ANALISIS LINGKUNGAN SEBAGAI DASAR DALAM PENENTUAN STRATEGI PENGADAAN BAHAN BAKU PADA PT. GLOBAL TROPICAL SEAFOOD KAPEDI SUMENEP

2012 ◽  
Vol 2 (1) ◽  
Author(s):  
Endang Widiyastuti
Keyword(s):  
Factor Analysis ◽  
External Factor ◽  
Internal Factor ◽  
Analysis Strategy

Sektor pemasaran sebagai salah satu fungsi dalam perusahaan yang diharapkan mampu berperan banyak dalam upaya meningkatkan volume penjualan. Fungsi dari kegiatan pemasaran adalah menyakinkan konsumen agar bersedia membeli produk yang ditawarkan. Untuk menghindari kesalahan sasaran program pemasaran perlu adanya suatu strategi pemasaran yang tepat, yaitu strategi pemasaran yang berwawasan lingkungan pemasaran. Hal ini penting untuk menghadapi setiap perubahan lingkungan pemasaran yang semakin tidak menentu dan agar dapat bersaing serta bertahan dalam percaturan bisnis dan untuk mengantisipasi kecenderungan pemasaran karena sifat pasar yang selalu berubah dan tidak  pasti.Tujuan dari penelitian ini adalah untuk menganalisis lingkungan internal (kekuatan dan kelemahan) serta eksternal (peluang dan ancaman) perusahaan, serta untuk mengidentifikasi strategi yang tepat bagi perusahaan.Dari hasil analisis data menunjukkan bahwa strategi pemasaran yang diterapkan adalah strategi pertumbuhan internal perusahaan dengan cara pendekatan penetrasi pasar. Alasan yang mendasari pemutusan  strategi tersebut adalah pada analisis lingkungan eksternal EFAS (External Factor Analysis Strategy) yang meliputi kondisi demografi, teknologi yang menunjukkan beberapa peluang bagi perusahaan yang bergerak dibidang perikanan untuk tumbuh dan berkembang. Pada analisis lingkungan internal IFAS (Internal Factor Analysis Strategy) perusahaan menunjukkan kondisi internal perusahaan masih perlu dibenahi karena kekuatan Sumber Daya Manusia merupakan potensi yang dapat dikembangkan dimasa yang akan datang.Berdasarkan hasil penelitian yang dilakukan maka dapat disimpulkan bahwa strategi pemasaran dalam hal ini untuk mengantisipasi lingkungan internal.  Kata kunci: analisis lingkungan, penentuan strategi.

SRAM Failure Analysis Strategy

2003 ◽  
Author(s):  
P. Egger ◽  
C. Burmer
Keyword(s):  
Liquid Crystal ◽  
Failure Analysis ◽  
The Other ◽  
Other Hand ◽  
Analysis Strategy ◽  
Emission Microscopy ◽  
Metal Layers ◽  
Failure Localization

Abstract The area of embedded SRAMs in advanced logic ICs is increasing more and more. On the other hand smaller structure sizes and an increasing number of metal layers make conventional failure localization by using emission microscopy or liquid crystal inefficient. In this paper a SRAM failure analysis strategy will be presented independent on layout and technology.

scholarly journals How to improve infectious disease prediction by integrating environmental data: an application of a novel ensemble analysis strategy to predict HFMD

2021 ◽  
Vol 149 ◽  
Author(s):  
Junwen Tao ◽  
Yue Ma ◽  
Xuefei Zhuang ◽  
Qiang Lv ◽  
Yaqiong Liu ◽  
...  
Keyword(s):  
Variable Selection ◽  
Mean Squared Error ◽  
Moving Average ◽  
Dynamic Bayesian Networks ◽  
Environmental Data ◽  
Control Measures ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Analysis Strategy ◽  
Ensemble Analysis

Abstract This study proposed a novel ensemble analysis strategy to improve hand, foot and mouth disease (HFMD) prediction by integrating environmental data. The approach began by establishing a vector autoregressive model (VAR). Then, a dynamic Bayesian networks (DBN) model was used for variable selection of environmental factors. Finally, a VAR model with constraints (CVAR) was established for predicting the incidence of HFMD in Chengdu city from 2011 to 2017. DBN showed that temperature was related to HFMD at lags 1 and 2. Humidity, wind speed, sunshine, PM10, SO2 and NO2 were related to HFMD at lag 2. Compared with the autoregressive integrated moving average model with external variables (ARIMAX), the CVAR model had a higher coefficient of determination (R2, average difference: + 2.11%; t = 6.2051, P = 0.0003 < 0.05), a lower root mean-squared error (−24.88%; t = −5.2898, P = 0.0007 < 0.05) and a lower mean absolute percentage error (−16.69%; t = −4.3647, P = 0.0024 < 0.05). The accuracy of predicting the time-series shape was 88.16% for the CVAR model and 86.41% for ARIMAX. The CVAR model performed better in terms of variable selection, model interpretation and prediction. Therefore, it could be used by health authorities to identify potential HFMD outbreaks and develop disease control measures.

scholarly journals Fused Graphical Lasso Recovers Flowering Time Mutation Genes in Arabidopsis Thaliana

Inventions ◽  
2021 ◽  
Vol 6 (3) ◽  
pp. 52
Author(s):  
Rajan Kapoor ◽  
Aniruddha Datta ◽  
Michael Thomson
Keyword(s):  
Arabidopsis Thaliana ◽  
Crop Improvement ◽  
Phenotypic Traits ◽  
The Novel ◽  
Model Plant ◽  
Graphical Lasso ◽  
Analysis Strategy ◽  
Model Plant Arabidopsis Thaliana ◽  
Beneficial Traits ◽  
Delayed Flowering

Conventional breeding approaches that focus on yield under highly favorable nutrient conditions have resulted in reduced genetic and trait diversity in crops. Under the growing threat from climate change, the mining of novel genes in more resilient varieties can help dramatically improve trait improvement efforts. In this work, we propose the use of the joint graphical lasso for discovering genes responsible for desired phenotypic traits. We prove its efficiency by using gene expression data for wild type and delayed flowering mutants for the model plant. Arabidopsis thaliana shows that it recovers the mutation causing genes LNK1 and LNK2. Some novel interactions of these genes were also predicted. Observing the network level changes between two phenotypes can also help develop meaningful biological hypotheses regarding the novel functions of these genes. Now that this data analysis strategy has been validated in a model plant, it can be extended to crop plants to help identify the key genes for beneficial traits for crop improvement.

scholarly journals Data-driven analysis of interactions between people with dementia and a tablet device

2017 ◽  
Vol 3 (2) ◽  
pp. 735-738
Author(s):  
Wolfgang Doneit ◽  
Jana Lohse ◽  
Kristina Glesing ◽  
Clarissa Simon ◽  
Monika Fischer ◽  
...  
Keyword(s):  
Data Mining ◽  
Facial Expressions ◽  
Music Videos ◽  
Data Driven ◽  
Tablet Devices ◽  
Log Files ◽  
People With Dementia ◽  
Analysis Strategy ◽  
Tablet Device ◽  
Data Mining Strategy

AbstractIn the project I-CARE a technical system for tablet devices is developed that captures the personal needs and skills of people with dementia. The system provides activation content such as music videos, biographical photographs and quizzes on various topics of interest to people with dementia, their families and professional caregivers. To adapt the system, the activation content is adjusted to the daily condition of individual users. For this purpose, emotions are automatically detected through facial expressions, motion, and voice. The daily interactions of the users with the tablet devices are documented in log files which can be merged into an event list. In this paper, we propose an advanced format for event lists and a data analysis strategy. A transformation scheme is developed in order to obtain datasets with features and time series for popular methods of data mining. The proposed methods are applied to analysing the interactions of people with dementia with the I-CARE tablet device. We show how the new format of event lists and the innovative transformation scheme can be used to compress the stored data, to identify groups of users, and to model changes of user behaviour. As the I-CARE user studies are still ongoing, simulated benchmark log files are applied to illustrate the data mining strategy. We discuss possible solutions to challenges that appear in the context of I-CARE and that are relevant to a broad range of applications.

scholarly journals Clus-DoC: a combined cluster detection and colocalization analysis for single-molecule localization microscopy data

2016 ◽  
Vol 27 (22) ◽  
pp. 3627-3636 ◽  
Author(s):  
Sophie V. Pageon ◽  
Philip R. Nicovich ◽  
Mahdie Mollazade ◽  
Thibault Tabarin ◽  
Katharina Gaus
Keyword(s):  
T Cell ◽  
T Cell Activation ◽  
Single Molecule ◽  
Cell Activation ◽  
Spatial Organization ◽  
Focal Adhesions ◽  
Cell Receptor ◽  
Intact Cells ◽  
Analysis Strategy ◽  
Signaling Activity

Advances in fluorescence microscopy are providing increasing evidence that the spatial organization of proteins in cell membranes may facilitate signal initiation and integration for appropriate cellular responses. Our understanding of how changes in spatial organization are linked to function has been hampered by the inability to directly measure signaling activity or protein association at the level of individual proteins in intact cells. Here we solve this measurement challenge by developing Clus-DoC, an analysis strategy that quantifies both the spatial distribution of a protein and its colocalization status. We apply this approach to the triggering of the T-cell receptor during T-cell activation, as well as to the functionality of focal adhesions in fibroblasts, thereby demonstrating an experimental and analytical workflow that can be used to quantify signaling activity and protein colocalization at the level of individual proteins.

scholarly journals Automatic Promotion and Student Dropout: Evidence from Uganda, Using Propensity Score in Difference in Differences Model

2018 ◽  
Vol 7 (2) ◽  
pp. 191 ◽  
Author(s):  
Jeje Moses Okurut
Keyword(s):  
Propensity Score ◽  
Dropping Out ◽  
Probit Regression ◽  
Difference In Differences ◽  
Student Dropout ◽  
Analysis Technique ◽  
Analysis Strategy ◽  
Negative Effect ◽  
And Control ◽  
The Impact

The impact of automatic promotion practice on students dropping out of Uganda’s primary education was assessed using propensity score in difference in differences analysis technique. The analysis strategy was instrumental in addressing the selection bias problem, as well as biases arising from common trends over time, and permanent latent differences between the treated and control groups. Probit regression results indicate a negative effect on the probability of students dropping out, but only at P3. There seems to be no policy effect at P6. Decomposing the effect incidence along school location shows the policy as having had an effect only on P3 students studying in urban schools; otherwise, there is no effect among students at P3 rural, P6 rural or P6 Urban. In terms of the gender component, automatic promotion appears to have had an effect on P3 male and female students and no effect on either sex at P6.

Sign in / Sign up

Export Citation Format

Share Document