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Indonesian Journal of Artificial Intelligence and Data Mining

IJAIDM
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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 207 Documents

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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

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

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.

Implementation of AD8232 ECG Signal Classification Using Peak Detection Method For Determining RST Point Martin Clinton Tosima Manullang; Jonathan Simanjuntak; Ahmad Luky Ramdani
Indonesian Journal of Artificial Intelligence and Data Mining Vol 2, No 2 (2019): September 2019
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

The medical world, especially those related to diseases and management of the heart uses ECG as a measurement tool. ECG has important points determined based on predetermined characteristics. The point is PQRST, where three of them are used as research objects in this paper. AD8232 is used as a research medium where the RST points must be determined in the AD8232 plot results by first determining the R points based on the highest peak. The results obtained were satisfactory wherein from 10 ECG graphic samples, 9 of them obtained RST point measurements which tended to be similar to conventional ECG measurements using millimeter paper as plotting media. Accuracy values reaching more than 90% indicate the reliability of the implementation results.

Implementation of C4.5 Algorithm for Critical Land Prediction in Agricultural Cultivation Areas in Pemali Jratun Watershed Deden Istiawan; Laelatul Khikmah
Indonesian Journal of Artificial Intelligence and Data Mining Vol 2, No 2 (2019): September 2019
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Watershed is a complex system that is built on physical systems, biological systems and human systems that are related to each other. Each component has a distinctive nature and its existence is related to other components so as to form a unified ecosystem. Land use that does not pay attention to the conservation requirements of land and water causes land degradation which ultimately results in critical land. The impact of critical land is not only the withdrawal of soil properties, but also results in a decrease in production functions. Prediction of the critical level of land is needed to reduce the level of damage to the watershed, so that it can be used for policy making by the relevant agencies. In this research C4.5 algorithm will be applied to predictions of critical land in agricultural cultivation areas using critical land parameters. Based on the results of the research on critical land classification of agricultural cultivation areas in the jratun pemali watershed it can be concluded that the C.45 algorithm can be implemented to predict critical land in agricultural cultivation areas with an accuracy rate of 92.47%.

K-Nearest Neighbor for Classification of Tomato Maturity Level Based on Hue, Saturation, and Value Colors Suwanto Sanjaya; Morina Lisa Pura; Siska Kurnia Gusti; Febi Yanto; Fadhilah Syafria
Indonesian Journal of Artificial Intelligence and Data Mining Vol 2, No 2 (2019): September 2019
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

The selection of tomatoes can use several indicators. One of the indicators is the fruit color. In digital image processing, one of the color information that could be used in Hue, Saturation, and Value (HSV). In this research, HSV is proposed as a color model feature for information on the ripeness of tomatoes. The total data of tomato images used in this research were 400 images from four sides. The maturity level of tomatoes uses five levels, namely green, turning, pink, light red, and red. The process of divide data uses K-Fold Cross Validation with ten folds. The method used for classification is k-Nearest Neighbor (kNN). The scenario of the test performed is to combine the image size with the parameter value of the neighbor (k). The image sizes tested are 100x100 pixels, 300x300 pixels, 600x600 pixels and 1000x1000 pixels. The “k” values tested were 1, 3, 5, 7, 9, 11, and 13. The highest accuracy reached 92.5% in the image size 1000x1000 pixels with a parameter “k” is 3. The result of the experiment showed that the image size has a significant influence of accuracy, but the parameter value of neighbor (k) has an influence that is not too significant.

Modeling Statistical Downscaling for Prediction Precipitation Dry Season in Bireuen District Province Aceh Juniana Husna; Sanusi Sanusi
Indonesian Journal of Artificial Intelligence and Data Mining Vol 2, No 2 (2019): September 2019
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v2i2.7518

The Asian-Australian monsoon circulation specifically causes the Indonesian region to go through climate changebility that impacts on rainfall variability in different Indonesia’s zone. Local climate conditions such as rainfall data are commonly simulated using GCM time series data. This study tries to model the statistical downscaling of GCM in the form of 7x7 matrix using Support Vector Regression (SVR) for rainfall forecasting during drought in Bireuen Regency, Aceh. The output yields optimal result using certain parameter i.e. C = 0.5, γ = 0.8, d = 1, and ↋= 0.01. The duration of computation during training and testing are ± 45 seconds for linear kernels and ± 2 minutes for polynomials. The correlation degree and RMSE values of GCM and the actually observed data at Gandapura wheather station are 0.672 and 21.106. The RSME value obtained in that region is the lowest compared to the Juli station which is equal to 31,428. However, the Juli station has the highest correlation value that is 0.677. On the other hand, the polynomial kernel has a correlation degree and RMSE value equal to 0.577 and 29,895 respectively. To summary, the best GCM using SVR kernel is the one at Gandapura weather station in consideration of having the lowest RMSE value with a high correlation degree.

Implementation of K-Medoids and FP-Growth Algorithms for Grouping and Product Offering Recommendations Imaduddin Syukra; Assad Hidayat; Muhammad Zakiy Fauzi
Indonesian Journal of Artificial Intelligence and Data Mining Vol 2, No 2 (2019): September 2019
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

212 Mart Rambutan Street on Pekanbaru City is a company engaged in retail. Meeting the needs of consumers and making the right decision in determining the sales strategy is a must. One way to find out market conditions is to observe sales transaction data using data mining. The data mining method commonly used to analyze market basket (Market Basket Analysis) is the Association Rule. The Association Rule can provide product recommendations and promotions, so that the marketing strategy is more targeted and the items promoted are the customer's needs. At 212 Mart, the determination of product promotion is obtained from the analysis of sales transaction data reports, which are based on the most sold products and the expiration date. Often the product being promoted does not fit the customer's needs. The purpose of this study is to apply the K-Medoids algorithm for clustering on FP-Growth in producing product recommendation rules on a large number of datasets so that they can provide technical recommendations / new ways to the 212 Mart in determining product promotions. The results obtained are from the experiments the number of clusters 3 to 9 obtained optimal clusters of 3 clusters based on the validity test of the Davies Bouldin Index with a value of 0.678. With a minimum support value of 5% - 9% and a minimum value of 50% confidence, the result is that the Association Rule is found only in cluster 3 with 5 rules.

Data Mining Predictive Modeling for Prediction of Gold Prices Based on Dollar Exchange Rates, Bi Rates and World Crude Oil Prices Iman Priyadi; Julius Santony; Jufriadif Na'am
Indonesian Journal of Artificial Intelligence and Data Mining Vol 2, No 2 (2019): September 2019
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v2i2.6864

Gold is an investment instrument that is quite safe from inflationary attacks, and gold is one aspect of initiating investment. Can by buying gold in physical form and then selling when the price has risen high or by digitally investing gold. One of them is by trading gold online. To maximize the benefits of gold trading, a gold price prediction (XAUUSD) is needed for traders. This study aims to (1) Analyze various factors that influence the price of gold (2) Provide recommendations about the prediction of gold prices. Materials that will be used as objects of research to produce gold price predictions include historical XAUUSD (Gold) data itself, historical crude oil data, historical dollar data (USD IDR) and BI 7-Day Repo Rate (BI Rate). ), in producing the prediction of the gold price used Mining Predictive Modeling data using the linear regression function. The results to be achieved from this study is to provide accurate gold price predictions so that it can be used as a reference in making decisions to buy / sell positions in trading. The prediction of the XAUUSD gold price generated is expected to provide significant interest to the investment players (traders) in order to maximize the profit generated.From the results of the trading tests that have been carried out, the implementation of predictive modeling data mining using a linear regression function produces recommendations for gold price predictions (XAUUSD) with an accuracy of 85%.

Implementation the Knuth Morris Pratt (KMP) Algorithm in Interactive Web Monitoring and Recording Rabbit Reproduction System Halimah Tus Sadiah; Muhamad Saad Nurul Ishlah
Indonesian Journal of Artificial Intelligence and Data Mining Vol 2, No 2 (2019): September 2019
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Recording and monitoring rabbit reproduction data at Balai Penelitian Ternak (Balitnak) are yet integrated using systematic recording and searching system approach. Thus the purpose of this study is to build an interactive web monitoring and recording rabbit reproduction system as well as implementing the Knuth Morris Pratt (KMP) algorithm in order to provide a reliable search function. This research produced an interactive monitoring of recording rabbit reproduction data which record important information about rabbit codes, dates (mating, palpation, childbirth, 21 days, 35 days), weight (mating, palpation, after giving birth, 21 days, 35 days ), the number of children born alive or dead (giving birth, 21 days, 35 days). The results of the implementation of the KMP algorithm generated a search with a time of 0.015095 milliseconds with an algorithm test based on the search for rabbit names as many as 20 types of rabbits.

Internet of Things (IOT) Development for The Chicken Coop Temperature and Humidity Monitoring System Based on Fuzzy Jamaluddin Husein; Oktaf Brillian Kharisma
Indonesian Journal of Artificial Intelligence and Data Mining Vol 3, No 1 (2020): March 2020
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v3i1.9294

The high increase in broiler each year there are several factors that must be considered, one of them is the temperature and humidity of the cage. The brooding cage space of 10 ekor/1. If more then that couse broiler is not optimal. The temperatur needed is 29°C-35°C and humidity 60%-70% for brooding. In general control of temperature and humidity, unable to maintain cage needs. Then it is necessary to do automatic control using the fuzzy logic method and the concept of Internet of Things (IoT). Base on the testing result the system has been able to maintain the set point of temperature and humidity and get broiler growth not same, The best stability value during the test is condition without DOC produces no overshoot temperature parameters and steady state error 0,48, no overshoot and steady state for humidity. On the condition with DOC produces overshoot temperature 0,06 and steady state error 0,15, and overshoot 0,1 and steady state 0,4 for humidity. While on the IoT concept has been able to display the temperature and humidity values on the cage

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Contact Name
Mustakim

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Mustakim

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Journal Mail Official
ijaidm@uin-suska.ac.id

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Location
Kab. kampar,

Riau

INDONESIA