Yuita Sari | University of Brawijaya (original) (raw)
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JUTI: Jurnal Ilmiah Teknologi Informasi, 2014
Fitur yang digunakan untuk mengenali jenis daun meliputi bentuk, warna, dan tekstur. Tidak semua ... more Fitur yang digunakan untuk mengenali jenis daun meliputi bentuk, warna, dan tekstur. Tidak semua jenis fitur perlu digunakan untuk melakukan komputasi hasil ektraksi, namun perlu diseleksi beberapa fitur yang paling berpengarauh dalam sistem temu kembali citra daun. Teknik seleksi fitur Correlation based Featured Selection (CFS) digunakan untuk melakukan pemilihan fitur berdasarkan korelasi antar fitur, sehingga dapat meningkatkan performa dari sistem temu kembali citra daun. Jenis seleksi fitur yang digunakan diantaranya menggunaka CFS, CFS dengan Genetic Search (GS), dan chi square. Analisis keterkaitan korelasi antar fitur melalui seleksi fitur juga dikombinasikan dengan penggunaan kedekatan dalam menghitung similaritas pada sistem temu kembali. Penggunaan kedekatan dengan Lp norm, manhattan, euclidean, cosine, dan mahalanobis. Hasil penelitian ini menunjukkan nilai temu kembali paling tinggi ketika menggunakan seleksi fitur CFS dengan pengukuran kedekatan mahalanobis.
Emotion detection is an application that is widely used in social media for industrial environmen... more Emotion detection is an application that is widely used in social media for industrial environment, health, and security problems. Twitter is ashort text messageknown as tweet. Based on content and purposes, the tweet can describes as information about a user‟s emotion. Emotion detection by means oftweet, is a challenging problem because only a few features can be extracted. Getting features related to emotion is important at the first phase of extraction, so the appropriate features such as a hashtag, emoji, emoticon, and adjective terms are needed. We propose a new method for analyzing the linkages among features and reducedsemantically using Non- Negative Matrix Factorization (NMF). The dataset is taken from a Twitter application using Indonesian language with normalization of informal terms in advance. There are 764 tweets in corpus which have five emotions, i.e. happy (senang), angry (marah), fear (takut), sad (sedih), and surprise(terkejut). Then, the percentage of user‟s emotion is computed by k- Nearest Neighbor(kNN) approach. Our proposed model achieves the problem of emotion detectionwhich is proved by the result near ground truth.
JUTI: Jurnal Ilmiah Teknologi Informasi, 2014
Fitur yang digunakan untuk mengenali jenis daun meliputi bentuk, warna, dan tekstur. Tidak semua ... more Fitur yang digunakan untuk mengenali jenis daun meliputi bentuk, warna, dan tekstur. Tidak semua jenis fitur perlu digunakan untuk melakukan komputasi hasil ektraksi, namun perlu diseleksi beberapa fitur yang paling berpengarauh dalam sistem temu kembali citra daun. Teknik seleksi fitur Correlation based Featured Selection (CFS) digunakan untuk melakukan pemilihan fitur berdasarkan korelasi antar fitur, sehingga dapat meningkatkan performa dari sistem temu kembali citra daun. Jenis seleksi fitur yang digunakan diantaranya menggunaka CFS, CFS dengan Genetic Search (GS), dan chi square. Analisis keterkaitan korelasi antar fitur melalui seleksi fitur juga dikombinasikan dengan penggunaan kedekatan dalam menghitung similaritas pada sistem temu kembali. Penggunaan kedekatan dengan Lp norm, manhattan, euclidean, cosine, dan mahalanobis. Hasil penelitian ini menunjukkan nilai temu kembali paling tinggi ketika menggunakan seleksi fitur CFS dengan pengukuran kedekatan mahalanobis.
Emotion detection is an application that is widely used in social media for industrial environmen... more Emotion detection is an application that is widely used in social media for industrial environment, health, and security problems. Twitter is ashort text messageknown as tweet. Based on content and purposes, the tweet can describes as information about a user‟s emotion. Emotion detection by means oftweet, is a challenging problem because only a few features can be extracted. Getting features related to emotion is important at the first phase of extraction, so the appropriate features such as a hashtag, emoji, emoticon, and adjective terms are needed. We propose a new method for analyzing the linkages among features and reducedsemantically using Non- Negative Matrix Factorization (NMF). The dataset is taken from a Twitter application using Indonesian language with normalization of informal terms in advance. There are 764 tweets in corpus which have five emotions, i.e. happy (senang), angry (marah), fear (takut), sad (sedih), and surprise(terkejut). Then, the percentage of user‟s emotion is computed by k- Nearest Neighbor(kNN) approach. Our proposed model achieves the problem of emotion detectionwhich is proved by the result near ground truth.