cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Heri Nurdiyanto
Contact Email
Heri Nurdiyanto
Phone
-
Journal Mail Official
internationaljournalair@gmail.com
Editorial Address
-
Location
Kota metro,
Lampung
INDONESIA
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.
Arjuna Subject : -
Articles 5 Documents
 1 
Search results for , issue "Vol 3, No 1 (2019): June 2019" : 5 Documents clear
Design and Analysis of an Intelligent Integrity Checking Watermarking Scheme for Ubiquitous Database Access Darwish, Saad Mohamed; Selim, Hosam A.
International Journal of Artificial Intelligence Research Vol 3, No 1 (2019): June 2019
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (26.216 KB) | DOI: 10.29099/ijair.v3i1.65

Abstract

As a result of the highly distributed nature of ubiquitous database accessing, it is essential to develop security mechanisms that lend themselves well to the delicate properties of outsourcing databases integrity and copyright protection. Researchers have begun to study how watermarking computing can make ubiquitous databases accessing more confident work environments. One area where database context may help is in supporting content integrity. Initially, most of the research effort in this field was depending on distortion based watermark while the few remaining studies concentrated on distortion-free. But there are many disadvantages in previous studies; most notably some rely on adding watermark as an extra attributes or tuples, which increase the size of the database. Other techniques such as permutation and abstract interpretation framework require much effort to verify the watermark. The idea of this research is to adapt an optimized distortion free watermarking based on fake tuples that are embedded into a separate file not within the database to validate the content integrity for ubiquitous database accessing. The proposed system utilizes the GA, which boils down its role to create the values of the fake tuples as watermark to be the closest to real values. So that it's very hard to any attacker to guess the watermark. The proposed technique achieves more imperceptibility and security. Experimental outcomes confirm that the proposed algorithm is feasible, effective and robust against a large number of attacks.
A modeling approach for short-term load forcasting using fuzzy logic type-2 in sulselrabar system Muhammad Ruswandi Djalal
International Journal of Artificial Intelligence Research Vol 3, No 1 (2019): June 2019
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v3i1.68

Abstract

This research proposed a modeling approach for 24-hour short-term load forcasting based on fuzzy logic type-2. In this research we get an approach in designing load forecasting model, where previously still using conventional fuzzy logic. Implementation of load forecasting in this research is done on electrical system 150 kV Sulselrabar. Sulselrabar electrical system in its development has grown rapidly, therefore needed a study that to improve system performance, one of which is the study of short-term load forcasting. As the input data used load data from 2010 to 2016 on the same day that is January 8th. To see the accuracy of the results, two approaches are performed, ie fuzzy logic type-1 modeled using Simulink and fuzzy logic type-2 modeled using m-file Matlab. From the analysis results obtained, Mean Percentage Error (MAPE) is the smallest by using Fuzzy Logic Type-2 method, compared with Fuzzy Logic Type-1 method.. Where, MAPE for fuzzy logic type-1 method is 2.133371219%, and by using fuzzy logic type-2 method, MAPE is 1.729778866%.
Machine Learning Based Prediction versus Human-as-a-Security-Sensor Haque, Safwana
International Journal of Artificial Intelligence Research Vol 3, No 1 (2019): June 2019
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (599.503 KB) | DOI: 10.29099/ijair.v3i1.83

Abstract

Phishing is one of the most common cyber threats in the world today. It is a type of social engineering attack where the attacker lures unsuspecting victims into carrying out certain tasks mostly to steal personal and sensitive information. These stolen information are exploited to commit further crimes e.g. blackmails, data theft, financial theft, malware installation etc. This study was carried out to tackle this problem by designing an anti-phishing learning algorithm to detect phishing emails and also to study the accuracies of human phishing prediction to machine prediction. A graphical user interface was designed to emulate an email-client system that popped-up a warning on detecting a phishing mail successfully and collection of predictions made by expert and non-expert users on anti-phishing techniques. These predictions were compared to the predictions made by the machine learning algorithm to compare the efficiencies of all predictions considered in this research. The performance of the classifier used was measured with metrics such as confusion matrix, accuracy, receiver operating characteristic curve and area under graph
The Implementation of AHP for Determining Dominant Criteria in Higher Education Competitiveness Development Strategy Based on Information Technology Yulmaini, Yulmaini; Sanusi, Anuar; Yusendra, M. Ariza Eka
International Journal of Artificial Intelligence Research Vol 3, No 1 (2019): June 2019
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (259.803 KB) | DOI: 10.29099/ijair.v3i1.85

Abstract

The existence of Higher Education has a huge role in nation and state’s life through tri dharma of Higher Education named education, research and community service. Higher Education can produce economic innovations based on knowledge so that, it will increase productivity and nation competitiveness. Higher Education must have strategies that will be carried out, therefore they are able to compete with other higher education according to stakeholder needs. The purposes of this research are to analyze 2 (two) models of information technology relations in the higher education competitiveness development strategy determining the most dominant criteria according to the higher education development direction (Relevance, Academic Atmosphere, Internal Management, Sustainability, Efficiency and Productivity, Access and Equit and Leadership). The method of this reserach is AHP method in wich the data are collected through questionnaires to respondents in collage. The criteria of this research are internal management & organization, academic atmosphere and university competitive sustainability. The results of this research are the information technology relations model with internal management, and the relation model between internal management and efficiency & productivities, and also the most dominant criteria in the higher education competitiveness development strategy are the criteria of Academic Atmosphere, Efficiency and Productivity.
Quantum Inspired Genetic Programming Model to Predict Toxicity Degree for Chemical Compounds Darwish, Saad Mohamed
International Journal of Artificial Intelligence Research Vol 3, No 1 (2019): June 2019
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (27.314 KB) | DOI: 10.29099/ijair.v2i2.64

Abstract

Cheminformatics plays a vital role to maintain a large amount of chemical data. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in domains such as cosmetics, drug design, food safety, and manufacturing chemical compounds. Toxicity prediction topic requires several new approaches for knowledge discovery from data to paradigm composite associations between the modules of the chemical compound; such techniques need more computational cost as the number of chemical compounds increases. State-of-the-art prediction methods such as neural network and multi-layer regression that requires either tuning parameters or complex transformations of predictor or outcome variables are not achieving high accuracy results.  This paper proposes a Quantum Inspired Genetic Programming “QIGP” model to improve the prediction accuracy. Genetic Programming is utilized to give a linear equation for calculating toxicity degree more accurately. Quantum computing is employed to improve the selection of the best-of-run individuals and handles parsimony pressure to reduce the complexity of the solutions. The results of the internal validation analysis indicated that the QIGP model has the better goodness of fit statistics and significantly outperforms the Neural Network model.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2018 2019


Reset
Filter By Issues
All Issue Vol 9, No 1.1 (2025) Vol 9, No 2 (2025): December Vol 9, No 1 (2025): June Vol 8, No 2 (2024): December 2024 Vol 8, No 1 (2024): June 2024 Vol 8, No 1.1 (2024) Vol 7, No 2 (2023): December 2023 Vol 7, No 1 (2023): June 2023 Vol 7, No 1.1 (2023) Vol 6, No 2 (2022): Desember 2022 Vol 6, No 1 (2022): June 2022 Vol 6, No 1.2 (2022) Vol 6, No 1.1 (2022) Vol 5, No 2 (2021): December 2021 Vol 5, No 1 (2021): June 2021 Vol 4, No 2 (2020): December 2020 Vol 4, No 1 (2020): June Vol 3, No 2 (2019): December 2019 Vol 3, No 1 (2019): June 2019 Vol 2, No 2 (2018): December 2018 Vol 2, No 1 (2018): June 2018 Vol 1, No 2 (2017): December 2017 Vol 1, No 1 (2017): June 2017 More Issue