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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.

Arjuna Subject : -

Articles 621 Documents

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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

A Novel Approach for Recognition and Identification of Low-Level Flight Military Aircraft using Naive Bayes Classifier and Information Fusion Arwin Datumaya Wahyudi Sumari; Afifah Millatina Nugraheni; Yoppy Yunhasnawa
International Journal of Artificial Intelligence Research Vol 6, No 2 (2022): Desember 2022
Publisher : STMIK Dharma Wacana

A problem that has been faced by the Radar is if the aircraft flies at low level or near to the surface so its coming in the aerial-surveillance airspace cannot be detected and endangers the air sovereignty. The aircraft can be recognized and identified by carrying out a technique called Visual Aircraft Recognition (VACR) using a binocular. This technique requires military personnel that has capability carrying out the air surveillance from the ground. Surveillance is a time-consuming and tiring task so it can cause fatigue and impact to the results of the recognition and identification. To cope with this problem, we have designed and implemented a novel recognition and identification method using the combination of Naive Bayes Classifier (NBC) and information fusion. By using a dataset that consists of 45 military aircrafts, 35 civilian aircrafts, 40 military helicopters, and 35 civilian helicopters with 80:20 dataset distribution for the training scheme and the validation one, we obtained the recognition accuracy of 87.1%. We also found that the recognition and identification process can be speeded up 1.2 seconds when using information fusion.

Analysis of Learning Algorithms for Multilayer Neural Networks Harahap, Muhammad Khoiruddin; Pramono, Eko; Novita, Hilda Yulia; Maharina, Maharina; Sasongko, Dimas; Zonyfar, Candra
International Journal of Artificial Intelligence Research Vol 6, No 1 (2022): June 2022
Publisher : Universitas Dharma Wacana

The modern stage of development of science and technology is characterized by a rapid increase in the complexity of the created technical systems. The management of such systems requires the development of new management methods, since the modification and improvement of traditional management techniques does not always ensure the fulfillment of stringent requirements for management quality indicators. Classical control methods are mainly based on the theory of linear systems, while most real objects are non-linear. The problem of the synthesis of control systems under conditions of uncertainty is currently one of the central problems in the modern theory of automatic control. The complexity of the control object itself, structural, parametric and information uncertainties in the description of the control object, and the complexity of control problems, the multi criteria of optimization problems, the lack of possible analytical solutions, the need to take into account all the properties of disturbances, etc. The solution to this problem requires a search for alternative approaches to the design of control systems, one of which involves the introduction of neural network systems. Neural network control systems are a high-tech direction of control theory and belong to the class of nonlinear dynamic systems. High performance due to parallelization of input information in combination with the ability to train neural networks makes this technology very attractive for creating control devices in automatic systems. Neural networks can be used to build regulating and switching devices, reference, adaptive, nominal and inverse-dynamic models of objects, on the basis of which objects are studied, analysis of the influence of disturbances acting on an object, determination of the optimal control law, search or calculating the optimal program for changing the impact when changing the values of the parameters of the object and the characteristics of the input data. In addition, neural networks can be used to identify objects, predict the state of objects, recognize, cluster, classify, analyze a large amount of data arriving at high speed from a large number of devices and sensors, and the like. The ability to learn according to a given principle of functioning allows creating automated control systems that are optimal in terms of speed, energy consumption, etc. Naturally, in this case, it is possible to implement several principles of functioning and the transition from one to another. They are a universal tool for modeling multidimensional nonlinear objects and finding solutions to ill-posed problems.

Smart Contract Blockchain Application Design Based on The Distribution of Product Return Transaction Data Ekawati, Ratna; Arkeman, Yandra; Suprihatinr, Suprihatinr
International Journal of Artificial Intelligence Research Vol 6, No 2 (2022): Desember 2022
Publisher : Universitas Dharma Wacana

In 2020, there will be 1% bulk sugar product returns. Direct return to warehouse; it is not known how much and what kind of sugar was returned. Changes in the number of uncontrolled product availability occur in the logistics sector. We designed a sugar volume return mechanism to verify the identity of the buyer, the amount and time of the transaction, using the steps of investigation, analysis, and system design that can implement. The application is based on the truffle test framework and smart contracts on the Ropsten test network on the Ethereum Metamask platform wallet, localhost memory, and a decentralized web-based dashboard. Input data on the smart contract so that during the Ropsten net test process, it will generate blocks, hash codes, and contract hashes as transaction details. It also displays a summary report and a blockchain transaction dashboard. How much volume will increase or decrease due to returns, buyers, type of sugar commodity, time, and volume of sugar during data transactions is known. The features developed for smart contracts are private, semi-public transactions with consensus proof of work as validation and verification of the success of transaction data records.

A Mobile Deep Learning Model on Covid-19 CT-Scan Classification Susanto, Prastyo Eko; Kurniawardhan, Arrie; Fudholi, Dhomas Hatta; Rahmadi, Ridho
International Journal of Artificial Intelligence Research Vol 6, No 2 (2022): Desember 2022
Publisher : Universitas Dharma Wacana

COVID-19 pandemic is currently happening in the world. Previous studies have been done to diagnose COVID-19 by identifying CT-scan images through the development of the novel Joint Classification and Segmentation System models that work in real-time. In this study, the author focuses on a different motivation and innovation focused on the development of mobile deep learning. Mobile Net, a deep learning model as a method for classifying the disease COVID-19, is used as the base model. It has a good level of efficiency and reliability to be implemented on devices that have small memory and CPU specifications, such as mobile phones. The used data in this study is a CT-scan image of the lungs with a horizontal slice that has been classified as positive or negative for COVID-19. To give a broader analysis, the author compares and evaluates the model against other architectures, such as MobileNetV3 Large, MobileNetV3 Small, MobilenetV2, ResNet101, and EfficientNetB0. In terms of the developed mobile architecture model, the classification of COVID-19 using MobileNetV2 obtained the best result with 0.81 accuracy.

iLearning Model Approach in Creating Blockchain Based Higher Education Trust Anwar, Aang Solahudin; Rahardja, Untung; Prawiyogi, Anggy Giri; Santoso, Nuke Puji Lestari; maulana, sabda
International Journal of Artificial Intelligence Research Vol 6, No 2 (2022): Desember 2022
Publisher : Universitas Dharma Wacana

Today, higher education presents challenges in terms of educational and industrial collaboration. Both theoretical and practical, formal and informal are also part of the application of blockchain in education. Moreover, the assessment is still quite difficult to do to measure the skill level of students so that they can compete for jobs in the future With the problem of the academic curriculum that still uses writing media on paper, problems often arise regarding the validity of reliable document validation. So it is necessary to compare the knowledge obtained to match their abilities. From these problems, the goal was created to improve the higher education curriculum in order to find a revolutionary solution for document validation beliefs.Â Evaluation of the iLearning learning system combined with blockchain technology (ledger) has the benefit of being able to support these problems.Â By using Blockchain technology, a new learning model innovation is created in the form of the SCi-B (Student-Centered iLearning Blockchain) framework. SCi-B is a new innovation in the learning model where all activities use Blockchain so that its existence is able to manage and store all transactions, competencies, and teaching that can provide intensive assessments through digital certificates for the academic world and the world of work. So that SCi-B has a significant impact on the confidence in the results that have been obtained.Â This paper aims to answer the challenges of the world of education which is currently getting wider, more open, and everywhere. The model in the SCi-B framework of this paper can be used for all training institutions because this model can adapt to the specific professional needs of the job sector. This model has been validated by the existence of a web application whose use is very satisfying.

Journal Unique Visitors Forecasting Based on Multivariate Attributes Using CNN Dewandra, Aderyan Reynaldi Fahrezza; Wibawa, Aji Prasetya; Pujianto, Utomo; Utama, Agung Bella Putra; Nafalski, Andrew
International Journal of Artificial Intelligence Research Vol 6, No 2 (2022): Desember 2022
Publisher : Universitas Dharma Wacana

Forecasting is needed in various problems, one of which is forecasting electronic journals unique visitors. Although forecasting cannot produce very accurate predictions, using the proper method can reduce forecasting errors. In this research, forecasting is done using the Deep Learning method, which is often used to process two-dimensional data, namely convolutional neural network (CNN). One-dimensional CNN comes with 1D feature extraction suitable for forecasting 1D time-series problems. This study aims to determine the best architecture and increase the number of hidden layers and neurons on CNN forecasting results. In various architectural scenarios, CNN performance was measured using the root mean squared error (RMSE). Based on the study results, the best results were obtained with an RMSE value of 2.314 using an architecture of 2 hidden layers and 64 neurons in Model 1. Meanwhile, the significant effect of increasing the number of hidden layers on the RMSE value was only found in Model 1 using 64 or 256 neurons.

Financial Management System (QRIS) based on UTAUT Model Approach in Jabodetabek Kadim, A; Sunardi, Nardi
International Journal of Artificial Intelligence Research Vol 6, No 1.1 (2022)
Publisher : STMIK Dharma Wacana

The focus of this research is the use of e-money information technology by adapting the UTAUT model to determine the effect of performance expectations, business expectations, social influences and facilitation conditions to public interest in using Server Base Payment System: Quick Response Code Indonesian Standard (QRIS) in Jabodetabek. This study uses a quantitative descriptive method. The population of this study was Jakarta, Bogor, Depok, Tangerang, Bekasi (Jabodetabek) citizen and the sample of fespondent are 125 by using targeted sampling method. Data analysis by lisrel. The results of this study informed that behavioral intersest was not affect with positive and significant by performance expectations and social influence but was affect with positive and signifianct by effort expectation. Jabodetabek citizens usage behavior in QRIS payment tools was affect with positive and significant by facilities conditions and behavioral interest.

Instagram: is it a Social Media Solution to Promote Sustainable Tourism? Aprih Santoso; Ardiani Ika sulistyawati; Vensy Vydia
International Journal of Artificial Intelligence Research Vol 6, No 1.1 (2022)
Publisher : International Journal of Artificial Intelligence Research

There is a tourism promotion business phenomenon that is carried out through social media Instagram in the city of Semarang. This phenomenon shows the current realization of tourism promotion activities through Instagram social media, with the aim of research to examine tourism promotion through Instagram social media carried out in Semarang City. This study uses qualitative research with a case study approach by taking tourism research objects in the city of Semarang. Data collection in this study was obtained through interviews, observations, documentation with informants, namely Instagram account admins, Instagram social media users, academics in the tourism sector, and travel agencies that use Instagram social media as a promotional media. The results of the analysis of tourism promotion in Semarang City through Instagram social media are carried out in stages, namely: content creation, platform determination, program planning, program implementation, and monitoring & evaluation. Analysis of the advantages of promotion through Instagram social media, namely promotional media that is not paid, can be used at any time, is easy to use, can be connected to other social media, and has many users. Analysis of the weakness of promotion through Instagram social media is that it must be updated regularly, the authenticity of the product is still in doubt, prone to spamming. Furthermore, the analysis of sharia tourism promotion through Instagram social media has very effective prospects for the future.

Carter Dimension Analysis of Customer Satisfaction In Improving Electronic Word Of Mouth (E-Wom) In Bank Syariah Mandiri Customers. Asmuni, Asmuni; Harahap, Isnaini; Saragih, Lenny Menara Sari
International Journal of Artificial Intelligence Research Vol 6, No 1.1 (2022)
Publisher : STMIK Dharma Wacana

This study aims to analyze the effect of CARTER dimensions consisting of tangible, assurance, reliability, tangible, empathy and responsiveness on customer satisfaction at Bank Syariah Mandiri in Medan City. electronic word of mouth. Respondents in this study were customers of Bank Syariah Mandiri in Medan City who were Muslim and used Mobile Banking. A total of 300 questionnaires were distributed, there were 220 questionnaires that met the criteria. So that the data can be processed as many as 220 samples. The analysis technique uses Structural Equation Modeling with AMOS.The results show that the Compliance dimension has a positive and significant effect on customer satisfaction at Bank Syariah Mandiri in Medan City, the assurance dimension has a significant positive effect on customer satisfaction at Bank Syariah Mandiri in Medan City, the reliability dimension has a positive direction but does not significantly affect customer satisfaction at Bank Syariah Mandiri in Medan. Medan City, the tangible dimension is the largest dimension that has a positive and significant effect on customer satisfaction of Bank Syariah Mandiri in the City, the dimension of empathy has a positive and significant effect on customer satisfaction of Bank Syariah Mandiri in the City, the dimension of responsiveness has a positive and significant effect on customer satisfaction of Bank Syariah Mandiri in Medan city. The variable of customer satisfaction has a positive and significant effect on electronic word of mouth on Bank Syariah Mandiri customers in Medan City. The Sobel test conducted shows that customer satisfaction has a significant effect as a mediator between the tangible, assurance, tangible, empathy and responsiveness dimensions and Electronic Word of Mouth.

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Contact Name
Heri Nurdiyanto

Contact Email
Heri Nurdiyanto

Phone
-

Journal Mail Official
internationaljournalair@gmail.com

Editorial Address
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Location
Kota metro,

Lampung

INDONESIA