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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 2 (2022): September 2022" : 7 Documents clear
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
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.
Algorithm Decission Tree C4.5 and Backpropagation Neural Network for Smarthpone Price Classification Muhammad Ridho Al Fathan; M Fadhil Arfa; Habibah Br. Lumbantobing; Rahmaddeni Rahmaddeni
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.v5i2.19064

Abstract

Smartphones are a necessity in this technological age. In fact, everyone has at least one smartphone, this is because of its role that can help daily activities. There are data smartphone prices from major companies from Kaggle. The data is divided into 2000 training data and 1000 test data, the price range of smartphones based on the features provided. The analysis needed is the relationship between the features of smartphone and the selling price. To get this information, data mining techniques can be used. This study uses the Decission Tree C4.5 algorithms and the Backpropagaition Neural Network algorithm for classification problems. The technique used will be compared to a better algorithm in carrying out the classification process. The classification method consists of predictor variables and one target variable. The software used to process the data is Rapid Miner software. The results of the study get the accuracy of the Backpropagation Neural Network algorithm 96.65% and the same data is also applied to the C4.5 algorithms with an accuracy of 83.75%. From the research results, it can be concluded that the backpropagation neural network algorithm is the best algorithm for smartphone price classification with accuracy 96.65%.

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