Niken Prasasti Martono

Niken Prasasti Martono
Tokyo University of Science | TUS · Department of Industrial Administration

Dr. Eng.

About

17
Publications
4,235
Reads
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44
Citations
Citations since 2017
9 Research Items
37 Citations
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201720182019202020212022202302468
201720182019202020212022202302468
201720182019202020212022202302468
Additional affiliations
April 2015 - present
Tokyo University of Science
Position
  • PhD Student
January 2012 - July 2014
Bandung Institute of Technology
Position
  • Research Assistant
Education
July 2013 - July 2014
Tokyo University of Science
Field of study
  • Machine Learning
July 2012 - July 2013
Bandung Institute of Technology
Field of study
  • Management Science
July 2007 - July 2011
Bandung Institute of Technology
Field of study
  • Engineering Physics

Publications

Publications (17)
Chapter
Machine learning has been gradually introduced into corporate financial distress prediction and several prediction models have been developed. Financial distress affects the sustainability of a company’s operations and undermines the rights and interests of its stakeholders, also harming the national economy and society. Therefore, we developed an...
Chapter
Automatic electrocardiogram (ECG) analysis is crucial in diagnosing heart arrhythmia but is limited by the performance of existing models owing to the high complexity of time series data analysis. Arrhythmia is a heart condition in which the rate or rhythm of the heartbeat is abnormal. The heartbeat may be excessively fast or slow or may have an ir...
Chapter
Full-text available
Research in virtual reality (VR) has resulted in the development of many applications in clinical settings in the areas of learning and therapy in psychology and neuropsychology because this technology can be flexible to the needs of the clinical application. VR technology has many implementations for cognitive training and as a screening tool for...
Article
Full-text available
This article seeks to utilize the data collected from virtual reality (VR)-based software and a leap-motion device used for learning of subtle errors in mild cognitive impairment (MCI) cases to enable early detection of MCI by analyzing the classification rules for errors (action slips) based on finger-action transitions when performing instrumenta...
Conference Paper
Full-text available
Developments in virtual reality (VR) have advanced numerous applications in clinical settings in the areas of learning and treatment in neuropsychology. Emerging VR applications today focus on the challenge of diagnosis and cognitive training of mild cognitive impairment (MCI) and dementia patients and address navigation and orientation, face recog...
Conference Paper
Recently, Japan has been experiencing a declining birthrate and an increasingly aging population; as a result, the number of dementia patients is increasing. Current medical science has no way to treat dementia completely after onset. Therefore, it is necessary to detect mild cognitive impairment (MCI) in the early stage just before dementia develo...
Article
Full-text available
Research in virtual reality (VR) has resulted in the development of many applications in clinical settings in the areas of learning and therapy in psychology and neuropsychology because this technology can be flexible to the needs of the clinical application. VR technology has many implementations for cognitive training and as a screening tool for...
Conference Paper
Full-text available
Difficulty in performing the activities of daily living is a key clinical feature of early cognitive decline in older adults and has also been associated with the early stage of dementia in mild cognitive impairment (MCI). As the number of individuals with dementia and the development of technology rise, an immersive virtual environment or virtual...
Research
Full-text available
Conference paper: 15th IEEE International Conference on COGNITIVE INFORMATICS & COGNITIVE COMPUTING
Conference Paper
Full-text available
This study model an integration between agent-based simulation and machine learning in order to have a comprehensive result of behavior prediction. Model are applied to a case of customer churning in a subscription-based business. To have a good model for behavior prediction, dynamic simulation based on social structure is required. In this study,...
Conference Paper
Full-text available
Land conversion is one complex problem which includes actors and factors with different social level. In the process of land use change, every small changes in decision making methods used by individuals may significantly affect the outcomes. Agent-based modeling and simulation (ABMS) is a common approach to analyze and simulate the process of land...
Conference Paper
Full-text available
This paper aims to provide a solution to the prediction of customer defection task in the growing market of cloud software industry. From the original unstructured data from the company, we proposed a procedure to first identify the real defection condition, whether the customer is defecting from the company or merely stop using current product to...
Conference Paper
Full-text available
Machine learning is an established method of predicting customer defection from a contractual business. However, no systematic comparison or evaluation of the different machine-learning techniques has been performed. In this study, we provide a comprehensive comparison of different machine-learning techniques with three different data sets of a sof...
Conference Paper
Full-text available
This paper proposes an estimation of Customer Lifetime Value (CLV) for a cloud-based software company by using machine learning techniques. The purpose of this study is twofold. We classify the customers of one cloud-based software company by using two classifications methods: C4.5 and a support vector machine (SVM). We use machine learning primari...

Questions

Questions (2)
Question
I am currently working on rare event prediction, which I have never done before (I used to work with simple prediction problem), and I looked up on this article about using LSTM for time series rare event classification.
It was very exciting to read since I think my case is a little similar: to predict calving time of cow, with historical activity feature, where calving is only happen once in the end of data collection.
However... aside from my data is way smaller than the example, I got too much confusion using LSTM. The good point about LSTM I am looking forward to is the "look back" feature that can let you decide for each output how many input in previous time you look back to.
My question will be: is there any "easier" or more simple machine learning method that works like LSTM for time series classification?
I tried to use simple ML such as decision tree, random forest, but I don't think it represents the problem well (many historical data into one output).
Question
Is there anyway to "visualize" the performance score in graph (concordance index, AUC, ROC curve?) ?

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