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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 6,923 Documents
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Pengembangan Aplikasi Pengorganisasian Tim Pengembang Perangkat Lunak dengan Mempertimbangkan Kepribadian Rony Hendiarto; Bayu Priyambadha; Mahardeka Tri Ananta
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 3 (2019): Maret 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (616.223 KB)

Abstract

In the past ten years, the process of software engineering is growing faster and also the development of software engineers should be able to follow the development of software engineering. Because of that, the project amount of software engineering is getting larger and variant. However, there are several obstacles in practices of software engineering. One of the most common obstacle is the inaccuracy of the composition of the software development team because of social problems and conflicts experienced by the team.The researcher found a direct correlation between the success achieved in particular job role and someone's personality. The composition of the team in software development project is a crucial factor that is able to failure of a software development project. Identifying that personality can be used to measure the compatibility of a software development team, so it is necessary to have a system that is able to accurately identify personality of a person as well as the suitability of work positions especially in software engineering. The system must be able to identify a person's personality. From the results of these personalities, the system is also able to determine recommendations software development positions based on their personality dimensions of Bigfive Personality Traits, including: Openness to Experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. From the results of Bigfive Personality Dimension, the system is also able to compile a software development team in working on a software project. The result of this research is had a three actor, eighteen functional requirements and also system are tested with a hundred percent valid.
Klasifikasi Jenis Citra Makanan Tunggal Berdasarkan Fitur Local Binary Patterns dan Hue Saturation Value Menggunakan Improved K-Nearest Neighbor Sarah Najla Adha; Yuita Arum Sari; Randy Cahya Wihandika
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

To fulfill their basic needs, living things need foods. Foods that have poor quality can cause disease. To avoid this, digital image processing can be used to create a food classification system. Digital image processing is used to analyze features contained in food images. In this study, the feature used to classify the types of food images is a feature of color and texture. Color feature extraction is done by Hue Saturation Value (HSV) color space and texture features using the Local Binary Patterns (LBP) method. Classification is done by the Improved K-Nearest Neighbor (Improved K-NN) method. The test results for the k value indicate that the highest accuracy is obtained at 90.476% with the value of k = 1. When the feature used is only a color feature, the highest accuracy value is obtained at 90.476% with a value of k = 1. When the feature used is only a texture feature, the highest accuracy value is obtained 85.714% with a value of k = 1. The results of testing the classification method showed that the Improved K-NN method produced higher accuracy than the K-NN method with an average accuracy of 80.306%. So the best classification results are obtained by using a combination of color and texture features with the Improved K-NN classification method.
Perbandingan Double Moving Average dan Double Exponential Smoothing untuk Peramalan Jumlah Kedatangan Wisatawan Mancanegara di Bandara Ngurah Rai Cinthia Vairra Hudiyanti; Fitra Abdurrachman Bachtiar; Budi Darma Setiawan
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

Every year the number of international tourist arrivals in Bali always increases (BPS, Statistics Indonesia). Increasing the number of international tourist arrivals will have an impact on the availability of facilities, infrastructure, and services for the airport or Angkasa Pura I. Many things affect foreign arrivals, resulting in the need forecasting the number of foreign arrivals whose results can be used by Angkasa Pura I as the airport manager and local government to improve services. This research forecasting is done using Double Moving Average and Double Exponential Smoothing. Accuracy calculation is done by using Mean Absoulte Percentage Error (MAPE). The data used are 120 data, from January 2008 to December 2017, and obtained from the official website of Statistics Indonesia. From this study testing in 2017 found the best time order value for the Double Moving Average is 2 and Double Exponential Smoothing with parameter 𝛼 = 0.4. From these parameter values, the MAPE Double Moving Average value is 10,522 and the MAPE Double Exponential Smoothing value is 3,355. At Double Exponential Smoothing has a value below 10, it is said to be very good, while the Double Moving Average with a value above 10 is said to be good. It can be concluded that Double Exponential Smoothing has better accuracy than Double Moving Average in forecasting the number of arrivals of foreign tourists at Ngurah Rai Airport.
Penentuan Penerimaan Beasiswa Menggunakan Metode Modified K-Nearest Neighbor Caesaredi Rama Raharya; Nurul Hidayat; 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

In determining the acceptance of students scholarship, the officers often face problem selecting student who are eligible for a scholarship that caused by several factors such as the number of students who apply for a scholarship while the quota for students who get scholarships is relatively small, the number of parameters used as a reference in determining the students who are eligible for a scholarship and the officers who are given only a relatively short time in determining the awardee. Therefore implement a classification system is required to this issue for help facilitate the scholarship selection officer. This research was using Modified K-Nearest Neighbor method. Modified Method K-Nearest Neighbor is modified method from K-Nearest Neighbor consists of the process of calculating distance euclidean, calculation of validity value and weight voting calculation. The highest average accuracy results obtained based on the tests and normalization data that have been done is 87.2%.
Prediksi Intensitas Curah Hujan Menggunakan Metode Jaringan Saraf Tiruan Backpropagation Defanto Hanif Yoranda; Muhammad Tanzil Furqon; Mahendra Data
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 10 (2018): Oktober 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The intensity of rainfall is quite difficult to predict. Many things can be the factor of rainfall, such as temperature, wind speed, humidity, air pressure, and others. This rainfall factor is a major component that is difficult to predict and calculated, therefore rainfall forecasting is a very interesting thing to discuss, because it will be very useful for various things. Many forecasting methods can be used for forecasting, such as the Backpropagation Neural Network used in this study. This research will use time-series data, monthly rainfall data obtained from Kab. Ponorogo. The best result of this research is test MAPE of 20.28% obtained from training using data from Balong rain gauge station. The training process uses 10 neurons on the input layer, training data from 1997 to 2015, test data in 2016, 40 neurons on the hidden layer, a MAPE limit of 20%, and a maximum of 200000 iterations. Test MAPE is classified as not very well and too high due to there are many 0 values in the data.
Peringkasan Teks Otomatis Pada Artikel Berita Hiburan Berbahasa Indonesia Menggunakan Metode BM25 Desy Andriani; Indriati Indriati; Muhammad Tanzil Furqon
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

One of the most often activitiy carried out by Indonesian internet users is reading news. More than 50% of Indonesian internet users use the internet to read news. However, problems will arise if the content of the article is a long text so that the reader needs time to read and understand the contents of the article. One way that users can still read and understand the contents of articles quickly is by reading the summary. Therefore we need an automatic text summarization system in entertainment news articles with the aim of emphasizing the main information and helping the reader get the main information from the text quickly and don't need to read the entire contents of the text or document. This study uses the BM25 method which is a method of weighting sentences that sort sentences based on terms that appear in each sentence in the document. BM25 is using tf idf weighting for word weighting and the relationship between terms and each sentence in the document is influenced by free parameters k1 and b. Based on the test results it was found that summarizing the text with the BM25 method obtained the best average precision result, recall and f-measure values ​​when the value of the compression rate used was 30%. Where the average values ​​of precision, recall, and f-measure are 0,730, 0,738 and 0,734.
Optimasi Peramalan Metode Backpropagation Menggunakan Algoritme Genetika pada Jumlah Penumpang Kereta Api di Indonesia Mohammad Birky Auliya Akbar; Indriati Indriati; Ahmad Afif Supianto
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

The train is a kind of massive land transport with a lot of users, base on the results presented by Statistics for Safety Index and service reached 4.09 from 5 in year 2014, also supported by the fact that exposed by the daily Tempo (www.bisnis.tempo.co) indicating that the train users from time to time inCreased. However, with the inCrease in the number of passengers on top of the train without any prediction will be bad for the train in Indonesia. For this need a method of predicting the results that can be answerable, using popular methods such as artificial neural network Backpropagation and optimizations to do in determining the initial weights (W) with Using numbered variables 800 for the population, 20 for a number of generations, the composition of the value of Mr = 0.3 and Cr = 0.7, with the main variant of the Backpropagation artificial neural network that consists of multiple iterations is 100 and a value of Alpha is 0.9, also with dataset on a monthly basis, start from January 2006 to June 2017 in timeseries form data, with 100 training data pattern as initial data and 10 pattern of test data of last data. So the result is the level of precision based on error value (MSE) results 0.065869861 from the results of the hybridization method backpropagation artificially neural networks using a genetic algorithm, while without using the hybridization error value is 0.072517977.
Prediksi Rating Novel Baru Berdasarkan Sinopsis Menggunakan Genre Based Collaborative Filtering dan Text Similarity Rhevitta Widyaning Palupi; Yuita Arum Sari; Putra Pandu Adikara
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

The novel is a story that has a long, imaginary plot. Based on the editor's choice on the Amazon.com website, 50 of the 100 best-selling books are novels. This shows that public interest in the novel is quite high as one type of reading. But when you want to choose a novel that you want to read, readers sometimes feel confused to know the quality of the novel. One reference in looking at the quality of a product is rating. The Goodreads site is one site that allows amateur reviewers to write reviews and ratings to help readers choose relevant books. But sometimes Goodreads users don't give ratings to a book so followers from that user want to know the rating given by the user in the book. This study uses the Genre Based Collaborative Filtering method as a calculation of rating predictions and Text Similarity to determine the value of similarity between documents with each other. The data used in this study were 31 users and 90 synopsis as training data and 35 synopsis as test data. System accuracy obtained from the classification results by using the similarity value on text similarity of 45,714286% and MAE value of 0,27742857 so that it can be concluded that the method of genre based collaborative filtering and text similarity can be used to make rating predictions.
Analisis Kinerja On-Path Caching Dan Off-Path Caching Pada Information-Centric Networking Muhamad Rizka Maulana; Achmad Basuki; Kasyful Amron
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 9 (2018): September 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The massively increasing Internet traffic is not supported by the current Internet architecture which is still based on host-centric communication. Information-centric networking (ICN) paradigm has been proposed to resolve that problem with content-centric communication. Some ICN architectures have been proposed, such as CCN, DONA, and NetInf. Those proposed architectures generally have three main concepts, which are publish-subscribe operation, in-network caching, and content-oriented security. In-network caching plays a very important role in ICN paradigm. It can improve network performance by saving content in caches that are spread in the network. This research aims to compare on-path caching and off-path caching strategies using Biznet topology on Icarus simulator. The result shows that off-path caching has 12.45% higher cache hit ratio than on-path caching, and on-path caching has 63.14% lower link load and 42,07% lower latency than off-path caching. In addition to that, it is worth noted that bigger cache capacity and α value results in higher cache hit ratio, lower link load, and lower latency.
Prediksi Jumlah Kendaraan Bermotor Di Indonesia Menggunakan Metode Average-Based Fuzzy Time Series Models Fajar Pangestu; Agus Wahyu Widodo; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 9 (2018): September 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (850.452 KB)

Abstract

Motor vehicles in Indonesia are growing in number each year. The high number of motor vehicles will affect various sectors. Impacts such as traffic congestion, pollution, accidents, and traffic violations. By predicting the number of motor vehicles, predicted data can be used by the government or related parties to create a program to reduce the impact of high number of motor vehicles. Fuzzy time series is one method for prediction. One type of fuzzy time series method is the average-based fuzzy time series. This method is an average-based fuzzy time series method that is able to determine the effective interval length, so as to provide predictive results with a good degree of accuracy. The data used in the study amounted to 45 data. The result of this research test, the average value of error calculated using Mean Absolute Percentage Error (MAPE) method is 12.67% error value indicating that this research is included in good category used in motor vehicle prediction in Indonesia because it has accuracy value below 20 %.

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