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International Journal of Artificial Intelligence Research
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
 1 
Search results for , issue "Vol 8, No 2 (2024): December 2024" : 10 Documents clear
Enhancing Electricity Consumption Prediction with Deep Learning through Advanced Data Splitting Techniques Pratiwi, Adinda Putri; Ginardi, Raden Venantius Hari; Saikhu, Ahmad
International Journal of Artificial Intelligence Research Vol 8, No 2 (2024): December 2024
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i2.1204

Abstract

Energy consumption is increasing due to population growth and industrial activity, making electricity essential in human life. With limited natural resources, effective management of electrical resources is crucial to reduce energy usage amidst rising demand. The current trends on using deep learning as prediction can enhance the performances. To have good performance it needs correct preprocessing data, so it will produce a model with less overfitting. This research proposes a model using time-series cross-validation as the splitting data and correlation to choose the best features set for the prediction of electricity consumption. Experiments will compare time-series cross-validation and holdout methods to see the performances of splitting data and enhancing the multi-horizon data.  The experiment used 8 sets of feature lists, which are paired in combination based on correlation to ensure the best features that are related. The result is splitting data using time-series cross-validation can maintain good perfomances on mode and holdout can maintain a good evaluation performance across the horizon. Feature sets that include temporal features have excellent results, especially when combined with features that have the strongest correlation relationship with electricity consumption, leading to an enhanced R2. Among all the models tested, CNN-GRU had the best model for multistep prediction across various every horizons and feature sets.
The Distribution of Legal Research Topics on Artificial Intelligence: A Bibliometric Study Muhammad Asrul Maulana; Savira Aristi
International Journal of Artificial Intelligence Research Vol 8, No 2 (2024): December 2024
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i2.1237

Abstract

This study aims to determine the distribution of legal research topics related to Artificial Intelligence. This study uses the lens of an indexing institution, to find raw data on the research distribution. The method used is Bibliometric analysis. The results of the research found that this study showed the keywords (Co-Occurrence) were divided into 3 clusters which had a total of 49 topics. Based on the collaboration of authors (Co-Authorship) has 1 cluster which includes 194 authors. Out of a total of 385,943 search results for scientific work, the most prolific author is Wei Wang with 361 documents created by journal articles. Meanwhile, research with the keyword Artificial Intelligence experienced fluctuating developments and the most publications occurred in 2022 with a total of 42,483 publications.
Cultural Change of Mathematics Teachers' Views on Technology: Navigating the Artificial Intelligence Revolution Utami, Niken Wahyu; Sagita, Laela; Rahmawati, Rina Dyah; Nurdianto, Heri
International Journal of Artificial Intelligence Research Vol 8, No 2 (2024): December 2024
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i2.1232

Abstract

The development of technology related to Artificial Intelligence is growing rapidly, and one of its implications is the teaching of mathematics in the classroom. Therefore, it is necessary to conduct research on the perspective of mathematics teachers in addressing the development of artificial intelligence (AI) used in mathematics learning. This study examines the role of AI technology in facilitating pedagogical reform in mathematics education from the perspective of teachers. Through a questionnaire distributed to mathematics teachers, this paper identifies teachers' perspectives on the development of AI and its use in their classrooms. A total of 56 mathematics teachers participated in this study. In addition to the five-item questionnaire, an open-ended questionnaire was also provided. A number of AI that teachers use in their teaching are also mentioned in this paper. The paper also discusses the challenges that mathematics teachers face when using AI in mathematics lesson planning in their classrooms. It concludes that mathematics learning using AI has significant potential to improve students' competencies by equipping them with essential skills for the digital age
The Classification Method is Used for Sentiment Analysis in My Telkomsel Hardiansyah, Deni; Aziz, RZ Abdul; Hasibuan, Muhammad Said
International Journal of Artificial Intelligence Research Vol 8, No 2 (2024): December 2024
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i2.1229

Abstract

User reviews significantly impact how mobile apps are perceived and provide developers with valuable insights into improving the functionality and quality of their products. Sentiment analysis of these evaluations helps identify the main issues faced by consumers, such as technical difficulties, costs, and service levels. The main objective of this study is to classify user sentiment into positive and negative categories, focusing on the MyTelkomsel app. With the use of Google Play Scraper, 39,493 reviews on various app versions and user experiences were collected. This data was analyzed using multiple machine learning models, including Support Vector Machines (SVM), Naive Bayes, Random Forest, and Gradient Boosting, alongside the Natural Language Processing (NLP) approach. The results show that 39.2% of the reviews are positive, while 60.8% reflect negative sentiment. Among the models, SVM showed the highest accuracy in sentiment classification with a value of (0.854792), while Naive Bayes (0.775541), Random Forest (0.829725), and Gradient Boosting (0.819344) also performed well in sentiment classification. These findings suggest that developers can leverage the insights gained from this analysis to proactively improve the performance and user experience of the MyTelkomsel app, by addressing technical and service-related issues identified in user reviews.
The Effectiveness of Augmented Reality with Adapted Books as Emotional Expression Media for Children with Autistic Spectrum Disorders (ASD) Tolle, Herman; Nerea López, Jonathan del Moral
International Journal of Artificial Intelligence Research Vol 8, No 2 (2024): December 2024
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i2.1200

Abstract

Autism Spectrum Disorder (ASD) is a neurological condition that affects a person's ability to recognize and understand emotions, thereby hindering social interactions. This research introduces AREmotion, an augmented reality (AR) mobile application integrated with an adapted book, designed to aid children with ASD in recognizing emotional expressions. Using images as markers in the AR application, AREmotion facilitates the learning process for these children. The study employed a pre-experimental design with a one-group pre-test and post-test to evaluate the application's effectiveness. Results indicated a significant improvement in the participant's ability to understand emotional expressions, with each respondent showing a significance value of less than 0.05. These findings demonstrate that combining AR technology with traditional printed books can effectively enhance emotional recognition in children with ASD, showcasing a promising advancement in this field.
Development of Detection and Mitigation of Advanced Persistent Threats Using Artificial Intelligence and Multi-Layer Security on Cloud Computing Infrastructure Hartono, Hartono; Wijaya, Ryan Aji; Khotimah, Khusnul
International Journal of Artificial Intelligence Research Vol 8, No 2 (2024): December 2024
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i2.1250

Abstract

This research proposes a novel approach for detecting and mitigating Advanced Persistent Threats (APTs) in cloud computing infrastruc ture, offering more comprehensive protection compared to previous methods. By integrating detection and mitigation, this study addresses the shortcomings of prior research that focused solely on detection. Based on the conducted research, Artificial Intelligence (AI) detected Cross-Site Scripting (XSS) attacks with an accuracy of 0.9951, SQL Injection (SQLI) at 0.9964, and Remote Code Execution (RCE) at 0.9876. In trials against new attacks, the detection success rates reached 70% for XSS, 98% for SQLI, and 100% for RCE. During the deployment phase, the system successfully identified 23.040 out of 108.394 requests as XSS attacks, 2.684 out of 128.750 as SQLI attacks, and 1.135 out of 46.450 as RCE attacks. The detection and mitigation methods were directly tested on cloud server experiencing APT attacks. The daily attacks on the server reached 1.980, with 663.000 requests. Additionally, the number of attacks directed at authentication or sensitive pages reached 17.913.701. Attack mitigation was tested through seven layers of security, including DNS Protection, Config Server Firewall (CSF), OWASP ModSecurity, HTTP middleware, data filter or sanitizer, template engine, and manual mitigation successfully blocking million of persistent attacks. The DNS protection layer successfully mitigated 59,000 out of a total of 19 million requests. The CSF layer mitigated 173 sources IP of DDoS attacks. The ModSecurity layer mitigated 17,916,204 attacks. All attacks were successfully mitigated before reaching the HTTP Middleware stage or next layer. The use of NIST 2.0 standards helps manage security risks through identification, protection, detection, response, and recovery. Test results indicate that this multi-layered system is more efficient and effective in detecting and mitigating attacks compared to traditional methods. However, the complexity of implementation and maintenance poses challenges that must be addressed. This research significantly contributes to a more adaptive and sustainable cybersecurity strategy.
Prediction of Performance and Emissions Diesel Engines Fueled-Biodiesel Using Artificial Neural Network (ANN) Resilient Backpropagation Algorithm (Rprop) Amrulloh, Riva; Widayat, Widayat; Warsito, Budi
International Journal of Artificial Intelligence Research Vol 8, No 2 (2024): December 2024
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i2.1265

Abstract

In order to increase energy security and improve environmental quality, the Indonesian Goverment set a target of 23% renewable energy mix in 2025, one of which is the Mandatory Bioediesel Program. A higher biodiesel blending ratio will affect the performance and emissions of diesel engines because biodiesel is chemically different from diesel oil. Research related to the prediction of diesel engine performance and emissions using Artificial Neural Network (ANN) has been conducted, but the author sees a research opportunity for the implementation of the ANN Resilient Backpropagation (Rprop) algorithm. The data used to create the ANN model prediction was secondary data from previous research. The model designed multi input and multi output (MIMO) with 4 input variables and 7 output variables. Model building done by varying the number of neurons and hidden layers. Model evaluation selected based on the largest coefficient of determination parameter R2  and the smallest RMSE or MAPE. The results showed that the ANN single layer 4-20-7 network architecture is the best model for predicting diesel engine performance and emissions with test data R2 , RMSE and MAPE of 0.962532, 6.699428 and 6.0% respectively, while for overall data testing has a performance of 0.982869, 3.908542 and 4.3%. The results also show that based on the ANN prediction results, the increasing biodiesel ratio can increase NOx emissions and decrease HC, CO and CO emissions2 . In terms of performance, the addition of biodiesel can increase BSFC and BP and decrease BTE. The results also show that the addition of ZnO concentration can reduce emissions while in terms of performance it will increase BTE and reduce BSFC and BP.
Optimizing Text Correction For Voice Based IoT Smart Building Virtual Assistants Shidiqi, Maulana Ahmad As; Hadi, Mokh Sholihul; Wibawa, Aji Prasetya; Mhd. Irvan, Mhd. Irvan
International Journal of Artificial Intelligence Research Vol 8, No 2 (2024): December 2024
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i1.1085

Abstract

The integration of Virtual Assistants (VAs) within Smart Building Internet of Things (IoT) ecosystems is increasingly critical, particularly for interpreting user commands via Automatic Speech Recognition (ASR). This paper presents an in-depth performance analysis of text correction algorithms on a Raspberry Pi 4—a cost-effective and widely used computing solution in smart building applications. Due to the absence of GPU acceleration for Python on ARM architecture, a specialized dataset was developed to benchmark algorithmic performance, focusing on correction times and accuracy. Our study utilized a near-real-world experimental setup, deploying Docker containers to simulate IoT MQTT brokers, a Smart Building Platform, and Rasa for dialogue management. Among the algorithms tested—Edit distance, Jaccard, FuzzPartialRatio, FuzzSortRatio, MLE, and Norvig Spell—the Edit distance and Norvig Spell emerged as leaders in accuracy, achieving an 84% success rate in text correction. Notably, the Edit distance algorithm demonstrated superior speed, vital for real-time processing demands. The Fuzz Sort Ratio algorithm distinguished itself with the fastest correction time at 31.6 milliseconds, albeit with a slight compromise on accuracy, attaining a 79% success rate. Consequently, the Edit distance algorithm is recommended for applications where accuracy and response time are paramount, while the Fuzz Sort Ratio is preferable for scenarios where speed is the overriding priority. This research paves the way for future exploration into the computational impacts of these algorithms and the exploration of neural network-based methods to further enhance text correction capabilities in smart building automation systems.
Melanoma Detection and Classification in Dermoscopic Images using resnet50 and Hair removal feature K P, Akshaya; Phalgunan, Prafulla
International Journal of Artificial Intelligence Research Vol 8, No 2 (2024): December 2024
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i2.1300

Abstract

Melanoma is the most common skin cancer, and it is increasing widely. Automatic skin lesion detection from dermoscopic images remains a challenging task. Many efforts have been dedicated to this challenge using various methods, but due to its poor robustness, it is not good for the analysis of melanoma skin lesions. Propose a method for skin lesion detection and classification tasks simultaneously to make sure feature learning is successful. The base of feature pyramid networks and region proposal networks is ResNet50, which is used here. The network learns features more quickly using a three-phase cooperative training technique. Before entering this model, the hairs from the images are removed
Identify the Usability of the Netraku Application System Using the Usability Testing Method Dewi, Alifa Permata; Sari, Amarria Dila; Iryani, Devina Inayah; Akhyari, Muhammad Wafa
International Journal of Artificial Intelligence Research Vol 8, No 2 (2024): December 2024
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i1.1472

Abstract

The Netraku application is aimed at the visually impaired who want to know the name of an object or the writing of an object or printed media, as well as knowing the nominal currency conveyed via voice in the application. The application works by pointing the camera at the object or currency you want to see the name or amount of. The Netraku application will detect it, and a voice will appear stating the information you want to convey. The problem found is that the Netraku application has not carried out usability testing for blind users. Therefore, it is necessary to develop usability testing to obtain user needs from the Netraku application, determine the effectiveness and efficiency values, and provide recommendations that can be applied to the Netraku application. The methods used in usability testing are performance measurement, focus group discussion, and participatory design. The results obtained on the effectiveness value received a success rate of 83.33% and a failure rate of 16.7%, concluding that the user can complete the task effectively. The efficiency value obtained from the average processing time is 10.34 seconds.

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