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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 11 Documents
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Search results for , issue "Vol 7, No 1 (2023): June 2023" : 11 Documents clear
Applied of Analytical Hierarchy Process and Fuzzy Time Series in Hybrid for Optimizing Smart Vertical Farming with Multi-Variety Plants Wibowo, Danang Arengga; Sendari, Siti; Wibawa, Aji Prasetya; Wibowo, Fauzy Satrio
International Journal of Artificial Intelligence Research Vol 7, No 1 (2023): June 2023
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.402

Abstract

Vertical Farming is a kind of modern agricultural methods, where the structure of growing racks are arranged upwards. This method aims to optimize the use of agricultural space. There are many plants, which are suitable to be planted for vertical farming, such as Strawberry, Tomatoes, Celery, Chili, Mint, Chives, Kuchay, Spinach, and Water spinach. The problem, which is studied in this paper, is how to control the environments of vertical farming with multi-variety plants. This paper proposed a hybrid method of Analytical Hierarchy Process and Fuzzy Time Series AHP-FTS, that is, plants with similar characteristics are placed at the same block area determined by the method of Analytical Hierarchy Process (AHP). Furthermore, controlling the environments regarding the needs of appropriate growing parameters for multi-variety plants, the Fuzzy Time Series (FTS) method is used. Then, time variable for activating actuators could be adjusted as a multi-control system. The effectiveness of the proposed method was evaluated with 365 record data in 12 months. The result shows that the AHP was successful to determine the multi-criteria to determine the zone and priority of plants. The second stage is that the FTS predicts the temperature to determine time variable for activating actuators, and the third stage is the implemented AHP-FTS as a hybrid system to evaluate the vertical Farming system. The results show that the proposed system works well as hybrid system of AHP-FTS
A contribution spanning three time periods offered by artificial intelligence and synthetic biology Sven, Diaconescu Ada; Bellman, Francesco; Nurdiyanto, Heri
International Journal of Artificial Intelligence Research Vol 7, No 1 (2023): June 2023
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.348

Abstract

The field of synthetic biology benefits significantly from the application of artificial intelligence. I'd want to make three suggestions, which all have something to do with the past, the present, and the future of artificial intelligence. The works of Turing and von Neumann in biology and artificial systems from the past are exciting to investigate within the new framework of synthetic biology, particularly regarding the concepts of self-modification and self-replication as well as their links to the emergence and the bottom-up approach. The ongoing epistemological investigation into the emergence and the research being conducted on swarm intelligence, superorganisms, and biologically inspired cognitive architecture may result in discoveries on the potential uses of synthetic biology to explain mental processes. Finally, the current discussion on the future of artificial intelligence and the rise of superintelligence may point to some research trends for the future of synthetic biology and help to better define the boundary of concepts such as "life," "cognition," "artificial," and "natural," as well as their interconnections in theoretical synthetic biology. In addition, the rise of superintelligence may point to some research trends for the future of synthetic biology
An extraction of shapes and support vector machine methods for identification of decorative wall “Lamin” motifs of the Dayak Kenyah Pampang tribe Haviluddin, Haviluddin; Wati, Masna; Alfred, Rayner; Burhandenny, Aji Ery; Pratama, Arief Ardi
International Journal of Artificial Intelligence Research Vol 7, No 1 (2023): June 2023
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.475

Abstract

One of the Dayak cultures of Kalimantan Island, Indonesia is a traditional house called Lamin where each wall is decorated according to tribal characteristics. This study aims to identify the image on the Lamin wall using the Support Vector Machine (SVM) method based on the eccentricity and metric parameter values. The data of this study consisted of 50 types of images of the Lamin wall motifs of the Dayak Kenyah tribe consisting of tebengaang, dragon, crocodile, tiger, and arch which were taken from the tourist village, Pampang, Samarinda, East Kalimantan. Based on the experiment, the shape feature extraction method has produced the highest value of the eccentricity parameter which is 0.6979 and the metric parameter is 0.9953 on the image of the arch. Motif identification using the SVM method using linear, Gaussian/RBF, and polynomial kernel parameters has resulted in the highest accuracy with 80% image composition of kernel polynomial at 85%, Gaussian/RBF at 80%, and linear at 78%.
Vulnerability Detection With K-Nearest Neighbor and Naïve Bayes Method using Machine Learning Herman, Herman; Riadi, Imam; Kurniawan, Yudi
International Journal of Artificial Intelligence Research Vol 7, No 1 (2023): June 2023
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.795

Abstract

In this day and age, the use of the Internet has increased. SQL injection is a serious security threat on the Internet for various dynamic websites. As the use of the Internet for various online services increases, so make the security threats that exist on the Web. SQL injection attacks are one of the most serious security vulnerabilities on the Web. Most of these vulnerabilities are caused by a lack of input validation and the use of SQL parameters. SQLMap is an application from the Kali Linux operating system that is useful for injecting data on a website by using the features available in this application. In this paper, author conducts a security assessment to detect attacks on a website, more precisely to detect SQL Injection attacks, using the K-Nearest Neighbor method and naïve bayes. The results obtained are that the website being tested has SQL Injection vulnerabilities, and the K-Nearest Neighbor method is the best method for this case because it has an accuracy of 94.2%. In comparison, the Naïve Bayes method has an accuracy of 80%.
Analysis of the Similiarity Level of Source Code in the Kotlin Programming Language using Winnowing Algorithm Astica, Yustikamasy; Utami, Ema; Hartanto, Anggit Dwi
International Journal of Artificial Intelligence Research Vol 7, No 1 (2023): June 2023
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.902

Abstract

Plagiarism is an act of imitating the work of others directly or indirectly. In an academic environment, plagiarism applies not only to textual documents but also to source code documents. Source code plagiarism in academia usually occurs when students copy another student's code and submit it as if it were the student's work. So that an automatic plagiarism check is needed, the winnowing algorithm will be used to help detect similarities in source code as a way to detect an act of plagiarism. The Winnowing algorithm, which is usually used to detect document plagiarism, this research detects the source code. The results produced in this study are that the degree of similarity in the two source codes will produce different similarity values if the dataset used has gone through the text preprocessing stage or without preprocessing. If the dataset has gone through the text preprocessing stage, the similarity value will be pretty low because the number of characters used is significantly reduced. The Winnowing and Jaccard Similarity algorithms quickly detect plagiarism in source code and can be used to minimize plagiarism.
Volume Determination of Symmetrical Object with Distance Parameter Using Linear Regression Method Suseno, Jatmiko Endro; Setyawan, Agus; Gunadi, Isnain
International Journal of Artificial Intelligence Research Vol 7, No 1 (2023): June 2023
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.906

Abstract

The object’s volume is a consideration in determining the quantity of products such as eggs, fruit, piles of rice, or sand. This research aims to obtain a system for determining the volume of a symmetrical object using the linear regression method in real-time, faster, more effective, and more enjoyable. This research uses segmentation methods and linear regression to determine the volume of a symmetrical object. The objects are a pile of rice and eggs which have symmetrical shapes. The shape of the symmetry in each object is a cone for a pile of rice and an oval for an egg. The results of this research are a system of symmetrical object volume determination using the linear regression method with an accuracy score of 96.48% for piles of rice and 97.84% for an egg. This system has limitations, there are the volume value must be in the data range that has been trained and the camera phone must be the same.
Classification of Cervical Cancer Images Using Deep Residual Network Architecture Fauzi, Hilman; Bima Ansori, Revydo; Siadari, Thomhert; Budi Harsono, Ali; Rahmah, Qisthi Nur
International Journal of Artificial Intelligence Research Vol 7, No 1 (2023): June 2023
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.955

Abstract

According to data from the World Health Organization (WHO), cervical cancer is ranked second, with a high mortality rate in women every year. Cervical cancer is caused by the presence of the Human Papilloma Virus (HPV), which directly attacks the cervix. Additionally, an unhealthy lifestyle can cause attacks of this disease. Several methods can be used to detect cervical cancer early, one of which is Visual Inspection with Acetic Acid (VIA). Through VIA, tests can determine whether patients are infected with the HPV virus. The results of the VIA test can be seen with the naked eye, but medical experts have different opinions about the diagnosis made using their vision. Therefore, to assist medical practitioners in diagnosing the results of VIA, an examination with a technological approach was carried out. Digital imagery was used for the analysis. A medical expert’s Android camera was used with .jpg image format to capture pictures of the VIA test results. In this study, cervical cancer image classification was carried out from the results of the VIA test examination that had been carried out at Hasan Sadikin Hospital, Bandung, with as many as 255 data points for Negative VIA and 65 data points for Positive VIA. In the image processing of the VIA test results, CLAHE images and Canny Edge Detection images are used. Deep learning was used with the ResNet-50 and ResNet-101 architectural models to classify images, and different hyperparameter configurations, such as optimizers, learning rates, batch sizes, and input sizes, were tested. In this study, the best results were obtained using Canny Edge Detection images with hyperparameter configurations using the SGD optimizer with a learning rate of 0.1, a batch size of 32, and an input size of 224 × 224.
A Study on The Job Replacement Impact of ChatGPT and Education Method Dong Hwa Kim; Aktansi Kindiasari
International Journal of Artificial Intelligence Research Vol 7, No 1 (2023): June 2023
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.1007

Abstract

This paper deals with the job impact of ChatGPT and education preparation for that, which will give an influence on many areas because it can be implemented with ease as just normal editing works and speak including code development by using huge data. Currently young generations will take a big impact on their job selection because ChatGPT can do well as much as human can do it in everywhere. Therefore, education method and system should be rearranged as new curriculums. However, government and officer do not understand well how it is serious in education. This paper provides education method and curriculum for AI education including ChatGPT through analyzing many papers and report, and experience
A Comparison Support Vector Machine, Logistic Regression And Naïve Bayes For Classification Sentimen Analisys user Mobile App Baihaqi, Kiki Ahmad; Setyawan, Iwan; Manongga, Danny; Purnomo, Hendryanto Dwi; Hendry, Hendry; Fauzi, Ahmad; Hananto, Aprilia
International Journal of Artificial Intelligence Research Vol 7, No 1 (2023): June 2023
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.962

Abstract

Data is the most important thing, the use of data can be useful to get an evaluation from the user of a system or application that is built based on mobile. Not only, the assessment or acceptance results of mobile applications during the trial stage are considered important, assessments and comments from direct users are also important things that can be input for mobile application developers. Data mining, or known in English as data mining, is the answer to the process of retrieving data on any media. In this research, data mining is carried out on the media mobile application download service provider Google Playstore, which provides data in the form of comments and ratings. After scraping the data and obtaining the latest data parameters determined by the latest 2000 comments, the data is pre-processed by removing the emot icon character and eliminating unneeded variables so that the data obtained can be processed to the next stage, namely classification based on ratings and sentiment comments. The algorithms used or compared in this research are Support Vector machine, logistic regression and naïve bayes which are known to be reliable in data mining processing. In this research, the accuracy results are 88% for SVM, 90.5% for Logistic Regression and 91% for naïve bayes.
Selecting the Optimal Location for a New Facility: A PROMETHEE II Analyst Harjanti, Trinugi Wira; Widjaja, Herry Rachmat; Nofirman, N; Sudipa, I Gede Iwan; Pramono, Susatyo Adhi; Rahim, Robbi
International Journal of Artificial Intelligence Research Vol 7, No 1 (2023): June 2023
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.738

Abstract

This paper presents a case study of location selection for a new facility using the multi-criteria decision making (MCDM) method, PROMETHEE II. The PROMETHEE II method is a widely used method for solving problems that involve multiple criteria and alternatives. The method allows for the ranking of alternatives based on their overall net flow, which is calculated by weighting and comparing the criteria values for each pair of alternatives. The case study evaluated five different locations based on criteria such as access to transportation, availability of skilled labor, and cost of living. The results of the analysis indicate that C1 was ranked as the most attractive location, with the highest scores for all criteria and the highest overall net flow among all alternatives. A sensitivity analysis was performed to ensure that the results were robust and not sensitive to small changes in the weight of the criteria. The results of this study demonstrate the utility and effectiveness of the PROMETHEE II method in practice and provide valuable insights for further research and practical action.

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