scholarly journals Applying Logistic Regression Data mining techniques for Ethiopian Government Agricultural Open Data Sets

2021 ◽  
Vol 10 (2) ◽  
pp. 473-488
Keyword(s):  
Data Mining ◽  
Logistic Regression ◽  
Agricultural Sector ◽  
Model Building ◽  
Open Data ◽  
Research Process ◽  
Attribute Selection ◽  
Sufficient Evidence ◽  
Vegetable Crops ◽  
Logistic Regression Algorithm

Ethiopia has a great agricultural potential because of its vast areas of fertile land, diverse climate, generally adequate rainfall, and large labor force. With its verified importance to the Ethiopian economy, there is sufficient evidence to show that the potential of the agricultural sector can be expanded considerably by attracting investors towards the sector. This study aims at applying classification techniques in developing a predictive model that can estimate yield production of vegetable crops and the correlation of crops based on their class. In the process of building a model, different steps were undertaken. Among the steps, data collection, data preprocessing and model building and validation were the major ones. Different tasks performed in each step are mentioned as follows. The data were collected Food and Agriculture Organization of the United Nations (FAO). Under preprocessing, data cleaning, discretization and attribute selection were done. The final step was model building and validation and it was performed using the selected tools and techniques. The data mining tool used in this research was Weka. In this software the logistic regression algorithm was selected since it is capable to score more accuracy. After successive experiments were done using this software, a model that can classify crop yield as high, medium and low with better accuracy to the extent of 88.6%. Experimental results show that logistic regression is a very helpful tool to depict the contribution of yield estimation and crop correlation. The reported findings are optimistic, making the proposed model a useful tool in the decision making process. Eventually, the whole research process can be a good input for further indepth research

scholarly journals Implementasi Teknik Bagging untuk Peningkatan Kinerja J48 dan Logistic Regression dalam Prediksi Minat Pembelian Online

2020 ◽  
Vol 7 (1) ◽  
pp. 16-19
Author(s):  
Eka Rahmawati ◽  
Candra Agustina
Keyword(s):  
Data Mining ◽  
Logistic Regression ◽  
Class Imbalance ◽  
Online Data ◽  
Ensemble Technique ◽  
Data Mining Algorithms ◽  
Ensemble Techniques ◽  
Logistic Regression Algorithm ◽  
A Site ◽  
F Measure

Abstract—The rapid growth of online shopping sites makes business in the virtual world very promising. Purchasing intentions is one of the keys to success in an online store. There are several data mining methods for making predictions on online purchase intentions datasets. Data can represent the characteristics or habits of each user who has visited a site whether it ends with a transaction or not. Some popular algorithms with good performance in data mining include J48 and Logistic Regression. However, in data sometimes there is a problem of class imbalance, so the ensemble technique needs to be applied. One technique that can be applied is bagging. This research examines data using bagging techniques to improve the performance of the J48 algorithm and Logistic Regression. The results of improving the performance of data mining algorithms with these techniques have an accuracy value of 89.68% for the J48 algorithm and 88.50% for the Logistic Regression algorithm. This figure shows an increase when compared with initial testing without using ensemble techniques. Increases were also experienced in Recall, F-Measure, and AUC values. Keywords—purchasing intentions; J48; Logistic Regression; Bagging; Abstrak— Pesatnya situs pembelanjaan online menjadikan bisnis di dunia virtual sangat menjanjikan. Minat pembelian menjadi salah satu kunci kesuksesan pada sebuah toko online. Terdapat beberapa metode data mining untuk melakukan prediksi pada dataset minat pembelian online. Data dapat mewakili karakteristik atau kebiasaan dari setiap user yang telah mengunjungi suatu situs baik berakhir dengan melakukan transaksi ataupun tidak. Beberapa algoritma yang populer dengan kinerja yang baik dalam data mining diantaranya J48 dan Logistic Regreession. Namun, dalam sebuah data terkadang terdapat masalah ketidakseimbangan kelas, sehingga perlu diterapkan teknik ensemble.  Salah satu teknik yang dapat diterapkan adalah teknik bagging. Penelitian kali ini mengujikan data dengan teknik bagging untuk meningkatkan kinerja algoritma J48 dan Logistic Regression. Hasil dari peningkatan kinerja algoritma data mining dengan teknik tersebut memiliki nilai akurasi 89.68% untuk algoritma J48 dan 88.50% untuk algoritma Logistic Regression. Angka tersebut menunjukan adanya peningkatan jika dibandingkan dengan pengujian awal tanpa menggunakan teknik ensemble. Peningkatan juga dialami pada nilai Recall, F-Measure, dan AUC. Keywords—Minat Pembelian, J48, Logistic Regression, Bagging

scholarly journals Perbandingan Algoritma Regresi Logistic Dan Neural Network Pada Prediksi Nilai Hasil Pembinaan Dan Kelulusan Tepat Waktu

Respati ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. 30
Author(s):  
Nahrowi Hamdani ◽  
Arief Setyanto ◽  
Sudarmawan Sudarmawan
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Logistic Regression ◽  
Character Education ◽  
Cross Validation ◽  
Learning Program ◽  
Data Registration ◽  
Logistic Regression Algorithm ◽  
The Relationship ◽  
Fold Cross Validation

INTISARIPenelitian ini didasari pada keinginan memanfaatkan informasi akademis mahasiswa yang tinggal di asrama yang memiliki pendidikan karakter dengan program pembelajaraan milik Universitas Muhammadiyah Yogyakarta yang disediakan untuk sebagian mahasiswanya. Hubungan antara pembinaan di asrama mahasiswa dengan prestasi di kampus belum pernah diteliti secara khusus. Penelitian sebelumnya yang penulis temukan menjelasakan hubungan antara nilai di kampus dan kelulusannya. Adanya visi asrama yang salah satunya adalah prestasi studi juga tersedianya data Nilai pendaftaran hingga raport hasil pembelajaran di Asrama serta data kelulusan di kampus, sehingga penulis ingin melihat apakah mahasiswa asrama dapat lulus tepat waktu di kampus, dibutuhkan data mining untuk memprediksi, dipilihlah algoritma Regresi Logisitic dan Neural Network. Dari hasil pengolahan data angkatan tahun 2014-2015 yang digunakan untuk training dan testing, didapatkan hasil dari 5x iterasi k-fold cross validation untuk Regresi Logistic dengan akurasi 65 % dan Neural Network 69%. Dengan begitu algoritma Neural network cendrung lebih baik Regresi Logistic. Kata kunci — data mining, kelulusan, klasifikasi, neural network, prediksi, regresi logistic ABSTRACTThis research is based on the desire to utilize the academic information of students living in dormitories who have character education with the learning program of the University of Muhammadiyah Yogyakarta provided for some of its students. The relationship between development in student dormitories with achievements on campus has not been specifically examined. Previous research that the authors found explained the relationship between grades on campus and graduation. The existence of a dormitory vision, one of which is the achievement of the study as well as the availability of data Registration value to report cards of learning outcomes at the Dormitory as well as graduation data on campus, so the writer wants to see whether boarding students can graduate on time on campus, data mining is needed to predict, chosen Logistic Regression algorithm and Neural Network. From the results of the 2014-2015 batch data processing used for training and testing, the results of 5 times the k-fold cross validation iteration for Logistic Regression with an accuracy of 65% and a 69% Neural Network. Thus the Neural network algorithm tends to be better than Logistic Regression. Keywords —  data mining, graduation, klasification, neural nework, prediction, regresi logistic.

Recognizing Job Apathy Patterns of Iraqi Higher Education Employees Using Data Mining Techniques

2019 ◽  
Vol 54 (4) ◽  
Author(s):  
Mustafa S. Abd ◽  
Suhad Faisal Behadili
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Psychiatric Patients ◽  
Human Life ◽  
A Priori ◽  
Psychological Research ◽  
Attribute Selection ◽  
Specific Class ◽  
Data Mining Techniques ◽  
Using Data

Psychological research centers help indirectly contact professionals from the fields of human life, job environment, family life, and psychological infrastructure for psychiatric patients. This research aims to detect job apathy patterns from the behavior of employee groups in the University of Baghdad and the Iraqi Ministry of Higher Education and Scientific Research. This investigation presents an approach using data mining techniques to acquire new knowledge and differs from statistical studies in terms of supporting the researchers’ evolving needs. These techniques manipulate redundant or irrelevant attributes to discover interesting patterns. The principal issue identifies several important and affective questions taken from a questionnaire, and the psychiatric researchers recommend these questions. Useless questions are pruned using the attribute selection method. Moreover, pieces of information gained through these questions are measured according to a specific class and ranked accordingly. Association and a priori algorithms are used to detect the most influential and interrelated questions in the questionnaire. Consequently, the decisive parameters that may lead to job apathy are determined.

scholarly journals Google Play Content Scraping and Knowledge Engineering using Natural Language Processing Techniques with the Analysis of User Reviews

2020 ◽  
Vol 30 (1) ◽  
pp. 192-208 ◽  
Author(s):  
Hamza Aldabbas ◽  
Abdullah Bajahzar ◽  
Meshrif Alruily ◽  
Ali Adil Qureshi ◽  
Rana M. Amir Latif ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Language Processing ◽  
Mobile Application ◽  
Knowledge Engineering ◽  
Machine Learning Algorithms ◽  
Application Development ◽  
User Reviews ◽  
N Gram ◽  
Logistic Regression Algorithm ◽  
Google Play

Abstract To maintain the competitive edge and evaluating the needs of the quality app is in the mobile application market. The user’s feedback on these applications plays an essential role in the mobile application development industry. The rapid growth of web technology gave people an opportunity to interact and express their review, rate and share their feedback about applications. In this paper we have scrapped 506259 of user reviews and applications rate from Google Play Store from 14 different categories. The statistical information was measured in the results using different of common machine learning algorithms such as the Logistic Regression, Random Forest Classifier, and Multinomial Naïve Bayes. Different parameters including the accuracy, precision, recall, and F1 score were used to evaluate Bigram, Trigram, and N-gram, and the statistical result of these algorithms was compared. The analysis of each algorithm, one by one, is performed, and the result has been evaluated. It is concluded that logistic regression is the best algorithm for review analysis of the Google Play Store applications. The results have been checked scientifically, and it is found that the accuracy of the logistic regression algorithm for analyzing different reviews based on three classes, i.e., positive, negative, and neutral.

Improved differentiation classification of variable precision artificial intelligence higher education management

2021 ◽  
pp. 1-10
Author(s):  
Chao Dong ◽  
Yan Guo
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Data Mining ◽  
Decision Tree ◽  
Classification Accuracy ◽  
Attribute Selection ◽  
Higher Education Management ◽  
Education Management ◽  
Decision Tree Classification

The wide application of artificial intelligence technology in various fields has accelerated the pace of people exploring the hidden information behind large amounts of data. People hope to use data mining methods to conduct effective research on higher education management, and decision tree classification algorithm as a data analysis method in data mining technology, high-precision classification accuracy, intuitive decision results, and high generalization ability make it become a more ideal method of higher education management. Aiming at the sensitivity of data processing and decision tree classification to noisy data, this paper proposes corresponding improvements, and proposes a variable precision rough set attribute selection standard based on scale function, which considers both the weighted approximation accuracy and attribute value of the attribute. The number improves the anti-interference ability of noise data, reduces the bias in attribute selection, and improves the classification accuracy. At the same time, the suppression factor threshold, support and confidence are introduced in the tree pre-pruning process, which simplifies the tree structure. The comparative experiments on standard data sets show that the improved algorithm proposed in this paper is better than other decision tree algorithms and can effectively realize the differentiated classification of higher education management.

scholarly journals Climate Change Trends and Impacts on California Agriculture: A Detailed Review

Agronomy ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. 25 ◽  
Author(s):  
Tapan Pathak ◽  
Mahesh Maskey ◽  
Jeffery Dahlberg ◽  
Faith Kearns ◽  
Khaled Bali ◽  
...  
Keyword(s):  
Climate Change ◽  
Agricultural Sector ◽  
Climate Adaptation ◽  
Crop Yields ◽  
Heat Waves ◽  
Sufficient Evidence ◽  
Future Climate Change ◽  
Future Research ◽  
Detailed Review ◽  
California Agriculture

California is a global leader in the agricultural sector and produces more than 400 types of commodities. The state produces over a third of the country’s vegetables and two-thirds of its fruits and nuts. Despite being highly productive, current and future climate change poses many challenges to the agricultural sector. This paper provides a summary of the current state of knowledge on historical and future trends in climate and their impacts on California agriculture. We present a synthesis of climate change impacts on California agriculture in the context of: (1) historic trends and projected changes in temperature, precipitation, snowpack, heat waves, drought, and flood events; and (2) consequent impacts on crop yields, chill hours, pests and diseases, and agricultural vulnerability to climate risks. Finally, we highlight important findings and directions for future research and implementation. The detailed review presented in this paper provides sufficient evidence that the climate in California has changed significantly and is expected to continue changing in the future, and justifies the urgency and importance of enhancing the adaptive capacity of agriculture and reducing vulnerability to climate change. Since agriculture in California is very diverse and each crop responds to climate differently, climate adaptation research should be locally focused along with effective stakeholder engagement and systematic outreach efforts for effective adoption and implementation. The expected readership of this paper includes local stakeholders, researchers, state and national agencies, and international communities interested in learning about climate change and California’s agriculture.

scholarly journals PENGARUH OPERATING CAPACITY, SALES GROWTH DAN ARUS KAS OPERASI TERHADAP FINANCIAL DISTRESS

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Annisa Livia Ramadhani ◽  
Khairun Nisa
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Financial Distress ◽  
Agricultural Sector ◽  
Stock Exchange ◽  
Sampling Technique ◽  
Cash Flows ◽  
Sales Growth ◽  
Operating Capacity ◽  
Operating Cash Flows

This study aims to determine how the influence of operating capacity, sales growth and operating cash flows on financial distress. The population in this study israll agricultural sector companies listed on the Indonesia Stock Exchange (IDX) in 2013-2017. The sampling technique in this study used purposive sampling which produced 8 samples in a period of 5 years, namely as many as 40 units of data samples. The analytical method used is logistic �regression analysis which is processed. using SPSS Version 25. Based on the results of this study, it was found that simultaneous operating capacity, sales growth and operating cash flows influence the occurrence of financial distress. Then partially, operating capacity and sales growth have no effect on the occurrence of financial distress, while operating cash flows have a positive and significant effect on the occurrence of financial distress.�Keyword : Financial Distress, Operating capacity, Sales growth, Operation cash flow.

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