scholarly journals Strategic Decision Models Cross-Validation by Use of Decision Reports Information Extraction

2014 ◽  
Vol 7 (5) ◽  
pp. 849
Author(s):  
Lucian Hancu
Keyword(s):  
Information Extraction ◽  
Cross Validation ◽  
Strategic Decision ◽  
Decision Models

The Structuring Of The Strategic-Decision-Making Process

2011 ◽  
pp. 213-240
Author(s):  
Tamio Shimizu ◽  
Marley Monteiro de Carvalho ◽  
Fernando Jose Barbin
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Decision Trees ◽  
Strategic Decision ◽  
Decision Models ◽  
Decision Problems ◽  
Strategic Decision Making ◽  
Decision Making Process ◽  
Decision Matrix

The basic types of decision models presented in the previous chapter (rational, descriptive, political, and ambiguous models) relies on quantitative values (money, time, or probabilities) that are most suitable for structured and semi-structured decision problems. These basic models can be used as starting models to guide the structuring process of strategic decision problems. First, a systematic procedure for structuring the strategic decision making process is presented, using decision matrix and decision trees. The need for the sensitivity analysis is introduced, and will be illustrated with more detail in the next chapter. Some problems that must be considered in this structuring process are illustrated in form of hidden traps and paradoxes. The first step in the decision-making process is to formulate the problem. It is possible that an inadequate formulation of the problem leads to a result that reduces efficiency and efficacy, since an incorrect formulation can define a wrong problem.

Strategic decision models: Integrating different perspectives

1991 ◽  
Vol 12 (5) ◽  
pp. 327-351 ◽  
Author(s):  
Michael A. Hitt ◽  
Beverly B. Tyler
Keyword(s):  
Strategic Decision ◽  
Decision Models

EXPRESS: REPUTATION BELONGS IN MORE STRATEGIC DECISION MODELS (AND SOME DATA TO MEASURE IT)

2021 ◽  
pp. 147612702110679
Author(s):  
Owen Nelson Parker ◽  
Ke Gong ◽  
Rachel Mui
Keyword(s):  
Decision Making ◽  
Managerial Discretion ◽  
Strategic Decision ◽  
Decision Models ◽  
Recent Theory ◽  
Strategic Decisions ◽  
Unique Role ◽  
Organizational Theories ◽  
Context Specific ◽  
Organization Research

Organizational reputation is compelling to layman audiences, it is critical for firm performance and myriad organizational phenomena, and recent theory articulates how it shapes the very managerial discretion underpinning strategic decisions. Yet, reputation is still excluded from much of mainstream strategic organization research. We make the case for reputation’s wider inclusion in studies of managerial discretion or strategic decision-making. We first demonstrate reputation’s potential theoretical importance in explaining nuances or non-findings in such studies, detail ways to measure reputation accurately, provide five sources of data for readers to facilitate the inclusion of reputation in their studies, and illustrate how scholars can use freelancers to collect their own archival data for their own, context-specific purposes. By shedding light on reputation’s unique role in shaping managerial discretion and, thereby, strategic decisions, we hope this essay helps scholars better account for decision-making patterns that might otherwise defy the predictions of other organizational theories.

scholarly journals Ekstraksi Informasi Kesehatan Masyarakat Dari Tweet Berbahasa Indonesia Berbasis Klasifikasi Dengan Algoritma Naive Bayes

Repositor ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 193
Author(s):  
Khoirir Rosikin ◽  
Setio Basuki ◽  
Yufis Azhar
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Text Mining ◽  
Information Extraction ◽  
Cross Validation ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Disease Information ◽  
Bayes Algorithm ◽  
Imbalance Dataset

AbstrakKesehatan merupakan kebutuhan utama manusia. Di Indonesia terdapat  permasalahan tentang kesehatan, yaitu meningkatnya penyakit menular dan penyakit tidak menular. Untuk mengatasinya perlu dilakukan tidakan pencegahan. Salah satu usaha untuk melakukan pencegahan penyakit, adalah dengan mengetahui informasi penyakit tersebut, temasuk tentang penyebab dan akibat yang ditimbulkan, sehingga bisa melakukan pencegahan. Informasi bisa didapatkan dengan berbagai macam cara, salah satunya diambil dari media sosial, terutama twitter. Twitter digunakan karena banyaknya tweet yang dihasilkan sehingga memunculkan fenomena big data. Karena hal itulah, penelitian ini bermaksud untuk melakukan suatu metode ekstraksi informasi. Ekstraksi informasi merupakan metode penerapan data mining terutama bidang text mining yang digunakan untuk mendapatkan informasi dari kumpulan banyak data. Informasi yang dimaksud adalah penyakit, akibat, dan penyebab. Penelitian ini menggunakan pendekatan ekstraksi informasi berbasis klasifikasi dengan algoritma Naive Bayes. Penelitian ini menggunakan 7 set fitur dan sebuah model algoritma klasifikasi yaitu Naive Bayes. Dalam ekstraksi fitur terjadi imbalance dataset, sehingga dilakukan resample filtering data. Pengujian dilakukan dengan 2 metode, yaitu pengujian model dengan menggunakan 10-folds cross-validation dan pengujian klasifikasi dengan menggunakan 100 data uji. Hasil dari pengujian model mendapatkan nilai akurasi 77,27% dan pengujian klasifikasi mendapatkan nilai akurasi 74,07%. AbstractHealth is a primary human need. In Indonesia there are health problems, namely the increase of infectious diseases and non-communicable diseases. To overcome this need to do precautionary measures. One effort to prevent disease, is to know the disease information, including about the causes and effects caused, so it can do prevention. Information can be obtained in various ways, one of which is taken from social media, especially twitter. Twitter is used because of the number of tweets produced resulting in big data phenomenon. Because of that, this research intends to perform an information extraction method. Information extraction is a method of application of data mining, especially the text mining field used to obtain information from a large collection of data. The information in question is a disease, effect, and cause. This research uses a classification-based information extraction approach with Naive Bayes algorithm. This research uses 7 feature sets and a model of classification algorithm that is Naive Bayes. In feature extraction there is imbalance dataset, so it is done resample filtering data. The test is done by 2 methods, namely model testing using 10-folds cross-validation and classification testing using 100 test data. The result of model test get the accuracy value 77,27% and the classification test get the accuracy value 74,07%.

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