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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 125 Documents
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Search results for , issue "Vol 3 No 5 (2019): Mei 2019" : 125 Documents clear
Klasifikasi Citra Jenis Makanan dengan Color Moments, Morphological Shape Descriptors, dan Gray Level Coocurrence Matrix menggunakan Neighbor Weight K-Nearest Neighbor Muhammad Abdan Mulia; Yuita Arum Sari; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The level of development of human growth depends on food consumed daily. The good condition of the human body comes from healthy and hygienic food. Recognizing a food becomes a problem for visitors or tourists who are visiting a food place that has no known nutrient levels and ingredients. To overcome this problem, research needs to be done to classify a food image. Morphological Shape Descriptors (MSD), Color Moments (CM), and Gray Level Co-ocurence Matrix (GLCM) features with Haralick have been shown to produce good features for classification. The Neighbor Weight K-Nearest Neighbor method is also an alternative to the image classification process.Based on the test results from k-fold cross validation with k = 10 and the evaluation method in the form of accuracy, obtained maximum accuracy of 0.86 with parameter values ​​E = 11 and k = 3 in the case of training data amounting to 530 images of single food which has been pre-processing. This shows that the classification of food images based on the extraction of textural features such as form (MSD), color (CM), and texture (GLCM) results in relatively better accuracy. In addition, the combination of the use of these three features affects the results of accuracy. This is indicated by testing which shows that the results of relative accuracy are better achieved in features of a combination of textures, shapes, and colors.
Prediksi Rating Pada Review Produk Kecantikan Menggunakan Metode Semantic Orientation Calculator dan Regresi Linier Bastian Dolly Sapuhtra; M. Ali Fauzi; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Crowded producers of beauty product produce good and varied products. This has attracted consumers to use these beauty products. More and more consumers are using these beauty products, making producers try various innovations on their products. Innovation can be obtained from many comments, advices, or reviews made by consumers on variety of products. Benefits of product reviews for consumers are also useful to obtain information before buy a product. Many results of the review are not accompanied by rating. This makes it difficult for producers to classify reviews into certain sentiments. In this research aims to classify review into certain sentiments automatically into rating. In this research built a system using Semantic Orientation Calculator and Linear Regression methods. Breaking sentences in a review into n-gram (bigram and trigram) and one sentence aims to improve the results of predictions. Results of testing on this system are 23%, 71%, 67% on accuracy of bigram, 24%, 71%, 67% on accuracy of trigram, and lowest 24%, 67%, 64% on accuracy of one sentence with tolerance 0, tolerance 1, and sentiment reviews. The best result of testing on breaking sentence using n-gram (bigram and trigram) was good enough to solve problem in this research.
Sistem Deteksi Api pada Quadcopter Ar Drone Menggunakan Metode Color Filtering HSV dan YCbCr Color Space Anata Tumonglo; Gembong Edhi Setyawan; Rizal Maulana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Fire events are disasters that occur because of the expansion of the source of the fire that propagates to potentially burned objects. In addition, these events are also often caused by irresponsible human negligence. Therefore risk management is carried out in anticipation of minimizing the impact of losses. One of them is by using a quadcopter to monitor and detect objects. This study was designed to detect hotspot objects in a room through image processing using the ArDrone front camera for sending raw frame data images at UDP port 5555. Image processing is implemented on the ROS platform which is used to connect quadcopter with GCS using OpenCv. This system utilizes the Hue Saturation Value (HSV) Color Filtering and YCbCr Color Space methods. The results of this study are in the form of a system on ArDrone that is capable of detecting hotspot objects. Testing on the system is done by doing detection tests with different distances with reference to the position of the hotspot object, testing the range of HSV value parameters along with YCbCr values in each condition, testing with a certain speed by reference to the position of hotspot objects and testing the system response performance. The results obtained are effective detection distance is 8 meters with an overall percentage of 83.33%, the speed of the appropriate ArDrone quadcopter if detected is with a speed of 1m/s and the system response performance to detect hotspot objects by 0.022 seconds.
Prediksi Suku Bunga Acuan (BI 7-Day Repo Rate) Menggunakan Metode Extreme Learning Machine (ELM) Yohana Yunita Putri; Putra Pandu Adikara; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Reference interest rates or often referred to as BI 7-Day Repo Rate is a policy interest rate that describes the establishment or view of monetary policy whose determination is made by Bank Indonesia which is then notified to the public.. BI 7-Day Repo Rate has an influence on economic activities, such as investment, inflation and currency changes. Investors and market players in making economic decisions will refer to the fluctuation of interest rates set by the central bank. Therefore, the prediction of the benchmark interest rate (BI 7-Day Repo Rate) is important. The purpose of the BI 7-Day Repo Rate prediction is to facilitate and assist investors and market players to make estimates of the decisions to be taken according to the prediction of the benchmark interest rate. This study uses the Extreme Learning Machine (ELM) method to predict the reference interest rate (BI 7-Day Repo Rate). The process of the first ELM algorithm is to normalize, then initialize the input and bias weights, then continue to carry out the training process and proceed with the testing process, then do the normalization to obtain the actual value. Based on the Extreme Learning Machine (ELM) algorithm that has been conducted, it produces the best Mean Absolute Percentage Error (MAPE) of 1,1% and the fastest processing time is 0.125 seconds using 50 hidden neurons, sigmoid activation function and 96 data counts.
Klasifikasi Senyawa Kimia dengan Notasi Simplified Molecular Input Line Entry System (SMILES) menggunakan Metode Extreme Learning Machine (ELM) Isti Marlisa Fitriani; Dian Eka Ratnawati; Syaiful Anam
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Indonesia has a huge natural's potential by the existence of various plants and animals discovery. This issue brings a good for Indonesian people through taking advantage of nature, especially in pharmacology. In pharmacology, active compounds can be used to prevent and cure disease. Therefore, a research is conducted in informatics's field by making an active compounds' classification system to determine its pharmacological benefits. SMILES is a chemical compound notation used in this research. SMILES's features which are used as many as 15, namely B, C, N, O, P, S, F, Cl, Br, I, OH, @, =, #, and (). ELM is an ANN method that can do a generalization better than conventional methods in a limited time. A number of hidden neurons test which were conducted using k-fold cross validation method in 2 classes produced the best accuracy, 85,03%, in Metabolism and Inflammation class scenario with a total of 5, 10, and 15 hidden neurons. A number of hidden neurons' test use k-fold cross validation method which were conducted in 3 classes produced the best accuracy, 55,06%, in Metabolism, Inflammation, and Cancer class scenario with a total of 300 hidden neurons. The best accuracy was obtained as many as 55,06% by testing 15 features with 300 hidden neurons, while in 11 features's test with 400 hidden neurons was found a number of 49,18% as the best accuracy.
Sistem Recruitment Pegawai (Studi Kasus: Mahar Agung Organizer) Rizkia Desi Yudiari; Fajar Pradana; Fitra Abdurrachman Bachtiar
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Mahar Agung Organizer has many parameters in selecting candidates that cause a lot of time during manual processing. Project manager's selection in February 2018 was then estimated at more than five hundred job applicants. In the August 2018 official crew selection, it was estimated that job applicants reached one thousand six hundred candidates. In addition, there was no process of classifying the types of events suitable for official crew after being accepted, causing the project manager to review the results of the interview to place the crew official in the list of events. The absence of a concise page that can display information related to official crew work performance to assess official crew performance is also one of the problems that is expected to be resolved through this research. This research will be conducted using an Object-oriented (OO) approach that will use Use Case Diagrams, Sequence Diagrams, and Class Diagrams in making systems. The development cycle uses the Prototype method by doing two iterations according to the agreement in the beginning with the stakeholders. This system will have three main features. The first feature will be implementing selection of candidates with Profile Matching. The second feature is a function for placing candidates who have been accepted into their divisions using the Naive Bayes algorithm. The third feature is displaying official crew performance in the form of dashboarding information. Validation testing using Black Box produces 100% validity. Unit testing and integration testing using White Box is in accordance with expected result.
Algoritme Information Gain Feature Selection pada Sistem Temu Kembali Citra Makanan Menggunakan Ekstraksi Fitur Warna dan Tekstur Dyva Agna Fauzan; Yuita Arum Sari; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The food name used as a keyword or query in conducting a food recipe search on the search system has limitations, namely the knowledge of the name of the food that the recipe wants to find. So another approach is needed to do recipe searches, namely by the display or the image of food. However, with the many features that are generated from the image it will cause high dimensional data which results in the effectiveness of the search system. For this reason, feature selection is needed to handle high-dimensional data. This research conducted a study of the effect of the number of returns that can provide the highest MAP value and the effect of the Information Gain feature selection on food image retrieval systems using texture feature extraction using Gray Level Co-occurance Matrix and color features using Color Moments and Color Histogram. The number of retrieves (r) of 5 is outperforming other r values with the value of MAP = 1 on the use of only color features and textures and the value of MAP = 0.98 in the combination of both. This indicates a smaller number of returns can give a higher MAP value. The effect of the Information Gain feature selection algorithm on the system is that it can provide the MAP = 1 value on the number of features (n) = 10 on the color feature, n = 5 on the texture feature, and n = 30 on the combination. This shows that the system with feature selection can provide results that are as good (in color and texture) and even better (in combination of features) with fewer features when compared to without feature selection.
Prediksi Kelulusan Mahasiswa Berdasarkan Kinerja Akademik Menggunakan Metode Modified K-Nearest Neighbor (MK-NN) Imaning Dyah Larasati; Ahmad Afif Supianto; Muhammad Tanzil Furqon
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In the Brawijaya University FILKOM Informatics Engineering study program, the academic performance of students in terms of study period is still a problem. In FILKOM's academic database, there are student academic data. The data can be carried out data mining by predicting students' graduation in the 5th semester. K-NN is a good method for predicting graduation. However, there is a method that has better accuracy than K-NN has been found in other cases, that is MK-NN. Therefore, this study using M-NN method for predict students' graduation based on academic performance by testing includes testing the effect of k value, the number of training data and the composition of training data. Furthermore, comparing the accuracy produced by MK-NN and K-NN methods. The highest accuracy of testing the effect of the value of k is when k = 5, which is equal to 82%. The highest accuracy from testing the effect of number of training data and the composition of training data reached 85,25% and 84%. From the comparison of the accuracy of MK-NN and K-NN it was concluded that MK-NN produced better accuracy than K-NN.
Implementasi Perancangan Tata Kelola Teknologi Informasi menggunakan Kerangka Kerja COBIT 4.1 pada Bidang Pengelolaan Teknologi Informasi dan Komunikasi (TIK) Diskominfo Kota Madiun Debrilla Ivanadya Pang; Suprapto Suprapto; Admaja Dwi Herlambang
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The management of ICT field of The Communication and Information office of Madiun is a government agency that has responsibility in managing of ICT to supporting technology information service so that it requires an evaluation of managing ICT. This research employs a COBIT 4.1 framework, which is focused on the domain Deliver and Support (DS) especially on DS1 the definition and management service level, DS5 ensure systems security, DS7 training and education of the user, DS11 manage data. The purpose of this research is to know the evidence of governance ICT, the result of maturity level and provide results recommendation to documentation design on governance such as policy, standards, guidelines and procedures. Based on this research, the average value of maturity level that is obtained from each subdomain DS1, DS5, DS7, and DS11 it is 2.83, 2.59, 2.75 and 2.58 and analytic value gap is 1,00. To achieve the maturity level of expectations 3.00 then the recommendation given is to make policy, to make standard procedure and to do documentation using standard format so that the management of ICT field can manage the ICT with a clear and controlled stage.
Implementasi TOPSIS Pada Sistem Rekomendasi Kafe di Kota Malang Berbasis Lokasi Wiandono Saputro; Ratih Kartika Dewi; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In Malang, there are at least 400 cafes and restaurants that college students can visit. A large number of cafes available creates its own problems for college students while looking for a gathering place for refreshing or doing a final assignment, which is choosing which cafe to visit. There are several factors that determine the choice of a cafe such as rating, distance, price, and facilities. To solve this problem, we need a system that can provide cafe recommendations to its users based on the location and the criteria of the cafe they are looking for, also provides information such as prices, menus, coffee beans, brewing methods and directions to the cafe. The cafe recommendation system developed using the TOPSIS. The TOPSIS method is used to generate alternative of cafe based on the concept that the best alternative is not only the the alternative with the shortest distance from the positive ideal solution but also with the farthest distance from the negative ideal solution. Alternative data is stored using the Firebase Realtime Database database service, and directions are given using third-party applications, namely Google Maps. From the testing the system obtained 100% valid on functional testing, 100% valid in the validation of algorithm that compares the system output with manual calculation, and the absence of ranking reversal on rank consistency testing which is done by adding new alternatives, and testing usability based on usefulness, ease of use, ease of learning, and satisfaction which give results in a percentage of 80.55%.

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