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Science in Information Technology Letters
Published by Association for Scientific Computing Electronics and Engineering
ISSN : -     EISSN : 27224139     DOI : https://doi.org/10.31763/SiTech
Core Subject : Science,
Computer Science & IT
Science in Information Technology Letters (SITech) aims to keep abreast of the current development and innovation in the area of Science in Information Technology as well as providing an engaging platform for scientists and engineers throughout the world to share research results in related disciplines. SITech is a peer reviewed open-access journal which covers four (4) majors areas of research that includes 1) Artificial Intelligence, 2) Communication and Information System, 3) Software Engineering, and 4) Business intelligence Submitted papers must be written in English for initial review stage by editors and further review process by minimum two international reviewers. Finally, accepted and published papers will be freely accessed in this website.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol 1, No 1: May 2020" : 5 Documents clear
Modification of a gray-level dynamic range based on a number of binary bit representation for image compression Arief Bramanto Wicaksono Putra; Supriadi Supriadi; Aji Prasetya Wibawa; Andri Pranolo; Achmad Fanany Onnilita Gaffar
Science in Information Technology Letters Vol 1, No 1: May 2020
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v1i1.17

Abstract

The unique features of an image can be obtained by changing the gray level by modifying the dynamic range of the gray level. The gray-level dynamic range modification technique is one technique to minimize the selected features.  Bit rate reduction uses coding information with fewer bits than the original image (image compression). This study using the dynamic level of the gray level of a modified image with the concept of binary bit representation or also called bit manipulation.  Using some binary bit representation options used: 4, 5, 6, and 7 of bit can obtain the best compression performance. Measurement of compression ratio and decompression error ratio to a benchmark comparison called compression performance, which is the ultimate achievement of this study. The results of this study show the use of 6-bit binary representation has the best performance, and the resulting image compression does not resize the resolution of the original image only visually looks different.
Healthcare analytics by engaging machine learning Pragathi Penikalapati; A Nagaraja Rao
Science in Information Technology Letters Vol 1, No 1: May 2020
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v1i1.32

Abstract

Precise prediction of chronic diseases is the very basis of all healthcare informatics. Early diagnosis of the disease is crucial in delivering any healthcare service. The modern times witness our general vulnerability to several health disorders due to a stressful lifestyle causing anxiety and depression, or susceptibility to hypertension and diabetics or major diseases such as cancer or cardiovascular ailments. Hence, we should undergo periodic screening and diagnostic tests for such possible disorders to lead healthy lives. In this context, Machine Learning technology can play a pivotal role in developing Electronic Health Records (EHR) for implementing quick and comprehensively automated procedures in disease detection among the at-risk individuals at an early stage, so that accelerated processes of referral, counseling, and treatment can be initiated. The scope of the current paper is to survey the utilization of feature selection and techniques of Machine Learning, such as Classification and Clustering in the specific context of disease diagnosis and early prediction. This paper purposes of identifying the best models of Machine Learning duly supported by their performance indices, utility aspects, constraints, and critical issues in the specific context of their effective application in healthcare analytics for the benefit of practitioners and researchers.
Hybrid approach redefinition with progressive boosting for class imbalance problem Hartono Hartono; Erianto Ongko
Science in Information Technology Letters Vol 1, No 1: May 2020
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v1i1.34

Abstract

Problems of Class Imbalance in data classification have received attention from many researchers. It is because the imbalance class will affect the accuracy of the classification results. The problem of the imbalance class itself will ignore the minority class, which is a class with a smaller number of instances even though the minority class is an exciting class to observe. In overcoming the imbalanced class problem, it is necessary to pay attention to diversity data, the number of classifiers, and also classification performance. Several methods have been proposed to overcome the imbalanced class problem, one of which is the Hybrid Approach Redefinition Method. This method is a good hybrid ensemble method in dealing with imbalance class problems, which can provide useful diversity data and also a smaller number of classifiers. This research will combine the Hybrid Approach Redefinition by replacing the use of SMOTE Boost by using Progressive Boosting to get better data diversity, a small number of classifiers, and better performance. This study will conduct testing in handling imbalance class problems using datasets sourced from the KEEL-Dataset Repository. The results of this study indicate that the Hybrid Approach Redefinition with Progressive Boosting will provide better results in the number of classifiers, data diversity, and classification performance.
Palm oil classification using deep learning Abdulrazak Yahya Saleh; Ermawatih Liansitim
Science in Information Technology Letters Vol 1, No 1: May 2020
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v1i1.1

Abstract

Deep Convolutional Neural Networks (CNNs) have been established as a dominant class of models for image classification problems. This study aims to apply and analyses the accuracy of deep learning for classifying ripes on palm oil fruit.  The CNN used to classify 628 images into 2 different classes. Furthermore, the experiment of CNN with 5 epochs gives promising classification results with an accuracy of 98%, which is better than previous methods.  To sum up, this study was successfully solving an image classification by detected and differentiated the ripeness of oil palm fruit.
Automated image captioning with deep neural networks Abdullah Ahmad Zarir; Saad Bashar; Amelia Ritahani Ismail
Science in Information Technology Letters Vol 1, No 1: May 2020
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v1i1.31

Abstract

Generating natural language descriptions of the content of an image automatically is a complex task. Though it comes naturally to humans, it is not the same when making a machine do the same. But undoubtedly, achieving this feature would remarkably change how machines interact with us. Recent advancement in object recognition from images has led to the model of captioning images based on the relation between the objects in it. In this research project, we are demonstrating the latest technology and algorithms for automated caption generation of images using deep neural networks. This model of generating a caption follows an encoder-decoder strategy inspired by the language-translation model based on Recurrent Neural Networks (RNN). The language translation model uses RNN for both encoding and decoding, whereas this model uses a Convolutional Neural Networks (CNN) for encoding and an RNN for decoding. This combination of neural networks is more suited for generating a caption from an image. The model takes in an image as input and produces an ordered sequence of words, which is the caption.

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