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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 6,945 Documents
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Evaluasi Proses Bisnis Bagian Produksi Dengan Metode Quality Evaluation Framework (QEF) Studi Pada CV. Kajeye Food Yusuf Reyhan Aditiawan; Aditya Rachmadi; Andi Reza Perdanakusuma
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 8 (2019): Agustus 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

CV. Kajeye Food is a company that originated from a home company engaged in the production of fruit processing, especially chips and fruit sweets with the brand SoKressH is in Malang. In the production process, there are several things that the company needs to consider, such as the efficiency of raw materials and workers used. There are some mistakes that occur as in the process of raw material processing, the quality of the raw materials ordered is sometimes not in accordance with the company's preset standards. In addition there is a problem that the absence of regular machine maintenance routine/scheduling, cleaning and checking of the machine is done when it is needed to cause unexpected engine damage and resulting production process Stalled. Therefore, evaluation of business processes in progress using the Quality Evaluation Framework (QEF) aimed at evaluating the quality of business processes is objective, quantitative and by fact. As well as the Failure Mode And Effect Analyst (FMEA) method to evaluate and search for root problems to find out which problems have higher levels of urgency. And identify the problems that have been defined in the QEF and FMEA methods using the Fishbone method. Business process evaluation can be used by the company to compare the business targets made before the activity begins with the end result of the business activities.problems to find out which problems have higher levels of urgency. And identify the problems that have been defined in the QEF and FMEA methods using the Fishbone method. Business process evaluation can be used by the company to compare the business targets made before the activity begins with the end result of the business activities.
Algoritme K-Nearest Neighbors Untuk Klasifikasi Jenis Makanan Dari Citra Digital Dengan Local Binary Patterns Dan Color Moments Gregorius Ivan Sebastian; Yuita Arum Sari; Randy Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 7 (2019): Juli 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Food is a primary need to help individuals in executing their daily activities. The nutritional value provided by certain food items affects one's performance in executing their daily activities. Individuals need to be assisted in identifying what food items are nutritious and those that are not, hence a classification algorithm is made for this task. Computer vision can be utilized to classify food items based on analyzing certain features. This research uses color and texture features to classify food items that are in images. Color feature extraction utilizes Color Moments (CM) using a Red, Green, and Blue (RGB) color channel, while Local Binary Patterns (LBP) is utilized for texture feature extraction. The k-Nearest Neighbors (k-NN) is used for the classification process. The digital images, from both the testing and training groups, will be preprocessed whose color features will be extracted with CM and the texture features with LBP. The extracted features will then be saved in a database, which will decrease computing time during the classification time. Varying the values of k in the k-nearest neighbors algorithm during testing and combinatios of features used, showed that the highest value for f1-score during evaluations was 0,89 when the value of k=1 and when only the color features from using color moments were used. Therefore the classification algorithm works efficiently on the dataset used in the research if only color features were used using k-NN as the classification algorithm.
Perbandingan dan Pengaruh Handover Terhadap Kinerja Penjadwalan Paket Round Robin dan Proportional Fair Pada Jaringan LTE Chandra Yogi Adhitama; Primantara Hari Trisnawan; Reza Andria Siregar
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

Long Term Evolution (LTE) is a network that has many users using it to get information. Because mobile users are currently on this network, a packet scheduling algorithm is needed to schedule packages for ubiquitous communication. This research will discuss the performance of the packet scheduler Round Robin and the Proportional Fair and the effect of the handover on that packet schedulers. This research was conducted by simulating an LTE network in the NS-3 simulator and produce data that is reprocessed so that a value is formed to measure the performance of the packet scheduling algorithm such as throughput, packet loss ratio, delay, and jitter. These data will be analyze by comparing them from the results of the Round Robin scheduling algorithm and the Proportional Fair scheduling algorithm.
Penerapan Algoritma Support Vector Regression Pada Peramalan Hasil Panen Padi Studi Kasus Kabupaten Malang Dhan Adhillah Mardhika; Budi Darma Setiawan; Randy Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Rice is one of the important resources in human life, in several surveys it was found that more than 59% of the world's population used rice from rice as food staple. But in another theory stated that the human population will continue to develop exponentially while it is difficult to be followed by the growth of food products, especially in this case rice. Support Vector Regression (SVR) method is a method that will be used in this study, this method has been used in several previous studies such as forecasting gold prices and forecasting electricity consumption. In this study we will focus on testing whether the Support Vector Regression (SVR) method is suitable for use in predicting rice yields, using a number of predetermined parameters, and by applying changes to the parameters, namely the number of iterations, Complexity, Epsilon, Sigma, cLR , Lambda. The best results obtained in this study reached MAPE error rate of 10.133%, these results were achieved with the following parameter values, Number of iterations: 50, Complexity: 1, Epsilon: 0.01, Sigma: 1, cLR: 0.1, Lambda: 1
Rancang Bangun Pot Cerdas Dengan Mengatur Suhu Ruangan, Kelembapan Tanah, dan Intensitas Cahaya Berbasis Arduino dengan Metode Jaringan Saraf Tiruan Backpropagation Indera Ulung Mahendra; Hurriyatul Fitriyah; Mochammad Hannats Hanafi Ichsan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 9 (2019): September 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Indonesia is one of the countries where the majority of the population work as farmers. Over time, agricultural land decreases. This encourages people to do farming activities in the home which is often called urban farming. One commodity that is often grown is chili. The growth process of the chili plant itself has several factors that must be considered in order to grow optimally. To support the urban farming activities, an intelligent pot frame was created that could regulate the temperature within the framework, soil moisture in the planting media, and also the intensity of the light in the smart pot frame. All of these functions are supported by using a backpropagation feed-forward neural network classification method. The DHT11 sensor is used to conduct temperature readings with an average reading error rate of 2.57% compared to digital thermometers. YL69 sensor is used to read the soil moisture results from the reading of the soil moisture sensor has a pretty good accuracy compared to the reading from the hygrometer. The LDR sensor is used to read the light intensity with an average error rate of 17.62% compared to digital luxmeter. The reading value of each sensor is then entered into the classification program, where the program takes 548 milliseconds to classify after 20 tests.
Klasifikasi Isu Suku, Antar Golongan, Ras, Agama (SARA) pada Twitter Berbahasa Indonesia menggunakan Metode Improved K-Nearest Neighbor (K-NN) Firhad Rinaldi Saputra; Indriati Indriati; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 1 (2020): Januari 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Twitter is a social network that has one of the most active users today. With the openness of information users move to send texts or tweets about other users, the number of Twitter users makes a lot of tweets related to ethnic issues, between groups, races, religions (SARA). Twitter cannot access the content of tweets that contain Sara's Issues, research is needed to classify tweets to understand including categories of Sara's Issues or Not Sara's Problems. Classification The Sara issue starts in several ways, namely preprocessing which consists of several stages, namely cleaning, folding cases, tokenisation, filtering and stemming. Followed by the term weghting process, to the classification process using the Improved K-Nearest Neighbor method. Based on the implementation and testing carried out in the research on Sara's Issue Classification on Twitter Using K-NN Increase, get the best results based on Precision averages of 0.976422, Remember at 1, F-Measure of 0.987944444 and Accuracy of 96%. Where the number of documents used as training data are 320 documents and test data as many as 80 documents. Where the number of documents, comparison or balance of training data and the value of k-value used determine the good or not classification process of the document.
Implementasi Regresi Linier Berganda Untuk Prediksi Jumlah Peminat Mata Kuliah Pilihan Nur Kholida Afkarina; Agus Wahyu Widodo; Muhammad Tanzil Furqon
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 11 (2019): November 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Higher education is a continuation of education at a higher level after completing secondary education. With so many elective courses with each interest, it makes students have difficulty in knowing the number of interested ones. To overcome the problems that exist, this study predicts the number of interested subjects with multiple linear regression methods. Therefore we need a system to predict the number of interested subjects. There are two features used, namely the average student score in the previous year, the number of interested ones in the previous year. The method used is multiple linear regression. The training data used to determine the number of interested parties in taking courses is the 2013-2017 student data. As for the test data using student data for 2018-2019. From this research, the predicted MAPE score of Fuzzy Logic (2017) is 61.52% and in 2016 is 49.64%
Pengembangan Aplikasi Kakas Bantu Untuk Menghitung Estimasi Nilai Modifiability Dari Class Diagram Heru Apriadi; Faizatul Amalia; Bayu Priyambadha
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 11 (2019): November 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In the initial stages of developing a software, estimation of software size, effort, and cost is a very important issue for a developer and project administrator. Studies report that more than 90% of the total cost spent on software is caused by maintenance and evolution. Therefore the stakeholders expect that a software is built with the best design to improve efficiency and speed of work if there is a change in the software. Class diagram is one diagram created at the design stage of software development. Measuring the quality of the class diagram design of the software to be built can reduce revisions that may occur in the future. In the calculation of the estimated quality of this class diagram will be done by estimating the value of modifiability. To calculate the estimated value of the class modifiability estimation value, this diagram can be done manually, but it will take a long time if the calculation is done on a class diagram that has a large and complex number of classes and relations. Therefore, based on the problems that have been described, a solution is needed, namely the development of assistive tools to calculate the estimated modifiability of class diagrams automatically by using the calculation method using modifiability metrics. By making this system, it is expected to be able to resolve the problems that have been described. The research methods carried out in this study include the study of literature, data collection, needs analysis, design and implementation, testing and conclusions and suggestions. The system has also been tested by unit testing, integration testing, and functional validation testing to produce a value of 100% valid and testing the accuracy of the system by comparing the results of calculations with the system with manual calculations that produce a value of 100% valid.
Peramalan Curah Hujan Menggunakan Metode Extreme Learning Machine Rich Juniadi Domitri Simamora; Tibyani Tibyani; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Rainfall is the height of rain water that is found and collected in a flat, not absorbed, does not evaporate and does not flow. Information about rainfall is very important especially in agriculture and civil. In agriculture, rainfall information is used to determine the type of plants to be planted in accordance with the intensity of rainfall, predicting the start of the growing season in the planting calendar to minimize the risk of planting. In the civil field, it is used as a determinant of engineering design standards in planning flood disaster control buildings. Above normal rainfall will cause natural disasters such as floods and landslides. Rainfall is part of the weather element and one of the meteorological processes that is quite difficult to predict. Rainfall forecasting is needed so that the community and the government can take preventative measures against the existing problems. The forecasting process is divided into several processes which include data normalization, forecasting with the Extreme Learning Machine algorithm, data denormalization and the results of errors with MAPE. Based on the test results using rainfall data in the Poncokusumo area with a span of years 2002 to 2015 obtained the smallest MAPE value of 3.6852%, with as many features as 4, many neurons in the hidden layer as much as 2, the percentage of training data 90%.
Analisis dan Perancangan Sistem Informasi Eksekutif Dashboard Data E-Government berbasis Service pada Pemerintah Kabupaten Sidoarjo Rivalno Al Fath Ismubandono; Widhy Hayuhardhika Nugraha Putra; Djoko Pramono
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 11 (2019): November 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The Government of Sidoarjo Regency especially the Communication and Information Office (Diskominfo) has the task to connect the Organisasi Perangkat Daerah (OPD) to present the government data from each OPD. During this time, the presentation and delivery of government data to the Mayor of Sidoarjo Regency still uses manual process that requires a lot of time and effort because Diskominfo must process data sent by the OPD in the form of Excel worksheet into diagrams and graphs so that the Mayor can easily understand the data from OPD. Data sent by OPD is still not in the form information packages, so data processing takes a long time. This will take a long time for the Mayor to make decisions. Based on this problem, the Executive Information System Dashboard E-Government needed is needed based on Android and Website so when Mayor need government's data can be accessed either through mobile or computer applications by taking data from the data warehouse. Analysis and design of this sistem will use object-oriented design analysis (OOAD) method. From the results of this study, current business processes and proposals are generated, user needs, system design and user interface. The results of the design of this system are evaluated by traceability matrix and consistency analysis. The results of the design evaluation show that user needs have been defined and facilitated and have a high level of consistency and can help the Mayor in viewing government data contained in the Executive Information System Dashboard E-Government.

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