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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 233 Documents
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Portable Device for Measuring Heart Rate of Pregnant Women Based on Ip Address With BPM Graph Display Wahyuni, Yuli
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.26786

Abstract

Checking the pregnant woman's womb during pregnancy is very important and should be done regularly. In the examination, the calculation of the number of heartbeats in a certain time interval is called beats per minute. The matrix of a person's heart condition is BPM (beats per Minute). This research is related to making a prototype of a pregnant woman's heart rate device using a fingerprint sensor. The tool can be used for health experts, namely midwives. The test results of abnormal heart rate of pregnant women are 1 person. This tool is very easy to use because it is portable (easy to carry everywhere) and its use by attaching the fingerprints of pregnant women to the max30100 sensor for 10-15 seconds using the index finger, the sensor will process the data read by the NodeMCU receiving data and the data transmission process is displayed on the IP Addres in the form of BPM. NodeMCU must be connected via wifi / hotspot with one Ip address and must have an account to be able to access on the internet platform, namely google chrome or mozila firefox so that it can be accessed via smartphone or personal computer.
Uncovering Malware Families Using Convolutional Neural Networks (CNN) Ruly Sumargo; Handri Santoso
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.27243

Abstract

Malware attacks pose significant cyber threats, with a rising number of vulnerability reports in security communities due to the continual introduction of mutations by malware programmers to evade detection. One of the most attractive targets which attacked by malware is the organization emails system. Malware’s mutations within the malware family, has complicating the development of effective machine learning-based malware analysis and classification methods. To answer this challenge, this research uses an agnostic deep learning solution inspired by ImageNet's success, which efficiently classifies malware into families by analyzing visual representations of malicious software as greyscale images using a Convolutional Neural Network (CNN). The Malwizard is a flexible Python tool suitable for both organizations and end-users enabling automated and rapid malware analysis within email system. Malwizard could be use as an Outlook Email’s add-in and an API service for SOAR platforms. The study evaluates this novel approach using the Microsoft Classification Challenge dataset, where image representations are encrypted to address privacy concerns. Experimental results show that the proposed approach performs comparably to the best existing model on plain text data, accomplishing the task in one-third of the time. For the encrypted dataset, adjustments to classical techniques are necessary for improved efficiency.
Training Evaluation Analysis Using Text Mining Nainunis, Mas Akhmad; Yuadi, Imam
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.27607

Abstract

Training evaluation is an evaluation of the results of training that has been carried out. This evaluation includes technical and non-technical factors which are very important for the company to pay attention to when implementing training in the future. Many companies only implement training evaluations as a formality and only include evaluations that are limited by choice, such as closed questionnaires, training evaluations using open questionnaires can provide the freedom to provide positive or negative input that can be of concern to the company. This research aims to find out the words or topics that appear most frequently in open comments on training evaluation results by using the FP Growth algorithm and association rules to find out the relationship between topics or words from the training evaluation results. They are applied to 516 open-ended comments submitted via the post-training questionnaire. The research results showed that 15 association rules were created using Rapidminer using the FP-Growth algorithm with a minimum support of 0.02 and a minimum confidence of 0.5. All rules have a lift value>1 which indicates that all rules are valid or have a strong association relationship. This research can determine the pattern of comments or suggestions given by workers regarding training evaluation.
Performance Comparison of Decision Tree J48, CART, and Naïve Bayes Algorithms for Predicting Chronic Kidney Disease Ikhsan, Ali Nur; Fadilah, Alif Nur; Iftinani, Alifah Dafa
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.26472

Abstract

Chronic Kidney Disease could be a worldwide issue that proceeds to extend with high treatment costs. Accurate diagnosis is essential for managing this disease. There is a requirement for a technique to anticipate chronic kidney disease, with prevalent use being made of Decision Tree J48, Naive Bayes, and CART algorithms which offer benefits like swift computation, ease of use, and high precision. The researchers aimed to determine the comparison results of Decision Tree J48, CART, and Naive Bayes algorithms for predicting chronic kidney disease. From the research findings, it was concluded that the CART algorithm had the highest accuracy rate of 97.25% in predicting chronic kidney disease, compared to the J48 Decision Tree algorithm and the Naïve Bayes algorithm with accuracy rates of 96.5% and 93.5% respectively. The CART algorithm can be utilized by pathologists to develop a program for predicting chronic kidney disease.
Improving K-Means Clustering Accuracy for Academic Success Investigation With Extreme Gradient Boosting Algorithm Darmayanti, Irma; Adhimah, Laily Farkhah; Sadewo, Rizki; Hidayati, Nurul; Subarkah, Pungkas
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.26657

Abstract

Human Resources (HR) has a very important role in the development of the nation, so to improve the quality of human resources, education is needed. Education has a role in developing science, disseminating, socializing, and applying it. So that education is one of the important factors in advancing a nation. However, there are still many challenges in achieving quality education, especially in developing countries such as Indonesia, such as parental education level, socioeconomic status, and environmental conditions can also affect the quality of education and students' opportunities for academic success. The research methods used in this research are problem identification, data collection, data analysis, and evaluation. The results in this study are an increase in accuracy of 38.55% from the difference in the K-Means accuracy value of 14% resulting from the David Bounded Index and the use of the extreme gradient adaboost algorithm.
Implementation of Convolutional Neural Network for Classification of Density Scale and Transparency of Needle Leaf Types Sriatna, Diah Adi; Andrian, Rico; Safei, Rahmat
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.26258

Abstract

Crown density and transparency are among the parameters in determining forest health using magic card. This is still less effective because it only relies on direct vision. Therefore, a more sophisticated and accurate application using digital image technology is needed. Convolutional Neural Network (CNN) is designed to help recognize objects in images with various positions. There are 1000 images of needle leaf types with ten classes of crown density and transparency for every kind of needle leaf, including araucaria heterophylla, cupressus retusa, pine merkusii, and shorea javanica, which are classified using AlexNet. AlexNet is a CNN architecture that has eight feature extraction layers. The AlexNet model succeeded in classifying coniferous trees on the scale of density and crown transparency with an accuracy level of 87.00% for araucaria heterophylla, cupressus retusa 96.00%, merkusii pine 86.00%, and shorea javanica 95.00%. Although some errors were still found in classification, this was caused by similar patterns and similar image positions. It is hoped that the results of this research will be used in monitoring forest health in the future.
Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) Methods to Forecast Daily Turnover at BM Motor Ngawi Larasati, Larasati; Saadah, Siti; Yunanto, Prasti Eko
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.27643

Abstract

The number of motorcycles on the report of Indonesian BPS statistics from the Indonesian State Police between 2019 to 2021 by its type has increased annually. Routine motorcycle checks, services, and maintenance are essential to keep a motorcycle in good condition and more durable; therefore, buying spare parts is enlarged in line with the growth of public motorcycle ownership. The necessity of buying spare parts increases with the growth of public motorcycle ownership. Numerous stores in Ngawi offer motorcycle spare parts and check services for routine motorcycle maintenance. One of these stores is BM Motor. To develop an effective product-selling strategy, it is essential to forecast the daily turnover of the shop. To achieve this, the present research aims to analyze the daily turnover using Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM). These methods were applied to a time-series dataset, allowing for an in-depth examination of the patterns and trends in the shop's turnover. The research compares several hyperparameter tunings and scenarios to optimize the models that forecast daily turnover data at the store. The outcomes presented that the LSTM model achieved a lesser MAE score of 0.087, while the RNN model scored 0.092. These findings proved that the LSTM model achieved lower MAE than the RNN model, it means LSTM is more accurate than the RNN model.
Early Prediction of Stroke Disease Diagnosis Patients Using Data Mining Algorithm Comparison Subarkah, Pungkas; Damayanti, Wenti Risma; Sabaniyah, Arbangi Puput
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.25955

Abstract

Stroke constitutes a medical emergency of paramount significance, characterized by a notably elevated mortality rate, and stands as the foremost cause of mortality within hospital settings. The dataset employed for this analysis is sourced from Kaggle, denoted as the Brain Stroke Dataset, encompassing a total of 4981 records. This research aims to carry out early prediction of stroke sufferers using several algorithms including the ANN algorithm, CART, KNN, and the NBC algorithm. The results obtained in the ANN algorithm obtained an accuracy of 93.53%, in the CART algorithm of 95.02%, in the KNN algorithm of 91.09% and in the NBC algorithm of 88.44%. With the outcomes of this research, the use of the cart set of rules may be used for early evaluation of stroke sufferers because it has a good degree of accuracy and is listed inside the excellent type kind
Evaluation of Support Vector Machine, Naive Bayes, Decision Tree, and Gradient Boosting Algorithms for Sentiment Analysis on ChatGPT Twitter Dataset Rabbani, Salsabila; Safitri, Dea; Try Puspa Siregar, Farida; Rahmaddeni, Rahmaddeni; Efrizoni, Lusiana
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.24662

Abstract

ChatGPT is a language model employed to produce text and engage in conversation with users. It serves as a tool for generating text and facilitating interactions in a conversational manner. The model was designed to provide relevant and useful responses based on the context of the ongoing conversation. By the increasing popularity of using ChatGPT, it makes it difficult for users to classify responses about the use of ChatGPT. Therefore, sentiment classification of ChatGPT is carried out. The dataset used is sourced from the kaggle website with a total of 20,000 data. The classification methods used in this research include Support Vector Machine (SVM), Naïve Bayes, Decision Tree, and Gradient Boosting. Through the research results, the Support Vector Machine algorithm had the highest accuracy value with 80% compared to other methods, when the data is divided by a ratio of 90:10. This research is expected to help developers and service providers to improve ChatGPT and understand user responses better.
Internet of Things (IoT) Based Temperature and Humidity Detector Prototype in the UHAMKA Data Center Room Rizkiawan, M. Asep; Ramza, Harry; Sofwan, Agus
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.28035

Abstract

Internet of Things (IoT) is a concept where an object or entity is imbued with technology such as sensors and software, aiming to communicate, control, connect, and exchange data with other devices as long as they remain connected to the internet. In this research, the developed IoT is employed to monitor and control the conditions of a data center space. The research methodology follows the system development life cycle, utilizing the Blynk application and a modified Arduino Uno with the esp8266 microcontroller, relay, and DHT-22 sensor for real-time temperature and humidity detection. The IoT development's outcomes were tested through black box and white box approaches. The research results demonstrate that the IoT network prototype functions effectively, enhancing the performance of the data center space. Temperature measurements were acquired from the DHT22 sensor, and alternative temperature measurements were taken without utilizing the DHT22 sensor, instead using a tool known as a thermometer, revealing measurement errors. Based on the calculation of the average percentage of temperature error on the DHT22 sensor, it can be concluded that the temperature error rate reaches 0.051%, while for humidity it reaches 0.064%, with an average delay time of 6.542 ms. Additionally, users have convenient access through both a website and mobile platform for seamless monitoring.

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