scholarly journals PENENTUAN KESESUAIAN LAHAN PERTANIAN TANAMAN CABAI MENGGUNAKAN METODE NAÏVE BAYES DI KABUPATEN KUPANG

2021 ◽  
Vol 9 (1) ◽  
pp. 56-64
Author(s):  
Welmy Sinlae ◽  
Sebastianus A. S. Mola ◽  
Nelci Dessy Rumlaklak
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Case Based Reasoning ◽  
Case Based

Tanaman cabai merupakan salah satu tanaman yang dibudidayakan di Nusa Tenggara Timur (NTT). Wilayah Kabupaten Kupang merupakan salah satu wilayah penghasil cabai yang ada di NTT. Produksi cabai secara keseluruhan di Kabupaten Kupang pada tahun 2019 sampai 2020 mengalami peningkatan. Namun peningkatan produksi ini belum maksimal mengingat banyaknya lahan yang belum dimanfaatkan sebagai lahan pertanian. Oleh karena itu dibutuhkan sebuah sistem yang membantu dalam menentukan kesesuaian lahan pertanian untuk penanaman cabai. Dalam penelitian ini penulis menerapkan penalaran berbasis kasus/case-based reasoning (CBR) dalam menentukan kesesuaian lahan pertanian tanaman cabai. Metode yang digunakan dalam penelitian ini adalah Naïve Bayes dengan 7 kriteria yaitu: curah hujan, drainase, tekstur tanah, kedalaman tanah, C-organik, kemiringan lahan dan bahaya terjadinya bencana. Proses pencarian solusi dimulai dengan mengeliminasi data yang tidak relevan menggunakan metode Naive Bayes dan berlanjut dengan perankingan nilai kemiripan terbaik menggunakan KNN. Berdasarkan hasil pengujian dengan 110 kasus lahan cabai didapatkan hasil akurasi tertinggi sebesar 92.2% dan rata-rata hasil akurasi dari keseluruhan fold sebesar 89.1%.      

A novel case-based reasoning system for breast cancer diagnosis: considering the effects of external characteristics of cases Abstract (Preprint)

2021 ◽  
Author(s):  
Dongxiao Gu ◽  
Wang Zhao ◽  
Xuejie Yang ◽  
Kaixiang Su ◽  
Changyong Liang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Naive Bayes ◽  
Breast Cancer Diagnosis ◽  
Naïve Bayes ◽  
Case Based Reasoning ◽  
Reasoning System ◽  
System P ◽  
External Characteristics ◽  
Case Based

BACKGROUND Artificial intelligence can help physicians improve the accuracy of breast cancer diagnosis. However, the effectiveness of AI applications is limited by doctors’ adoption of the results recommended by the AI systems. A case-based reasoning system for breast cancer diagnosis (CBR-BCD) that considers the effects of external characteristics of cases (ECC) can not only provide doctors with more accurate results for auxiliary diagnosis, but also improve doctors’ trust in the results, so as to encourage doctors to adopt the results recommended by the system. OBJECTIVE The objective of our study is to develop a novel integrated case-based reasoning (CBR) framework based on Naive Bayes and K-Nearest Neighbor (KNN) algorithms considering the effects of external characteristics of cases (CBR-ECC) and a corresponding system named CBR-BCD to assist in diagnosis and promote adoption by doctors. METHODS We used a real-world data set from the Maputo Central Hospital in Mozambique and constructed the CBR-ECC model and corresponding CBR-BCD system. We performed data processing and obtained six internal features and three external features of the cases. We randomly divided the 1214 cases into a training group and a testing group. The performance of the model was evaluated by accuracy and the area under the receiver operating characteristic curve (AUC). RESULTS The system based on the CBR-ECC model was developed. In the first stage of this model, Naive Bayes showed the best performance, compared with KNN and J48 decision tree classifiers, with an accuracy rate of 95.87%. In the second stage, the accuracy of the KNN model with the optimal K value of 2 was 99.40%. In the third stage, after considering the external characteristics of the cases, the rankings of recommendation changed. Finally, we report the users’ evaluation of the novel CBR system in a real hospital scenario; we found that it is superior to the original system. CONCLUSIONS CBR-BCD not only enables accurate case recommendations to support health practitioners in diagnosing breast cancer and reducing diagnostic inaccuracies, but also facilitates the adoption of system-recommended results by physicians, which is valuable for clinicians to assist in diagnosis. It enables the early screening of breast cancer to improve the quality of breast cancer management and reduces the socioeconomic burden compared to traditional methods.

scholarly journals ANALISIS PERBANDINGAN TIGA METODE UNTUK MENDIAGNOSA PENYAKIT MATA PADA MANUSIA

2021 ◽  
Vol 8 (4) ◽  
pp. 1654-1664
Author(s):  
Ahmad Fahmi Adam
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Case Based Reasoning ◽  
Certainty Factor ◽  
Case Based

Untuk mendiagnosa penyakit mata pada manusia diperlukan perhitungan probabilitas yang terbaik. Karena mata merupakan salah satu bagian terpenting pada tubuh manusia yang harus di jaga kesehatannya. Penelitian ini bertujuan untuk menganalisis perbandingan dari 3 metode diantaranya : metode Case-Based Reasoning, Naïve Bayes dan Certainty Factor sehingga bisa diketahui metode mana yang terbaik untuk melakukan pendiagnosaan. Setelah melakukan perbandingan, untuk perhitungan metode Case-Based Reasoning didapatkan hasil probabilitas 61,6 %, metode Naïve Bayes didapatkan hasil 56,36% dan metode Certainty Factor didapatkan hasil 90,4%. Dapat disimpulkan, metode Certainty Factor adalah metode yang terbaik untuk melakukan pendiagnosaan penyakit mata pada manusia. Setelah itu, akan dibuatkan suatu sistem pakar menggunakan metode Certainty Factor untuk mendiagnosa penyakit mata pada manusia. Sistem pakar merupakan peniru suatu pakar dalam melakukan diagnosis suatu penyakit. Tujuan dibuatkan sistem pakar ini, supaya dapat membantu pasien untuk mendiagnosa jenis penyakit mata apa berdasarkan gejala gejala yang dialaminya.

A novel case-based reasoning system for breast cancer diagnosis: considering the effects of external characteristics of cases (Preprint)

2021 ◽  
Author(s):  
Dongxiao Gu ◽  
Wang Zhao ◽  
Xuejie Yang ◽  
Kaixiang Su ◽  
Changyong Liang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Naive Bayes ◽  
Breast Cancer Diagnosis ◽  
Naïve Bayes ◽  
Case Based Reasoning ◽  
Reasoning System ◽  
System P ◽  
External Characteristics ◽  
Case Based

BACKGROUND Artificial intelligence can help physicians improve the accuracy of breast cancer diagnosis. However, the effectiveness of AI applications is limited by doctors’ adoption of the results recommended by the AI systems. A case-based reasoning system for breast cancer diagnosis (CBR-BCD) that considers the effects of external characteristics of cases (ECC) can not only provide doctors with more accurate results for auxiliary diagnosis, but also improve doctors’ trust in the results, so as to encourage doctors to adopt the results recommended by the system. OBJECTIVE The objective of our study is to develop a novel integrated case-based reasoning (CBR) framework based on Naive Bayes and K-Nearest Neighbor (KNN) algorithms considering the effects of external characteristics of cases (CBR-ECC) and a corresponding system named CBR-BCD to assist in diagnosis and promote adoption by doctors. METHODS We used a real-world data set from the Maputo Central Hospital in Mozambique and constructed the CBR-ECC model and corresponding CBR-BCD system. We performed data processing and obtained six internal features and three external features of the cases. We randomly divided the 1214 cases into a training group and a testing group. The performance of the model was evaluated by accuracy and the area under the receiver operating characteristic curve (AUC). RESULTS The system based on the CBR-ECC model was developed. In the first stage of this model, Naive Bayes showed the best performance, compared with KNN and J48 decision tree classifiers, with an accuracy rate of 95.87%. In the second stage, the accuracy of the KNN model with the optimal K value of 2 was 99.40%. In the third stage, after considering the external characteristics of the cases, the rankings of recommendation changed. Finally, we report the users’ evaluation of the novel CBR system in a real hospital scenario; we found that it is superior to the original system. CONCLUSIONS CBR-BCD not only enables accurate case recommendations to support health practitioners in diagnosing breast cancer and reducing diagnostic inaccuracies, but also facilitates the adoption of system-recommended results by physicians, which is valuable for clinicians to assist in diagnosis. It enables the early screening of breast cancer to improve the quality of breast cancer management and reduces the socioeconomic burden compared to traditional methods.

Case Based Reasoning And Naive Bayes Implementation In Laptop Purchasing Recommender System

2021 ◽  
Vol 9 (3) ◽  
pp. 411
Author(s):  
I Gusti Ngurah Agung Dharmawangsa ◽  
I Wayan Supriana
Keyword(s):  
Recommender System ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Case Based Reasoning ◽  
The Right ◽  
Case Based ◽  
Selection Of ◽  
The Ideal

Purchasing a new laptop will be difficult if we do not know what the ideal laptop specification for our needs. Especially with a wide selection of laptops. From this problem, system that can give a recommendation to choose the right laptop based on purchaser’s specification choice is needed. This research using two method, Case Based Reasoning and Naive Bayes. The concept of Case Based Reasoning is the process of solving new problems based on the solutions of similar past problems, While Naive Bayes assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. Naive bayes will be implemented in retrive process of case based reasoning. The recommender system utilizing 7 feature, Kecepatan Processor, Kapasitas Ram, Tipe Grafis, Ukuran Layar, Ukuran Harddisk, Kecepatan Layar, and Harga. The percentage of respondents who said the system was successful in providing the right recommendations was 70 percent of the total respondents.

scholarly journals DIAGNOSA HAMA DAN PENYAKIT PADA TANAMAN PADI MENGGUNAKAN METODE NAIVE BAYES DAN K-NEAREST NEIGHBOR

2020 ◽  
Vol 8 (2) ◽  
pp. 156-162
Author(s):  
Restanti M Bianome ◽  
Yelly Y Nabuasa ◽  
Derwin R Sina
Keyword(s):  
Test Data ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Case Based Reasoning ◽  
K Nearest Neighbor ◽  
Rice Plants ◽  
Average Value ◽  
Bayes Algorithm ◽  
Degree Of Similarity

This study builds systems Case Based Reasoning (CBR) to diagnose pests and diseases in rice plants using Naïve Bayes algorithm and K-Nearest Neighbor. CBR is one method of solving the problem with new cases of decision making based on the solution of previous cases by calculating the degree of similarity (similarity), The case consists of 13 species and 10 types of disease pests of rice plants. The degree of similarity can be determined by indexing and nonindexing. Indexing is the process of grouping the cases by classes that have been determined, while nonindexing a process without grouping cases. Based on cross validation testing using average values obtained accuracy of 92.88% to 153 test data on testing using the indexing and the average value of 89.63% accuracy of the test data in the test 153 using nonindexing.

Implementation of the CBR (Case Based Reasoning) Method in Cases of Cesarean section

2020 ◽  
Vol 8 (2) ◽  
pp. 181
Author(s):  
Ni Wayan Wiantari ◽  
I Wayan Supriana
Keyword(s):  
Blood Pressure ◽  
Cesarean Section ◽  
Naive Bayes ◽  
Training Data ◽  
Similarity Criteria ◽  
Case Based Reasoning ◽  
Section Data ◽  
Testing Data ◽  
Case Data ◽  
Case Based

CBR (Case Based Reasoning) method is a reasoning method that uses old knowledge to overcome new problems. CBR will provide solutions to new cases by looking at old cases that are closest to new cases. One case that can use the CBR method is a case of cesarean section because there are several factors that affect cesarean section as well as features in the system, including: age, number of pregnancies, time of delivery, blood pressure, and heart status so that not everyone can do surgery cesar. In this study a system was used to determine whether a patient could have a cesarean section or not by using the CBR method and calculate similarity using Naive Bayes. The percentage correlation value of each feature is sought using SPSS because each feature has a different effect on the results. The number of cesarean section data was 80 data, in this study were divided into 70% training data (56) and 30% testing data (24). Where the new case data will be compared with the old case data in the database, and then the similarity criteria are calculated based on the existing formula. The results of testing of 24 data testing there are 5 data whose results are incompatible and 19 data whose results are in accordance with the data before it is shared. So that the accuracy of the cesarean section with the CBR method using Nayve Bayes is 79%.

Study of Sentiment of Governor's Election Opinion in 2018

2018 ◽  
pp. 231-238
Author(s):  
Agung Eddy Suryo Saputro ◽  
Khairil Anwar Notodiputro ◽  
Indahwati A
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Addition Method ◽  
Sentiment Mining ◽  
Positive Sentiment ◽  
Trending Topics ◽  
Election News

In 2018, Indonesia implemented a Governor's Election which included 17 provinces. For several months before the Election, news and opinions regarding the Governor's Election were often trending topics on Twitter. This study aims to describe the results of sentiment mining and determine the best method for predicting sentiment classes. Sentiment mining is based on Lexicon. While the methods used for sentiment analysis are Naive Bayes and C5.0. The results showed that the percentage of positive sentiment in 17 provinces was greater than the negative and neutral sentiments. In addition, method C5.0 produces a better prediction than Naive Bayes.

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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