Improving K-Nearest Neighbor Efficiency for Text Categorization

Barigou, Fatiha

doi:10.14311/nnw.2016.26.003

Cited by 9 publications

(6 citation statements)

References 26 publications

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“…Dapat disimpulkan model yang digunakan untuk mewakili dan memilih dokumen yang relevan didasarkan pada kata-kata. Namun, penggunaan kata-kata untuk mewakili isi dokumen masih menimbulkan dua masalah, yakni ambiguitas semantik kata-kata menyiratkan bahwa dokumentasi pelatihan yang tidak relevan, berbagi kata-kata yang sama dengan yang ingin kita klasifikasikan nanti dipilih bisa meningkatkan noise, berbeda dengan kasus diagnosa penyakit yang sudah jelas atribut yang digunakan merupakan hasil analisa pakar dan dari data rekam medis [5].…”

Section: Gambar 1 Grafik Jumlah Kasus Difteri Indonesiaunclassified

Case Based Reasoning Diagnosis Penyakit Difteri dengan Algoritma K-Nearest Neighbor

Fatoni¹,

Noviandha²

2018

citec

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Akhir tahun 2017, masyarakat Indonesia ramai dengan maraknya kematian pada anakanak dan orang dewasa akibat penyakit Difteri. Ditemukan sebanyak 12 orang meninggal dunia dari 318 kasus Difteri menurut catatan Dinas Kesehatan Jawa Timur. Padahal di tahun 2016 kasus Difteri di Jawa Timur tercatat sebanyak 4 orang meninggal dunia dari 209 kasus. Hal tersebut menjadi perhatian bagi pemerintah dan tercatat sebagai kejadian luar biasa (KLB). Kenaikan angka kasus Difteri ini disebabkan karena kurangnya kesadaran masyarakat akan pentingnya imunisasi. Semakin banyaknya kasus Difteri yang terjadi dan minimnya pengetahuan masyarakat tentang Difteri, maka dibutuhkan suatu sistem pakar yang mampu membantu masyarakat maupun pemerintah dalam mendiagnosis penyakit Difteri. Penelitian mengenai Difteri ini menggunakan metode algoritma K-Nearest Neighbour (K-NN) dimana dilakukan perhitungan similaritas pada kasus lama dengan kasus baru. Penelitian penyakit Difteri ini disempurnakan dengan menggunakan penalaran berbasis kasus atau Cased Based Reasoning (CBR) agar hasil diagnosis lebih akurat. Output dari penelitian ini yaitu berupa hasil diagnosa penyakit Difteri berdasarkan gejala-gejala yang dialami dengan hasil akurasi pengujiannya sebesar 95,17%.End of 2017, the people of Indonesia enlivened so many of deaths in children and adults due to Diphtheria. Found 12 people died from 318 cases of Diphtheria according to East Java Health Office records. Whereas in the year 2016 Diphtheria cases in East Java recorded and reported as many as 4 people died from 209 cases. It's of particular concern to government and is noted as an extraordinary event (KLB). The increase in the number of Diphtheria cases is due to a lack of public awareness of the importance of immunization. Increasing number of Diphtheria cases and the lack of public knowledge about Diphtheria, it needs an expert system capable of assisting the public and the government in diagnosing Diphtheria. This research on Diphtheria uses the K-Nearest Neighbors (K-NN) algorithm method in which a similarity case study in the old case with new cases is used. The research of Diphtheria disease is enhanced by using casebased reasoning or Cased Based Reasoning (CBR) to make the diagnosis more accurate. The output of this research is the result of diagnosis of Diphtheria disease based on the symptoms experienced by the result of the accuracy of the test is 95,17%.

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Section: Gambar 1 Grafik Jumlah Kasus Difteri Indonesiaunclassified

Case Based Reasoning Diagnosis Penyakit Difteri dengan Algoritma K-Nearest Neighbor

Fatoni¹,

Noviandha²

2018

citec

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“…Natural language processing has extensive research scope, which includes sentiment analysis, machine translation, text classification, semantic analysis, etc. [7][8][9]. As the practicality of sentiment analysis has gradually become a research hotspot in recent years.…”

Section: Related Workmentioning

confidence: 99%

A Method of Fine-Grained Short Text Sentiment Analysis Based on Machine Learning

Chang¹,

Huo²

2018

NNW

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Text sentiment analysis plays an important role in social network information mining. It is also the theoretical foundation and basis of personalized recommendation, circle of interest classification and public opinion analysis. In view of the existing algorithms for feature extraction and weight calculation, we find that they fail to fully take into account the influence of sentiment words. Therefore, this paper proposed a fine-grained short text sentiment analysis method based on machine learning. To improve the calculation method of feature selection and weighting and proposed a more suitable sentiment analysis algorithm for features extraction named N-CHI and weight calculation named W-TF-IDF, increasing the proportion and weight of sentiment words in the feature words Through experimental analysis and comparison, the classification accuracy of this method is obviously improved compared with other methods.

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“…The first research is about how K-Nearest Neighbor can identify efficiency in the selection of documents based on the categories taken in the document. It was concluded in the study that the model used to process and select similar documents was based on words, but the use of words to process document content still caused two problems, namely semantic confusion, words that implied that training documentation was inappropriate, shared the same words that we want to classify and then choose can increase noise, in contrast to cases of disease diagnosis that clearly the attributes used are the results of expert analysis and from medical record data [2]. The second study applied 60 medical record data on intestinal diseases taken from RSUD dr. Soetrasno Rembang with a scenario of 40 source cases and 20 target cases, it can be seen that the accuracy of the system diagnosis is 95%, but in this study there is no greater amount of data, and the attributes used are all related.…”

Section: Article Historymentioning

confidence: 99%

Expert system for diagnosing diphtheria with k-nearest neighbor method

Fatoni¹,

Utami²,

Wibowo³

2018

IJARLIT

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The Diphtheria cases have special concern by the Indonesian government and are recorded as an extraordinary case (KLB) in 2017. Diphtheria is an infectious disease and cause complications of dangerous and deadly diseases if have not any treated immediately. Along this time, the communities often underestimate the common symptoms of diseases, such as throat pain, flu, and fever. The similarity of Diphtheria symptoms with common diseases and complications such as myocarditis, obstruction on breath, Acute Kidney Injury (AKI), making Diphtheria are rather difficult to treat due to the infections spread quickly. Some complications of diphtheria can cause a death if have not treated immediately and there must be any identification early for diphtheria. Then, an expert system is needed to help the community and the government in diagnosing the diphtheria. An expert system is an information system containing knowledge from experts in order provide information to be used for consultation. The knowledge from experts in this particular system is used as a basis by the Expert System to answer the questions (consultation). The study used the K-Nearest Neighbor (KNN) method, which the method calculates the similarity value of Diphtheria disease symptom. As the result, it can provide an initial diagnosis for Diphtheria before complications occur. The output of this study is the diagnosis of diphtheria based on the symptoms with the accuracy results of 93.056%, as well as providing an initial diagnosis in order to have immediately treating the diphtheria.

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Improving K-Nearest Neighbor Efficiency for Text Categorization

Cited by 9 publications

References 26 publications

Case Based Reasoning Diagnosis Penyakit Difteri dengan Algoritma K-Nearest Neighbor

Case Based Reasoning Diagnosis Penyakit Difteri dengan Algoritma K-Nearest Neighbor

A Method of Fine-Grained Short Text Sentiment Analysis Based on Machine Learning

Expert system for diagnosing diphtheria with k-nearest neighbor method

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