Articles
<![CDATA[Implementasi High Performance Computing Cluster Menggunakan Rocks Cluster ]]>
<![CDATA[Richie R. Tokan]]>;
<![CDATA[Mochammad Hannats Hanafi Ichsan]]>;
<![CDATA[Agung Setia Budi]]>
<![CDATA[ Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 8 (2020): Agustus 2020 ]]>
Publisher : <![CDATA[Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya ]]>
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<![CDATA[Rocks Cluster is a linux distribution that is devoted to building a High Performance Cluster Computing by connecting several personal computers which are then called nodes. Cluster performance is measured by looking at the system's ability to process floating point numbers by using the LINPACK benchmark library and the OpenBLAS arithmetic library. In this study also used SLURM (Simple Linux Utility for Resource Management) to run an interactive job on the cluster by adjusting the resource requirements of the cluster to run the task. From the benchmark test results obtained a value of 15.0323 GigaFlops within 692.35 seconds or about 11.5 minutes on testing with 25000 matrix. For the results of interactive jobs testing using SLURM, the system shows efficient performance for the use of system resources for workload, cpu, memory and network ussage.]]>
<![CDATA[Sistem Pendeteksi Penyakit Diabetes Melitus berdasarkan Kondisi Urin dan Gas Buang Pernapasan menggunakan K-Nearest Neighbor berbasis Arduino ]]>
<![CDATA[Farah Amira Mumtaz]]>;
<![CDATA[Rizal Maulana]]>;
<![CDATA[Agung Setia Budi]]>
<![CDATA[ Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 8 (2020): Agustus 2020 ]]>
Publisher : <![CDATA[Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya ]]>
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<![CDATA[One of the most dangerous chronic diseases in Indonesia is diabetes mellitus. This disease has symptoms characterized by high levels of glucose in human blood. Examination of diabetes mellitus that currently available is still invasive by taking blood samples using a needle into the patient's finger. The long period of time and the high cost are the constraint in getting the results of the examination. Research to detect diabetes mellitus in a non-invasive manner, which requires a small amount of money, and saves more time, is needed to overcome the existing problems. This research uses features in the form of urine and respiratory gas. In the patient's urine, the color and the level of ammonia gas will be detected. Meanwhile, in the patient's respiratory gas levels, methane gas will be detected. Data processing in the system using the Arduino Mega microcontroller. The processed data obtained from the output of the color sensor and gas sensor. The sensors are TCS3200 as a color sensor and the MQ-135 and MQ-4 sensors as gas sensors. The results of data processing will be classified using the K-Nearest Neighbor or K-NN method into Normal and Diabetes conditions. Testing using 12 test data and 24 training data with a value of K = 3 resulted in an accuracy of 91.67% because there was 1 data mismatch at the time of testing. The average system performance obtained based on 10 tests is 3061.9 ms.]]>
<![CDATA[Rancang Bangun Sistem Penghematan Energi Listrik menggunakan Komunikasi Bluetooth Low Energy dan WiFi berbasis Cloud Server Blynk ]]>
<![CDATA[Muchtar Ardhiansyah]]>;
<![CDATA[Mochammad Hannats Hanafi Ichsan]]>;
<![CDATA[Agung Setia Budi]]>
<![CDATA[ Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 8 (2020): Agustus 2020 ]]>
Publisher : <![CDATA[Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya ]]>
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<![CDATA[The use of electricity is a basic requirement for humans today, therefore it is necessary to have savings in its use so that lower costs and the supply of electrical energy in an area can be saved for something unexpected. Internet of Things is a device or system that is connected and communicates in an internet network. By using IoT, we can carry out a long distance communication which makes the performance of a system more effective and efficient. The communication module that is quite popular in IoT is Bluetooth Low Energy (BLE), but in BLE there are still drawbacks, namely the inability to connect to the internet so that a gateway is needed to communicate with the cloud server. This study aims to design an electrical energy saving system. The results of this study indicate that the system can work well. The delay obtained from sending data at the gateway to the Blynk Cloud Server has an average delay of 1,930 s with a jitter of 0.754 s, the average delay from the sensor node to the gateway is 0.672 s. The next test used a variation scenario of 1 meter - 11 meter distance with uccess ratio parameter. The highest success ratio is 90% at a distance of 1 meter and the lowest success ratio is 0% at a distance of 11 meters. The saving of electrical energy is up to 59.04%.]]>
<![CDATA[Implementasi Pengiriman Data Multi-Node Sensor Menggunakan Metode Master-slave pada Komunikasi LoRa ]]>
<![CDATA[Agiya Yoshua]]>;
<![CDATA[Rakhmadhany Primananda]]>;
<![CDATA[Agung Setia Budi]]>
<![CDATA[ Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 10 (2020): Oktober 2020 ]]>
Publisher : <![CDATA[Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya ]]>
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<![CDATA[Wireless technology has begun to develop and connect with each other, so this is what makes a basis for the development of the Internet of Thing technology or abbreviated as IoT. There are several elements in helping the performance of IoT technology, one of which is in terms of communication. LoRa is a wireless transmission communication technology using Chirp Spread Spectrum (CSS) as a modulation technique of its delivery. LoRa uses Pure ALOHA as an access method for sending data where two or more nodes send data at the gateway without coordinating with each other, causing the risk of data collisions. In this study using the master-slave method to send data to avoid data collisions. Gateway node acts as a master whose job is to send data request messages to the sensor node. The sensor node acts as a slave whose duty is to send sensor data to the gateway node that acts as the master only if the master requests the sensor node specifically based on the slave ID that the sensor node has. Functionality and non-functional tests are carried out to test whether the system is operating properly. The smallest percentage of packet loss is experienced under test conditions at a distance of 50 meters where packet loss only occurs in the process of sending from sensor node 1 to the gateway, while the largest percentage of packet loss is experienced under test conditions at a distance of 200 meters on a 30 minute test duration of 8.99% . The smallest average delay test results are experienced in testing conditions at a distance of 200 meters with an average delay of up to 160.64 milliseconds, while the largest is experienced at a distance of 100 meters with an average delay of up to 405.67 milliseconds.]]>
<![CDATA[Sistem Deteksi Perpindahan Kendaraan Bermotor Berdasarkan Data GPS dan Sensor IMU Menggunakan Naive Bayes ]]>
<![CDATA[Wirafadil Nugraha]]>;
<![CDATA[Dahnial Syauqy]]>;
<![CDATA[Agung Setia Budi]]>
<![CDATA[ Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 1 (2021): Januari 2021 ]]>
Publisher : <![CDATA[Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya ]]>
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<![CDATA[The security system in vehicles is something that must be considered because vehicles are often stolen which results in losses to the vehicle owner. Ease of control and a security system that can be monitored using only one communication device such as a mobile phone that can support a wireless connection is an opportunity to develop a vehicle security system. By designing a vehicle displacement detection system based on GPS data and the IMU sensor, it will become a more accurate tool to determine the condition of vehicles. In order for the system to classify the current situation more accurately, the Naive Bayes algorithm is applied. The use of GPS data in this study uses the NEO-6M module because a vehicle must be closely related or always have a coordinate location as long as it is within satellite coverage. This data is used as input along with data from the IMU sensor used in this study using the GY-61 accelerometer sensor. From this research test, the NE0-6M module has an accuracy value of 99.99%, the GY-61 sensor has an accuracy of 87,68%, and the SIM800 module can functionally run well. In the classification process that using Naive Bayes, an accuracy of 95% is obtained.]]>
<![CDATA[Implementasi Self-Tuning Pada Sistem Database Untuk Meningkatkan Kinerja Query Dengan Menggunakan Metode Gradient Descent ]]>
<![CDATA[Zaky Farsi]]>;
<![CDATA[Agung Setia Budi]]>;
<![CDATA[Rakhmadhany Primananda]]>
<![CDATA[ Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 1 (2021): Januari 2021 ]]>
Publisher : <![CDATA[Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya ]]>
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<![CDATA[Self-tuning the database system is a database performance improvement process that focuses on the timing of queries performed by the database. Self-tuning determines the parameter value to be used by the database based on the query time generated by the database. Self-tuning is known as a system that helps improve performance with human assistance or without human intervention. Self-tuning itself can only be used on systems whose performance can be improved, one of which is the database. Therefore, in this study we propose a new self-tuning system using the Gradient Descent method. In the first stage, we determine the parameters that can affect the performance of the database by paying attention to the response time value of the query when changes to the parameters are made. In this self-tuning database research with the Gradient Descent method, the parameters used are buffer_pool_size, read_io_threads, write_io_threads and io_capacity. Each of these parameters will be processed in the self-tuning system and will output the query time and will be processed in the Gradient Descent method to determine the new parameter values. Self-tuning will be carried out on the MariaDB DBMS by using test data with a different number of iterations in each self-tuning process. The results of the self-tuning test with the Gradient Descent method get an increase in the value of each parameter with a number of iterations of 20.40, 60 and 100. From the test results, the increase in database performance itself shows an increase of 67.92% with an average query time of 20 seconds. at 80-100 iterations.]]>
<![CDATA[Implementasi Skema Anti-Collision Menggunakan Metode TDMA dan TPSN pada Sistem WSN Berbasis LoRa ]]>
<![CDATA[Fajar Hamid Embutara Ratuloli]]>;
<![CDATA[Agung Setia Budi]]>;
<![CDATA[Adhitya Bhawiyuga]]>
<![CDATA[ Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 1 (2021): Januari 2021 ]]>
Publisher : <![CDATA[Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya ]]>
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<![CDATA[The usage of LoRa-based Wireless Sensor Network (WSN) overcomes a common problem of WSN, that its communication capabilities over a near range. LoRa is a radio spectrum modulation technology that accommodates long distances communication with low power consumption. But the current weakness of system, the delivery data is interfering with each other which causes data loss. Time Division Multiple Access (TDMA) method is used to avoid this problem. TDMA is a scheduling method that divides by timeslot, and it compatible with LoRa because it works on single frequency. In a TDMA implementation, all device times must be synchronized with each other so that sending scheduling can occur. Time Synchronization Protocol for Sensor Network (TPSN) method is used as a time synchronization method that supports the WSN system. The implementation of TPSN is divided into 2 phase, namely the Discovery Phase and the Synchronization Phase. Functionality testing is carried out so that the system can works as needed. The accuracy testing of time using the TPSN method obtained results of 79.7%. Performance testing of delivery scheduling using the TDMA method shows that the increase in delivery time is 28.37 milliseconds at sensor node 1 and an increase in delivery time is 37.11 milliseconds at sensor node 2.The success rate in delivery using the TPSN and TDMA methods is 96%.]]>
<![CDATA[Implementasi Pemrosesan Paralel untuk Ekstraksi Fitur QRS pada data Electrocardiography (ECG) menggunakan Algoritma Pan-Tompkins dengan Framework Renderscript ]]>
<![CDATA[Sabit Ihsan Maulana]]>;
<![CDATA[Agung Setia Budi]]>;
<![CDATA[Adhitya Bhawiyuga]]>
<![CDATA[ Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 3 (2021): Maret 2021 ]]>
Publisher : <![CDATA[Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya ]]>
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<![CDATA[Telemedicine technology is increasingly needed for remote patient health care. Especially for patients with a high risk/severe disease such as heart disease. Due to its important role and benefits, a lot of research has been carried out to improve this technology so that it's relevant with current state-of-the-art technologies. One of the latest technologies that have been adapted on telemedicine research is cloud computing. With this combination, CPU intensive task can be transferred to the cloud. However, a new problems arose. The resulting system now will always depend on the internet, while not all areas are covered or have a good internet connection. Therefore, this researcher proposes a QRS detection system with parallel processing on the edge device using the Pan-Tompkins algorithm and Renderscript framework. This system is an Android-based application that receives ECG data and then performs the QRS detection process by utilizing Renderscript to perform parallel processing. Execution time on the detection process will be measured to determine the difference in performance with serial processing. The results of the detection process will be compared to determine the level of accuracy of the system. A profiler application will also be used to find out the CPU Utilization level. In testing, the system obtained 3 times smaller execution time with an average processing speed of 301,340 row/second. The system also obtained a sensitivity value of 99.64% and a positive predictivity value of 99.47%. The average CPU utilization on the system is at 82%, 32% higher than serial counterpart.]]>
<![CDATA[Pengembangan Sistem Monitoring Smarthome Secara Pervasif Dengan Nodemcu Dan Smartphone ]]>
<![CDATA[Reva Ade Wardana]]>;
<![CDATA[Agung Setia Budi]]>
<![CDATA[ Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 2 (2021): Februari 2021 ]]>
Publisher : <![CDATA[Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya ]]>
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<![CDATA[Smarthome or what we usually call smart home is a house or building equipped with high technology that allows various systems and devices at home to communicate with each other. In Indonesia alone, smarthome technology is still in the introductory and familiarization stage to the community, the challenge of the smarthome concept is entering the realm of the smallest and most intimate community, namely the family, There are so many ways to implement a smarthome in daily life, such as the use of sensors to detect changes in the physical or chemical environment, turning on or turning off devices using smartphones, or simply monitoring the condition of the house remotely. In addition, the concept of IoT (Internet of Things) is one part that cannot be separated in the development of this technology, this concept can greatly help humans to streamline their time and help carry out activities with this problem. this system parameters are used to monitor. There are four sensors to monitor the state of the house, this system works automatically without user intervention. Then the Pervasive Computing method will communicate between Nodes. In this study the estimated time used for communication between nodes is approximately 4578- 5097ms.]]>
<![CDATA[Pengenalan Perangkat Dan Sensor Secara Otomatis Menggunakan Metode Scanning Pada Komunikasi I2c ]]>
<![CDATA[Noor ilmi]]>;
<![CDATA[Agung Setia Budi]]>
<![CDATA[ Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 2 (2021): Februari 2021 ]]>
Publisher : <![CDATA[Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya ]]>
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<![CDATA[Currently, many vendors create products that make it easier for users to make IoT devices, especially sensors and microcontrollers. A sensor is an electronic device that can be used to sensing a situation around us and a microcontroller is used to help process data and as a communication line between sensors one and another. Because there are many different vendors, the more sensors are created and the more needs that use multiple sensors from different vendors are combined into one for a specific purpose. Therefore, something is needed to connect between various sensors from various vendors. sometimes has its own configuration. Based on this, a microcontroller is needed to bridge sensors to one another and can recognize these sensors automatically. And the communication line used is I2C (Intergrated Circuit) which is connected using SDA (Serial Data) and (Serial Clock) ). And from the results of testing the microcontroller is able to recognize the connected sensor.]]>
