Klasifikasi Berita Bahasa Indonesia Menggunakan Mutual Information dan Support Vector Machine

Irham, Lalu Gias; Adiwijaya, Adiwijaya; Wisesty, Untari Novia

doi:10.30865/mib.v3i4.1410

Cited by 16 publications

(25 citation statements)

References 15 publications

(20 reference statements)

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“…Hasil penelitian menunjukkan bahwa akurasi klasifikasi mengalami peningkatan sebesar 20.4% dengan menggunakan stopword removal, stemming, dan feature selection. Kemudian penelitian [6] melakukan pengujian teknik preprocessing dalam klasifikasi. Hasil pengujian menunjukkan bahwa penggunaan teknik preprocessing cleaning, case folding, dan stemming tanpa menggunakan stopword removal mampu meningkatkan akurasi sistem, dengan perolehan akurasi sebesar 94.24%.…”

Section: Pendahuluanunclassified

“…Berdasarkan hasil penelitian [5,6,7], menunjukkan bahwa preprocessing memiliki pengaruh yang cukup baik dalam meningkatkan kinerja sistem. Namun, dari penelitian sebelumnya tidak membahas mengenai pengaruh dari berbagai teknik preprocessing yang digunakan dan juga belum diketahui kombinasi preprocessing seperti apa yang menghasilkan kinerja analisis sentimen yang optimal.…”

Section: Pendahuluanunclassified

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Pengaruh Text Preprocessing terhadap Analisis Sentimen Komentar Masyarakat pada Media Sosial Twitter (Studi Kasus Pandemi COVID-19)

Khairunnisa

Adiwijaya

Faraby

2021

mib

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COVID-19 is a pandemic that is troubling many people. This has led to a lot of public comments on Twitter social media. The comments are used for sentiment analysis so that we know the polarity of the sentiment that appears, whether it is positive, negative, or neutral. The problem when using twitter data is that the tweet data still contains many non-standard words such as abbreviated writing due to the maximum limitation of characters that can be used in one tweet. Preprocessing is the most important initial stage in sentiment analysis when using Twitter data, because it affects the classification performance results. This study specifically discusses the preproceesing technique by performing several test scenarios for the combination of preprocessing techniques to determine which preprocessing technique produces the most optimal accuracy and its effect on sentiment analysis. Feature extraction using N-Gram and word weighting using TF-IDF. Mutual Information as a feature selection method. The classification method used is SVM because it is able to classify high-dimensional data according to the data used in this study, namely text data. The results of this study indicate that the best performance is obtained by using a combination of cleaning and stemming; and normalization of words, cleaning, and stemming with the same accuracy of 77.77%. the use of unigram results in higher accuracy compared to bigram. Mutual Information is able to reduce overfitting problems by reducing irrelevant features so that train and test accuracy is quite stable

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Section: Pendahuluanunclassified

Pengaruh Text Preprocessing terhadap Analisis Sentimen Komentar Masyarakat pada Media Sosial Twitter (Studi Kasus Pandemi COVID-19)

Khairunnisa

Adiwijaya

Faraby

2021

mib

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“…Hasil ini dibuktikan oleh beberapa penelitian sebelumnya mengenai klasifikasi teks juga antara lain, penelitian mengenai klasifikasi topik berita menggunakan SVM. Hasil penelitian ini menyimpulkan bahwa SVM sebagai metode klasifikasi memberikan hasil prediksi yang paling akurat dibandingkan dengan metode yang lainnya yaitu sebesar 94,24% [6]. Penelitian lainnya yaitu penelitian mengenai klasifikasi jenis tiket helpdesk menggunakan SVM, akurasi yang diperoleh cukup tinggi yaitu sebesar 81% [7].…”

Section: Pendahuluanunclassified

Klasifikasi Jenis Pantun Dengan Metode Support Vector Machines (SVM)

Irmanda¹,

Astriratma²

2020

RESTI

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This study aims to create a model for categorizing pantun types and analyze the accuracy of support vector machines (SVM). The first stage is collecting pantun that have been labeled with pantun category. The pantun categories consist of pantun for children, pantun for young people, and pantun for elder. After collecting data, the next stage is pre-processing. This pre-processing stage makes data ready to be processed on the extraction stage. The pre-processing stage consists of text segmentation, case folding, tokenization, stop word removal, and stemming. The feature extraction stage is intended to analyze potential information and represent terms as a vector. Separating training data and testing data is necessary to be conducted before the classification process. Then the classification process is done by using multiclass SVM. The results of the classification are evaluated to obtain accuracy and will be analyzed whether the classification model is proper to be used. The results showed that SVM classified the types of pantun with accuracy of 81,91%.

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“…Untuk mereduksi data dengan dimensi vektor yang tinggi perlu dilakukannya feature selection untuk menyeleksi kata atau fitur yang dianggap kurang relevan dalam pembentukan suatu model. Mutual Information (MI) digunakan sebagai metode feature selection karena memiliki kemampuan untuk menunjukan seberapa berpengaruh suatu fitur dalam melakukan prediksi yang tepat terhadap suatu kelas [7] [8].…”

Section: Pendahuluanunclassified

“…MI merupakan salah satu metode yang sudah digunakan secara luas sebagai untuk melakukan feature selection. MI mengukur berapa banyak informasi yang ada pada fitur, sehingga dapat diketuahui pengaruh fitur tersebut untuk membuat keputusan klasifikasi yang tepat [22] [7]. Rumus perhitungan nilai MI secara formal dapat dilihat pada persamaan (1).…”

Section: E Mutual Information (Mi)unclassified

Klasifikasi Multi Label pada Hadis Bukhari Terjemahan Bahasa Indonesia Menggunakan Mutual Information dan k-Nearest Neighbor

2020

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Hadith is the second source of law for Muslims after the Qur'an which comes from various forms of the words, actions and stipulations of the Prophet Muhammad or referred to as his sunnah. In order to make it easier for Muslims to apply the teachings of the hadiths, a classification system is needed that can categorize a hadith into a class or a combination of two of the three classes which called a multi-label classification. In building a text classification system, there are various classification techniques, one of which is k-Nearest Neighbor (KNN). KNN is a simple and effective classification method for text classification, but has a weakness in processing data with high vector dimensions so that the computation time is higher and the efficiency of text classification is very low. Mutual Information (MI) is used as a feature selection method to reduce vector dimensions because it has the ability to show how strong a feature is in making a correct prediction of a class. In this study Problem Transformation Method with the Binary Relevance (BR) approach is used so that the multi label classification process can be accomplished. The optimum results obtained in this study shows the value of hamming loss is 0.0886 or about 91.14% of data were correctly classified and computational time for 595 seconds by using MI as a feature selection, but without stemming.

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Klasifikasi Berita Bahasa Indonesia Menggunakan Mutual Information dan Support Vector Machine

Cited by 16 publications

References 15 publications

Pengaruh Text Preprocessing terhadap Analisis Sentimen Komentar Masyarakat pada Media Sosial Twitter (Studi Kasus Pandemi COVID-19)

Pengaruh Text Preprocessing terhadap Analisis Sentimen Komentar Masyarakat pada Media Sosial Twitter (Studi Kasus Pandemi COVID-19)

Klasifikasi Jenis Pantun Dengan Metode Support Vector Machines (SVM)

Klasifikasi Multi Label pada Hadis Bukhari Terjemahan Bahasa Indonesia Menggunakan Mutual Information dan k-Nearest Neighbor

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