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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
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Search results for , issue "Vol 5, No 1 (2022): March 2022" : 7 Documents clear
Stock Price Prediction Using XCEEMDAN-Bidirectional LSTM -Spline Kelvin Chen; Ronsen Purba; Arwin Halim
Indonesian Journal of Artificial Intelligence and Data Mining Vol 5, No 1 (2022): March 2022
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v5i1.14424

Abstract

Bidirectional Long Short Term Memory (Bidirectional LSTM) is a machine learning technique with the ability to capture data context by traversing backward data to forward data and vice versa. However, the characteristics of stock data with large fluctuations, high dimensions and non-linearity become a challenge in obtaining high stock price prediction accuracy values. The purpose of this study is to provide a solution to the problem of stock data characteristics with large fluctuations, high dimensions and non-linearity by combining the Complete Ensemble Empirical Mode Decomposition With Adaptive Noise method for exogenous features (XCEEMDAN), Bidirectional Long Short Term Memory (LSTM), and Splines. The predicted data will go through normalization and preprocessing using XCEEMDAN then the XCEEMDAN decomposition results are divided into high and low frequency signals. The bidirectional LSTM handles high frequency signals and the Spline model handles low frequency signals. The test is carried out by comparing the proposed XCEEMDAN-Bidirectional LSTM-Spline model with the XCEEMDAN-LSTM-Spline model using the same parameters and changing the noise seed randomly 50 times. The test results show that the proposed model has the smallest RMSE average value of0.787213833 while model which is compared only has the smallest RMSE average value of 0.807393567.
Human Face Identification Using Haar Cascade Classifier and LBPH Based on Lighting Intensity Hutama Hadi; Hasdi Radiles; Rika Susanti; Mulyono Mulyono
Indonesian Journal of Artificial Intelligence and Data Mining Vol 5, No 1 (2022): March 2022
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v5i1.15245

Abstract

The problem in implementing online learning during the Covid-19 era is the lack of internet access for video streaming, especially in small towns or villages. The solution idea is to minimize the video bandwidth quota by only showing emoticons. The first step of the process is the system must be able to lock the face area to be translated. This study aims to identify areas of the human face based on camera captures. The research was conducted using the Haar cascade classifier algorithm to recognize the facial area of the captured image. Then the Local Binary Pattern Histogram algorithm will recognize the identity of the face. The lighting scenario will be used as a distracting effect on the image. The results showed that based on 30 sets of images tested in bright conditions, the system was able to recognize facial identities up to 62%, normal conditions 51% and dark conditions 46%.
Association Between Spritual Intelligence, Social Support and Mental Health of University Student During Covid-19 Based on Dependency Degree and Ordinal Logistics Regression Reza Agustina; Reza Selfiana; Auzia N. Oktavani; Risa K. Sari; Veny Alvionita; Maulidya T. Putri; Yusnita Hasibuan; Riswan Efendi
Indonesian Journal of Artificial Intelligence and Data Mining Vol 5, No 1 (2022): March 2022
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v5i1.15276

Abstract

The modeling of mental health has been widely studied using statistical approaches such as the chi-square test, regression analysis, and ordinal logistics. However, not many studies apply ordinal logistic regression and dependency degree into this domain, where both of these approaches are good statistical and non-statistical approaches for investigating categorical data. In this study, the researcher is interested in combining the dependency degree and ordinal logistic regression and identifying the mental health factors of students in the Covid-19 era. The research sample was students majoring in Al-Qur'an and Tafsir Study at UIN Suska Riau. The results indicated that a significant relationship between the informative support dimension to the disorder, the instrumental dimension to avoidance and increased self-awareness with an average dependency value between dimensions of 4.13% and 4.53%. Through this relationship, it can be seen that students are more likely to experience stress disorder (PTSD) if they are reluctant to do good than the number of awards received by students.
Fuzzy Sugeno with Gain Compensator Based on Pole Placement for Controlling Coupled Water Tank System Halim Mudia; Ahmad Faisal; Marhama Jelita
Indonesian Journal of Artificial Intelligence and Data Mining Vol 5, No 1 (2022): March 2022
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v5i1.16350

Abstract

The control of liquid level in tanks is a classic problem in process industries. Most of the liquid will be processed by chemical or mixing treatment in the tanks. Because of that, the liquid level in the tanks must be regulated, so that in order for this system to work as we want, it needs a control strategy. Therefore, this research will use a control strategy using fuzzy sugeno with a gain compensator based on pole placement for controlling level of tank 2 in the coupled water tank system with setpoint is 10 centimeters at time 0 seconds and 8 centimeters given at time 1000 seconds. Wherein, the gain compensator based on pole placement is used to make the output system robust to changes in setpoint with zero steady-state error and fuzzy sugeno for faster time response. The results show that using the fuzzy sugeno with a gain compensator based on pole placement can follow setpoint given with 0 centimeters of steady-state error, 0% for overshoot, 44,6538 seconds for rising time, 62,2688 seconds for settling time and can follow setpoint changes in 58,8662 seconds.
Sentiment Analysis of Expedition Customer Satisfaction using BiGRU and BiLSTM Salsabila Zahirah Pranida; Arrie Kurniawardhani
Indonesian Journal of Artificial Intelligence and Data Mining Vol 5, No 1 (2022): March 2022
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v5i1.17361

Abstract

The occurrence of a pandemic caused behavioral changes that occurred in Indonesian society, especially in increasing interest in online purchases. The increased purchases of goods increased the volume of four expeditions, namely: JNE, JNT Express, Sicepat, and Anteraja. To find out the customer satisfaction of the users of the four expeditions automatically, sentiment analysis was conducted based on the thousand tweet data from the opinions of expedition users in three-class categories, which are positive, negative, and neutral. Two deep learning methods were used to analyze the sentiment of expedition customer satisfaction: BiGRU and BiLSTM. The activities conducted during the sentiment analysis were crawling, preprocessing, data labeling, modeling, and evaluation. The performance evaluation results of the two methods use an accuracy matrix over 1,217 test data. The BiGRU method produces an accuracy performance of 71.5% and the BiLSTM method produces an accuracy performance of 66.5%.
Implementation of Decision Tree Algorithm Machine Learning in Detecting Covid-19 Virus Patients Using Public Datasets Nadiah Nadiah; Sopian Soim; Sholihin Sholihin
Indonesian Journal of Artificial Intelligence and Data Mining Vol 5, No 1 (2022): March 2022
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v5i1.17054

Abstract

The advancement of AI (Artificial Intelligence) technology has been widely implemented in numerous sectors of daily life. Machine Learning is one of the subfields of Artificial Intelligence. Using statistics, mathematics, and data mining, machine learning is developed so that machines may learn by assessing data without being reprogrammed. At this time the world is on alert for the spread of a popular virus, the corona virus. Coronaviruses are part of a family of viruses caused by diseases ranging from the flu. The disease caused by the coronavirus is known as Covid-19. Therefore, to help identify whether a somebody has coronavirus disease based on certain symptoms, a model is created that can classify people with the covid-19 virus using machine learning. The classification methods utilized in this study are decision trees and large-scale machine learning projects. The study employed Python 3.7 as its programming language and PyCharm as its Integrated Development Environment (IDE). Based on the results, the accuracy rate as expected after conducting various trials is 99%.
Identifying Characteristics of Households Recipient of the Government’s Social Protection Program Nofrida Elly Zendrato; Bagus Sartono; Utami Dyah Syafitri
Indonesian Journal of Artificial Intelligence and Data Mining Vol 5, No 1 (2022): March 2022
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v5i1.18579

Abstract

According to Statistics Indonesia, the number of poor people increased by 1,12 million people in March 2020. In March 2021, the percentage of poor people increased by 0,36 points compared to March 2020. The percentage of poor people in Banten Province has increased in the last three years (2019-2021). One way to reduce poverty by the government is to increase social protection programs. The characteristics of households receiving social protection programs were identified by modeling the classification of households using the random forest technique, obtaining important variables using the permutation feature importance and Shapley additive explanations interpretation techniques, and analyzing the most important variables from the two interpretations methods. Handling the imbalance data on the response variables using SMOTE technique and evaluating the classification model obtained an AUC value of 0,718. The important variables were selected from the permutation feature importance and Shapley additive explanation methods based on a consistent ranking at the top. Shapley’s additive explanation was more consistent than permutation feature importance. Six important, namely capita expenditure, education of the head of household, age of head of household, source of drinking water, floor area, and the number of household members.

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