Heru Sukoco

Reliable data delivery mechanism on irrigation monitoring system

Monitoring activity is one of implementation of automatically irrigation system. This activity is closely related to data transmission. Wireless communication channels are not free of noise interferences. This paper proposes the development of monitoring irrigation system with a reliable data delivery mechanisms in which TCP protocol handles senders sending their data to a receiver. The research designs an irrigation system architecture consisting of main and field controllers. The main controller is a receiver as a server that designs with concurrent and connection-oriented mode. The field controller consist of gateway (Raspberry Pi) as a sender, sensor node (Arduino Leonardo), sensor and actuator. In order to ensure the design of the data delivery architecture is well defined, analysis is carried out to calculate throughput, packet loss ratio, and packet delay to verify the design. In addition, the error control features will be added to complement these mechanisms in further research.

Selecting User Influence on Twitter Data Using Skyline Query under MapReduce Framework

The aim of this research was to select and identify user influence on Twitter data. In identification stage, the method proposed in this study was matrix Twitter approach, sentiment analysis, and characterization of the opinion leader. The importan characteristics included external communication, accessib ility, and innovation. Based on these characteristics and information from Twitter data through matrix Twitter and sentiment analysis, a algorithm of skyline query was constructed for the selection stage. Algorithm of skyline query selected user influence by comparing with other users according to values of each characteristic. Thus, user influence was indicated as user that was not influenced b y other users in any combination of skyline objects. The use of MapReduce framework model in identification and selection stage, support whole operation where Twitter had b ig size data and rapid changes. The results in identification and selection of user influence exhib ited that MapReduce framework minimized the execution time, whereas in parallel skyline query could reveal user influence on the data.

Sistem Akuisisi Data Multi Node untuk Irigasi Otomatis Berbasis Wireless Sensor Network

Watering plants is one of farmer’s activities. Most of Indonesian farmers use traditional watering method to water plants. It causes water productivity unmanaged properly and soil moisture level can not be monitored. To resolve these problems, an automatic watering system is developed. This system uses soil moisture sensors which provide real-time data. Data from multiple sensor node will be transmitted through wireless sensor network. LED in actuator node will turn on or off based on lower and upper set point values transmitted from coordinator node. Soil moisture sensors are calibrated using groundwater level to obtain correlation between sensor and groundwater level. Delay, throughput, and packet loss ratio are measured and result 0.2 seconds, 1.6 kbps, and 1.6%, respectively. These values showed that all automatic watering system were well implemented.

Parallelization of star alignment

Pair-wise sequence alignment is a technique of comparing the similarity of two organisms. It is the basic technique in DNA sequence alignment. There is an extraordinary number of data sequences when they are compared. Problems when comparing the huge data sequences are accuracy and efficiency. These parameters are contradiction which means reaching faster speed will decrease accuracy, and vice versa. Older method such as Needleman-Wunchs has extremely high computational complexity of O(n2). Similarly, multiple sequence alignment that processes the sequences one by one, called Star Alignment, takes time until O(k2n2). Therefore, they have a timing issue problem while processing the data. However, the computation result still has high accuracy. Consequently, it is very important to get a better way to improve the performance. One of the methods for increasing the speed is parallel computation by using multiple computers work together as a system. This research focuses on finding a faster method to process multiple sequence alignment using the Star method with a parallel computer. Our proposed method is implemented using Message Passing Interface (MPI). The results show that the paralellization of the Star Alignment increased speed up 4-6 times compared to that of using single CPU.