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Communications in Science and Technology
ISSN : 25029258     EISSN : 25029266     DOI : -
Core Subject : Engineering,
Communication in Science and Technology [p-ISSN 2502-9258 | e-ISSN 2502-9266] is an international open access journal devoted to various disciplines including social science, natural science, medicine, technology and engineering. CST publishes research articles, reviews and letters in all areas of aforementioned disciplines. The journal aims to provide comprehensive source of information on recent developments in the field. The emphasis will be on publishing quality articles rapidly and making them freely available to researchers worldwide. All articles will be indexed by Google Scholar, DOAJ, PubMed, Google Metric, Ebsco and also to be indexed by Scopus and Thomson Reuters in the near future therefore providing the maximum exposure to the articles. The journal will be important reading for scientists and researchers who wish to keep up with the latest developments in the field.
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Articles 6 Documents
Search results for , issue "Vol 4 No 2 (2019)" : 6 Documents clear
MACHINE LEARNING ALGORITHM FOR IMPROVING PERFORMANCE ON 3 AQ-SCREENING CLASSIFICATION Pratama, Taftazani Ghazi; Hartanto, Rudy; Setiawan, Noor Akhmad
Communications in Science and Technology Vol 4 No 2 (2019)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (293.516 KB) | DOI: 10.21924/cst.4.2.2019.118

Abstract

Autism Spectrum Disorder (ASD) classification using machine learning can help parents, caregivers, psychiatrists, and patients to obtain the results of early detection of ASD. In this study, the dataset used is the autism-spectrum quotient for child, adolescent and adult, namely AQ-child, AQ-adolescent, AQ-adult. This study aims to improve the sensitivity and specificity of previous studies so that the classification results of ASD are better characterized by the reduced misclassification. The algorithm applied in this study: support vector machine (SVM), random forest (RF), artificial neural network (ANN). The evaluation results using 10-fold cross validation showed that RF succeeded in producing higher adult AQ sensitivity, which was 87.89%. The increase in the specificity level of AQ-Adolescents is better produced using an SVM of 86.33%.
KANSEI ENGINEERING APPROACH FOR DEVELOPING ELECTRIC MOTORCYCLE Baroroh, Dawi Karomati; Amalia, Mya; Lestari, Nur Puji
Communications in Science and Technology Vol 4 No 2 (2019)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (478.107 KB) | DOI: 10.21924/cst.4.2.2019.119

Abstract

Electric vehicles are considered one of the solutions that can reduce vehicle emissions into the environment. However, the enthusiasts' number of the electric motorcycle is still relatively really low. In order to escalate product and competitive value in the market, the design elements of the electric motorcycle have to develop. The aim of this research is to design electric motorcycle that appropriate with user needs and desires by utilizing Kansei Engineering. This study involved 212 respondents for the Semantic Differential I and 204 respondents for the Semantic Differential II. The results of this study were 14 pairs of kansei words which were used to evaluate 11 sample designs where there were 5 items and 14 design categories. The design specifications of the most dominant electric motorcycle were angled seat, type 1 of front hood, 2 slots for luggage box, type 3 of headlight, and type 2 of body hood.
TOWARD A TAXONOMY OF MICRO AND SMALL MANUFACTURING ENTERPRISES Wibowo, Budhi; Masrurah, Nur Aini; Kasanah, Yulinda Uswatun; Trapsilawati, Fitri; Subagyo; Ilhami, Muhammad Adha
Communications in Science and Technology Vol 4 No 2 (2019)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (546.225 KB) | DOI: 10.21924/cst.4.2.2019.120

Abstract

The business sustainability of micro and small manufacturing enterprises (M-MSEs) are substantial to drive productivity and employment rate in many developing countries. However, due to various limitations, these enterprises are struggling to develop themselves into larger enterprises. For this reason, the government of Indonesia is enthused to provide technical and managerial supports for these M-MSEs. This study aims to provide an objective taxonomy of Indonesian M-MSEs as a guidance for the authorities to deliver the appropriate supports. The taxonomy was developed based on the survey to 735 M-MSEs in the Yogyakarta area. By using cluster analysis, we found that the M-MSEs can be classified into four distinct groups. Each group has its development strategies, ranging from adopting lean philosophy to instituting relationship marketing. This taxonomy provides useful directions for the authorities to support the development of M-MSEs in Yogyakarta and also serves as part of a broader effort to construct M-MSEs taxonomy in Indonesia.
AEROSOL OPTICAL DEPTH (AOD) RETRIEVAL FOR ATMOSPHERIC CORRECTION IN LANDSAT-8 IMAGERY USING SECOND SIMULATION OF A SATELLITE SIGNAL IN THE SOLAR SPECTRUM-VECTOR (6SV) Basith, Abdul; Nuha, Muhammad Ulin; Prastyani, Ratna; Winarso, Gathot
Communications in Science and Technology Vol 4 No 2 (2019)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (475.45 KB) | DOI: 10.21924/cst.4.2.2019.122

Abstract

Atmospheric correction has been challenging task in digital image processing. It requires several atmospheric parameters in order to obtain accurate surface reflectance of objects within the image scene. One of the most crucial parameters required for accurate atmospheric correction is aerosol optical depth (AOD). AOD can be obtained by in-situ measurement or estimated from remote sensing observation. In this experiment, atmospheric correction was performed using second simulation of a satellite signal in the solar spectrum-vector (6SV) algorithm on Landsat-8 imagery in which AOD parameter was retrieved from surface reflectance inversion involving daily-global surface reflectance product of moderate resolution imaging spectroradiometer (MODIS). Furthermore, AOD retrieved from surface reflectance inversion was also validated using ground-based sun photometer observation data from aerosol robotic network (AERONET) station in Bandung, Indonesia. Our experiment shows the consistency between AOD from surface reflectance inversion and AOD from ground-based observation. Finally, 6SV was performed on Landsat-8 imagery to obtain the surface reflectance. We further compared surface reflectance of 6SV atmospheric correction and surface reflectance of Landsat-8 Level 2 product. The atmospherically corrected image also shared agreeable result with Landsat 8 Level-2 product.
OPTIC CUP SEGMENTATION USING ADAPTIVE THRESHOLD AND MORPHOLOGICAL IMAGE PROCESSING Adi Nugroho, Hanung; Kirana, Thea; Pranowo, Vicko; Hutami, Augustine Herini Tita
Communications in Science and Technology Vol 4 No 2 (2019)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (329.362 KB) | DOI: 10.21924/cst.4.2.2019.125

Abstract

Glaucoma is a chronic optic neuropathy. It was predicted that people with bilateral blindness caused by glaucoma will increase each year. Hence, computer-aided diagnosis of glaucoma was proposed to assist ophthalmologist to conduct a fast and accurate glaucoma screening. One of the ocular examination in screening is optic nerve examination called disc damage likelihood scale (DDLS). It is important to find the optic disc and the optic cup to determine the narrowest width of the neuroretinal rim when using DDLS. To find the optic cup, this study proposed a segmentation scheme consisting of pre-process, segmentation, convex hull and morphological opening operation. In pre-process the blood vessel was removed to make the segmentation process of the optic cup easier. The segmentation process was done by using an adaptive thresholding followed by morphological image processing such as convex hull, opening and erosion. This algorithm was applied on Magrabia dataset and attained accuracy, specificity and sensitivity of 99.50%, 99.75% and 75.19% respectively.
MALARIA PARASITE SEGMENTATION USING U-NET: COMPARATIVE STUDY OF LOSS FUNCTIONS Abraham, Julisa Bana
Communications in Science and Technology Vol 4 No 2 (2019)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (529.435 KB) | DOI: 10.21924/cst.4.2.2019.128

Abstract

The convolutional neural network is commonly used for classification. However, convolutional networks can also be used for semantic segmentation using the fully convolutional network approach. U-Net is one example of a fully convolutional network architecture capable of producing accurate segmentation on biomedical images. This paper proposes to use U-Net for Plasmodium segmentation on thin blood smear images. The evaluation shows that U-Net can accurately perform Plasmodium segmentation on thin blood smear images, besides this study also compares the three loss functions, namely mean-squared error, binary cross-entropy, and Huber loss. The results show that Huber loss has the best testing metrics: 0.9297, 0.9715, 0.8957, 0.9096 for F1 score, positive predictive value (PPV), sensitivity (SE), and relative segmentation accuracy (RSA), respectively.

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