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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.
Arjuna Subject : -
Articles 77 Documents
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Radial Basis Function Neural Network Control for Coupled Water Tank Halim Mudia
Indonesian Journal of Artificial Intelligence and Data Mining Vol 3, No 2 (2020): Spetember 2020
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v3i2.10002

Abstract

The level and flow control in tanks are the heart of all chemical engineering system. The control of liquid level in tanks and flow between tanks is a basic problem in the process industries. Many times the liquids will be processed by chemical or mixing treatment in the tanks, but always the level of fluid in the tanks must be controlled and the flow between tanks must be regulated in presence of non-linearity. Therefore, in this paper will use neural network based on radial basis function (RBF) to control of  level 2 in the tank 2 with the setpoint of 10 centimeters and can follow the setpoint changes to 8 centimeters given in 225 seconds. The results show that neural netwotk based on radial basis function can follow setpoint given with steady state error is 0 cm, overshoot is 0%, rising time is 48 seconds, settling time is 52 seconds and can follow setpoint changes in 51 seconds.
Local Binary Pattern and Learning Vector Quantization for Classification of Principal Line of Palm-Hand Suwanto Sanjaya; Ulfah Adzkia; Lestari Handayani; Febi Yanto
Indonesian Journal of Artificial Intelligence and Data Mining Vol 3, No 2 (2020): Spetember 2020
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v3i2.10236

Abstract

Biometrics such as DNA, face, fingerprints, and iris still had disadvantages. The principal line of palm-hand biometric was expected to cover the weakness of the other biometric. This research was used dataset amounted to 150 images of palms-hand of the left-hand side. The dataset sourced 15 people who captured 10 times. The cropping technique that has used is the Region of Interest (ROI). Local Binary Pattern (LBP) was used to feature extraction. The feature extraction consists of the five parameters statistical. They were mean, variance, skewness, kurtosis, and entropy. Learning Vector Quantization (LVQ) was used to train the weight to produce optimal weight. The Confusion matrix method was used to evaluate the accuracy of the classification. The experiment was used the learning rates 0.01; 0.05; 0.1; 0.5; and 0.7. Based on testing and the experimental results, the highest accuracy obtained was on the learning rate value 0.5 which achieve 80%. In future work, we can explore with added the second-order statistics feature for better result.
Clustering Productivity of Rice in Karawang Regency Using the Fuzzy C-Means Method Suna Mulyani; Betha Nurina Sari; Azhari Ali Ridha
Indonesian Journal of Artificial Intelligence and Data Mining Vol 3, No 2 (2020): Spetember 2020
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v3i2.10415

Abstract

Rice is a major food commodity that has a strategic role in the development of community nutrition, agriculture and the economy in Indonesia. Karawang Regency is known as a city of rice barns which is one of the largest rice producing and supplying regions in the province of West Java and even Indonesia. The importance of rice as a staple food in Karawang Regency needs to ensure rice productivity remains stable. Data Mining is a data mining technique that produces an output in the form of knowledge. The purpose of this study is to classify the productivity of rice plants so as to know the area of high rice productivity in Karawang Regency. The data used in this study were 180 data from 30 districts. Data grouping will use the Fuzzy C-Means (FCM) algorithm which is a data clustering technique where the existence of each data point in a cluster is determined by the degree of membership. With Silhouette Coefficient evaluation techniques the results of clustering obtained in 2010, 2011, 2013, 2014 and 2015 show that the results of grouping have a good structure that is above 0.5. Only in 2012 showed that the grouping results had a weak structure of 0.49.
Data Train Reduction on Data Image With K Support Vector Nearest Neighbor (Case Study : Maize Leaf Image) Marlinda Vasty Overbeek; Yampi R Kaesmetan
Indonesian Journal of Artificial Intelligence and Data Mining Vol 3, No 2 (2020): Spetember 2020
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v3i2.10451

Abstract

In this study, we applied the K Support Vector Nearest Neighbor algorithm to reduce data train on data image. The data image that we used is the maize leaves image infected with fungi and healthy maize leave. The aim of data train reduction in this study is to get faster and more accurate prediction results. This because by using the K Support Vector Nearest Neighbor algorithm, a support vector that is formed from the algorithm really characterize the objective function of the problem. The accuracy obtained from this study is 0.20 or 20% mean error for the value of nearest neighbor K  = 3 and using K Nearest Neighbor as a model construction algorithm. The error value is smaller than when we compared to the construction of the model without performing data train reduction. The error value if not doing any reduction is 0.209 or 20.9%. Whereas in terms of time efficiency, working with the K Support Vector Nearest algorithm is 24 seconds faster than without performing data train reduction 
Comparison Of Data Mining In E-Learning Learning Based On Log Aktivity On PSO-Based Nural Network Algorithms With PSO-Based SVM Elin Panca Saputra; Supriatiningsih Supriatiningsih; Indriyanti Indriyanti
Indonesian Journal of Artificial Intelligence and Data Mining Vol 3, No 2 (2020): Spetember 2020
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v3i2.10519

Abstract

The purpose of this research is to find a higher or better level of accuracy, we make a comparison between the Neural Network method based on Particle Swarm Optimization and the Particle Swarm Optimization-based support vector machine method, from evaluation on e-learning based learning systems is very important to determine the level. accuracy in learning.. In addition, the purpose of this study is to find the attributes of the highest predictive results of student learning who follow the e-learning learning system. The data we use are 641 users which are taken from the log of student learning activities from the LMS. The logs we use are Gender, Excercise, Forum, Chat, Diskusi, Upload An Assgmnt, Message, Excercise Quiz, dan Total Log. All logs will be recorded in the LMS. The data used in this study, the results of the tests we conducted, the results obtained using the PSO-based Neural Network (NN) method obtained an accuracy value of 97.35%, and the results of the AUC value were 98.60%. Then we did the second trial using the PSO-based support vectore machine (SVM) method to get an accuracy value of 88.47% and an AUC value of 93.80%. Then the conclusion is that using the neural network method is higher than using the spport vector machine method with an accuracy difference of 8.88% while the AUC accuracy value is a difference of 4.8%.
Improving Stock Price Prediction with GAN-Based Data Augmentation Julisa Bana Abraham
Indonesian Journal of Artificial Intelligence and Data Mining Vol 4, No 1 (2021): March 2021
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v4i1.10740

Abstract

The stock price is one of the most studied time series data because it is deemed to be profitable doing so, however stock price data is still difficult to predict because it is non-linear, non-parametric, non-stationary, and chaotic. One of the methods that most recently used to predict stock price data is deep learning. Although deep learning has a good performance to solve various problems, deep learning must be trained using a lot of data or this method will experience overfitting. This paper proposes a scheme to train a classifier model for predicting stock price time series data using augmented time-series data generated using GAN. Evaluation shows that the classifier model trained using augmented data has better performance on the AMZN dataset of 24.47% and 30.27% lower RMSE and MAE respectively compared to just using the real data and FB dataset of 15.84% and 13.88% lower RMSE and MAE respectively compared to just using the real data, but for the GOOG dataset it does not show a significant change in RMSE that is 0.52% lower and even the MAE value is increased slightly by 2.62% compared to just using the real data
Automatic Car Detection Using Haar Cascade Classifier and Convolutional Neural Network for Traffic Density Estimation Miftahul Hasanah; Gulpi Qorik Oktagalu Pratamasunu; Ratri Enggar Pawening
Indonesian Journal of Artificial Intelligence and Data Mining Vol 4, No 1 (2021): March 2021
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v4i1.10785

Abstract

Based on a survey released by the TomTom Traffic Index in 2018, Indonesia was ranked seventh in the category of the most congested country in the world. One of the factors affecting traffic congestion in Indonesia is an inflexible and conventional traffic management system. In this regard, it is necessary to have a better traffic management system such as a Smart Traffic Light. One way to implement a smart traffic light system is to make a vehicle detection and counting system on the traffic CCTV video automatically. The methods used in this research are Haar Cascade Classifiers and Convolutional Neural Network. Haar Cascade Classifiers have fast computation processes and CNN is applied to validate the detection results of the Haar Cascade method for better accuracy. The average level of accuracy achieved by the system on quiet test data is 82%, normal test data is 69%, and busy test data is 60%. Meanwhile, the average computation time needed by the system for the quiet test data is 0.63 seconds, the normal test data is 0.52 seconds, and the busy test data is 1.05 seconds.
C4.5, K-Nearest Neighbor, Naïve Bayes, and Random Forest Algorithms Comparison to Predict Students’ On Time Graduation Gunawan Gunawan; Hanes Hanes; Catherine Catherine
Indonesian Journal of Artificial Intelligence and Data Mining Vol 4, No 2 (2021): September 2021
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v4i2.10833

Abstract

Study program performance can be seen from the achievement of accreditation status, where one of the assessment instruments related to the graduate profile is the length of study. Graduation on time is one indicator of student’s success in obtaining a bachelor's degree and is an important attribute, because by being able to predict the period of study, universities can minimize student graduation failures by making more intensive planning, study escort, and guidance. Data mining classification techniques can be used to predict students graduation on time. Many data mining classification algorithms can be used, so it is necessary to make comparisons to determine the level of accuracy of each algorithm. The algorithms that will be compared in this study are C4.5, K-Nearest Neighbor, Naive Bayes, and Random Forest. The data used were 2,022 graduates from Informatics Engineering and Information System Study Program of STMIK Mikroskil Medan from 2011 to 2014, in which the attributes used include gender, regional origin, time of study, grade of Entrance Screening Examination, and Grade Point Average (GPA). The results of the classification process are evaluated using cross validation and confusion matrix to determine the most accurate data mining classification algorithm for predicting student graduation on time, where the K-Nearest Neighbor and Random Forest algorithms have the highest accuracy of 72,651%, followed by the C4.5 algorithm 72,453%, and the Naïve Bayes algorithm 71,860%.
Consumer Opinion Extraction Using Text Mining for Product Recommendations On E-Commerce Erlina Halim; Ronsen Purba; Andri Andri
Indonesian Journal of Artificial Intelligence and Data Mining Vol 4, No 1 (2021): March 2021
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v4i1.10834

Abstract

This study aims to evaluate consumer opinions in text form on e-commerce to determine the accuracy of ratings given by consumers with opinions using text mining with the lexicon approach. The research data was obtained online using a crawling technique using the API provided by Shopee. The conditions of diverse opinions and use of non-standard words are challenges in processing opinions. Opinion must be processed normalization and repairs using dictionary of words before going to extract using lexicon approach. Dictionary of words contain opinions with weights that are worth 1 to 5 for positive opinions and are worth -1 to -5 for negative opinions. For each opinion will be classified using the maximum ratio of the weight of positive opinion compared to the weight of negative opinion. The classification of opinion produced is positive, negative or neutral. Opinion classification is then compared with the rating classification to work out the extent of accuracy. The comparison produces an accuracy of 80.34% by completing an opinion dictionary.
Implementation of Genetic Algorithms in The Application of Car Racing Games Ade Pujianto
Indonesian Journal of Artificial Intelligence and Data Mining Vol 4, No 1 (2021): March 2021
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v4i1.10835

Abstract

The car racing game is a game that has always been popular from the past until now. where this game has lots of interesting gameplay, especially when adding artificial intelligence (AI) which makes the gameplay of the game even more challenging because the game is more dynamic with various levels of difficulty. However, most research on the application of artificial intelligence (AI) to the gameplay of car racing games is only limited to the application of game opponents. using genetic algorithms for innovations in game gameplay. Optimization of the game configuration will also be carried out to determine the level or difficulty level of the game's gameplay. The research flow to be implemented is the indie development method where the development method is used by indie game makers. The output of this research is to make scientific publications in accredited national journals and patent rights for car racing game game products. The contribution to this research is to make gameplay that is different from previous research, namely in making gameplay or applying artificial intelligence (AI) to making arena or racing circuits and optimizing genetic algorithms by adding configuration settings to the genetic algorithm

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