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Indonesian Journal of Artificial Intelligence and Data Mining
Published by Universitas Islam Negeri Sultan Syarif Kasim Riau
ISSN : 26143372     EISSN : 26146150     DOI : -
Core Subject : Science,
Computer Science & IT
Indonesian Journal of Artificial Intelligence and Data Mining (IJAIDM) is an electronic periodical publication published by Puzzle Research Data Technology (Predatech) Faculty of Science and Technology UIN Sultan Syarif Kasim Riau, Indonesia. IJAIDM provides online media to publish scientific articles from research in the field of Artificial Intelligence and Data Mining. IJAIDM will be published 2 (two) times a year, in March and September, each edition contains 7 (seven) articles. Articles may be written in English or Indonesia.
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Articles 7 Documents
 1 
Search results for , issue "Vol 2, No 1 (2019): March 2019" : 7 Documents clear
Prediction of Student Graduation Time Using the Best Algorithm Verry Riyanto; Abdul Hamid; Ridwansyah Ridwansyah
Indonesian Journal of Artificial Intelligence and Data Mining Vol 2, No 1 (2019): March 2019
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v2i1.6424

Abstract

Data mining has a very important role in the world of education can help education institutions in predicting and making decisions related to student academic status. We use the NN, SVM and DT algorithms to predict the graduation time of academic students at one of the private universities in Indonesia. The results of this study indicate that the three models produce the accuracy of more than 80%, and the SVM model has an accuracy of 85.18% higher than the other two models. The results arising from this study provide important reference material for planning the future success of students and faculty in early warning to students in the future.
Prediction of Successful Elearning Based on Activity Logs with Selection of Support Vector Machine based on Particle Swarm Optimization Elin Panca Saputra; Sukmawati Angreani Putri; Indriyanti Indriyanti
Indonesian Journal of Artificial Intelligence and Data Mining Vol 2, No 1 (2019): March 2019
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v2i1.6500

Abstract

Prediction is a systematic estimate that identifies past and future information, we predict the success of learning with elearning based on a log of student activities. In our current study we use the Support vector machine (SVM) method which is comparable with Particle Swarm Optimization. It is known that SVM has a very good generalization that can solve a problem. however, some of the attributes in the data can reduce accuracy and add complexity to the Support Vector Machine (SVM) algorithm. It is necessary for existing tribute selection, therefore using the Particle swarm optimization (PSO) method is applied to the right attribute selection in determining the success of elearning learning based on student activity logs, because with the Swarm Optimization (PSO) method can increase accuracy in determining selection of attributes.
Data Mining Optimization Using Sample Bootstrapping and Particle Swarm Optimization in the Credit Approval Classification Andre Alvi Agustian; Achmad Bisri
Indonesian Journal of Artificial Intelligence and Data Mining Vol 2, No 1 (2019): March 2019
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v2i1.6299

Abstract

Credit approval is a process carried out by the bank or credit provider company. Where the process is carried out based on credit requests and credit proposals from the borrower. Credit approval is often difficult for banks or credit providers. Where the number of requests and classifications must be made on various data submitted. This study aims to enable banks or credit card issuing companies to carry out credit approval processes effectively and accurately in determining the status of the submissions that have been made. This research uses data mining techniques. This study uses a Credit Approval dataset from UCI Machine Learning, where there is a class imbalance in the dataset. 14 attributes are used as system inputs. This study uses the C4.5 and Naive Bayes algorithms where optimization is needed using Sample Bootstrapping and Particle Swarm Optimization (PSO) in the algorithm so that the results of the research produce good accuracy and are included in the good classification. After using the optimization, it produces an accuracy rate of C4.5 which is initially 85.99% and the AUC value of 0.904 becomes 94.44% with the AUC value of 0.969 and Naive Bayes which initially has an accuracy value of 83.09% with an AUC value of 0.916 to 90 , 10% with an AUC value of 0.944.
Prediction Of Amount Of Use Of Planning Family Contraception Equipment Using Monte Carlo Method (Case Study In Linggo Sari Baganti District) Rani Yunima Astia; Julius Santony; Sumijan Sumijan
Indonesian Journal of Artificial Intelligence and Data Mining Vol 2, No 1 (2019): March 2019
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v2i1.5825

Abstract

Family planning aims to minimize birth rates in Indonesia. To conduct socialization, it is carried out to existing fertile couples. Pus is a married couple whose wife is in the range of 15-49 years. Contraception itself consists of 2 periods, namely short and long. Where the pus can choose according to what they want, therefore there is often a lack of stock. Thus it is necessary to predict how many contraceptives are used with a method to be more efficient. The Monte Carlo method is used which is a numerical analysis method that involves a sample of random numbers. Where to use the previous year's data to get the predicted results of the next year in the form of numbers. After passing the simulation series the percentage results have been obtained with an average of over 80%.
Spam Classification on 2019 Indonesian President Election Youtube Comments Using Multinomial Naïve-Bayes Jonathan Radot Fernando; Raymond Budiraharjo; Emeraldi Haganusa
Indonesian Journal of Artificial Intelligence and Data Mining Vol 2, No 1 (2019): March 2019
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v2i1.6445

Abstract

Text classification are used in many aspect of technologies such as spam classification, news categorization, Auto-correct texting. One of the most popular algorithm for text classification nowadays is Multinomial Naïve-Bayes. This paper explained how Naïve-Bayes assumption method works to classify 2019 Indonesian Election Youtube comments. The output prediction of this algorithm is spam or not spam. Spam messages are defined as racist comments, advertising comments, and unsolicited comments. The algorithms text representation method used bag-of-words method. Bag-of-words method defined a text as the multiset of its words. The algorithm then calculate the probability of a word given the class of spam or not spam. The main difference between normal Naïve-Bayes algorithm and Multinomial Naïve-Bayes is the way the algorithm treats the data itself. Multinomial Naïve-Bayes treats data as a frequency data hence it is suitable for text classification task.
Expert System For Diagnosing Hemophilia In Children Using Case Based Reasoning Subrianto Chandra; Sumijan Sumijan; Eka Praja Wiyata Mandala
Indonesian Journal of Artificial Intelligence and Data Mining Vol 2, No 1 (2019): March 2019
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v2i1.6681

Abstract

Many people who have children do not know about hemophilia, because this disease is one of the rare diseases. Hemophilia is a genetic disorder in the blood caused by a lack of blood clotting factors. Therefore there is a need for information for the public to be able to find out about this disease, so that when there is an unnatural bleeding, early treatment can be done properly.Therefore an expert system was designed to diagnose early hemophilia in children.The method used in this expert system is the Case Based Reasoning method. The Case Based Reasoning method is a method used to solve a new case by adapting the symptoms found in previous cases that are similar to the new case.This expert system can provide solutions / early prevention of the diagnostic process carried out. Expert system applications are designed based on websites using the PHP programming language.
Frameworks Comparative Study of Classification Models Based on Variable Extraction Model for Status Classify of Contraception Method in Fertile Age Couples in Indonesia Laelatul Khikmah
Indonesian Journal of Artificial Intelligence and Data Mining Vol 2, No 1 (2019): March 2019
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (647.23 KB) | DOI: 10.24014/ijaidm.v2i1.7568

Abstract

In terms of minimizing the risk of death in mothers the use of contraceptive methods really needs to be improved and the success of the use of contraceptive methods. This study aims to compare several popular classification models used to classify the status of the use of contraceptive methods in fertile age couples in Indonesia so that they can be used and the implementation of policies that are more impartial using the variable extraction integration method. The proposed model in this study is a comparative study of classification models include Logistic Regression (LR), k-Nearest Neighbor (k-NN), Naïve Bayes (NB), C4.5, and CART. For the purpose of testing the model, Accuracy, AUC, F-measure, Sensitivity (SN), Specificity (SP), Positive Predictive Value (PPV), and Negative Predictive Value (NPV) are used to test frameworks comparative study of classification models. Based on the experimental results, RL shows superior and stable performance compared to other methods. It can be concluded, the RL method is the right choice method to classify the status of use of contraceptive methods in couples of childbearing ages in Indonesia.

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