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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 207 Documents
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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%.
Classification Between Suicidal Ideation and Depression Through Natural Language Processing Using Recurrent Neural Network Rhenaldy Rhenaldy; Ladysa Stella Karenza; Hidayaturrahman Hidayaturrahman; Muhamad Keenan Ario
Indonesian Journal of Artificial Intelligence and Data Mining Vol 5, No 2 (2022): September 2022
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v5i2.17485

Abstract

The use of machine learning has been implemented in various ways, including to detect depression in individuals. However, there is hardly any research done regarding classification between suicidal ideations and depression among individuals through text analysis. Differentiating between depression and suicidal ideation is crucial, considering the difference in treatment between the two mental illness. In this paper, we propose a detection model using Recurrent Neural Network (RNN) in the hopes to improve previous models made by other researchers. By comparing the proposed model with the previous works as the baseline model, we discovered that the proposed model (RNN) performed better than the baseline models, with the accuracy of 86.81%, precision of 97.13%, recall score of 94.69%, f1 score of 95.90%, and area under the curve (AUC) score of 92.84%.
Application of Data Mining to Group the Spread of Covid-19 in West Java Province, Indonesia Using the K-Means Algorithm Ronald Sebastian; Christina Juliane
Indonesian Journal of Artificial Intelligence and Data Mining Vol 5, No 2 (2022): September 2022
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v5i2.18721

Abstract

Covid-19 cases in Indonesia have not subsided. The spread of COVID-19 cases has reached provinces in Indonesia such as West Java, which is one of the many locations where the virus has been detected. COVID-19 cases have spread to 28 districts and cities in West Java. Researchers must determine the level of distribution of COVID-19 cases which are divided into three clusters, namely high, medium, and low clusters, so that the West Java Regional Government can take action in an effort to prevent the spread of COVID-19 cases. Researchers use data mining and the K-means Clustering algorithm. to examine the distribution of COVID-19 cases. This data set for the study of the spread of COVID-19 in West Java Province, covers data for the period August 1, 2020 to July 15, 2022. To perform K-means Clustering on the data set, researchers used RapidMiner Studio 9.10. The results of this study indicate that in West Java there are two cities with the highest Covid-19 clusters, namely Bekasi and Depok, six cities and district in the medium cluster, namely city of Bogor, Bandung, and Karawang District, Bekasi, Bandung and Bogor, and The twenty district/cities in the lowest cluster for the spread of COVID-19 cases are the cities of Banjar, Cimahi, as well as the districts of West Bandung, Ciamis, Cianjur, Cirebon, Garut, Indramayu, Kuningan, Majalengka, Pangandaran, Purwakarta, Subang, Sukabumi, Sumedang, Tasikmalaya.
Identification of Diabetes Mellitus Risk Factors With a Data Mining Classification Approach Ade Agustina; Galih Ady Permana; Christina Juliane
Indonesian Journal of Artificial Intelligence and Data Mining Vol 5, No 2 (2022): September 2022
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v5i2.18841

Abstract

Diabetes mellitus is a chronic disease characterized by an increase in the frequency of eating, drinking and urinating due to the failure of the process of sugar entering the body to be converted into energy due to the pancreas function not being able to produce enough insulin or not producing insulin at all. The purpose of writing this paper is to test the accuracy of the decision tree and rules generated by the ID3 algorithm and correlate it with literature studies from research that has been carried out by researchers in the health sector related to diabetes and the results of this classification are expected to be used as a reference. For everyone to be able to change their lifestyle to avoid the risk of developing diabetes mellitus by looking at the attributes of the dataset. In this study, the application of data mining with the classification method with the ID3 algorithm using datasets from the BRFSS survey results was carried out. The results of data testing can be obtained from the accuracy of the rules generated by the ID3 algorithm with an accuracy rate of 85.95%. The rules generated by the ID3 algorithm are also correlated with the literature from research that has been carried out by researchers in the health sector, and the results are that the rules generated from the attribute indicators of the dataset have relevance and suitability
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.
Application of Data Mining Using the K-Means Clustering Algorithm for Opening Industrial Classes in Vocational High Schools Aan Rosydiana; Dian Sediana; Christina Juliane
Indonesian Journal of Artificial Intelligence and Data Mining Vol 5, No 2 (2022): September 2022
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v5i2.19172

Abstract

Vocational High School has a goal to enter the world of work, meaning that it must have a skill program to be relevant to the industrial world. However, adapting to the industrial world is difficult, one of the things that is happening between industries is increasing. Various efforts continue to be made, among others, by establishing an industrial class, the formation of an industrial class is expected to produce students who have competencies in accordance with the standards required by the collaborating industries. The formation of an industrial class can be done by applying data mining methods, in order to form the right industrial class and in accordance with predetermined criteria. This study aims to classify new student registration data at State Vocational Schools at the Regional Education Office XIII Branch of West Java Province in 2022 and the results of the grouping are used to form industrial classes. The clustering process is carried out using the K-Means algorithm and cluster analysis is carried out with the help of RapidMiner software. The results showed that the data clustering was formed into 4 clusters. The cluster that has the highest number is cluster 1 and the cluster that has the lowest number is cluster 0. There are variables used for data grouping, including school variables and expertise programs, from these variables it is obtained that the schools selected by students are based on the highest order and have expertise programs contained in their clusters, which need to be considered when opening industrial classes.
Potential for Improvement of Student's English Language with the C4.5 Algorithm Cyntia Lasmi Andesti; Fitria Lonanda; Nur Azizah
Indonesian Journal of Artificial Intelligence and Data Mining Vol 5, No 2 (2022): September 2022
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v5i2.17333

Abstract

Proficiency in English is not a barrier for the Millennial Generation today. Sophisticated technology can also help increase proficiency in English. However, there are still many who do not use this technology to support English proficiency. Apart from not using technology, the millennial generation is also lacking in practicing English in everyday life. There are several factors that can predict the potential for increasing proficiency in English, namely Reading (C1), Practice (C2), Pronunciation (C3), Environment (C4), Technology (C5), English Club (C6), and Listening (C7). These factors become parameters in solving problems that occur. These parameters are used in the Data Mining method, namely Classification C4.5 or what is often called the C4.5 Algorithm. This study aims to determine the potential for increasing proficiency in English. The data processed in this study were 90 respondents from the results of the questionnaire data distributed. The software used in the processing is WEKA 3.8.6 Software. The processing steps are to calculate the Entropy value and Gain value of each attribute, form the root node (node) based on the highest gain value and form a decision tree. The results of the discussion on the Weka 3.8.6 software, the data accuracy rate is 90 % or 81 data and the error rate is around 10 % or 9 Data. From the data of 90 respondents, the factors that influence the potential for increasing proficiency in English are Practice (C2).
Handling Outliers in The Stochastic Frontier Model Using Cauchy and Rayleigh Distributions to Measure Technical Efficiency of Rice Farming Bussiness Retna Nurwulan; Anik Djuraidah; Anwar Fitrianto
Indonesian Journal of Artificial Intelligence and Data Mining Vol 5, No 2 (2022): September 2022
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v5i2.19597

Abstract

Technical Efficiency (TE) is one of the essential indicators used to evaluate the development of the agricultural sector. Generally, the statistical model used to measure TE is a stochastic frontier model with the noise being normally distributed and the inefficiency being half-normally distributed. The problem is that the model is not robust when outlier observations occur. This study proposed a stochastic production frontier model with a fat-tailed distribution to overcome outlier observations. This study used two stochastic models with fat-tailed distribution used in this study: Chaucy-half normal and normal-Rayleigh stochastic models. The translog production function was selected as a connecting function between the input and output. These two models were applied to estimate the technical efficiency of rice farming in Central Kalimantan. The results showed that the proposed model could reduce or eliminate outliers in the remaining inefficiencies. In addition, the range of technical efficiency values had also narrowed. Thus, the Chaucy-half normal and normal-Rayleigh stochastic models can handle outliers.

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