Technology Reports of Kansai University

Technology Reports of Kansai University (ISSN: 04532198) is a monthly peer-reviewed and open-access international Journal. It was first built in 1959 and officially in 1975 till now by kansai university, japan. The journal covers all sort of engineering topic, mathematics and physics. Technology Reports of Kansai University (TRKU) was closed access journal until 2017. After that TRKU became open access journal. TRKU is a scopus indexed journal and directly run by faculty of engineering, kansai university.

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Submission Deadline

Volume - 66 , Issue 01
20 Jan 2024

Upcoming Publication

Volume - 66 , Issue 01
31 Jan 2024

Aim and Scope

Technology Reports of Kansai University (ISSN: 04532198) is a peer-reviewed journal. The journal covers all sort of engineering topic as well as mathematics and physics. the journal's scopes are in the following fields but not limited to:

Electrical Engineering and Telecommunication Section:

Electrical Engineering, Telecommunication Engineering, Electro-mechanical System Engineering, Biological Biosystem Engineering, Integrated Engineering, Electronic Engineering, Hardware-software co-design and interfacing, Semiconductor chip, Peripheral equipments, Nanotechnology, Advanced control theories and applications, Machine design and optimization , Turbines micro-turbines, FACTS devices , Insulation systems , Power quality , High voltage engineering, Electrical actuators , Energy optimization , Electric drives , Electrical machines, HVDC transmission, Power electronics.

Computer Science Section :

Software Engineering, Data Security , Computer Vision , Image Processing, Cryptography, Computer Networking, Database system and Management, Data mining, Big Data, Robotics , Parallel and distributed processing , Artificial Intelligence , Natural language processing , Neural Networking, Distributed Systems , Fuzzy logic, Advance programming, Machine learning, Internet & the Web, Information Technology , Computer architecture, Virtual vision and virtual simulations, Operating systems, Cryptosystems and data compression, Security and privacy, Algorithms, Sensors and ad-hoc networks, Graph theory, Pattern/image recognition, Neural networks. Lizi Jiaohuan Yu Xifu/Ion Exchange and Adsorption Fa yi xue za zhi

Civil and architectural engineering :

Architectural Drawing, Architectural Style, Architectural Theory, Biomechanics, Building Materials, Coastal Engineering, Construction Engineering, Control Engineering, Earthquake Engineering, Environmental Engineering, Geotechnical Engineering, Materials Engineering, Municipal Or Urban Engineering, Organic Architecture, Sociology of Architecture, Structural Engineering, Surveying, Transportation Engineering.

Mechanical and Materials Engineering :

kinematics and dynamics of rigid bodies, theory of machines and mechanisms, vibration and balancing of machine parts, stability of mechanical systems, mechanics of continuum, strength of materials, fatigue of materials, hydromechanics, aerodynamics, thermodynamics, heat transfer, thermo fluids, nanofluids, energy systems, renewable and alternative energy, engine, fuels, nanomaterial, material synthesis and characterization, principles of the micro-macro transition, elastic behavior, plastic behavior, high-temperature creep, fatigue, fracture, metals, polymers, ceramics, intermetallics.

Chemical Engineering :

Chemical engineering fundamentals, Physical, Theoretical and Computational Chemistry, Chemical engineering educational challenges and development, Chemical reaction engineering, Chemical engineering equipment design and process design, Thermodynamics, Catalysis & reaction engineering, Particulate systems, Rheology, Multifase flows, Interfacial & colloidal phenomena, Transport phenomena in porous/granular media, Membranes and membrane science, Crystallization, distillation, absorption and extraction, Ionic liquids/electrolyte solutions.

Food Engineering :

Food science, Food engineering, Food microbiology, Food packaging, Food preservation, Food technology, Aseptic processing, Food fortification, Food rheology, Dietary supplement, Food safety, Food chemistry.

Physics Section:

Astrophysics, Atomic and molecular physics, Biophysics, Chemical physics, Civil engineering, Cluster physics, Computational physics, Condensed matter, Cosmology, Device physics, Fluid dynamics, Geophysics, High energy particle physics, Laser, Mechanical engineering, Medical physics, Nanotechnology, Nonlinear science, Nuclear physics, Optics, Photonics, Plasma and fluid physics, Quantum physics, Robotics, Soft matter and polymers.

Mathematics Section:

Actuarial science, Algebra, Algebraic geometry, Analysis and advanced calculus, Approximation theory, Boundry layer theory, Calculus of variations, Combinatorics, Complex analysis, Continuum mechanics, Cryptography, Demography, Differential equations, Differential geometry, Dynamical systems, Econometrics, Fluid mechanics, Functional analysis, Game theory, General topology, Geometry, Graph theory, Group theory, Industrial mathematics, Information theory, Integral transforms and integral equations, Lie algebras, Logic, Magnetohydrodynamics, Mathematical analysis.

Latest Articles of

Technology Reports of Kansai University

Journal ID : TRKU-11-02-2021-11421
Total View : 584

Title : Boarding House Recommendation Application Using Collaborative Filtering Algorithm

Abstract :

Boarding houses are an inseparable part of students, mainly migrant students. In addition to resting, boarding can also be a place for study and discussion. Therefore, it needs reasonable consideration before choosing a boarding house. Choosing the right boarding house can increase student productivity. Currently looking for boarding houses you don't need to go around the campus, but you can use other boarding search applications. The application only displays data from the database directly without processing it first. When we open the application, the first display is boarding A. Then someone else opens the application the first to appear is boarding A again. Why not Boarding B? Even though the willingness of each user is different. Therefore, we need a recommendation algorithm to overcome these problems. In this research, the author uses the Collaborative Filtering algorithm. The dataset used comes from the Koseeker dataset from March 2020 - June 2020, totaling 1,897. The result of the calculation of Mean Absolute Error (MAE) is 0.4780. Applications made have an accuracy rate of 88.05%.

Full article
Journal ID : TRKU-11-02-2021-11420
Total View : 565

Title : Classification of Political Data on Social Media with Support Vector Machine Algorithm

Abstract :

This study will classify sentiment and analysis of general topics against President Joko Widodo taken through social media Twitter. The influence and benefits of sentiment are so significant that the study of analytical sentiment is increasing. This research has several stages, including data collection, data cleaning, data extraction, data classification using the Support Vector Machine method, and topic analysis using the Latent Dirichlet Allocation method. This study's results took data on Twitter taken from September 23, 2018, to December 15, 2020, as many as 1200 pieces of data in the form of 600 negative data and 600 positive data. In conclusion, the topic analysis system and sentiment classification performed the classification results using the Support Vector Machine method in this research, which obtained 86.39% accuracy by 85.75%, recall by 85.86%, and F1 score 85.79%—as for topic analysis using Latent Dirichlet Allocation got a perplexity value of 7.1049 on positive sentiment and 7.3165 on negative sentiment.

Full article