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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 Background Subtraction Untuk Klasifikasi Keripik Kentang Berbasis Raspberry Pi Menggunakan Metode Naive Bayes Yongki Pratama; Fitri Utaminingrum; Wijaya Kurniawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 1 (2019): Januari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Potatoes are a kind of vegetables that can be processed into various foods, one of which is the chips. Many big companies in Indonesia produce potato chips. One of them is Istana Factory located in Batu Tourism City. The potatoes processed have various sizes from the largest size (Super), medium size (AB), and then the smallest size (A). The process of sorting at the factory has been done by human manually, then it will produce less relative output. Therefore, it is needed a research about a tool that can sort the potato chips automatically. In this study, the system made is in the form of conveyor, which a webcam is installed as a censor to take pictures from potato chips, then those are processed in Raspberry Pi using image processing of Background Subtraction method. Potato chips will be classified based on size read by the system by using the w and h value parameters or the width and height of the bounding box of potato chips that are converted to actual size by millimeters. The value is used as a reference to be classified with the Naive Bayes method. Naive Bayes is used for the classification method because it is a method that has high performance and has excellent accuracy for classification. From the results of test conducted Background Subtraction can read the image of potatoes well. The reading of potato chip size from the system gets a small error of 3.73%. Then the accuracy obtained with Naive Bayes method in chips classification with 90 training data and 30 test data is worth 93.33 having an average processing time speed of 1.7 ms from 30 times of the test. Then it is performed a test of hardware servo that has been running based on the system.
Implementasi Algoritme K-Means Clustering Dan Naive Bayes Classifier Untuk Klasifikasi Diagnosa Penyakit Pada Kucing Puji Indah Lestari; Dian Eka Ratnawati; Lailil Muflikhah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 1 (2019): Januari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

At this time cat has become a popular pet community. This is because there are many benefits that exist from cats, such as an entertainer, and now developed countries many cats contested in the show cat. Treatment for cats is mainly because some of the cat's disease can spread to humans. The limitations of dentists in diagnosing diseases with a pattern of having the same symptoms as some diseases, are important in making a diagnosis. Therefore there need a system that can diagnose diseases that can be accessed by the cat owners and be dealt immediately. In this study can use K-Means Naive Bayes (KMNB) method for diagnosis in cats. The KMNB approach is formed by the incorporation of clustering and classification techniques. In the beginning Clustering on K-Means was used to group the same data. Further classification of data by category using Naive Bayes method. The data that have errors in the first stage are then organized by the second category. Identify data with the same character or data that shows similar characteristics from the start. Based on the results of tests that have been done by comparing the results of grouping on conventional K-Means proves that KMNB can produce the highest average of 90% while conventional K-Means has the highest average of 71, 379%.
Implementasi Low Power Mode Pada Pendeteksi Kebocoran Gas Dengan Atmega328P Berbasis NRF24L01 Ahmad Fikri Marzuqi; Sabriansyah Rizqika Akbar; Rakhmadhany Primananda
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 1 (2019): Januari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

LPG (Liquefied Petroleum Gas) is an alternative fuel for petroleum and gas replacement. Gas fuel could explode because of gas leaks, the design of the system of gas leaks detection, is important for minimizing fire of gas leaks. It uses the low power performance with atmega based NRF24L01 for device. The reason of using low power with atmega328p's microcontroller on transmitter node is for saving a power consumption, and also using wireless sensor network based NRF24L01 to minimize cable installation. The method consists of 3 node sensors those are transmitter and 1 receiver node sensor, then it will be linked with personal computer. The result of testing was concluded that the system can work as principle. Transmitter node can detected the presence of gas, some features were turned off from sleep node applied and develop system that comes of external interrupt and then data was submitted by wireless. And the result, average current value of sleep mode is 137,84mA and average current value of normal mode is 157,25mA. And the different between sleep mode and normal mode are 20,59mA.
Algoritma Genetika Untuk Optimasi Fuzzy Time Series Dalam Memprediksi Debit Air (Studi Kasus: PDAM Indramayu) Mohamad Alfi Fauzan; Budi Darma Setiawan; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 1 (2019): Januari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The availability of water in the country of Indonesia reaches 694 billion m3 per year, where the amount is a potential that can be utilized but only about 23% is utilized. With the increasing number of people needing clean water but low water debit distribution, the concept of forecasting or prediction is needed as one of the inputs in making decisions to increase the flow of water to be distributed. To solve these problems in this study fuzzy time series methods are optimized with genetic algorithms in predicting the distribution of water discharge. Genetic algorithm is used to optimize sub intervals in fuzzy time series. Based on the results of the test, the accuracy of the prediction results obtained using the Average Forecasting Error Rate (AFER) method obtained the percentage error rate of 15.33% which included in the good qualifications.
Implementasi Metode Fuzzy K-Nearest Neighbor Untuk Identifikasi Cedera Pada Pemain Futsal Rizki Wulyono Propana Sodiq; Nurul Hidayat; Ratih Kartika Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 1 (2019): Januari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The injury is common place occurs in a sports game. The risk of injury is a State when the individual risk hazards due to perceptual deficit gets or physiological, lack of awareness about the dangers, or old age. Injuries that are often experienced by invistasi futsal futsal players between HIGH SCHOOL/Central Java-se Equal the year 2013, namely the head injuries that often occur in the eyes of 31.8%, upper limb injuries often occur at the wrist, 33 3%, a lower limb injury often occurs in the knee injury to 36% and togok an injury often occurs at the waist 65.38%. Being the overall percentage of injuries that happen on most body limbs down 47.18%, especially on the knees of 36%. Fuzzy K-NN classification is a method that combines technique with fuzzy K-Nearest Neighbor Classifier. Algorithms of Fuzzy K-Nearest Neighbor gives value to the membership class on test data rather than putting test data on a particular class. FK-NN classification is a method used to predict the test data using the value of the degrees of membership test data on each class. The required variable in this study was injury symptoms. Highest accuracy of the test results is when K = 5 the value of 94,29%.
Diagnosis Penyakit Sapi Menggunakan Metode Promethee Bayu Kusuma Pradana; Nurul Hidayat; Randy Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 1 (2019): Januari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Indonesia has the potential of a large farm with superior products such as dairy cattle and beef cattle, the superior products of these farms are growing and concentrated in the development area of ​​the production center. With large amounts of production, the need for animal protein in Indonesia is increasing with increasing public awareness of the importance of nutritional intake. Therefore, the health of livestock raised by farmers is important to meet the nutritional needs and in addition to income for the livestock owners themselves. Promethee is a method of determining the order (priority) in multicriteria analysis. The key issues are simplicity, clarity, and stability. Promethee method is well used for the diagnosis of disease in cattle because it produces an accuracy of 92.73%.
Klon Perilaku Menggunakan Jaringan Saraf Tiruan Konvolusional Dalam Game SuperTuxKart Arrizal Amin; Yuita Arum Sari; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 1 (2019): Januari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

One of the important component of the video game is an artificial intelligence to make the game more competitive. Artificial intelligence used to decide action to reach goal in the game and challenge game player. In the process of developing artificial intelligence, developer needs to program an aritficial intelligence to make a decision for action for each states possible in the game. In this research, artifical neural network will be used as an artificial intelligence inside video game. Neural network will simplify process of developing artificial intelligence because developer does not have to program an algorithm to decide each action for each possible states in the game. Furthermore, neural network can learn or clone gamer's behavior while playing the game. In this research, SuperTuxKart will be used for an example to develop artificial intelligence inside video game. Artificial Intelligence with learning rate 0.0001, momentum 0.3 and epoch 100 reaches accuracy 86.72% for cloning game's behavior while playing video game. So this research concluded that neural network can be used as an artificial intelligence inside game.
Peramalan Harga Cabai Menggunakan Metode High Order Fuzzy Times Series Multifactors Ridho Agung Gumelar; Budi Darma Setiawan; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 1 (2019): Januari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The daily needs of Indonesian people can not be separated from agricultural commodities such as chili, onion, garlic, tomatoes and others. Some of these agricultural commodities have sharp price fluctuations, such as chili. When the supply of chilli in the market decreases, the price can be soar higher than the normal price. Conversely, when the supply of chili is excessive, the price will be fall well below the normal price. This is influenced by various factors such as the harvest season, the amount of production, the amount of public consumption, the area of the harvest area and others. Therefore we need a method to estimate the price off chili so that it can be used to support decision-making related to price issues. Forecasting is one solution to be able to estimate the price movement of chili commodities. The method used to forecast the price of chili is High Order Fuzzy Times Series Multifactors. In this method the formation of subinterva is done by using Fuzzy C-means. For calculate forecasting error results in this research using Mean Square Error (MSE). Based on the results of the test, the value of training data and orders used in forecasting does not guarantee a low error rate. The results of forecasting the price of chili using the method of High Order Fuzzy Times Series Multifactors get the best MSE results of 20,374.19.
Implementasi Protokol Routing Directed Diffusion Pada Wireless Sensor Network Menggunakan Media Komunikasi RF Elsandio Bramudya Putra Fathoni; Sabriansyah Rizqika Akbar; Mochammad Hannats Hanafi Ichsan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 1 (2019): Januari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Implementation of wireless sensor network routing protocols has been carried out specifically on flat topology architectures. Where in the flat topology there are several routing protocols that has been implemented, one of which is directed diffusion routing protocol. Unfortunately, the implementation is done using a network simulation application. Therefore, in this research, the authors implemented routing protocols directed diffusion on a wireless sensor network on a real device that uses RF communication media. Hardwares to build a node using arduino nano 3.0 microcontroller as sensor data processor and NRF24L01 module as data transmission medium. At the time of application of directed diffusion, the sink node send interest or request data containing the coordinates of the sender, the type of data and the coordinates of the destination node. The data used are the temperature and soil moisture data which is selected randomly. The research is done by testing the status of nodes that can be displayed on the serial monitor, each sensor node can perform sensors data and moisture data, as well as testing the directed diffusion as functional requirements. The results of the research is each node successfully displays the node status on the serial monitor, each sensor node can perform sensor data and moisture data, and the system successfully implements the direct diffusion routing protocol.
Peramalan Tingkat Produksi Gula Menggunakan Multi Factor Fuzzy Time Series yang Dioptimasi dengan Algoritme Genetika Kholifa'ul Khoirin; Budi Darma Setiawan; Agus Wahyu Widodo
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

Sugar is one of the strategic commodities that affect the Indonesia's economy. This is because sugar is one of a essencial staple for Indonesian society. But on the other hand, the large demand of sugar consumption in Indonesian cannot meet with the low production of sugar. One of sugar factories is PG Candi Baru Sidoarjo. Besides production processing's factors, the factory is experiencing difficulties in its planning. Which in the production planning, the sugar factory will set targets that must to be achieved in future production. In an effort to overcome these problems, this study is expected to provide forecasting to see the possibility of achieving sugar production targets. Multi Factor Fuzzy Time Series method optimized with Genetic Algorithm by considering several factors influencing sugar production process such as number of milling days in one month, percentage of rendeman, and number of milled sugarcane. The genetic algorithm is used to perform subinterval optimization. Forecasting results of sugar production using a combination of these two methods get RMSE of 424,70. These results are smaller than the Multi Factor Fuzzy Time Series method without optimizing the subintervals that yield RMSE 6168,7437. Thus, it can be concluded that the proposed method is capable of forecasting better results than the unoptimized Multi Factor Fuzzy Time Series method.

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