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Indian Traffic Signboard Recognition and Driver Alert System Using Machine Learning Yadav, Shubham; Patwa, Anuj; Rane, Saiprasad; Narvekar, Chhaya
International Journal of Applied Sciences and Smart Technologies Volume 01, Issue 01, June 2019
Publisher : Universitas Sanata Dharma

Sign board recognition and driver alert system which has a number of important application areas that include advance driver assistance systems, road surveying and autonomous vehicles. This system uses image processing technique to isolate relevant data which is captured from the real time streaming video. The proposed method is broadly divided in five part data collection, data processing, data classification, training and testing. System uses variety of image processing techniques to enhance the image quality and to remove non-informational pixel, and detecting edges. Feature extracter are used to find the features of image. Machine learning algorithm Support Vector Machine(SVM) is used to classify the images based on their features. If features of sign that are captured from the video matches with the trained traffic signs then it will generate the voice signal to alert the driver. In India there are different traffic sign board and they are classified into three categories: Regulatory sign, Cautionary sign, informational sign. These Indian signs have four different shapes and eight different colors. The proposed system is trained for ten different types of sign . In each category more than a thousand sample images are used to train the network.

Development Study of Deep Learning Facial Age Estimation Adi, Puspaningtyas Sanjoyo
International Journal of Applied Sciences and Smart Technologies Volume 01, Issue 01, June 2019
Publisher : Universitas Sanata Dharma

Human age estimation is one of the most challenging problem because it can be used in many applications relating to age such as age-specific movies, age-specific computer applications or website, etc. This paper will contribute to give brief information about development of age estimation researches using deep learning. We explore three recent journal papers that give significant contribution in age estimation using deep learning. From these papers, they selected classification methods and there is gradual improvement in result and also in selected loss function. The best result gives MAE (mean average error) 2.8 years and VGG-16 is the most selected CNN architecture.

Implementation of k-Medoids Clustering Algorithm to Cluster Crime Patterns in Yogyakarta Atmaja, Eduardus Hardika Sandy
International Journal of Applied Sciences and Smart Technologies Volume 01, Issue 01, June 2019
Publisher : Universitas Sanata Dharma

The increase in crime from day to day needs to be a concern for the police, as the party responsible for security in the community. Crime prevention effort must be done seriously with all knowledge that they have. To increase police performance of crime prevention effort, it is necessary to analyze crime data so that relevant information can be obtained.This study tried to analyze crime data to obtain relevant information using clustering in data mining.Clustering is a data mining method that can be used to extract valuable information by grouping data into groups that have similar characters.The data used in this study were crime patterns which were then grouped using K-medoids clustering algorithm.The obtained results in this study were three crime groups, namely high crime levelwith 4 members, medium crimelevel with 6 members and low crime level with 8 members.It is expected that this information can be used as material for consideration in crime prevention effort

Factors Influencing the Difficulty Level of the Subject: Machine Learning Technique Approaches Suparwito, Hari
International Journal of Applied Sciences and Smart Technologies Volume 01, Issue 01, June 2019
Publisher : Universitas Sanata Dharma

The difficulty level of a subject is needed either to understand the student acceptance of the subject and the highest level of student achievement in it. Some factors are considered, what kind of instructions, the readiness of the instructor and students in teaching and learning, evaluation and monitoring systems, and student expectations. Many factors are involved, and educators should know this. It is better if they can discern which are the prime factors and which the secondary factors. The purpose of the study is to find out the determinant factors in establishing the difficulty level of the subject from the students?, teachers? and infrastructure point of view using three machine learning techniques. The MSE and the variable importance measurement were used to predict between some factors such as Attendance, Instructors, and other factors as independent variables and the difficulty level of the subject as a dependent variable. The study result showed that Gradient Boosting Machine obtained the MSE value result 1.14 and 1.30 for training and validation dataset. The model generated five variable importance as an independent factor, i.e. Attendance, Instructor, The course can give a new perspective to students, The quizzes, assignments, projects and exams contributed to helping the learning, and The Instructor was committed to the course and was understandable. The Gradient Boosting Machine is superior to other methods with the lowest MSE and MAE values results. Two methods, Gradient Boosting Machine and Deep Learning, have produced the same five main factors that influenced the difficulty of the subject. It means these factors are significant and should get intention by the stakeholders

Influences of Annealing on the Electrical Properties of Ba0,5Sr0,5TiO3 Rositawati, Dwi Nugraheni
International Journal of Applied Sciences and Smart Technologies Volume 01, Issue 01, June 2019
Publisher : Universitas Sanata Dharma

The research aims to investigate influences of annealing on the electrical properties of Ba0,5Sr0,5TiO3. Ba0,5Sr0,5TiO3 material which was annealed at 900°C for 1, 2 and 4 hours has better mechanical properties. It needs investigation for its electrical contribution, namely the correlation between grain and grain boundaries to values of resistance and capacitance. The changing of electrical properties was controlled by grain, grain boundary and the area between the sample and contact. The electrical properties of Ba0,5Sr0,5TiO3 were investigated by impedance spectroscopy in the room temperature. This method is able to separate the electrical and dielectric properties of the grain, grain boundary and the area between contact with the sample. ZsimpWin software was used to find out the equivalent electrical circuit, the resistance and capacitance value. It was observed that with the increase in annealing time the small grains resistance, the grain boundaries resistance, and the large grain capacitance value also increases. The resistance values of small grains and large grains were smaller than the grain boundaries resistance. The value of capacitance-resistance of the small grains and large grains were obtained values that tend to be smaller.

The Improvement of Watershed Algorithm Accuracy for Image Segmentation Handwritten Numbered Musical Notation Pinaryanto, Kartono
International Journal of Applied Sciences and Smart Technologies Volume 01, Issue 01, June 2019
Publisher : Universitas Sanata Dharma

In the Implementation of image processing to translate the image of the numbered musical notation into a numerical character requires some initial process that must be passed like image segmentation process. The advantage of successful segmentation process is that it can reduce the failure rate in the object recognition process. Segmentation process determines the success of object recognition process, it takes segmentation algorithm that can perform accurate object separation. The combination segmentation process developed in this research used projection profile algorithm, watershed and non object filtering. Profile projection algorithm is used to crop the image of the musical horizontally and vertically. The watershed algorithm is used to segment the numerical object of numerical notation generated from the projection profile process. Non object filtering is a continuation of the watershed algorithm that includes the non-object reduction process and the process of combining objects so that the original object segment will be generated. The based on the results of the research, the accuracy of the segment on watershed segmentation is 99.74% higher than watershed segmentation without combination of 94.82%.

Spur Gears Transmission Analysis on Countinous Passive Motion Machine Design for Shoulder Joint Nugraha, Felix Krisna Aji; Noviyanto, Antonius Hendro
International Journal of Applied Sciences and Smart Technologies Volume 01, Issue 01, June 2019
Publisher : Universitas Sanata Dharma

An analysis of gear transmission on a countinous passive machine (CPM) from the 3-dimensional design has been carried out using Solidworks software. Analysis of the strength of the gear structure is affected by the weight of the patient's arm.Analysis of gear transmission that is affected by the load of the passive arm uses static simulation, by entering the patient's arm load. The facilities used are static simulation with the condition of fixed geometry in the parts related of the shaft, the effect of gravity of 10 m/s2, making mesh, and running simulation.The maximum stress that occurs in gear3 z = 100 is 4.5524e + 006 N/m2, the maximum stress on gear2 z = 80 is 4.81729e + 006 N/m2, the maximum stress on gear100 z = 20 is 9.08982e + 006 N/m2

Development Study of Deep Learning Facial Age Estimation Puspaningtyas Sanjoyo Adi
International Journal of Applied Sciences and Smart Technologies Volume 01, Issue 01, June 2019
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/ijasst.v1i1.1899

Human age estimation is one of the most challenging problem because it can be used in many applications relating to age such as age-specific movies, age-specific computer applications or website, etc. This paper will contribute to give brief information about development of age estimation researches using deep learning. We explore three recent journal papers that give significant contribution in age estimation using deep learning. From these papers, they selected classification methods and there is gradual improvement in result and also in selected loss function. The best result gives MAE (mean average error) 2.8 years and VGG-16 is the most selected CNN architecture.

Indian Traffic Signboard Recognition and Driver Alert System Using Machine Learning Shubham Yadav; Anuj Patwa; Saiprasad Rane; Chhaya Narvekar
International Journal of Applied Sciences and Smart Technologies Volume 01, Issue 01, June 2019
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/ijasst.v1i1.1843

Sign board recognition and driver alert system which has a number of important application areas that include advance driver assistance systems, road surveying and autonomous vehicles. This system uses image processing technique to isolate relevant data which is captured from the real time streaming video. The proposed method is broadly divided in five part data collection, data processing, data classification, training and testing. System uses variety of image processing techniques to enhance the image quality and to remove non-informational pixel, and detecting edges. Feature extracter are used to find the features of image. Machine learning algorithm Support Vector Machine(SVM) is used to classify the images based on their features. If features of sign that are captured from the video matches with the trained traffic signs then it will generate the voice signal to alert the driver. In India there are different traffic sign board and they are classified into three categories: Regulatory sign, Cautionary sign, informational sign. These Indian signs have four different shapes and eight different colors. The proposed system is trained for ten different types of sign . In each category more than a thousand sample images are used to train the network.

Factors Influencing the Difficulty Level of the Subject: Machine Learning Technique Approaches Hari Suparwito
International Journal of Applied Sciences and Smart Technologies Volume 01, Issue 01, June 2019
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/ijasst.v1i1.1869

The difficulty level of a subject is needed either to understand the student acceptance of the subject and the highest level of student achievement in it. Some factors are considered, what kind of instructions, the readiness of the instructor and students in teaching and learning, evaluation and monitoring systems, and student expectations. Many factors are involved, and educators should know this. It is better if they can discern which are the prime factors and which the secondary factors. The purpose of the study is to find out the determinant factors in establishing the difficulty level of the subject from the students, teachers and infrastructure point of view using three machine learning techniques. The MSE and the variable importance measurement were used to predict between some factors such as Attendance, Instructors, and other factors as independent variables and the difficulty level of the subject as a dependent variable. The study result showed that Gradient Boosting Machine obtained the MSE value result 1.14 and 1.30 for training and validation dataset. The model generated five variable importance as an independent factor, i.e. Attendance, Instructor, The course can give a new perspective to students, The quizzes, assignments, projects and exams contributed to helping the learning, and The Instructor was committed to the course and was understandable. The Gradient Boosting Machine is superior to other methods with the lowest MSE and MAE values results. Two methods, Gradient Boosting Machine and Deep Learning, have produced the same five main factors that influenced the difficulty of the subject. It means these factors are significant and should get intention by the stakeholders

Home Page

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Reviewer

Google Scholar

Contact Name
I Made Wicaksana Ekaputra

Contact Email
made@usd.ac.id

Phone
+62274883037

Journal Mail Official
editorial.ijasst@usd.ac.id

Editorial Address
Kampus III Universitas Sanata Dharma, Paingan, Maguwoharjo, Depok, Yogyakarta

Location
Kab. sleman,

Daerah istimewa yogyakarta

INDONESIA

Abstract

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name I Made Wicaksana Ekaputra

Contact Email made@usd.ac.id

Phone +62274883037

Journal Mail Official editorial.ijasst@usd.ac.id

Editorial Address Kampus III Universitas Sanata Dharma, Paingan, Maguwoharjo, Depok, Yogyakarta

Location Kab. sleman, Daerah istimewa yogyakarta INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
I Made Wicaksana Ekaputra

Contact Email
made@usd.ac.id

Phone
+62274883037

Journal Mail Official
editorial.ijasst@usd.ac.id

Editorial Address
Kampus III Universitas Sanata Dharma, Paingan, Maguwoharjo, Depok, Yogyakarta

Location
Kab. sleman,

Daerah istimewa yogyakarta

INDONESIA