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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 Penyakit Jantung Menggunakan Kombinasi Metode K-Nearest Neighbor dan Naive Bayes Syafitri Hidayatul Annur Aini; Yuita Arum Sari; Achmad Arwan
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

Heart disease is one of the non contagious diseases that can lead to death. This disease occurs because of the narrowing of blood vessels that cause impairment of heart function. The death rate that caused by a heart disease is continuing increase and according by the Ministry of Health of the Republic of Indonesia research, in 2030 it reach 23.3 million peoples. It should be anticipated because the number of cardiologists in Indonesia is still very minimal. This research proposes framework Information Gain selection features with combination K-Nearest Neighbor and Naive Bayes to overcome the problems on the effectiveness and accuracy in classification heart disease. Information Gain algorithm used for reduce variable dimention to get relevant variables. After Information Gain selection features process is completed, the next process is classify numeric atributes with KNN and categorical atributes with Naive Bayes. The results of this research indicate an accuracy of 92.31% when the class distribution testing is balanced using 6 features with value of K=25 and when the class distribution testing is not balanced using 4 features with value of K=35. Based on these results, can be concluded that features selection Information Gain with combination KNN and Naive Bayes algorithm can be used for classifying heart disease.
Analisis Sentimen pada Review Konsumen Menggunakan Metode Naive Bayes dengan Seleksi Fitur Chi Square untuk Rekomendasi Lokasi Makanan Tradisional Novan Dimas Pratama; Yuita Arum Sari; Putra Pandu Adikara
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

Consumer reviews at a restaurant are very influential in the quality of the restaurant itself. Many of the consumers pour critics or opinions through the internet media. The purpose of this study was to analyze the opinion sentiment from traditional food consumers as well as provide location recommendations with the desired keywords. Naive Bayes is a machine learning technique that is often used to classify text data. Chi Square is a feature selection used to calculate the level of a feature's dependencies on a class. In this study, Chi Square method gives value to the feature which is then sorted and selected according to percentage tested. Selected features are used for the classification process using the Naive Bayes method. The result of classification accuracy with 25% feature selection is 81%, with 50% feature selection is 80% and with 77% feature selection is 80%. From this test it can be concluded that feature selection is not so influential on the result value accuracy. It can be seen the difference of the accuracy value between using feature selection and without using a feature selection that is not very significant.
Pengenalan Emosi Berdasarkan Ekspresi Mikro Menggunakan Metode Local Binary Pattern Nova Amynarto; Yuita Arum Sari; Randy Cahya Wihandika
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 basic human emotions have been widely investigated cross-culturally, one of them by using facial expressions. Through the micro expression on the face can be known even one's psychological emotions. Basic expression is universal means that the child or the blind can know or form a basic expression. Micro expression is an expression that appears subtle and unconscious. Micro expression is very difficult or even can not be hidden. This research uses Local Binary Pattern (LBP) method to get features of facial micro expression and classification using K-Nearest Neighbor (K-NN) method to determine the emotion of micro expression. The result of k-value determination test on K-NN classification method shows that when k value 5 and 7 is able to recognize emotion based on micro expression with 56,03% accuracy. The result of determination test of R value and P value on LBP method showed an increase of accuracy in emotional recognition to 63,83%. The test results on the dimension of the image shows that the dimension of the image that produces the best accuracy is 200×200 pixels with an accuracy value of 63.83%. The observation using distance method on K-NN classification shows that Manhattan distance calculation method can increase accuracy in emotional recognition to 70.21%.
Pembangkitan Aturan Pengenalan Emosi Pada Twitter Menggunakan Metode Fuzzy-C Means Farid Rahmat Hartono; Yuita Arum Sari; Putra Pandu Adikara
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

In this digital era, social media users are growing more rapidly and more mediasocial applications. One of the most widely used social media today is Twitter, with users reaching over hundreds of millions of people in the world. Twitter is a mobile or desktop application where users can create an article that can reflect their emotions through a short text form status with a maximum of 140 characters. With so many active users up to now then on every status created by Twitter users can reflect their emotions. It takes a pesikolog to see an emotion from the status of people in social media because there is no automatic system to determine one's emotions through its status on Twitter. The system in this research is made using Fuzzy C-Means (FCM) method. The FCM method can be used to generate rules that can replace the role of a psychologist to determine a person's emotions from a status he or she creates on Twitter's social media. The Term Frequency & Invers Document Frequency (TF-IDF) weighting method in text mining is used to process textual data into numerical data to be able to be processed by FCM. Based on the test results, this system produces an highest accuracy of 70% so it can be concluded that the FCM method is good used in the formation of a person's emotional determination of a status on social media Twitter.
Sistem Temu Kembali Citra Lubang Jalan Aspal Berdasarkan Tingkat Kerusakan Menggunakan Ekstraksi Fitur Gray Level Co-occurrence Matrix Anggita Mahardika; Yuita Arum Sari; Candra Dewi
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

One factors of the road repair process that takes a long time caused by the process of recording the condition of road damage that is still done manually by human labor. Along with the development of technology, many research related to road damage detection system using digital image processing. The purpose of this research is to build a retrieval system of asphalt pavement image based on damage level. The process begins with pre-processing to get a segmented hole area. Furthermore, utilizing feature extraction of Gray Level Co-occurrence Matrix (GLCM) texture. Features used in this research are as many as 52 features derived from 13 features with angles 0o, 45o, 90o and 135o. Of the 52 features performed feature selection using Wrapper and CFS (Correlation Based Feature Selection) methods. Based on the results of the tests that have been done we get the image of 117 holes that successfully segmented successfully on the diameter of 101x101, = 75 and =75. Use of the Wrapper feature selection method gives higher average accuracy and MAP (Mean Average Precision) results than using the CFS feature selection method or not using feature selection. Accuracy and MAP resulting from Wrapper method with d = 1 respectively that is equal to 55.61% and 0.710.
Rekomendasi Lokasi Wisata Kuliner Menggunakan Metode K-Means Clustering Dan Simple Additive Weighting Nugroho Dwi Saksono; Yuita Arum Sari; Ratih Kartika Dewi
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

Culinary tour is one of the activities that are often done when visiting a city. the mistakes often made because lack of information regarding a location of a culinary tourism can cause a problem for tourists. The purpose of this study is to help the tourists in determining the location of culinary tourism that has the facilities in accordance with what they desired. K-Means Clustering is a method that groups data according to their clusters. Simple Additive Weighting (SAW) is a method for the ranking process by using a preference value. In this study, K-Means Clustering method will divide the location according to the distance calculated from the initial position of the user to the address of the location, then SAW method will sort which location best suit the user's wishes. The testing used 49 location data. The testing process is a accuracy test by comparing result from the system and results from 30 respondents. The results of the testing process is obtained an accuracy of 63.33% for very close category, 40% for near category, and 46,67% for medium category. This system can provide recommendations for very close categories with fairly accurate although for the near and medium category is still not accurate enough.
Prediksi Rating Otomatis pada Ulasan Produk Kecantikan dengan Metode Naive Bayes dan N-gram Irma Pujadayanti; Mochammad Ali Fauzi; Yuita Arum Sari
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

The rise of beauty products also pound Indonesia especially imported products. This has triggered intense competition between local and foreign beauty products industry players. Therefore, the need for innovation in their products. The large number of review data in various online sources is useful as a review material for producers to innovate their products. For Consumer the data is useful as information before buying the product. The review data is often also has not been accompanied by a rating that makes manufacturers have difficulty in categorizing into a certain sentiment. In this study helps to accelerate the categorization of reviews into sentiment in the form of rating. The system built on this research uses the naive bayes classification method and the addition of n-gram method to pre-processing. The use of n-grams including unigram, bigram and combination of unigram and bigram aims to improve the classification results. On testing the best result system in full pre-processing scenario on all n-grams. Accuracy of 50%, 93%, 93% unigram while the accuracy of bigram is 39%, 87%, 83% and the highest accuracy is a combination of 49%, 97%, 96% with tolerance 0, tolerance 1 and sentiment reviews. The results showed that the use of n-grams was enough effective in solving the problems in the study.
Klasifikasi Berita pada Twitter Menggunakan Metode Naive Bayes dan Query Expansion Hipernim-Hiponim Fakhruddin Farid Irfani; Mochammad Ali Fauzi; Yuita Arum Sari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The large number of posted tweets resulted in scattered tweets and appearing on the Twitter homepage very diverse and not classified by categories such as health, sports, technology, economics, tourism and so on. The absence of categorization causes the user difficulty to read or retrieve information related to certain desired categories. Solution that can be done is by the method of text classification, which in the process of classification is able to classify automatically against some categories on unstructured text with natural language. In this research will be done classification process using Naive Bayes method with additional query expansion to add term in initial document. The addition of term aims to optimize the classification process because the tweet is a short text that can lead to ambiguity of classification classi. The additions made are hyponym and hypernym from original documents extracted from WordNet. Accuracy calculation method used is k-fold that aims to test the robustness of system. The accuracy obtained was 72% for the classification without query expansion, 65.75% for hyponym and hypernym addition, 66.3% for hyponym addition, and 67.5% for hypernym addition. It can be concluded that the addition of queries made less effective to improve the accuracy of the classification process.
Analisis Sentimen Opini Film Menggunakan Metode Naive Bayes dengan Ensemble Feature dan Seleksi Fitur Pearson Correlation Coefficient Fachrul Rozy Saputra Rangkuti; Mochammad Ali Fauzi; Yuita Arum Sari; Eka Dewi Lukmana Sari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Microblogging has become the media information that is very popular among internet users. Therefore, the microblogging became a source of rich data for opinions and reviews especially on movie reviews. We proposed, sentiment analysis on movie review using ensemble features and Bag of Words and selection Features Pearson's Correlation to reduce the dimension of the feature and get the optimal feature combinations. Use the feature selection is done to improve the performance of the classification, reducing the dimension of the feature and get the optimal feature combinations. The process of classification using several models of Naive Bayes i.e. Bernoulli Naive Bayes for binary data , Gaussian Naive Bayes for continuous data and Multinomial Naive Bayes for numeric data. The results of this study indicate that by using the non-standard word on tweet evaluation results obtained accuracy 82%, precision 86%, recall 79.62% and f-measure 82.69% using Feature Selection 20%. Then after using manual standardization of word the evaluation results on the accuracy increased by 8% and then the accuracy becomes 90%, precision 92%, recall 88.46% and f-measure 90.19% using 85% feature selection. Based on these results it can be concluded that by using the standardization of word can improve the performance of classification and feature selection Pearson's provide optimal feature combinations and reducing the total number of dimensions feature.
Perolehan Informasi Rating Buku Berdasarkan Gambar Sampul Buku Menggunakan Metode Scale-Invariant Feature Transform Hamim Fathul Aziz; Yuita Arum Sari; Randy Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 1 (2019): Januari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Along with the development of technology, almost kinds of all information are available on media online. The expanding technology are expected to make it easier to access all kinds of information in media online. As an example by searching book's rating automatically in media online by utilizing book cover. With the existence of a system that can find the book's rating by using the image on the cover getting from camera phone hopefully can make it easier and make it more fast to get rating information from the book, so it can make the customer do less mistaken when buying a book. Based on explanation above this research will uses scale-invariant feature transform to recognize the book's object in an image. Before find the appropriate image of the book; First, preprocessing will be doing on the image by searching for scale space; Then, find the key point or key point localization; Next, by calculation of pixel's angle or orientation assignment; Finally, transforming of the image descriptor or key point descriptor. On this research the image will be tested of this effect on light intensity, image rotation, and scaling. The result by matching test the image of book's cover using scale-invariant feature transform method has high accuracy in condition of bright light intensity and it has low accuracy when using image rotation and image scaling. The average accuracy can obtain in bright light conditions, rotation, and scaling are 90%, 57.5%, and 46.6% respectively.
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