Article Per Year (5 Year)
p-Index From 2021 - 2026
3.611
P-Index
This Author published in this journals
All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Community Empowerment
Yuita Arum Sari
Fakultas Ilmu Komputer , Universitas Brawijaya
Arts Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Public Health Social Sciences

Published : 105 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 105 Documents
Search

 5   6   7   8   9 
Convolution Neural Network (CNN) Untuk Pengklasifikasian Citra Makanan Tradisional Akhmad Rohim; Yuita Arum Sari; Tibyani Tibyani
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 7 (2019): Juli 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (944.313 KB)

Abstract

People in this digital era take a picture before eating is one of lifestyle. Then the result of the picture will be uploaded to social media. Traditional food's pictures dissemination still less identified encourages this research about the classification of traditional food's image. Extraction of classification features food image is difficult because of food can vary dramatically in appearances such as shape, texture, color, and other visual properties. Convolution Neural Network (CNN) is a method that can learn its own features on a complex image. Hopefully, CNN evaluation results for the classification image of traditional food can provide a solution to identify the image of traditional food. Result of this research in building the architecture of the Convolutional Neural Network model for classification of the traditional food image required 4 conditional layers, 4 max-pooling layers, and 2 fully connected layers. That architecture obtained because it gets the smallest loss value with 0.000044 value on the 15 epoch during the learning process and gets a 73% precision, 69% recall, and 69% F-score.
Segmentasi Citra Kue Tradisional menggunakan Ruang Warna Hue Saturation Value dan Otsu Thresholding Ani Enggarwati; Yuita Arum Sari; Randy Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 8 (2019): Agustus 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (309.142 KB)

Abstract

There are some problems regarding food that occur. One of them is nutrition and the quality of food that still needs attention. To find out the nutrients content in food we can use food classification using digital images. Classification requires an initial process called segmentation. In this case, the color space that used is Hue Saturation Value (HSV) and Otsu thresholding. Segmentation in this thesis uses 50 traditional cake images, begins with converting RGB images to HSV images. The Otsu thresholding is performed on each color component. Based on the results of these studies, the Value component of color gives the opposite result, the background is white and the foreground is black. Therefore, invert is applied to it. After thresholding on each color component, accuracy, specificity and sensitivity are obtained. Hue color component has an average accuracy rate of 42.64%, Saturation color component has an average accuracy rate of 94.34%, Value color component has an average accuracy rate of 70.68%. Tests for specificity and sensitivity show that Saturation color component has a higher value than other color components, with values 82.08% and 91.30%. Thus the Saturation color component is best used for segmentation using Otsu Thresholding.
Temu Kembali Citra pada Kue Tradisional berdasarkan Ekstraksi Fitur Color Histogram dan Color Moment menggunakan Algoritme Perhitungan Jarak Minkowski Andina Dyanti Putri; Yuita Arum Sari; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 9 (2019): September 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (475.365 KB)

Abstract

Traditional cookies represent every single place in our country with its uniqueness. We can find the information on those culinary through various media such as books or the internet. It is straightforward if we know the name of the foods. However, the limitation of one's knowledge will bring different problems in acquiring the information, especially if it is text-based. The solution to that problem is to use Content-Based Image Retrieval (CBIR), a technique that automatically uses an image as a query and display a series of the similar image just like the query. We use feature extraction in CBIR, which is also a fundamental part of Image Analysis. The feature extraction is used Color Histogram and Color Moment. This research is using Minkowski distance calculation with following values: p = 1, p = 2, p = 3 and p = 5. The value of p = 3 in Minkowski distance calculation gives the best result to the combination of these two features is valued at 0,720498. The MAP average value which is acquired from top K-rank: k = 5, k = 10, k = 15, k = 20, and k = 25, is valued at 0,7119 for the features combination. We can conclude from this result that the feature extraction Color Histogram and Color Moment gives an excellent result for traditional cookies image analysis.
Bray-Curtis Distance Untuk Pencarian Resep Kue Tradisional Berdasarkan Ketersediaan Bahan Makanan Febriana Ranta Lidya; Yuita Arum Sari; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (340.111 KB)

Abstract

In modern times like today there are several problems, especially regarding the introduction of traditional food which is one of the cultural heritages. Most Indonesian people today prefer modern cuisine to traditional food, especially for general snacks such as cakes. The lack of introduction of traditional cakes to the community is currently the background of a bray-curtis distance study to search for traditional cake recipes based on the availability of food ingredients. In this study the data used are food recipes obtained from Twitter users using the number of likes and retweets and are processed using the Bray-Curtis Distance calculation, and also compare with Euclidean Distance, the influence of Like and Retweet are also seen to find out whether the likes and retweets can provide better traditional cake search results. The Bray-Curtis Distance and Euclidean Distance Calculation Results for MAP produce the MAP value for Bray-Curtis Distance on Top-1 is 0.76 while for Euclidean Distance on Top-1 gives 0.18 results. In this case it can be seen that Bray-Curtis Distance is superior compared to Euclidean Distance, this is because in Euclidean Distance occurs in words that have high dimensions so it cannot work properly, because Euclidean Distance will assume that there is a high degree of dependency between vectors coordinates on the vector.
Klasterisasi Data Titik Api Menggunakan Metode Self Organizing Map di Wilayah Jawa Dika Perdana Sinaga; Putra Pandu Adikara; Yuita Arum Sari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (516.013 KB)

Abstract

The events of forest fires can occur naturally or artificial that impact environmental damage and loss of all aspects. Indonesia's wildfires are increasing annually. This is because Indonesia has many peatlands and rainfall in the dry season is less than half the normal rainfall or known as the El Nino Southern Oscillation (ENSO) phenomenon. Early indications forest fires can be known through a fire point (hotspot). In the years 2010 to 2018 found 14.070 fire points in Java region. One way to detect land fires is to divide the data of the fire points into groups using the Self Organizing Map (SOM) method. To measure the quality of the formed cluster, the Silhouette Coefficient (SC) algorithm is used. Based on the test results obtained the highest SC value of 0.248945455 with the value of neuron count is 3, alpha value is 0.1, maximum epoch value is 18 and the value of reduction of learning rate is 0.1. In 2017 the resulting SC value was 0,23416068940874324. The result is that East Java region has a big chance of land fires if seen from the point of fire that appears and confidence value.
Pencarian Resep Kue Tradisional berdasarkan Jumlah Likes dan Retweet menggunakan Metode Generalized Vector Space Model Berlian Bidari Ratna Sari B; Yuita Arum Sari; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (468.66 KB)

Abstract

Traditional cakes is one diversity form of indonesia. Traditional baking requires a prescription as the basis of material needed to process. Housewives today don't know recipe and how to make traditional cakes who is now drowning by things junk food so they feel lazy to make traditional cakes. Based on the problems that have been described , carried out a search to be the recipe for food in the form of traditional cakes. To get that recipe desirable, the method of Generalized Vector Space Model is needed to determine relevance that recipe desirable. That recipe is in the training having the number of likes and retweet supporting selection a document relevant. Documents recipe used 100 data recipe with 10 types of tradisional cakes. After testing 25 documents, the best Mean Average Preecision was 0.583 using the Generalized Vector Space Model method with weighted likes and retweets. This proves that the search results approach the query entered by the user.
Klasifikasi Citra Kue Tradisional Indonesia Berdasarkan Ekstraksi Fitur Warna RGB Color Moment Menggunakan K-Nearest Neighbor Fida Dwi Febriani; Yuita Arum Sari; Randy Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (275.03 KB)

Abstract

Food is one of the needs in primary needs which is very important for humans. These needs must be met every day, but the human tendency for food needs changes with the times. Society in general will choose fast food that is ready to be served rather than choosing traditional food. In this day and age, most people tend to capture the moment when they want to enjoy a food that will be consumed. Taking pictures (photos) is one way, from which the images are obtained an image on food. The image will display several different colors, so the color will be a feature that can be used for extraction. One method used to extract color features in images is Color Moment. This feature will produce three main values namely mean, standard deviation, and skewness. In addition, this feature together with the K-Nearest Neighbor (K-NN) algorithm will classify the extracted colors based on training data taken as many as k values. In this study, there are 29 Indonesian traditional cake objects that will be used, where the test scenario is divided into 29 classes, 8 classes, 5 classes, and 3 classes. By using the K-NN method and the Color Moment feature, the highest evaluation value obtained is 60% for the test scenario of 3 classes.
Prediksi Jumlah Kunjungan Wisatawan Mancanegara Pada Negara Singapura Menggunakan Algoritme Extreme Learning Machine Muhammad Sanzabi Libianto; Tibyani Tibyani; Yuita Arum Sari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (362.714 KB)

Abstract

Tourism is one of the important aspects contributing to income for the country, especially Singapore. Singapore is ranked 2nd on the Asian continent and ranks 13th in the world in the tourism sector. One of Singapore's state revenue generated in the tourism sector is 14.8%. More than 16 million foreign tourists come to Singapore every year. However, the number of tourist visits has increased and decreased every month. Changes in the value of the fluctuations can lead to less than the maximum tourism industry in Singapore, especially the infrastructure sector. Because the accommodations are limited in accommodating a large number of tourists. Therefore, a prediction of the number of tourist visiting to the country of Singapore is needed, so that it becomes material for consideration in preparing better accommodation. One prediction algorithm that can be used is Extreme Learning Machine (ELM). From the results of the research that has been done, the optimal algorithm parameters on ELM are, the number of feature data = 5, the ratio of training data and testing data = 80%: 20%, and hidden neuron = 10 with the data on the number of tourist visiting in January to December from 2010 to 2016, the error value obtained using MAPE was 7.41%.
Prakiraan Penggunaan Volume Air PDAM Kota Malang Menggunakan Metode Support Vector Regression dengan Ant Colony Optimization Akmilatul Maghfiroh; Agus Wahyu Widodo; Yuita Arum Sari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (508.718 KB)

Abstract

Water is a very important element in the life of all living things. Humans need water to survive and carry out daily activities. Regional Drinking Water Company (PDAM) is a Regional-Owned Company (BUMD) that provides clean water for every region in Indonesia, one of them is PDAM in Malang. The population continues to grow every year causing an increase in the need for clean water. Considering the increasing need for clean water and limited water sources, PDAM Malang must distribute the water optimally and efficiently so that consumers can fulfill their needs for clean water. Therefore, by forecasting the water volume that can be used, it is hoped that it can help PDAM Malang to estimate the volume of water that needs to be distributed efficiently and on target. Some water forecasting methods such as "An Enhanced Differential Evolution Based Gray Model For Forecasting Urban Water Consumption" has a pretty good MAPE value of 2,285%. Then for the SVR-ACO method used in the research "Support Vector Regression and Ant Colony Optimization for HVAC Cooling Load Prediction" has an NMSE of 0.241.
Penerapan Metode Extreme Learning Machine Untuk Prediksi Konsumsi Batubara Sektor Pembangkit Listrik Tenaga Uap Rosintan Fatwa; Imam Cholissodin; Yuita Arum Sari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 11 (2019): November 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (420.754 KB)

Abstract

PLTU is a power station that utilizes coal as fuel. The PLTU sector is a dominant sector in absorbing domestic coal. During the period 2010 - 2015, coal consumption continued to increase along with the 35,000 MW power plant project which was designed in the 2015-2019 period, 19,940 MW (56%) was a coal-fired power plant. Based on data from the Director General of Mineral and Coal at the Ministry of Energy and Mineral Resources, said that the increase in coal consumption is due to the growing PLTU and the economic development which is directly proportional to the increase in national coal consumption. Based on these problems, the prediction of coal consumption in the power plant sector is needed so that coal consumption can be controlled in accordance with its production. In this study, the prediction process is carried out in several processes, namely data normalization, prediction calculation using Extreme Learning Machine, data denormalization, and error values ​​using MAPE. Based on the results of tests conducted on daily coal consumption data for 2018 at the Tanjung Jati B PLTU Unit 1 & 2 obtained the smallest MAPE value of 6.603% with many features 2, the number of hidden neurons as much as 4, and the comparison of the percentage of training data and testing data 70 %: 30% using the Sigmoid activation function.
Co-Authors Achmad Arwan Achmad Dinda Basofi Sudirman Ade Kurniawan Adella Ayu Paramitha Adi Mashabbi Maksun Adinugroho, Sigit Agus Wahyu Widodo Ahmad Efriza Irsad Ahmad Fauzi Ahsani Akbar Imani Yudhaputra Akhmad Muzanni Safi'i Akhmad Rohim Akmilatul Maghfiroh Alip Setiawan Amalia Safitri Hidayati Amelia Kosasih Andina Dyanti Putri Anggita Mahardika Ani Enggarwati Arrizal Amin Barbara Sonya Hutagaol Bayu Rahayudi Berlian Bidari Ratna Sari B Binti Najibah Agus Ratri Budi Darma Setiawan Cahya Chaqiqi Candra Dewi Chindy Putri Beauty Dea Valentina Delischa Novia Sabilla Destin Eva Dila Purnama Sari Devinta Setyaningtyas Atmaja Dhimas Anjar Prabowo Dian Eka Ratnawati Dika Perdana Sinaga Dyva Agna Fauzan Edy Santoso Eka Dewi Lukmana Sari Eka Novita Shandra Fachrul Rozy Saputra Rangkuti Fadhil Yusuf Rahadika Fajar Pradana Fakhruddin Farid Irfani Faraz Dhia Alkadri Farid Rahmat Hartono Fatwa Reza Rizqika Febriana Ranta Lidya Fida Dwi Febriani Fira Sukmanisa Fitra Abdurrachman Bachtiar Fitria Indriani Frisma Yessy Nabella Gabriel Mulyawan Gagas Budi Waluyo Galuh Fadillah Grandis Gregorius Ivan Sebastian Hafid Satrio Priambodo Hamim Fathul Aziz Haris Bahtiar Asidik Ian Lord Perdana Ibnu Rasyid Wijayanto Imam Cholissodin Imam Cholissodin Inas Istiqlaliyyah Indriati Indriati Irma Pujadayanti Ivan Ivan Juniman Arief Karunia Ayuningsih Kenza Dwi Anggita Kresentia Verena Septiana Toy Kukuh Wiliam Mahardika Lita Handayani Tampubolon M. Ali Fauzi M. Ali Fauzi Mala Nurhidayati Marji Marji Moch Alyur Ridho Moch. Ali Fauzi Mohammad Rizky Hidayatullah Muh. Arif Rahman Muhammad Abdan Mulia Muhammad Bima Zehansyah Muhammad Faiz Al-Hadiid Muhammad Rizky Setiawan Muhammad Sanzabi Libianto Muhammad Tanzil Furqon Muhammad Zaini Rahman Nadhif Sanggara Fathullah Noerhayati Djumaah Manis Nova Amynarto Novan Dimas Pratama Novanto Yudistira Nugroho Dwi Saksono Nur Aisyah Asriani Ofi Eka Novyanti Panji Gemilang Panji Prasuci Saputra Pretty Natalia Hutapea Putra Pandu Adika Putra Pandu Adikara Putri Harnis Raditya Rinandyaswara Randy Cahya Wihandika Randy Ramadhan Rasif Nidaan Khofia Ahmadah Ratih Kartika Dewi Ratna Tri Utami Refi Fadholi Renaza Afidianti Nandini Rendi Cahya Wihandika Restu Amara Rezza Pratama Rhevitta Widyaning Palupi Rifki Akbar Siregar Rizky Ardiawan Rizky Maulana Iqbal Rosintan Fatwa Safira Dyah Karina San Sayidul Akdam Augusta Sarah Najla Adha Sarah Yuli Evangelista Simarmata Sigit Adi Nugroho Sigit Adinugroho Sinta Kusuma Wardani Sulaiman Triarjo Supraptoa Supraptoa Sutrisno Sutrisno Tibyani Tibyani Tri Rahayuni Tuahta Ramadhani Utaminingrum, Fitri Vriza Wahyu Saputra Wahyuni Lubis Willy Karunia Sandy Yosua Dwi Amerta