardiogram (ECG) as a detection tool of the condition of human heartbeat is still considered difficult. So, the idea of implementing this sensor is to enhance the quality of service and to minimize the cost of using and maintaining by determining the best way in implementing and monitoring ECG sensor. This research proposes a novel approach to observe the condition of human heartbeat using ECG sensor with e-Health sensor and Raspberry Pi, mini single-board computer with robust capability, which acts as central processing unit. Those hardware sets were assembled to collect ECG voltage signals in human body. The signals are transmitted into the device and the data are stored in MySQL database for long-term monitoring access. The experimental results show effectiveness of this approach as it is successfully fulfill the idea of Body Area Network (BAN).

Wireless Body Area Network (WBAN) implements real time health monitoring by outfitting patients with wireless vital sign sensors. One of the parts of WBAN is glucometer sensor which determining the approximate concentration of glucose in the blood. In this paper, we develop glucometer sensor monitoring system based on WBAN by using e-Health sensor (glucometer sensor), arduino uno board, and ZigBee module. The experiment results show that the desktop-based, web-based, mobile-based applications can be used well bu using doctor/nurse to monitor the patient's glucose levels without having to see the patient directly.

Matagaruda is an Intrusion Detection System Application Framework. It provides four capabilities: seeing not only attacking phase, learning the local traffic and generating local rules, adapting the framework programming and reducing false alarm by using intelligence. In this research we develop a new features called Network Situational Awareness which represent the seeing capabilities in Matagaruda. This research creates 4 features supports in web-based and interactive user interface. We use two attacking scenarios: SQL Injection and DOS for testing purpose. We found that our module meets the requiment of network situational awareness implementation.

Wireless Sensor Network (WSN) is a promising technology for many applications, such as industrial, environment, and health-care application. The first requirement of WSN is energy efficiency in order to increase the network node lifetime. The second is reliability because packet drop cannot be tolerated in critical time applications. In this paper, we use NS2 simulation to evaluate and analyze the IEEE 802.15.4 standard in multi-hop WSN with a tree topology base on IEEE 802.15.4. Beacon enabled mode is used slotted CSMA/CA and non-beacon enabled mode is used unslotted CSMA/CA. Performance of beacon enabled and non-beacon enabled were evaluated and analyzed to understand the impact of protocol parameters such as Superframe Order (SO), Beacon Order (BO), and Traffic Load. The performance of the network which have been evaluated in detail are the throughput, energy consumption, end to end delay, packet success probability, drop packets and the percentage of the energy efficiency. The simulation result show that of beacon enabled is better than non beacon enabled in terms of the throughput, drop packet, energy consumption, and energy efficiency.

Many computer-based devices are now connected to the internet technology. These devices are widely used to manage critical infrastructure such energy, aviation, mining, banking and transportation. The strategic value of the data and the information transmitted over the Internet infrastructure has a very high economic value. With the increasing value of the data and the information, the higher the threats and attacks on such data and information. Statistical data shows a significant increase in threats to cyber security. The Government is aware of the threats to cyber security and respond to cyber security system that can perform early detection of threats and attacks the internet. The success of a nation's cyber security system depends on the extent to which it is able to produce independently their cyber defense system. Independence is manifested in the form of the ability to process, analyze and create an action to prevent threats or attacks originating from within and outside the country. One of the systems can be developed independently is Intrusion Detection System (IDS) which is very useful for early detection of cyber threats and attacks. The advantages of an IDS is determined by its ability to detect cyber attacks with little false. This study learn how to implement a combination of various methods of machine-learning to the IDS to improve the accuracy in detecting attacks. This study is expected to produce a prototype IDS. This prototype IDS, will be equipped with a combination of machine-learning methods to improve the accuracy in detecting various attacks. The addition of machine-learning feature is expected to identify the specific characteristics of the attacks occurred in the Indonesian Internet network. Novel methods used and techniques in implementation and the national strategic value are becoming the unique value and advantages of this research.