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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 8 Documents
 1 
Search results for , issue "Vol 4, No 1 (2021): March 2021" : 8 Documents clear
Classification of Pineapple Fruit Comosus Merr (Nanas) Quality Using Learning Vector Quantization Method Efendi, Muhamad; Defit, Sarjon; Nurcahyo, Gunadi Widi
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.v2i2.4621

Abstract

The demands of publics for these fruits Ananas Comosus Merr (Pineapple) became higher years to years because of the fruit has so many virtues for human healthy and the taste of this fruit is sweet and fresh. Therefore the pineapple farmers have to protect the quality and quantity of this plant in order to get high produce. This research help the pineapple farmers to classify to quality of pineapple fruits by using neural network with Learning Vector Quantization method which has 2 classes, such as: First quality (1st) and Second quality (2nd) quality. This method has 2 process they are : training process and testing process. To input data in the training and testing process are using uniformity, characteristic of varieties, the rate of aging, hardness, size, stem, crown, manure, destroyer, spoilage, rotten and the total solid content of the least was taken by observed the crop of pineapple farmers in the Teluk Batil village Sungai Apit district Siak Riau province. Learning Vector Quantization method automatically will classify the pineapple into their class. The result of the testing classification has gotten the accuracy 65.56% for the first (1st) quality and 34.44% for the second (2nd) quality. At the second testing has gotten 66.67% the accuracy for the first (1st) quality and 33.33% for the second (2nd) quality. At the third (3rd) testing has gotten 64.44% the accuracy for first (1st) quality and 35.56% for the second (2nd) quality.
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
Optimization of Technical and Economical Objective Functions of Hybrid Renewable Energy Generation Based Genetic Algorithm Novendra Setyawan; Zulfatman Zulfatman; Haris Rahmana Putra; Muhammad Ikhwanul Khair
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.v4i1.11690

Abstract

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.
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
Clustering Analysis of Financial Distress on Tourism Sector Companies Go-Public Due to LSSR Ahmad Firman Maulana; Camelia Iltazami Ulva; Fath Esa Prasanti Kusuma; Faza Budiarti; Moh. Fadli Hidayat DJ Makaraseng; Nyoman Putri Pradievy Syanthi; Rani Nooraeni
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.v4i1.11303

Abstract

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.
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.
A Review Comparative Mamography Image Analysis on Modified CNN Deep Learning Method Siti Ramadhani
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.v4i1.10891

Abstract

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

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