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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

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Seleksi Fitur Information Gain untuk Klasifikasi Informasi Tempat Tinggal di Kota Malang Berdasarkan Tweet Menggunakan Metode Naive Bayes dan Pembobotan TF-IDF-CF Ahmad Efriza Irsad; Yuita Arum Sari; M. Ali Fauzi
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

Malang city is a city that has a significant increase in population, which is around 50 thousands people in just period of 5 years. One of the reasons is because Malang city is a city of education, the reasons its called city of education is because in this City there are a lot of public university and private university that are quite popular, such as Universitas Brawijaya (UB), Universitas Islam Malang (Unisma), etc. This resulted many migrants from outside the area of Malang city study in Malang city. There are some things that might be the reasons why migrants choose Malang city, such as the Malang city have one of the best quality university in Indonesia. When becoming a migrant, the most needed thing is certainly a place to live in a long term, because of that the migrants need information on where to live in the form of boarding house or rent house to live in, we can get this kind of information trough social media like Twitter, but on Twitter there is still no category for this kind of information. By seeing this problem, we can use Classification technique to classified the information in the form of living quarters in the city of Malang. In this study Naive Bayes method is used as the classification method, and Information gain as the feature selection method. Before entering the classification process the weighting is done first using TF-IDF-CF method. This study uses 150 training data and 60 testing data. The highest accuracy value in this study are 71,66% using 33% of feature, using TF-IDF-ICF weighthing and, without using number feature.
Pengenalan Citra Jenis Makanan Menggunakan Klasifikasi Naive Bayes Dengan Ekstraksi Fitur Hue Saturation Intensity Color Moments Dan Morphological Shape Descriptors Ian Lord Perdana; Yuita Arum Sari; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 6 (2019): Juni 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The process of determining some kind of food become important because it will determine the food that will be processed in the system that recorded the food for health and diet purpose. The process of determining food consist of preprocessing process and then changing the color model of the image from RGB to HSI. The next process is color feature extraction with Color Moment method that will generate the mean feature, standard deviation feature, and skewness feature from every color channel. Then, for shape feature extraction will using Morphological Shape Descriptors that will generate the length feature, width feature, diameter feature, and aspect ratio feature from the image. After the feature get extracted from the image, do the classification process with Naive Bayes Method with the help of LogSumExp for the probability calculation. The result in the testing of the effect of testing data generate 78% accuracy value when using 100 testing data. The result in the testing the effect of image dimension generate 81% accuracy value when using 300x300 pixel image for testing. The result in the testing the effect of number feature used generate 83% accuracy value when using feature from Color Moment only. The conclusion is, the feature extraction Color Moment and Morphological Shape Descriptors with Naive Bayes classification can be used to determine the kind of some food.
Klasifikasi Citra Makanan Menggunakan HSV Color Moment dan Haralick Feature Extraction dengan Naive Bayes Classifier Gabriel Mulyawan; Yuita Arum Sari; Muhammad Tanzil Furqon
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 6 (2019): Juni 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

As living things, humans need to survive. One of the basic need human's bodies require to survive is food. Foods provide nutrients that contain carbohydrates, protein, minerals, fats, and vitamins for boosting endurance. Basically, foods can be easily identified with human's eyes. But it is not like the brain-computer that require the introduction or features extraction from food objects for classification. The features extraction used are HSV Color Moment for color features and Haralick for texture features. Then, the results of the features extraction will be classified using the Naive Bayes classifier method. The data set used are based of the primary data that contains pictures and the pictures were taken by the smartphone camera consist of 276 foods images.. This research uses 2 testing processes, that are the comparison of the amount of the training data and testing data, and the testing of the used features. Based on the testing of the comparison of the amount of the training data and the training data using K-Fold Cross Validation, it showed that the best accuracy is 61,95% that using 166 training data images and 110 training data images. Then, the accuracy from the features test that was just using the HSV Color Moment feature is about 57,66%. The accuracy from test that using the Haralick feature is 36,67%. The accuracy from the combination of 2 features of the HSV Color Moment and Haralick are better than only using the texture features with the 56,33% accuracy. The image processing technique using HSV Color Moment and Haralick features extraction can be used for foods image classification using the Naive Bayes classifier method.
Temu Kembali Citra Makanan Menggunakan Color Histogram Dan Local Binary Pattern Chindy Putri Beauty; Yuita Arum Sari; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 6 (2019): Juni 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Technology makes it easier for humans. One of them is the ease of finding food on the internet. However, in general, search engines are available using text queries with image file names, a textual based approach. It is difficult to be done on large-scale imagery. Image search based on visual image content commonly known as image retrieval system based on content or Content Based Image Retrieval (CBIR) can be used as a solution. Food image has different colors and textures. The texture feature extraction method used in this research is Local Binary Pattern (LBP) and for the color feature extraction is Color Histogram. The image used is 444 data, 413 data as data training and 31 data as data testing. Based on feature extraction, similarity can be calculate using Euclidean distance. The result get by calculating Mean Average Precision (MAP). The best MAP obtained when the n value is 2 with MAP 0,919354 which n is the number of document that displayed on result. For the feature comparison testing, the use of color features only provides better results than using the texture feature or both features
Prediksi Rating pada Ulasan Produk Kecantikan menggunakan Metode SO-CAL in an Inheritance-based Lita Handayani Tampubolon; Mochammad Ali Fauzi; Yuita Arum Sari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 6 (2019): Juni 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The development of technology as an access to information about beauty products offered through internet is getting faster, especially about the review of beauty products that can help manufacturers to find out feedback about products from users, and help consumers to choose the appropriate beauty products easily. The product user can provide ratings and reviews on the sites that have been provided. Sometimes manufacturers and consumers have difficulty in differentiating and categorizing reviews into a rating as a determinant of the quality of a product. Therefore, a system is needed to simplify the right prediction of consumers or users of products on beauty products. In this study, a system was built using the calculation of the SO-CAL in an Inheritance-based method which applied on the K-NN algorithm and linear regression in the rating prediction. Results shows that the study using the SO-CAL in Inheritance-based method by testing using the Cross Validation / k-fold method obtained the average linear regression accuracy of 66% while the highest average accuracy of k-NN is 50% at Tolerance testing model 1. The average RMSE results in linear regression is 1.3628 while the k-NN algorithm is 2.1314. Hence, it can be concluded that the SO-CAL in Inheritance-based method is preferably applied to linear regression compared to the k-NN algorithm in the predicted rating.
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.
Pengenalan Citra Makanan Tradisional menggunakan Fitur Hue Saturation Value dan Fuzzy k-Nearest Neighbor Refi Fadholi; Yuita Arum Sari; Fitra Abdurrachman Bachtiar
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

Traditional food and snacks are typical of ancestors usually used for events or traditions. Some people consider that traditional food and snacks are obsolete, so that more traditional food and snacks are left behind by the people, and begin to shift to modern life, whereas traditional snacks themselves are one form of ancestral heritage that must preserved by next generation. Making system that can identify traditional types of food can be done using digital image processing. In this study, the image data used the result of image after segmentation with three types of data on testing, that is 300 images of the best segmentation result, 300 data of images with the amount of data each class is almost same and 400 data of images with the best segmentation results. Image feature used Hue Saturation Value (HSV) that contains mean, standard deviation, skewness and curtosis each color dimensions. Classification using the Fuzzy k-NN method and k-Fold Cross Validation. The test results based on values of k (k-Fold) and k (k-NN) obtained highest average accuracy at 53.33%. The test results also show that high color similarity between classes, poor image quality data and uneveness amount of data makes the test result decreased.
Segmentasi Citra Kue Tradisional menggunakan Otsu Thresholding pada Ruang Warna CIE LAB Putri Harnis; Yuita Arum Sari; Muh. Arif Rahman
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

Traditional cakes are one of the cultural products that deserve to be treated equally like other cultural assets, including by acknowledging them and preserving their existence. One way is to reintroduce traditional food through digital image processing technology. Taking an image allows us to get the information contained in it. Image segmentation is a technique developed to extract information contained in images which can be used as a reference source. This study proposes the application of traditional cake image segmentation using otsu thresholding method in the CIE LAB color space as extraction of color features. RGB images are used as initial images which are converted into LAB images, each LAB channel then converted into an otsu image. The results of the otsu thresholding image will be compared with the image of ground truth. The results of the study showed that the test values on groundtruth images have different effects on spesific colour of each channel. Of the three channels tested, channel A has the highest accuracy value of 89.65% as well as its specificity and sensitivity values of 87.825 and 95.818%. This indicates that channel A is a channel that can be used on common objects for segmentation with good results than L and B channels.
Algoritme Enhanced K-Means dengan Ekstraksi Fitur Local Binary Pattern dan Color Moment untuk Pengelompokan Citra Makanan Mohammad Rizky Hidayatullah; 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 source of our energy for doing our daily activities. Food have each color and texture for their identity. Using color and texture from the food, we can feel the taste in our mind while we see that food. In this paper, we want to know about what information we can get with color and texture of food. To do that, we use clustering to see how color and texture can show any information like nutrition inside the food. We used Enhanced K-means for grouping food image because we want to get a consistent results cause in Enhanced K-means, the initialization didn't use random data. The food image is grouping by color and texture cause they are two thing who can increase someones appetite. To get the color feature we used Color Moment and for texture feature we used Local Binary Pattern. For the result of evaluation using Coefficient Silhouette (CS) and Davies-Boulding Index (DBI), clustering using color texture get best result with DBI score is 0.957 and Silhouette score is 0.399 whereas when we use color and texture, result for DBI score is just 1.058 and Silhouette score is 0.31.
Klasifikasi Jenis Citra Makanan menggunakan Color Histogram dan Gray Level Co-occurrence Matrix dengan K-Nearest Neighbour Hafid Satrio Priambodo; Yuita Arum Sari; Agus Wahyu Widodo
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

The habit of consuming food irregularly is one factor increasing health risks. One solution to make it easier for the public to know, record and monitor the types of food consumed is to create an intelligent system. To support this solution, research is conducted to identify the type of food that will be consumed. The initial stage in making an introduction is to classify these foods. The classification process is done from the value of the feature extraction used. The introduction process begins with the image preproccessing process which is then performed a feature extraction. Feature extraction used is Color Histogram and Gray Level Co-occurrence Matrix. In feature extraction using the Color Histogram using 3 colors namely red, green, blue with each color having the mean, standard deviation, and skewness features. In addition, feature extraction with the Gray Level Co-occurrence Matrix has 6 types of features such as contrast, dissimilarity, homogeneity, angular second moment, energy, and entropy with the angle of taking pixel values ​​0o, 45 o, 90 o, and 135 o. The method applied to classify the value of the feature extraction results can use is the K-Nearest Neighbor method. The results of the average accuracy produced by these methods amounted to 93.33%. This proves that the methods used in this study are able to classify the types of food images.
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