scholarly journals COMPARING NAIVE BAYES, K-NEAREST NEIGHBOR, AND NEURAL NETWORK CLASSIFICATION METHODS OF SEAT LOAD FACTOR IN LOMBOK OUTBOUND FLIGHTS

2019 ◽  
Vol 16 (2) ◽  
pp. 187
Author(s):  
Mega Luna Suliztia ◽  
Achmad Fauzan
Keyword(s):  
Neural Network ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Load Factor ◽  
Classification Methods ◽  
Affecting Factors ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Ticket Prices

Classification is the process of grouping data based on observed variables to predict new data whose class is unknown. There are some classification methods, such as Naïve Bayes, K-Nearest Neighbor and Neural Network. Naïve Bayes classifies based on the probability value of the existing properties. K-Nearest Neighbor classifies based on the character of its nearest neighbor, where the number of neighbors=k, while Neural Network classifies based on human neural networks. This study will compare three classification methods for Seat Load Factor, which is the percentage of aircraft load, and also a measure in determining the profit of airline.. Affecting factors are the number of passengers, ticket prices, flight routes, and flight times. Based on the analysis with 47 data, it is known that the system of Naïve Bayes method has misclassifies in 14 data, so the accuracy rate is 70%. The system of K-Nearest Neighbor method with k=5 has misclassifies in 5 data, so the accuracy rate is 89%, and the Neural Network system has misclassifies in 10 data with accuracy rate 78%. The method with highest accuracy rate is the best method that will be used, which in this case is K-Nearest Neighbor method with success of classification system is 42 data, including 14 low, 10 medium, and 18 high value. Based on the best method, predictions can be made using new data, for example the new data consists of Bali flight routes (2), flight times in afternoon (2), estimate of passenger numbers is 140 people, and ticket prices is Rp.700,000. By using the K-Nearest Neighbor method, Seat Load Factor prediction is high or at intervals of 80% -100%.

scholarly journals Comparative Analysis of K-NN and Naïve Bayes Methods to Predict Stock Prices

2021 ◽  
Vol 2 (2) ◽  
pp. 49-53
Author(s):  
Budi Soepriyanto
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
High Accuracy ◽  
Naïve Bayes ◽  
Share Price ◽  
Classification Methods ◽  
K Nearest Neighbor ◽  
Bayes Methods

Abstract— Buying and selling shares is a transaction that is widely carried out at this time, especially buying and selling stocks online which are widely available in the market, to make buying and selling shares require ability or knowledge so that the buying and selling of shares are profitable, to be able to help economic players predict prices. Profit shares or not purchased in the future, this research will conduct stock price predictions using classification methods, namely K-Nearest Neighbor and Naïve Bayes, to predict the stock price data used for one month in minute levels totalling 39065 data, based on prediction results. The highest results obtained were using Naïve Bayes with an accuracy value of 69.38 then the K-Nearest Neighbor method with a K = 5 value of 67.25%, based on these results it can be concluded that the use of the K-Nearest Neighbor and Naïve Bayes methods for prediction share price not yet owned I high accuracy, so it can be combined with other methods or by using other variable predictors.

scholarly journals KOMPARASI NAÏVE BAYES, SUPPORT VECTOR MACHINE DAN K-NEAREST NEIGHBOR UNTUK MENGETAHUI AKURASI TERTINGGI PADA PREDIKSI KELANCARAN PEMBAYARAN TV KABEL

2019 ◽  
Vol 11 (1) ◽  
pp. 11-16
Author(s):  
Mohamad Efendi Lasulika
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Types ◽  
Neural Network Algorithm ◽  
Bayes Algorithm

One obstacle of the default payment is the lack of analysis in the new customer acceptance process which is only reviewed from the form provided at registration, as for the purpose of this study to find out the highest accuracy results from the comparison of Naïve Bayes, SVM and K-NN Algorithms. It can be seen that the Naïve Bayes algorithm which has the highest accuracy value is 96%, while the K-Neural Network algorithm has the highest accuracy at K = 3 which is 92%, while Support Vector Machine only gets accuracy of 66%. The ROC Curve results show that Naïve Bayes achieved the best AUC value of 0.99. Comparison between data mining classification algorithms namely Naïve Bayes, K-Neural Network and Support Vector Machine for predicting smooth payment using multivariate data types, Naïve Bayes method is an accurate algorithm and this method is also very dominant towards other methods. Based on Accuracy, AUC and T-tests this method falls into the best classification category.

scholarly journals KOMPARASI METODE KLASIFIKASI PADA ANALISIS SENTIMEN USAHA WARALABA BERDASARKAN DATA TWITTER

2019 ◽  
Vol 15 (2) ◽  
pp. 267-274
Author(s):  
Tati Mardiana ◽  
Hafiz Syahreva ◽  
Tuslaela Tuslaela
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Support Vector ◽  
K Nearest Neighbor

Saat ini usaha waralaba di Indonesia memiliki daya tarik yang relatif tinggi. Namun, para pelaku usaha banyak juga yang mengalami kegagalan. Bagi seseorang yang ingin memulai usaha perlu mempertimbangkan sentimen masyarakat terhadap usaha waralaba. Meskipun demikian, tidak mudah untuk melakukan analisis sentimen karena banyaknya jumlah percakapan di Twitter terkait usaha waralaba dan tidak terstruktur. Tujuan penelitian ini adalah melakukan komparasi akurasi metode Neural Network, K-Nearest Neighbor, Naïve Bayes, Support Vector Machine, dan Decision Tree dalam mengekstraksi atribut pada dokumen atau teks yang berisi komentar untuk mengetahui ekspresi didalamnya dan mengklasifikasikan menjadi komentar positif dan negatif.  Penelitian ini menggunakan data realtime dari  tweets pada Twitter. Selanjutnya mengolah data tersebut dengan terlebih dulu membersihkannya dari noise dengan menggunakan Phyton. Hasil  pengujian  dengan  confusion  matrix  diperoleh  nilai akurasi Neural Network sebesar 83%, K-Nearest Neighbor sebesar 52%, Support Vector Machine  sebesar 83%, dan Decision Tree sebesar 81%. Penelitian ini menunjukkan metode Support Vector Machine  dan Neural Network paling baik untuk mengklasifikasikan komentar positif dan negatif terkait usaha waralaba.  

scholarly journals Classification of Dengue Hemorrhagic Fever (DHF) Spread in Bandung using Hybrid Naïve Bayes, K-Nearest Neighbor, and Artificial Neural Network Methods

2021 ◽  
Vol 7 (1) ◽  
pp. 10-20
Author(s):  
Fatri Nurul Inayah ◽  
Sri Suryani Prasetiyowati ◽  
Yuliant Sibaroni
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Dengue Fever ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Hemorrhagic Fever ◽  
Naïve Bayes ◽  
K Nearest Neighbor ◽  
Artificial Neural

Dengue fever is a dangerous disease caused by the dengue virus. One of the factors causing dengue fever is due to the place where you live in the tropics, so that cases of dengue fever in Indonesia, especially in the Bandung Regency area, will continue to show high numbers. Therefore, information is needed on the spread of this disease by requiring the accuracy and speed of diagnosis as early prevention. In terms of compiling this information, classification techniques can be done using a combination of methods Naïve Bayes, K-Nearest Neighbor(KNN), and Artificial Neural Network(ANN) to build predictions of the classification of dengue fever, and the data used in this Final Project are dataset affected by the spread of dengue fever in Bandung regency in the 2012-2018 period. The hybrid classifier results can improve accuracy with the voting method with an accuracy level of 90% in the classification of dengue fever.

scholarly journals Performance comparison between naive bayes and k- nearest neighbor algorithm for the classification of Indonesian language articles

2021 ◽  
Vol 10 (2) ◽  
pp. 452
Author(s):  
Titin Winarti ◽  
Henny Indriyawati ◽  
Vensy Vydia ◽  
Febrian Wahyu Christanto
Keyword(s):  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Performance Comparison ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm ◽  
Bayes Algorithm

<span id="docs-internal-guid-210930a7-7fff-b7fb-428b-3176d3549972"><span>The match between the contents of the article and the article theme is the main factor whether or not an article is accepted. Many people are still confused to determine the theme of the article appropriate to the article they have. For that reason, we need a document classification algorithm that can group the articles automatically and accurately. Many classification algorithms can be used. The algorithm used in this study is naive bayes and the k-nearest neighbor algorithm is used as the baseline. The naive bayes algorithm was chosen because it can produce maximum accuracy with little training data. While the k-nearest neighbor algorithm was chosen because the algorithm is robust against data noise. The performance of the two algorithms will be compared, so it can be seen which algorithm is better in classifying documents. The comes about obtained show that the naive bayes algorithm has way better execution with an accuracy rate of 88%, while the k-nearest neighbor algorithm has a fairly low accuracy rate of 60%.</span></span>

scholarly journals Perbandingan Metode Klasifikasi Multiclass untuk Pemetaan Zona Risiko COVID-19 di Pulau Jawa

2021 ◽  
Vol 9 (1) ◽  
pp. 98-107
Author(s):  
Jesica Nauli Br. Siringo Ringo ◽  
Wahyu Joko Mursalin ◽  
Nisrina Citra Nurfadilah ◽  
Dwiky Rachmat Ramadhan ◽  
Wa Ode Zuhayeni Madjida
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Imbalanced Data ◽  
Naïve Bayes ◽  
K Nearest Neighbor ◽  
Missing Value

Penambahan kasus COVID-19 yang besar di Indonesia, khususnya Pulau Jawa, membutuhkan berbagai upaya untuk mengendalikannya. Salah satu upaya efektif yang dapat dilakukan adalah tindakan preventif dengan memberi informasi mengenai kondisi suatu wilayah. Sebagai peringatan kepada masyarakat dan sebagai upaya pengambilan kebijakan daerah, Indonesia mengeluarkan zona risiko sampai pada tingkat kabupaten/kota melalui Satgas Penanganan COVID-19. Pembentukan level zona risiko tersebut menggunakan teknik konvensional yaitu pembobotan skor menggunakan informasi dari tiga jenis indikator. Dengan mempertimbangkan bahwa zona risiko merupakan hal yang penting dalam penentuan kebijakan terkait COVID-19, penelitian ini bertujuan untuk membangun model klasifikasi zona risiko kabupaten/kota di Pulau Jawa menggunakan beberapa teknik klasifikasi data mining dan menentukan model klasifikasi terbaik berdasarkan hasil evaluasi. Teknik klasifikasi yang digunakan sebagai perbandingan dalam penelitian ini adalah naive Bayes, decision tree, k-nearest-neighbor, dan neural network. Sebelum dilakukan pemodelan, data disesuaikan terlebih dahulu pada tahap preprocessing di mana pada tahap tersebut teridentifikasi terdapat permasalahan missing value dan imbalanced data. Permasalahan tersebut diatasi dengan imputasi data dan teknik oversampling. Hasil penelitian menunjukkan bahwa model k-nearest-neighbor merupakan model terbaik dibandingkan tiga model lainnya. Hasil tersebut didasarkan pada ukuran evaluasi keempat model di mana model k-NN memiliki nilai acccuracy, nilai rata-rata makro untuk sensitivitas, spesifisitas, dan ukuran F1 paling tinggi dibandingkan model lainnya.

scholarly journals Perbandingan Kinerja Metode Naive Bayes dan K-Nearest Neighbor untuk Klasifikasi Artikel Berbahasa indonesia

2018 ◽  
Vol 5 (4) ◽  
pp. 427 ◽  
Author(s):  
Riri Nada Devita ◽  
Heru Wahyu Herwanto ◽  
Aji Prasetya Wibawa
Keyword(s):  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Classification Methods ◽  
K Nearest Neighbor ◽  
Bayes Method ◽  
K Nearest Neighbors ◽  
Naive Bayes Method ◽  
Main Factor

<p class="Abstrak">Kecocokan isi artikel dengan sebuah tema jurnal menjadi faktor utama diterima tidaknya sebuah artikel. Tetapi masih banyak mahasiswa yang bingung untuk menentukan jurnal yang sesuai dengan artikel yang dimilikinya. Untuk itu diperlukannya sebuah metode klasifikasi dokumen yang dapat mengelompokkan artikel secara otomatis dan akurat. Terdapat banyak metode klasifikasi yang dapat digunakan. Metode yang digunakan dalam penelitian ini adalah <em>Naive Bayes</em> dan sebagai <em>baseline </em>digunakan metode <em>K-Nearest Neighbor</em>. Metode <em>Naive Bayes </em>dipilih karena dapat menghasilkan akurasi yang maksimal dengan data latih yang sedikit. Sedangkan metode <em>K-Nearest Neighbor</em> dipilih karena metode tersebut tangguh terhadap data <em>noise</em>. Kinerja dari kedua metode tersebut akan dibandingkan, sehingga dapat diketahui metode mana yang lebih baik dalam melakukan klasifikasi dokumen. Hasil yang didapatkan menunjukkan metode <em>Naive Bayes </em>memiliki kinerja yang lebih baik dengan tingkat akurasi 70%, sedangkan metode <em>K-Nearest Neighbor </em>memiliki tingkat akurasi yang cukup rendah yaitu 40%.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstrak"><em>One way to be accepted in a journal conference and get the publication is to create an article with perfect suitability content of the journal. Matching the content of the article with a journal theme is the main factor for acceptability an article. But there are still many students who are confused to choose the journal in accordance with the articles it has. So we need a method to classification article documents category automatically and accurately group articles. There are many classification methods that can be used. The method used in this study is Naive Bayes and as a baseline the K-Nearest Neighbor method. Naive Bayes method is chosen because it can produce maximum accuracy with little training data. While K-Nearest Neighbor method was chosen because the method is robust to data noise. The performance of the two methods will be compared, so we can be known which method is better in classifying the document. The results show that the Naive Bayes method performs is more accurate with 70% accuracy and K-Nearest Neighbors method has a fairly low accuracy of 40% on classification test.</em></p>

scholarly journals ANALISA 4 ALGORITMA DALAM KLASIFIKASI LIVER MENGGUNAKAN RAPIDMINER

2020 ◽  
Vol 6 (2) ◽  
pp. 1-9
Author(s):  
Annisa Putri Ayudhitama ◽  
Utomo Pujianto
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Data Mining ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
World Health ◽  
K Nearest Neighbor ◽  
Health Organization

Hati merupakan salah satu organ penting dalam tubuh manusia yang berfungsi untuk detoksifikasi racun atau penetral racun dari segala sesuatu yang masuk ke dalam tubuh kita, sehingga tubuh menjadi lebih sehat. Hati dapat terserang suatu penyakit yang mampu mengganggu tugasnya, apabila penyakit hati sudah menyerang maka racun akan tersebar ke seluruh tubuh dan membuat tubuh menjadi tidak sehat. Penyakit liver merupakan penyakit hati yang disebabkan oleh virus, alkohol, pola hidup dan lainnya. Menurut data WHO (World Health Organization) menunjukkan hampir 1,2 juta orang per tahun khususnya di Asia Tenggara dan Afrika mengalami kematian akibat terserang penyakit liver. Seseorang sering tidak menyadari atau terlambat mengetahui penyakit liver sehingga ketika diperiksa penyakit liver sudah parah, akan lebih baik apabila dilakukan penanganan lebih awal dengan mengetahui gejala-gejala yang diderita. Data mining mampu membantu diagnosa penyakit liver dengan lebih mudah terutama untuk membantu para dokter dalam menentukan apakah pasien menderita penyakit liver atau tidak, dengan gejala hampir mendekati penyakit liver. Proses diagnosa penyakit liver dilakukan dengan proses klasifikasi dan hasilnya berupa pasien tersebut menderita liver atau tidak. Penelitian ini menggunakan 4 algoritma data mining yaitu Naïve Bayes, K-Nearest Neighbor (KNN), Decision Tree dan Neural Network. Dataset yang digunakan yaitu Indian Liver Patient Dataset (ILPD) dari website UCI Machine Learning Repository. Keempat algoritma tersebut dibandingkan manakah yang lebih baik akurasinya untuk kasus diagnosa penyakit liver. Hasilnya menunjukkan bahwa algoritma Naïve Bayes memiliki akurasi 55,75%, algoritma K-Nearest Neigbor memiliki akurasi 66,36%, algoritma Decision Tree memiliki akurasi 67,04%, dan algoritma Neural Network memiliki akurasi 70,50%. Akurasi tersebut tergolong rendah karena kelas atau label antara pasien penyakit liver dan pasien tidak memiliki liver tidaklah seimbang, kelas pasien penyakit liver lebih banyak dibandingkan pasien tidak memiliki liver, sehingga banyak data yang diklasifikasikan sebagai pasien penyakit liver. Keywords— Data Mining, Decision Tree, Klasifikasi, KNN, Liver, Naïve Bayes, Neural Network

scholarly journals ANALISIS PERBANDINGAN KLASIFIKASI PREDIKSI PENYAKIT HEPATITIS DENGAN MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR, NAÏVE BAYES DAN NEURAL NETWORK

Dinamik ◽  
2020 ◽  
Vol 24 (2) ◽  
pp. 82-91
Author(s):  
Sulastri Sulastri ◽  
Kristophorus Hadiono ◽  
Muchamad Taufiq Anwar
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
K Nearest Neighbor

Hepatitis merupakan penyakit yang diderita oleh banyak orang, bahkan bisa menyebabkan kematian. Prediksi awal dapat mencegah kematian tersebut yaitu denganmengumpulkan data pasien hepatitis yang dilihat dari faktor - faktornya. Faktor-faktor tersebut antara lain Protime, Alk Phosphat, Albumin, Bilirubin dan Usia. Untuk mengolah datatersebut, dibutuhkan Data Mining. Salah satu metode data mining yang digunakan pada penelitian ini adalah klasifikasi.Tujuan penelitian ini yaitu bagaimana memprediksi hidup atau meninggalnya pasien penyakit hepatitis dengan tingkat akurasi dan mencari atribut paling berpengaruh terhadapprediksi hidup atau meninggalnya pasien penyakit hepatitis dengan menggunakan algoritma Algoritma K-Nearest Neighbor, Naïve Bayes Dan Neural Network dan kemudianmembandingkan ketiga hasil analisis dari ketiga algoritma tersebut.Dari hasil analisis 20 atribut dilakukan 3 kali percobaan dengan algoritma Naïve Bayes didapat model klasifikasi dengan tingkat akurasi yang terbaik yaitu 76.92 %, tingkat error23.01% dan atribut Acites dan Spider merupakan atribut yang berpengaruh terhadap keputusan hidup atau meninggalnya pasien yang terkena penyakit hepatitis.Dengan menggunakanAlgoritma Neural Network didapat model klasifikasi dengan tingkat akurasi yang terbaik yaitu 82,97%, tingkat error 17.03% dan atribut yang paling berpengaruh yaitu anorexia, spiders dan protime. Dengan menggunakan algoritma K-Nearest Neighbor didapat model klasifikasi dengan tingkat akurasi terbaik yaitu 93%, tingkat error 7% dan atribut yang paling berpengaruh terhadap penderita penyakit hepatitis yaitu Albumin.

scholarly journals Classification methods comparison for customer churn prediction in the telecommunication industry

2021 ◽  
Vol 8 (12) ◽  
pp. 1-8
Author(s):  
Makruf et al. ◽  
Keyword(s):  
Decision Tree ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Telecommunication Service ◽  
Support Vector ◽  
Telecommunication Industry ◽  
Classification Methods ◽  
K Nearest Neighbor ◽  
Customer Churn

The need for telecommunication services has increased dramatically in schools, offices, entertainment, and other areas. On the other hand, the competition between telecommunication companies is getting tougher. Customer churn is one of the areas that each company gains more competitive advantage. This paper proposes a comparison of several classification methods to make a prediction whether the customers cancel the subscription to a telecommunication service by highlighting key factors of customer churn or not. The comparison is non-trivial due to the urgent requirements from the telecommunication industry to infer the most appropriate techniques in analyzing their customer churn. This comparison is often of huge commercial value. The result shows that Artificial Neural Network (ANN) can predict churn with an accuracy of 79%, Support Vector Machine (SVM) with 78% accuracy, Gaussian Naïve Bayes, and K-Nearest Neighbor (KNN) with 75% accuracy, while Decision Tree with 70% accuracy. Moreover, the technique with the highest F-Measure is Gaussian Naïve Bayes with 65% and the technique with the lowest one is Decision Tree with 49%. Hence, ANN and Gaussian Naïve Bayes are two methods with high recommendation to predict the customer churn in the telecommunication industry.

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