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INDONESIA
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Published by Universitas Brawijaya
ISSN : -     EISSN : -     DOI : -
Core Subject : Science, Education, Engineering,
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering
Jurnal Pengembangan Teknlogi Informasi dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya merupakan jurnal keilmuan dibidang komputer yang memuat tulisan ilmiah hasil dari penelitian mahasiswa-mahasiswa Fakultas Ilmu Komputer Universitas Brawijaya. Jurnal ini diharapkan dapat mengembangkan penelitian dan memberikan kontribusi yang berarti untuk meningkatkan sumber daya penelitian dalam Teknologi Informasi dan Ilmu Komputer.
Arjuna Subject : -
Articles 6,850 Documents
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Implementasi Algoritme Grain V1 Dan 128 Bit Pada Raspberry PI Syahifudin Shahid; Ari Kusyanti; Rakhmadhany Primananda
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 4 (2018): April 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

NIST (National Institute of Standard Technology) is the institution that regulates data security standards. NIST held a project called eSTREAM to update old outdated stream ciphers. The grain algorithm is selected as a security algorithm on the hardware side and replaces the previous stream cipher. Grain algorithm has 2 versions, 80 bit and 128 bit. Security at the hardware level is also required to keep information data. With the update of security standards from NIST, this research is conveying the idea to utilize the grain algorithm for data security that will be implemented in raspberry pi. This study chose raspberry pi due to the large use of this mini PC in the Internet of Things and there is no update data stream cipher security. Raspberry pi that has been implemented with grain will do 3 testing that is test of keystream validation with test vector, time of execution process of IV and Key to produce keystream and encryption and data decryption. The test of execution time, will be analyzed using Independent t test test, the result of testing of keystream execution time on grain v1 and 128 algorithm is -2,146 with probability (Sig.) 0,000 indicates a significant difference, as well as with execution time of 8, 12 and 16 bit encryption and decryption. This research concluded raspberry pi performance for both stable versions.
Rancang Bangun Sistem Penghitung Jumlah Orang Melewati Pintu menggunakan Sensor Infrared dan Klasifikasi Bayes Raden Galih Paramananda; Hurriyatul Fitriyah; Barlian Henryranu Prasetio
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 3 (2018): Maret 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The growing of visitor number lead to a new problem that is, the fullness of the place when the visitors is not comparable with the available standard capacity of the building. Counting the number of visitors in a shopping center can provide an information for the manager to optimizing the place, as well as to evaluating the attraction in some shopping areas. Area managers can analyze and monitor the center state of the crowd. From this problem, it is need an automate system to calculate the visitor who passes through the door simultaneously. In this study, the parameter that used by reseaecher is the detection of passing objects. The researcher used infrared sensor switch E18-D80NK that will be processed used Bayes classification to calculate the total of visitor who passes through the sensor in the door. The Bayes method was chosen as one of the techniques for decision-making classification counters of people who passing through the door simultaneously, this method is one of the classification method is quite simple and easy to be understood. So the accuracy obtained by this system using Bayes method is 79.24%. In this case, reseacher using the door size width of 200 cm and 190 cm high with the computation time of sensor readings until the calculation of 679.2 ms or about 0.6792 seconds.
Prediksi Jumlah Produksi Kelapa Sawit Dengan Menggunakan Metode Extreme Learning Machine (ELM) (Studi kasus: PT. Sandabi Indah Lestari Kota Bengkulu) Ema Agasta; Imam Cholissodin; Dian Eka Ratnawati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Palm oil is a plantation that became the number one sector in Indonesia. This plant has a cost and a better production than other plantation crops such as sugar cane and rubber. In a company, palm oil production becomes the driving force of the economy, as well as what happened to PT. Sandabi Indah Lestari. In every week the company plans to predict the production. Planning done sometimes still give less than optimal results. This is because the calculation process is still using manual analysis. In this research will use four prediction features that are plant age, number of trees, land, and production. The prediction technique used is the learning method of Extreme Learning Machine (ELM). This method has advantages in learning speed and accuracy in predicted results. The calculation process starts from the process of data normalization, training a number of training data and test data, calculation of the prediction error value and produce the final value. The data used is production data in the period 2015 - 2017 with a total of 297 data. From a number of data will be divided into two data with percentage of 80% training data and 20% test data. The result of the research was obtained the optimal parameter value that is 13 hidden neuron in testing the number of neurons with Mean Absolute Perscentage (MAPE) value of 21.25%, 20.42% on the data feature test with the best 2 technical features and 20,19% on testing the pattern with the final result of the data pattern 1.
Pembangunan Kakas Bantu Untuk Mengukur Maintainability Index Pada Perangkat Lunak Berdasarkan Nilai Halstead Metrics dan McCabe's Cyclomatic Complexity: English Rasio Ganang Atmaja; Bayu Priyambadha; Fajar Pradana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 3 (2019): Maret 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In the software development cycle there is a maintenance phase. In this phase, errors or defects of the software that have not been found on development or testing phase will be corrected. In this phase, software is also changing to fit the new system environment and stakeholder needs. In software development there are several reasons why it is necessary to calculate maintainability value of the software, such as the value of maintainability can help in deciding whether a software is easy to maintain or needs to be redesigned. There are several methods that can be used to measure maintainability value of the program, one is the Maintainability Index (MI). Maintainability Index is calculated based on the value of Halstead's Volume, McCabe's Cyclomatic Complexity, and line of codes. The Maintainability Index calculations system provide features for calculate Maintainability Index values of the Java source code and display graph visualizations using Java technologies. This system has been tested using unit testing, integration testing that uses Whitebox methods and validations testing that use Blackbox methods. This system has an accuracy of 98% and the time for calculating one method only takes less than 1000ms
Optimasi Penjadwalan Shift Jaga Dokter di IGD Menggunakan Algoritme Genetika (Studi Kasus Rumah Sakit di Malang) Annisaa Amalia Safitri; Imam Cholissodin; Lailil Muflikhah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Emergency room (ER) is one of the units in a hospital who the first of receiving patients in case of an emergency. In ER, there are doctors who should be available for 24 hours to deal with patient who come everytime when an emergency form happen. To keep the performance of doctors who working 24 hours in ER, then we make a schedule that use with shift system. For 1 month scheduling, 11 doctors will split into 3 shift work in a day. In order to optimize and make the best combination in doctor's schedule at ER, then made the doctor's scheduling system in a ER using genetic algorithm. Reproductive process using 2 ways, first the process of crossover by using extended intermediate crossover and second the mutation process by using a reciprocal exchange mutation, and then will use the last process of algorithm and the name is elitism selection process. Testing that is used for doctor's scheduling system in a ER is there are 3 types of testing. The first test is testing the number of popSize, with the highest fitness at a value of 40 with an average of 1,766, the second test is testing the value of generation with the highest fitness at generation value 40 with an average value of fitness 1,608, and the last test is combination of cr mr with the highest fitness value on a combination of 0,7 and 0,3 with average fitness 2,064. From those results, do more testing to compare the fitness value of fitness values of the system with real data provided by the hospital. And the results show that the value of fitness of the system = 11,111 is greater than the value of the data on real fitness given hospital = 7,692.
Optimasi Bobot pada Extreme Learning Machine untuk Prediksi Beban Listrik menggunakan Algoritme Genetika (Studi Kasus: PT. PLN (Persero) APD Kalsel dan Kalteng) Vina Meilia; Budi Darma Setiawan; Nurudin Santoso
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The growth of electrical consumers in Indonesia continues to increases every year, but it is not matched by the provision of adequate infrastructure that available. This causes the available electrical capacity can't fulfill the demand for electricity. As an anticipation, beside to add more electrical capacities which will need a lot of costs. PLN also do operations management systems, which is electrical load forecasting. In this study, a smart computing system is build to solves the problem. Electrical load data per hour is being used as an input to do the electrical load forecasting with Extreme Learning Machine method. Extreme Learning Machine method uses random input weight within range -1 to 1. Before the electric load prediction process runs, genetic algorithms first optimizing the input weight. Mean Absolute Percentage Error (MAPE) is being used to calculate the accuration of prediction results. According to the test results with weight optimization, MAPE average error rate is 0.799% while without weight optimization the rate rise to 1.1807%. Thus this study implies that Extreme Learning Machine method with weight optimization using genetic algorithm can be used in electrical load forecasting problem and give better prediction result.
Implementasi Metode Dempster-Shafer untuk Diagnosis Penyakit pada Tanaman Kedelai Rahmat Arbi Wicaksono; Nurul Hidayat; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 3 (2018): Maret 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Soybean is one of the main sources of food commodities in Indonesia that not only serves as raw materials for the food industry but also non-food industries. But the lack of knowledge of farmers of soybeans crops about the various symptoms and types of diseases that attack soybean plants are problems that have a negative impact on soybean cultivation. Therefore needed a system that can solve problem of soybean disease diagnosis quickly and precisely. In this research, the writer will implement Dempster-shafer method to diagnose soybean plant disease. This soybean plant diagnosis system can detect 5 types of diseases with 16 symptoms. The results of accuracy tested on 25 data cases obtained an accuracy of 92%, so it can be said that the system works well enough and can be applied.
Sistem Deteksi Dan Perhitungan Otomatis Bakteri Salmonella dengan Pengolahan Citra Menggunakan Metode Object Counting Lashot Ria Ingrid Melanika; Hurriyatul Fitriyah; Gembong Edhi Setiawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Salmonella is one of the pathogenic bacteria that can cause disease in human digestive tract. The microscopic size makes the Salmonella bacteria invisible without the aid of a microscope. Detection and calculation of bacterial colonies during microscopic observations are still done manually until now. Therefore designed a system that can detect and perform calculations on Salmonella bacteria colonies automatically using the method of color segmentation and object counting. Samples of Salmonella bacteria isolated from chicken meat were made on glass preparations and gram staining was done to make it easier to observe. Bacteria shooting is using the camera on a digital microscope with a resolution of 5 mega pixels. The first process in image processing is image enhancement, then converting RGB image to HSV image. After that performed closing morphological operation, then bacterial colonies were calculated by object counting method. The processing of image processing algorithms is performed on MATLAB, and the system will be displayed on an interface to make it easier for user. The accuracy of input and image information to obtain results with successful status. The average time required during the execution of the system is 4.59 seconds, and the accuracy of detection and calculation of the number of bacterial colonies has an accuracy by 80.81%.
Implementasi Sistem Real Time Peringatan Kebakaran Pada Terminal Listrik Rumah Tangga Muhamad Irfanul Hadi; Sabriansyah Rizqika Akbar; Rizal Maulana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Increased use of electricity because all equipment requires an energy source. And electricity is the energy source that is most widely used in various equipment. Because more and more electricity users need a system to monitor electricity usage. There are several studies that make a system for monitoring electricity use. Existing systems are limited to monitoring electricity usage without a security system. While electricity is the cause of most of Indonesia's fires according to the central statistics agency. Because there is still no system that can detect fires due to electricity, a realtime fire warning system is created at the household electricity terminal. This study uses FreeRTOS to process sensor data. In this study, reading the current sensor, voltage sensor and temperature sensor will be more accurate in reading. Reading using FreeRTOS will produce eroor at a temperature sensor of 0.609%, a voltage sensor of 0.685% and a temperature sensor of 2.02%. Whereas when using a sequential program or an ordinary program it will produce an error of 2.219% on the current sensor, 1.05% on the voltage sensor and 4.607% this proves that there is an error difference of 1.61% on the current sensor, 0.365% on the voltage sensor and 2.587% on the sensor temperature is compared without using the FreeRTOS method with better accuracy. However, when using FreeRTOS the execution time will be 89.3 ms longer than without using FreeRTOS. With such high accuracy the system can detect that there will be a fire accurately and give the notification appropriately.
Klasifikasi Risiko Gagal Ginjal Kronis Menggunakan Extreme Learning Machine Dimas Prenky Dicky Irawan; Imam Cholisoddin; Edy Santoso
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Kidney is an organ in humans that have a very important role in the process of managing fluid and electrolyte needs. Chronic renal failure is a disease of kidney that occurs due to kidney infection and the existence of blockage due to kidney stones. To perform the classification of chronic renal failure medical personnel are still not maximally in handling it, to deal with this problem researchers use the Extreme Learning Machine to perform the classification of chronic renal failure. The Extreme Learning Machine is a classification algorithm in which this algorithm is part of a neural network that has a good learning speed and also according to existing research results in a good accuracy value when compared to using other algorithms. This study obtained a comparison of the value of training data as well as the optimal test data with a 70:30 ratio value, many hidden layer neurons of 10 and using the bipolar sigmoid activation function of these parameters resulted in an accuracy of 99.13%. From the results of accuracy obtained, indicating that the method of Extreme Learning Machine is good enough to be used for the process of classification of chronic renal failure.

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