Sebelas Maret University

About: Sebelas Maret University is a education organization based out in Surakarta, Indonesia. It is known for research contribution in the topics: Population & Public health. The organization has 10901 authors who have published 10832 publications receiving 33057 citations. The organization is also known as: Universitas Negeri Surakarta & Universitas Sebelas Maret.

Face to Face or Online Learning: Students’ Perspectives on Blended Learning in Indonesia

Abstract: The implementation of blended learning (BL) continues to grow and encouraged in many contexts of teaching. While previous investigations had largely focused on BL implementation and effect on learning, the number of studies highlighting the students’ point of view is limited. This survey study investigated students’ perspective on the implementation of BL in the context of tertiary education. The respondents of this study were 75 students from three tertiary institutions in Indonesia. The data were collected utilizing a questionnaire which was adapted from and developed based on the PLEQ II to meet this present context of the study. Thematic analysis of five possible situations of blended learning resulted in the identification of the attributes that hinder and facilitate learning in the context of BL from the perspective of the students. This study indicated that students preferred and felt that they learned better in face to face session. The students acknowledged advantages but found online sessions more problematic. The study also confirms the self-regulatory attribute as a vital component in blended learning. The findings imply that blended learning, as opposed to blended teaching, requires careful tailoring to meet specific context and purpose of learning.

Genome wide association study discovers genomic regions involved in resistance to soybean cyst nematode (Heterodera glycines) in common bean.

Abstract: Common bean (Phaseolus vulgaris L.) is an important high protein crop grown worldwide. North Dakota and Minnesota are the largest producers of common beans in the USA, but crop production is threatened by soybean cyst nematode (SCN; Heterodera glycines Ichinohe) because most current cultivars are susceptible. Greenhouse screening data using SCN HG type 0 from 317 plant introductions (PI's) from the USDA core collection was used to conduct a genome wide association study (GWAS). These lines were divided into two subpopulations based on principal component analysis (Middle American vs. Andean). Phenotypic results based on the female index showed that accessions could be classified as highly resistant (21% and 27%), moderately resistant (51% and 48%), moderately susceptible (27% and 22%) and highly susceptible (1% and 3%) for Middle American and Andean gene pools, respectively. Mixed models with two principal components (PCs) and kinship matrix for Middle American genotypes and Andean genotypes were used in the GWAS analysis using 3,985 and 4,811 single nucleotide polymorphic (SNP) markers, respectively which were evenly distributed across all 11 chromosomes. Significant peaks on Pv07, and Pv11 in Middle American and on Pv07, Pv08, Pv09 and Pv11 in Andean group were found to be associated with SCN resistance. Homologs of soybean rhg1, a locus which confers resistance to SCN in soybean, were identified on chromosomes Pv01 and Pv08 in the Middle American and Andean gene pools, respectively. These genomic regions may be the key to develop SCN-resistant common bean cultivars.

Effect of alkali treatment of Salacca Zalacca fiber (SZF) on mechanical properties of HDPE composite reinforced with SZF

Abstract: Advanced developments of design and structure industries require modern material which both complies with green environment issues to reduce non-renewable resources, and also has the prospect to be fundamental material. The aim of the research was to investigate the interaction between alkali treated of Salacca Zalacca Fiber (SZF) and High Density Polyethylene (HDPE) by using microscopic examination, chemical analysis, contact angle test, interfacial shear strength and flexural tests of composites. SZF was chemically treated using a 5% NaOH solution for 0, 1, 2,3, 4, and 5 h to modify the fiber's properties. Chemical analysis shows that hemicellulose was decomposed after alkalization-ray diffraction found that the crystalline index (CrI) was changed as a result of alkalization. Micrograph observations by scanning electron microscope (SEM) show the complete impurities removal and revealed the parallelly aligned cellulose microfibrils, while atomic force microscopy (AFM) result shows the decrease of surface roughness as seen in 2D roughness and 3D surface topography. Contact angle measurement was found that the contact angle decreases with increased treatment time which indicated the improvement on SZF wettability. From the IFSS test, it was found that the overall fiber-matrix adhesion of the composite was improved with peak adhesion obtained after 5-hour treatment.

The Analysis Effect of Cluster Numbers On Fuzzy C-Means Algorithm for Blood Vessel Segmentation of Retinal Fundus Image

Abstract: The diagnosis of hypertensive retinopathy can be done using analysis of the retinal fundus image. The initial analysis that can be done is about the curvature of the blood tortuosity. The analysis was carried out by segmenting existing blood vessels. Segmentation can be done using the clustering algorithm, one of which is the fuzzy c-means (FCM) algorithm. This study aims to analyze the performance of the FCM algorithm with a variable number of cluster functions. The method used is divided into three stages, namely preprocessing, segmentation and performance analysis, Preprocessing consists of channel separation, CLAHE, and median filtering processes. The segmentation process consists of 2D to 1D conversion, clustering, thresholding and masking processes. The last process is to perform a performance analysis to compare with manual segmentation. Parameter performance used is sensitivity, specificity and are under the curve (AUC). The test results show, when using the DRIVE dataset the best performance is in a low number of clusters, while STARE is in a large number of clusters. The conclusion obtained is that increasing the number of clusters does not guarantee the best performance, but the number of clusters affects the performance of segmentation.

A fuzzy collaborative supply chain inventory model with controllable setup cost and service level constraint for imperfect items

Abstract: This paper presents a fuzzy collaborative vendor-buyer production-inventory model under service level constraint. The vendor's production process is imperfect and an order lot may contain a certain number of imperfect items with a known probability density function. This article assumes that setup cost and lead time can be reduced at an investing cost and at a crashing cost, respectively. The fuzzy total joint cost under fuzzy average demand is also considered. In this study, we consider two cases of lead time demand that are normal distribution and distribution free. The objective is to simultaneously optimise the lot size, lead time, reorder point, setup cost and number of shipment in one production cycle, constrained on a service level, such that minimise the total joint cost. In distribution free case, we apply a minimax distribution free procedure to determine the optimal solution. Numerical examples are used to illustrate the benefits of integration.