Indonesian Journal of Artificial Intelligence and Data Mining
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.
Articles
207 Documents
<![CDATA[Implementation of Genetic Algorithms in The Application of Car Racing Games ]]>
<![CDATA[Ade Pujianto]]>
<![CDATA[ Indonesian Journal of Artificial Intelligence and Data Mining Vol 4, No 1 (2021): March 2021 ]]>
Publisher : <![CDATA[Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/ijaidm.v4i1.10835
<![CDATA[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]]>
<![CDATA[Radial Basis Function Neural Network Control for Coupled Water Tank ]]>
<![CDATA[Halim Mudia]]>
<![CDATA[ Indonesian Journal of Artificial Intelligence and Data Mining Vol 3, No 2 (2020): Spetember 2020 ]]>
Publisher : <![CDATA[Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/ijaidm.v3i2.10002
<![CDATA[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.]]>
<![CDATA[Clustering Productivity of Rice in Karawang Regency Using the Fuzzy C-Means Method ]]>
<![CDATA[Suna Mulyani]]>;
<![CDATA[Betha Nurina Sari]]>;
<![CDATA[Azhari Ali Ridha]]>
<![CDATA[ Indonesian Journal of Artificial Intelligence and Data Mining Vol 3, No 2 (2020): Spetember 2020 ]]>
Publisher : <![CDATA[Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/ijaidm.v3i2.10415
<![CDATA[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.]]>
<![CDATA[Optimization of Technical and Economical Objective Functions of Hybrid Renewable Energy Generation Based Genetic Algorithm ]]>
<![CDATA[Novendra Setyawan]]>;
<![CDATA[Zulfatman Zulfatman]]>;
<![CDATA[Haris Rahmana Putra]]>;
<![CDATA[Muhammad Ikhwanul Khair]]>
<![CDATA[ Indonesian Journal of Artificial Intelligence and Data Mining Vol 4, No 1 (2021): March 2021 ]]>
Publisher : <![CDATA[Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/ijaidm.v4i1.11690
<![CDATA[This study is aimed to optimize the technical and economic objective functions of a renewable energy hybrid generator system by using genetic algorithms (GA) in order to create a balanced and optimal power generation system configuration. The technical and economic aspects used were the Loss of Power Supply Probability (LPSP) and Annualized Cost of System (ACS), respectively. The objective functions of GA method were LPSP and ACS. The types of power plants used in this hybrid system were photovoltaic (PV), Wind Turbine (WT), battery, and Micro Hydro Power Plant (MHPP). Validation on the GA method was done by simulation in Matlab. Results of the simulation show that the use of the GA offers the most balanced system configuration with less expensive costs and a very good level of system reliability against hybrid systems. The use of the objective function with penalty factor scenario in GA is not as effective as the conventional GA, following the weakness of its evaluation results.]]>
<![CDATA[Local Binary Pattern and Learning Vector Quantization for Classification of Principal Line of Palm-Hand ]]>
<![CDATA[Suwanto Sanjaya]]>;
<![CDATA[Ulfah Adzkia]]>;
<![CDATA[Lestari Handayani]]>;
<![CDATA[Febi Yanto]]>
<![CDATA[ Indonesian Journal of Artificial Intelligence and Data Mining Vol 3, No 2 (2020): Spetember 2020 ]]>
Publisher : <![CDATA[Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/ijaidm.v3i2.10236
<![CDATA[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.]]>
<![CDATA[Improving Stock Price Prediction with GAN-Based Data Augmentation ]]>
<![CDATA[Julisa Bana Abraham]]>
<![CDATA[ Indonesian Journal of Artificial Intelligence and Data Mining Vol 4, No 1 (2021): March 2021 ]]>
Publisher : <![CDATA[Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/ijaidm.v4i1.10740
<![CDATA[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]]>
<![CDATA[Clustering Analysis of Financial Distress on Tourism Sector Companies Go-Public Due to LSSR ]]>
<![CDATA[Ahmad Firman Maulana]]>;
<![CDATA[Camelia Iltazami Ulva]]>;
<![CDATA[Fath Esa Prasanti Kusuma]]>;
<![CDATA[Faza Budiarti]]>;
<![CDATA[Moh. Fadli Hidayat DJ Makaraseng]]>;
<![CDATA[Nyoman Putri Pradievy Syanthi]]>;
<![CDATA[Rani Nooraeni]]>
<![CDATA[ Indonesian Journal of Artificial Intelligence and Data Mining Vol 4, No 1 (2021): March 2021 ]]>
Publisher : <![CDATA[Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/ijaidm.v4i1.11303
<![CDATA[Large-Scale Social Restriction Policy (LSSR) to prevent the spread of COVID-19 has a big impact on economic activities, one of which is activities in the tourism sector. Restrictions on outdoor activities reduce the productivity of companies that can lead to bankruptcy. By knowing the financial condition of the company, we can predict whether the company will experience financial pressures or not. This paper tries to analyze the grouping of 100 companies in the tourism sector before (the first quarter of 2020) and after (the second quarter of 2020) the application of LSSR conditions. This paper uses the K-Means grouping method and the financial ratio of each company. Then, the variables in the analysis are Return on Asset (ROA), Total Asset Turn Over Ratio (TATO), Debt to Equity Ratio (DER), and Price to Earning Ratio (PER). The results showed that in the second quarter of 2020 or after the implementation of LSSR, almost all companies tend to be in a financially depressed condition. The number of companies that are under financial pressure after the implementation of this policy is 98 companies.]]>
<![CDATA[IoT-based Architecture for Automatic Detection of Fall Incident using Accelerometer Data ]]>
<![CDATA[I Wayan Wiprayoga Wisesa]]>;
<![CDATA[Genggam Mahardika]]>
<![CDATA[ Indonesian Journal of Artificial Intelligence and Data Mining Vol 3, No 2 (2020): Spetember 2020 ]]>
Publisher : <![CDATA[Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/ijaidm.v3i2.9686
<![CDATA[Fall is an unintentional incident that could happened in our daily life. For the elderly, fatal fall incident might increase the risk of death. There is a need to quickly do the first aid after fall incident occur. IoT based architecture made it possible to monitor fall incident remotely. The monitoring device records the activity and object movement using tri-axial accelerometer sensor attached to user’s waist. The system implemented simple thresholding technique based on total acceleration recorded over time. Various scenarios were performed in order to test the system including normal daily activities and fall incident. Using sensitivity and specificity measurement to evaluate the system, the proposed system achieved the value of 98% and 96% respectively.]]>
<![CDATA[Automatic Car Detection Using Haar Cascade Classifier and Convolutional Neural Network for Traffic Density Estimation ]]>
<![CDATA[Miftahul Hasanah]]>;
<![CDATA[Gulpi Qorik Oktagalu Pratamasunu]]>;
<![CDATA[Ratri Enggar Pawening]]>
<![CDATA[ Indonesian Journal of Artificial Intelligence and Data Mining Vol 4, No 1 (2021): March 2021 ]]>
Publisher : <![CDATA[Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/ijaidm.v4i1.10785
<![CDATA[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.]]>
<![CDATA[A Review Comparative Mamography Image Analysis on Modified CNN Deep Learning Method ]]>
<![CDATA[Siti Ramadhani]]>
<![CDATA[ Indonesian Journal of Artificial Intelligence and Data Mining Vol 4, No 1 (2021): March 2021 ]]>
Publisher : <![CDATA[Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/ijaidm.v4i1.10891
<![CDATA[This study aims to review classification of breast abnormality acuracy on deep learing using comparative CNN development of concepts and models in various cases and implementation. The CNN based breast mass detection approach to simultaneously localize and classify the mass into either benign or malignant abnormality by exploring all major types of medical image modalities that collected on dataset and hospital. This CNN method modified to R-CNN and SD-CNN based on modification on feature extraction to improve acuracy level. R-CNN adopt RPN and ROI for Feature extraction. The model designed, trained and evaluated to achieved detection acuracy. The proposed model on R-CNN achieved detection accuracy of up to 91.86%, sensitivity of 94.67% and AUC-ROC of 92.2%. SD-CNN study the two-fold applicability of CNN to improve the breast cancer diagnosis. This method recombined images from CEDM in helping the diagnosis of breast lessons using a Deep-CNN method with virtual feature image. The experiment shows the features from LE images can achieve from accuracy of 0.85 and AUC of 0.84, then when adding the recombined imaging features, model performance improves to accuracy of 0.89 with AUC of 0.91 until 0.92]]>
