scholarly journals Klasifikasi Ujaran Kebencian pada Cuitan dalam Bahasa Indonesia

2019 ◽  
Vol 10 (2) ◽  
pp. 164
Author(s):  
Kevin Antariksa ◽  
Y. Sigit Purnomo WP ◽  
Ernawati Ernawati
Keyword(s):  
Logistic Regression ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bahasa Indonesia

Banyaknya ujaran kebencian yang ada di media sosial sudah membuat jengah. Ujaran kebencian tersebut makin marak dijumpai namun masih belum ada upaya preventif dari media sosial untuk menangkal ujaran kebencian. Deteksi ujaran kebencian yang sudah dibuat juga belum tersedia dalam Bahasa Indonesia. Pada tugas akhir ini, akan dibuat model pembelajaran mesin yang dapat mengenali ujaran kebencian dengan Bahasa Indonesia. Dalam model tersebut akan membandingkan beberapa metode-metode pembelajaran mesin yang ada. Metode yang digunakan dalam pengujian adalah Naïve Bayes, SVM, dan Logistic Regression. Dalam pengujian, beberapa parameter akan diubah-ubah sehingga didapatkan nilai paling maksimal dalam deteksi ujaran kebencian. Hasil yang diharapkan adalah sebuah model pembelajaran mesin. Model tersebut diharapkan dapat mengenali ujaran kebencian berbahasa Indonesia secara akurat. Akurasi yang diharapkan adalah sebesar >85%.Kata Kunci: Deteksi ujaran kebencian, model pembelajaran mesin, media sosial, Bahasa Indonesia, cuitan

PREDIKSI JURUSAN PADA SELEKSI NASIONAL MASUK PERGURUAN TINGGI NEGERI (SNMPTN) MENGGUNAKAN METODE KLASIFIKASI NAÏVE BAYES

2020 ◽  
Vol 4 (1) ◽  
pp. 55
Author(s):  
Herliyani Hasanah ◽  
Nugroho Arif Sudibyo ◽  
Edy Kurniawan
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Bahasa Indonesia

SNMPTN merupakan salah satu jalur masuk perguruan tinggi negeri yang banyak diminati siswa karena hanya menggunakan parameter nilai raport, prestasi siswa, dan prestasi sekolah. Setiap jurusan mempunyai nilai diterima minimal yang berbeda-beda, besaran kuota yang ditetapkan LTMP 2019 minimal hanya 20% dari daya tampung program studi di setiap perguruan tinggi negeri. Besarnya minat siswa dan kecilnya jumlah kuota tidak sebanding sehingga menyebabkan persaingan diterima pada jalur ini semakin ketat. Namun, masih banyak siswa yang belum mempertimbangkan parameter tersebut saat mendaftar sehingga kemungkinan diterima pada jalur SNMPTN semakin kecil. Oleh karena itu diperlukan sebuah sistem yang dapat memprediksi kemungkinan diterimanya siswa pada jurusan SNMPTN berdasarkan atribut yang sudah ditentukan. <em>Naïve Bayes</em> diterapkan untuk mencari nilai probabilitas terbesar dalam setiap variabel yang ada. Variabel yang digunakan meliputi nilai rata-rata matematika, bahasa indonesia, dan bahasa inggris semester 1 sampai 5 serta prestasi siswa yang dilampirkan saat mendaftar dan prestasi sekolah. Hasilnya dengan <em>naïve bayes</em> mampu menghasilkan akurasi sebesar 83,3%.

scholarly journals Determining the Eligibility of Providing Motorized Vehicle Loans by Using the Logistic Regression, Naive Bayes and Decission Tree (C4.5)

2020 ◽  
Vol 1641 ◽  
pp. 012061
Author(s):  
Harsih Rianto ◽  
Amrin ◽  
Rudianto ◽  
Omar Pahlevi ◽  
Paramita Kusumawardhani ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Naive Bayes ◽  
Naïve Bayes

scholarly journals Evaluation of Prognosis in Nasopharyngeal Cancer Using Machine Learning

2020 ◽  
Vol 19 ◽  
pp. 153303382090982
Author(s):  
Melek Akcay ◽  
Durmus Etiz ◽  
Ozer Celik ◽  
Alaattin Ozen
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Naive Bayes ◽  
Nasopharyngeal Cancer ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Tumor Diameter ◽  
Survival Prognosis ◽  
Data Set

Background and Aim: Although the prognosis of nasopharyngeal cancer largely depends on a classification based on the tumor-lymph node metastasis staging system, patients at the same stage may have different clinical outcomes. This study aimed to evaluate the survival prognosis of nasopharyngeal cancer using machine learning. Settings and Design: Original, retrospective. Materials and Methods: A total of 72 patients with a diagnosis of nasopharyngeal cancer who received radiotherapy ± chemotherapy were included in the study. The contribution of patient, tumor, and treatment characteristics to the survival prognosis was evaluated by machine learning using the following techniques: logistic regression, artificial neural network, XGBoost, support-vector clustering, random forest, and Gaussian Naive Bayes. Results: In the analysis of the data set, correlation analysis, and binary logistic regression analyses were applied. Of the 18 independent variables, 10 were found to be effective in predicting nasopharyngeal cancer-related mortality: age, weight loss, initial neutrophil/lymphocyte ratio, initial lactate dehydrogenase, initial hemoglobin, radiotherapy duration, tumor diameter, number of concurrent chemotherapy cycles, and T and N stages. Gaussian Naive Bayes was determined as the best algorithm to evaluate the prognosis of machine learning techniques (accuracy rate: 88%, area under the curve score: 0.91, confidence interval: 0.68-1, sensitivity: 75%, specificity: 100%). Conclusion: Many factors affect prognosis in cancer, and machine learning algorithms can be used to determine which factors have a greater effect on survival prognosis, which then allows further research into these factors. In the current study, Gaussian Naive Bayes was identified as the best algorithm for the evaluation of prognosis of nasopharyngeal cancer.

scholarly journals Diagnosis of Diabetes Mellitus in Women of Reproductive Age using the Prediction Methods of Naive Bayes, Discriminant Analysis, and Logistic Regression

2021 ◽  
Vol 6 (2) ◽  
pp. 96-104
Author(s):  
Yulia Resti ◽  
Endang Sri Kresnawati ◽  
Novi Rustiana Dewi ◽  
Des Alwine Zayanti ◽  
Ning Eliyati
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Model Validation ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Reproductive Age ◽  
Training Data ◽  
Diabetes Diagnosis ◽  
Women Of Reproductive Age ◽  
Diabetes Status

Diabetes is a chronic disease that can cause serious illness. Women are four times more likely to develop heart problems caused by diabetes. Women are also more prone to experience complications due to diabetes, such as kidney problems, depression, and decreased vision quality. Nearly 200 million women worldwide are affected by diabetes, with two out of five affected by the disease being women of reproductive age. This paper aims to predict women with at least 21 years of age having diabetes based on eight diagnostic measurements using the statistical learning methods; Multinomial Naive Bayes, Fisher Discriminant Analysis, and Logistic Regression. Model validation is built based on dividing the data into training data and test data based on 5-fold cross-validation. The model validation performance shows that the Gaussian Naïve Bayes is the best method in predicting diabetes diagnosis. This paper’s contribution is that all performance measures of the Multinomial Naïve Bayes method have a value greater than 93 %. These results are beneficial in predicting diabetes status with the same explanatory variables.

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