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International Journal of Artificial Intelligence Research

IJAIR
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Published by STMIK Dharma Wacana

ISSN : - EISSN : 25797298 DOI : -

Core Subject : Science, Engineering,

Computer Science & IT Electrical & Electronics Engineering

International Journal Of Artificial Intelligence Research (IJAIR) is a peer-reviewed open-access journal. The journal invites scientists and engineers throughout the world to exchange and disseminate theoretical and practice-oriented topics of Artificial intelligent Research which covers four (4) majors areas of research that includes 1) Machine Learning and Soft Computing, 2) Data Mining & Big Data Analytics, 3) Computer Vision and Pattern Recognition, and 4) Automated reasoning. Submitted papers must be written in English for initial review stage by editors and further review process by minimum two international reviewers.

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Articles 10 Documents

Search results for , issue "Vol 5, No 1 (2021): June 2021" : 10 Documents clear

Analysis of Expert System for Early Diagnosis of Disorders During Pregnancy Using the Forward Chaining Method Basiroh, Basiroh; Priyatno, Priyatno; Kareem, Shahab Wahhab; Nurdiyanto, Heri
International Journal of Artificial Intelligence Research Vol 5, No 1 (2021): June 2021
Publisher : Universitas Dharma Wacana

Nowadays technological developments are increasingly having a positive influence on the development of human life, including in the health sector. One of them is an expert system that can transfer an expert's knowledge into a computer application to simplify and speed up the diagnosis of a disorder or disease in humans. The purpose of this final project is to design an application to diagnose diseases that occur during pregnancy which is caused by the existence of these pregnancies to simplify and speed up the diagnosis of diseases experienced by pregnant women. This study uses the forward chaining method. By involving experts in this expert system analysis according to current needs. Users are given easy access to information on several types of pregnancy disorders and their symptoms, as well as consultation through several questions that the user must answer to find out the results of the diagnosis. While experts are facilitated in system management, both the process of adding, updating and, deleting data.

Determination of Student Subjects in Higher Education Using Hybrid Data Mining Method with the K-Means Algorithm and FP Growth Rani, Larissa Navia; Defit, Sarjon; Muhammad, L. J.
International Journal of Artificial Intelligence Research Vol 5, No 1 (2021): June 2021
Publisher : Universitas Dharma Wacana

The large number of courses offered in an educational institution raises new problems related to the selection of specialization courses. Students experience difficulties and confusion in determining the course to be taken when compiling the study plan card. The purpose of this study was to cluster student value data. Then the values that have been grouped are seen in the pattern (pattern) of the appearance of the data based on the values they got previously so that students can later use the results of the patterning as a guideline for taking what skill courses in the next semester. The method used in this research is the K-Means and FP-Growth methods. The results of this rule can provide input to students or academic supervisors when compiling student study plan cards. Lecturers and students can analyze the right specialization subject by following the pattern given. This study produces a pattern that shows that the specialization course with the theme of business information systems is more followed by students than the other 2 themes

Uplift modeling VS conventional predictive model: A reliable machine learning model to solve employee turnover Wijaya, Davin; DS, Jumri Habbeyb; Barus, Samuelta; Pasaribu, Beriman; Sirbu, Loredana Ioana; Dharma, Abdi
International Journal of Artificial Intelligence Research Vol 5, No 1 (2021): June 2021
Publisher : Universitas Dharma Wacana

Employee turnover is the loss of talent in the workforce that can be costly for a company. Uplift modeling is one of the prescriptive methods in machine learning models that not only predict an outcome but also prescribe a solution. Recent studies are focusing on the conventional predictive models to predict employee turnover rather than uplift modeling. In this research, we analyze whether the uplifting model has better performance than the conventional predictive model in solving employee turnover. Performance comparison between the two methods was carried out by experimentation using two synthetic datasets and one real dataset. The results show that despite the conventional predictive model yields an average prediction accuracy of 84%; it only yields a success rate of 50% to target the right employee with a retention program on the three datasets. By contrast, the uplift model only yields an average accuracy of 67% but yields a consistent success rate of 100% in targeting the right employee with a retention program.

Fuzzy C-Means Clustering Algorithm For Grouping Health Care Centers On Diarrhea Disease Chusyairi, Ahmad; Saputra, Pelsri Ramadar Noor; Zaenudin, Efendi
International Journal of Artificial Intelligence Research Vol 5, No 1 (2021): June 2021
Publisher : Universitas Dharma Wacana

In Indonesia, public health services at the city or district level are carried out by regional public hospitals or â€œpuskesmasâ€ (health care centers), especially in Banyuwangi regency, East Java, Indonesia that has 45 health care centers spread throughout the villages. This research focused on the deaths of babies caused by diarrhea diseases, which are the second leading cause of death among children younger than 5 years globally. All of the health care centers need to be divided into 3 groups to find out which health care centers have the least, most moderate, and many diarrhea sufferers. Fuzzy C-Means algorithm is used to overcome this problem. The result from this research shown that 2 health care centers have the smallest member of diarrhea sufferers, 14 health care centers have a medium member of diarrhea sufferers, and the rest have a large number of diarrhea sufferers. From the result of this study, it can be a reference for the health department center in dealing with diarrheal diseases, accordingly, the infant mortality rate due to diarrheal diseases can be lowered to health care centers that have high diarrhea sufferers.

Covid-19: Implementation e-voting Blockchain Concept Kamil, Mustofa; Bist, Ankur Singh; Rahardja, Untung; Santoso, Nuke Puji Lestari; Iqbal, Muhammad
International Journal of Artificial Intelligence Research Vol 5, No 1 (2021): June 2021
Publisher : Universitas Dharma Wacana

The current situation of the Covid-19 pandemic is currently increasing public concern about the community. The government has especially recommended Stay at Home and the implementation of PSBB in various regions. One of the concerns is when the election of regional leaders to the general chairman. Even though there is already a safeguard regulation, this is not considered safe in the current Covid-19 pandemic. The solution in this research is the use of a blockchain-based E-voting system to help tackle election unrest during Covid-19. Where e-voting with blockchain technology can be carried out anywhere through the device without the need to be present in the voting booth, reducing data fraud, accurate and decentralized voting results that can be accessed by the public in real-time. The use of cryptographic protocols is applied for data transfer between system components as well as valid system security. This research method uses SUS trial analysis in a significant system of the Covid-19 pandemic situation. The implication that the SUS Score analysis shows 90 shows an acceptable E-voting system, meaning that the community can accept it because it brings positive and significant impacts such as effectiveness and efficiency.

Causal Relations of Factors Representing the Elderly Independence in Doing Activities of Daily Livings Using S3C-Latent Algorithm Tou, Nurhaeka; Rahmadi, Ridho; Effendy, Christantie
International Journal of Artificial Intelligence Research Vol 5, No 1 (2021): June 2021
Publisher : Universitas Dharma Wacana

The growth of the elderly population in Indonesia from year to year has always increased, followed by the problem of decreasing physical strength and psychological health of the elderly. These problems can affect the increase in dependence and decrease the independence of the elderly in ADL. In previous studies, various factors affect independence in ADLs such as cognitive, psychological, economic, nutrition, and health. However, In general, these studies only focus on predictive analysis or correlation of variables, and no research has attempted to identify the casual relationship of the elderly independence factors. Therefore, this study aimed to determine the mechanism of the causal relationship of the factors that influence the independence of the elderly in ADLs using a casual method called the Stable Specification Search for Cross-Sectional Data With Latent Variables (S3C-Latent). In this research we found strong causal and associative relationships between factors.The causal relationship of elderly independence in ADLs was influenced by cognitive, psychological, nutritional and health factors and gender with Î± values respectively (0.61; 0.61;1.00, 0.65;0.70). Cognitive factors associated with psychological, economic, nutrition, and health with a value of Î± (0.77; 1.00; 1.00; 0.64). Furthermore, psychological factors associated with economy, nutrition, and health with a value of Î± (0.77; 0.95; 0.63). Bisides, economic factors are associated with nutrition and health with Î± values of ( 0.86; 0.75) and nutrition with health with Î± values of 0.64. The last association was found between nutritional factors and gender with a value of Î± 0.76. This research is expected to increase the independence of the elderly in carrying out daily activities.

Best Cluster Optimization with Combination of K-Means Algorithm And Elbow Method Towards Rice Production Status Determination Hasugian, Paska Marto; Sinaga, Bosker; Manurung, Jonson; Al Hashim, Safa Ayoub
International Journal of Artificial Intelligence Research Vol 5, No 1 (2021): June 2021
Publisher : Universitas Dharma Wacana

Indonesia is the third-largest country in the world with rice production reaching 83,037,000 and became the highest production in southeast Asia spread in several provinces in Indonesia The problem found that such product has not been able to cover the needs of Indonesian people with a very high population so that in the research conducted information excavation to generate potential to the pile of data that has been described and analyzed by BPS with clustering topics. Clustering will help related parties, especially the ministry of agriculture, in determining land development priorities and can minimize the shortage of rice production nationally. Grouping process by involving the K-means algorithm to group rice production with a combination of the elbow method as part of determining the number of clusters that will be recommended with attributes supporting the area of harvest, productivity, and production. Method of researching with data cleaning activities, data integration, data transformation, and application of K-means with a combination of elbow and pattern evaluation. The results achieved based on the work description with a combination of K-Means and elbow provide cluster recommendations that are the best choice or the most optimal is iteration 2 which is the lowest rice production group with a total of 22 provinces, rice production with a medium category of 9 and production with the highest category with 3 regions

Intelligent Traffic Monitoring Systems: Vehicle Type Classification Using Support Vector Machine Ika Candradewi; Agus Harjoko; Bakhtiar Alldino Ardi Sumbodo
International Journal of Artificial Intelligence Research Vol 5, No 1 (2021): June 2021
Publisher : STMIK Dharma Wacana

In the automation of vehicle traffic monitoring system, information about the type of vehicle, it is essential because used in the process of further analysis as management of traffic control lights. Currently, calculation of the number of vehicles is still done manually. Computer vision applied to traffic monitoring systems could present data more complete and update.In this study consists of three main stages, namely Classification, Feature Extraction, and Detection. At stage vehicle classification used multi-class SVM method to evaluate characteristics of the object into eight classes (LV-TK, LV-Mobil, LV-Mikrobis, MHV-TS, MHV-BS, HV-LB, HV- LT, MC). Features are obtained from the detection object, processed on the feature extraction stage to get features of geometry, HOG, and LBP in the detection stage of the vehicle used MOG method combined with HOG-SVM to get an object in the form of a moving vehicle and does not move. SVM had the advantage of detail and based statistical computing. Geometry, HOG, and LBP characterize complex and represents an object in the form of the gradient and local histogram.The test results demonstrate the accuracy of the calculation of the number of vehicles at the stage of vehicle detection is 92%, with the parameters HOG cellSize 4x4, 2x2 block size, the son of vehicle classification 9. The test results give the overall mean recognition rate 91,31 %, mean precision rate 77,32 %, and mean recall rate 75,66 %.

Extractive Text Summarization of Student Essay Assignment Using Sentence Weight Features and Fuzzy C-Means Suwija Putra, I Made; Adiwinata, Yonatan; Singgih Putri, Desy Purnami; Sutramiani, Ni Putu
International Journal of Artificial Intelligence Research Vol 5, No 1 (2021): June 2021
Publisher : Universitas Dharma Wacana

One of the main tasks of a lecturer is to give students an academic assessment in the learning process. The assessment process begins with reading or checking the answers of student assignments that contain a combination of very long sentences such as essay or report assignments. This certainly takes a lot of time to get the primary information contained therein. It is necessary to summarize the answers so that the lecturer does not need to read the whole document but is still able to take the essence of the response to the task. This study proposes the application of summarizing text documents of student essay assignments automatically using the Fuzzy C-Means method with the sentence weighting feature. The sentence weighting feature is used by selecting the sentence with the highest weight in one cluster, helping the system to get the primary information from a document quickly. The results of this study indicate that the system succeeds in summarizing text with an average evaluation of the values of precision, recall, accuracy, and F-measure of 0.52, 0.54, 0.70, and 0.52, respectively.One of the main tasks of a lecturer is to give students an academic assessment in the learning process. The assessment process begins with reading or checking the answers of student assignments that contain a combination of very long sentences such as essay or report assignments. This certainly takes a lot of time to get the primary information contained therein. It is necessary to summarize the answers so that the lecturer does not need to read the whole document but is still able to take the essence of the response to the task. This study proposes the application of summarizing text documents of student essay assignments automatically using the Fuzzy C-Means method with the sentence weighting feature. The sentence weighting feature is used by selecting the sentence with the highest weight in one cluster, helping the system to get the primary information from a document quickly. The results of this study indicate that the system succeeds in summarizing text with an average evaluation of the values of precision, recall, accuracy, and F-measure of 0.52, 0.54, 0.70, and 0.52, respectively.

Stochastic Perturbations on Low-Rank Hyperspectral Data for Image Classification Sumarsono, Alex; Ganjeizadeh, Farnaz; Tomasi, Ryan
International Journal of Artificial Intelligence Research Vol 5, No 1 (2021): June 2021
Publisher : Universitas Dharma Wacana

Hyperspectral imagery (HSI) contains hundreds of narrow contiguous bands of spectral signals. These signals, which form spectral signatures, provide a wealth of information that can be used to characterize material substances. In recent years machine learning has been used extensively to classify HSI data. While many excellent HSI classifiers have been proposed and deployed, the focus has been more on the design of the algorithms. This paper presents a novel data preprocessing method (LRSP) to improve classification accuracy by applying stochastic perturbations to the low-rank constituent of the dataset. The proposed architecture is composed of a low-rank and sparse decomposition, a degradation function and a constraint least squares filter. Experimental results confirm that popular state-of-the-art HSI classifiers can produce better classification results if supplied by LRSP-altered datasets rather than the original HSI datasets.Â

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Contact Name
Heri Nurdiyanto

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Heri Nurdiyanto

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Journal Mail Official
internationaljournalair@gmail.com

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Location
Kota metro,

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INDONESIA