cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Mustakim
Contact Email
Mustakim
Phone
-
Journal Mail Official
ijaidm@uin-suska.ac.id
Editorial Address
-
Location
Kab. kampar,
Riau
INDONESIA
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
 4   5   6   7   8 
A Testing of Case-Base Reasoning for Covid-19 Patient Status Confirmation Salamun Salamun; Diki Arisandi; Luluk Elvitaria; Liza Trisnawati
Indonesian Journal of Artificial Intelligence and Data Mining Vol 4, No 2 (2021): September 2021
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v4i2.11990

Abstract

Currently, the world is facing a global pandemic that attacks all countries. In Indonesia, there are three types of status for suspected patients: asymptomatic person, Person Under Supervision, and Patient Under Supervision. The statuses are issued by a paramedic, conducting medical examinations or direct interviews with patients with several criteria. We conducted several non-medical experiments to assist medical personnel in determining the asymptomatic. We exploit the case-based reasoning (CBR) for determining the suspected patients, and the K-NN (K-Nearest Neighbor) for data grouping based on the level of similarity. The patients will be interviewed regarding their travel history, direct contact history, health status, and some other information for the past 14 days. This combination delivers the information of the similarity level from the given data and previous data. As a conclusion, the percentage level of similarity can be used by a medical officer to issue the status of patients and giving several recommendations to follow health protocols.
Selection of Superior Rice Seed Features Using Deep Learning Method Dinda Ayusma Tonael; Yampi R Kaesmetan; Marinus I. J. Lamabelawa
Indonesian Journal of Artificial Intelligence and Data Mining Vol 4, No 2 (2021): September 2021
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v4i2.13947

Abstract

Indonesia is a tropical country known as an agricultural country, where 88.57% of the population works in the agricultural sector (BPS Indonesia, 2020). Indonesia is rich in agricultural products such as rice, soybeans, corn, peanuts, cassava and sweet potatoes. Rice (Oryza sativia L) is one of the most dominant food commodities for the people of Indonesia. The carbohydrate content per 100 grams of rice reaches 79.34 grams. The main benefit of rice is as a source of carbohydrates and a source of energy for the body. Seed is one of the factors that play a role as a carrier of technology in advanced agriculture, therefore the seeds used must be of good quality. Farmers tend to equate rice seeds from previous harvests, the rice seed classification process is carried out manually through visual observation and soaking rice seeds in a container filled with water, submerged and floating rice seeds are selected for use, and those that float are discarded. But in reality it still produces less than optimal results, for example rice that is less dense and cracked. This study uses a color moment to be extracted using GLCM (gray level co-occurence matrix) then classified with k-NN to determine the class, then uses the SVM model to display the best hyperplane line to separate the two classes, namely superior and non-superior classes after that system tested with confusion matrix. With a continuous and more intense work process, the research entitled Selection of Superior Rice Seed Features Using Deep Learning Methods. The output of this research leads to a conclusion which rice seeds are superior and which are not superior, aiming to optimize the yield of rice with better quality. The research was successfully carried out using the deep learning method with the highest accuracy of 92.85%.
Implementation of Levenshtein Distance Algorithm in the Digital Biology Dictionary Search Function Khalidah Khalidah
Indonesian Journal of Artificial Intelligence and Data Mining Vol 4, No 2 (2021): September 2021
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v4i2.13989

Abstract

Digital biology dictionary is important to develop as it assists biology students, laboratory assistants, and general users in searching for biology terms. Sometimes, users mistype the biological terms in Latin on the term search form in the biology dictionary. Thus, it is important to implement the Levenshtein distance algorithm to provide query suggestion information to users. This study aims to implement the Levenshtein distance algorithm in the digital biology dictionary search function. This research consists of several stages, namely the development of a search module on the digital biology dictionary, implementation of the Levenshtein Distance algorithm, query suggestion validation. The levenshtein distance algorithm had been successfully implemented in the digital biology dictionary by providing query suggestion output for mistyping words. The results of this study indicate that the system was able to evaluate words with the query suggestion function with an accuracy value of 90%.
Real-Time Detection of Face Masked & Face Shield Using YOLO Algorithm with Pre-Trained Model and Darknet Muhamad Muhaimin; Wan Sen Tjong
Indonesian Journal of Artificial Intelligence and Data Mining Vol 4, No 2 (2021): September 2021
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v4i2.14235

Abstract

There are new regulations requiring the use of masks or face shields to prevent the transmission of Covid-19. Using deep learning, a model can be made to detect faces that use masks and face shields by training the model using the previous pre-trained model and using a custom dataset. The purpose of this study is to create a deep learning model that can detect faces with and without masks and as well as face shields for the prevention of covid-19 transmission using YOLO (You Only Look Once) with pre-trained models and custom datasets in real-time. In this study, using pre-trained models from YOLOv3, YOLOv3-Tiny, YOLOv4, YOLOv4-Tiny, and YOLOv4-Tiny-3l with Darknet Framework and compare between average pooling and max pooling in the convolutional neural network YOLO to detect face masks and face shields as a real-time. From experiment the highest mAP was obtained from YOLOv4 using average pooling with a value is 97.64% although the difference is not too much with YOLOv4 using max pooling with value 97.57% and the lowest was YOLOv3-Tiny using max pooling, which was 94.09%, and for the highest FPS was obtained by YOLOv4-Tiny with Fps values is 171 and mAP 96.75%. And for real-time detection of face masks and face shields, the best model used in testing using webcam 1080p is from YOLOv4-Tiny, because the FPS is quite good and the mAP is quite high.
C4.5, K-Nearest Neighbor, Naïve Bayes, and Random Forest Algorithms Comparison to Predict Students’ On Time Graduation Gunawan Gunawan; Hanes Hanes; Catherine Catherine
Indonesian Journal of Artificial Intelligence and Data Mining Vol 4, No 2 (2021): September 2021
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v4i2.10833

Abstract

Study program performance can be seen from the achievement of accreditation status, where one of the assessment instruments related to the graduate profile is the length of study. Graduation on time is one indicator of student’s success in obtaining a bachelor's degree and is an important attribute, because by being able to predict the period of study, universities can minimize student graduation failures by making more intensive planning, study escort, and guidance. Data mining classification techniques can be used to predict students graduation on time. Many data mining classification algorithms can be used, so it is necessary to make comparisons to determine the level of accuracy of each algorithm. The algorithms that will be compared in this study are C4.5, K-Nearest Neighbor, Naive Bayes, and Random Forest. The data used were 2,022 graduates from Informatics Engineering and Information System Study Program of STMIK Mikroskil Medan from 2011 to 2014, in which the attributes used include gender, regional origin, time of study, grade of Entrance Screening Examination, and Grade Point Average (GPA). The results of the classification process are evaluated using cross validation and confusion matrix to determine the most accurate data mining classification algorithm for predicting student graduation on time, where the K-Nearest Neighbor and Random Forest algorithms have the highest accuracy of 72,651%, followed by the C4.5 algorithm 72,453%, and the Naïve Bayes algorithm 71,860%.
Optimization of the Naïve Bayes Classifier (NBC) Algorithm Using the Sparrow Search (SSA) Algorithm to Predict the Distribution of Goods Receipts Rachma Oktari; Tjong Wan Sen
Indonesian Journal of Artificial Intelligence and Data Mining Vol 4, No 2 (2021): September 2021
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v4i2.15339

Abstract

Distribution must be able to meet all needs based on sales orders from consumers, be responsible for the delivery order process running optimally, and ensure the good receipt process is in accordance with consumer sales order requests. PT. Diamond Cold Storage currently uses Enterprise Resource Planning (ERP) to record all reports from production to sales. But in reality there are still some obstacles in the distribution section. In the good receipt process, several items were found that did not match the sales order, such as: the item did not match the order request or the item did not match the order request. The process of mismatching the good receipt with the sales order will be met with the completion of the good receipt process or the bad thing is that there is a cancellation, so this causes a loss for the company. This study uses data mining techniques with the Naïve Bayes Classifier algorithm to predict the distribution of goods receipts based on distribution data, and uses the Sparrow Search Algorithm (SSA) algorithm to optimize the Nave Bayes Classifier by selecting features to improve accuracy. In this study, the results obtained that the SSA algorithm can improve the performance of NBC from 95.05% to 97.95%.
Image Processing Technology for Motif Recognition Mandar Silk Fabric Android Based Andi Emil Multazam; Akhmad Qashlim; UL Khairat
Indonesian Journal of Artificial Intelligence and Data Mining Vol 4, No 2 (2021): September 2021
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v4i2.14470

Abstract

People use traditional equipment to produce silk fabrics for generations. Various motifs have been created and along with their development, sarong motifs are increasingly diverse and almost similar to one another. To be able to distinguish objectively, a technological approach will be used. An Image Processing technology that can identify the motifs and patterns of each silk fabric, this technology will be implemented so that it is used to help identify the names of each silk fabric motif or pattern, in addition, this technology will contribute and become a new way of preserving culture and customs. The bounding box technique is used to identify the motif pattern, then the Receiver Operating Character (ROC) method is used for the accuracy of the detection results. Research data in the form of 13 samples of sarong motifs will be collected and inventoried to create a training data table, after that, the user interface design of the application system will be built based on Android so that it is easy to use anytime. To ensure the consistency of the detection results, the system calibration is carried out with lighting techniques and identification distances. The results show that the precision value is 100% and the accuracy is 50%. However, we note that the recall value is only 60%. This shows that this system can only detect a small amount of saqbe sarong and there are still many sarongs that cannot be detected.
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.

Page 6 of 21 | Total Record : 207

 4   5   6   7   8 

Filter by Year

2018 2025


Reset
Filter By Issues
All Issue Vol 8, No 2 (2025): July 2025 Vol 8, No 1 (2025): March 2025 Vol 7, No 2 (2024): September 2024 Vol 7, No 1 (2024): March 2024 Vol 6, No 2 (2023): September 2023 Vol 6, No 1 (2023): Maret 2023 Vol 5, No 2 (2022): September 2022 Vol 5, No 1 (2022): March 2022 Vol 4, No 2 (2021): September 2021 Vol 4, No 1 (2021): March 2021 Vol 3, No 2 (2020): Spetember 2020 Vol 3, No 1 (2020): March 2020 Vol 2, No 2 (2019): September 2019 Vol 2, No 1 (2019): March 2019 Vol 1, No 2 (2018): September 2018 Vol 1, No 1 (2018): March 2018 More Issue