Indra Budi

Association rules mining for name entity recognition

We propose a new name entity class extraction method based on association rules. We evaluate and compare the performance of our method with the state of the art maximum entropy method. We show that our method consistently yields a higher precision at a competitive level of recall. This result makes our method particularly suitable for tasks whose requirements emphasize the quality rather than the quantity of results.

Named entity recognition for the indonesian language: combining contextual, morphological and part-of-speech features into a knowledge engineering approach

We present a novel named entity recognition approach for the Indonesian language. We call the new method InNER for Indonesian Named Entity Recognition. InNER is based on a set of rules capturing the contextual, morphological, and part of speech knowledge necessary in the process of recognizing named entities in Indonesian texts. The InNER strategy is one of knowledge engineering: the domain and language specific rules are designed by expert knowledge engineers. After showing in our previous work that mined association rules can effectively recognize named entities and outperform maximum entropy methods, we needed to evaluate the potential for improvement to the rule based approach when expert crafted knowledge is used. The results are conclusive: the InNER method yields recall and precision of up to 63.43% and 71.84%, respectively. Thus, it significantly outperforms not only maximum entropy methods but also the association rule based method we had previously designed.

Business Process Requirements for Indonesian Small Medium Enterprises (SMEs) in Implementing Enterprise Resource Planning (ERP) and ERP Systems Comparison

Based on Central Agency on Statistic (Badan Pusat Statistik – BPS), the growth of SMEs in Indonesia is increasing rapidly. In order to increase their competitive advantage, SMEs need to implement ERP system. However, most of ERP systems in the today market are very complex and not suitable for Indonesian SMEs. This paper presents the requirements of Indonesian SMEs’ main business processes which are focusing in marketing, distributing, selling and production processes by randomly distributing questionnaires to SMEs. Based on our analysis, those processes are urgently needed to be standardized and implemented in ERP system due to their strategies to increase their market share. In addition, this study also conducts the comparison of ERP systems through thorough observation to give insight for SMEs owners if they choose to buy an ERP system. According to our observation, SAP Business One or Compiere Community Edition could be selected by SMEs due to their detailed and complete processes.

Application of association rules mining to Named Entity Recognition and co-reference resolution for the Indonesian language

In this paper, we propose a new method, association rules mining for Named Entity Recognition (NER) and co-reference resolution. The method uses several morphological and lexical features such as Pronoun Class (PC) and Name Class (NC), String Similarity (SP) and Position (P) in the text, into a vector of attributes. Applied to a corpus of newspaper in the Indonesian language, the method outperforms state-of-the-art maximum entropy method in name entity recognition and is comparable with state-of-the-art machine learning methods, decision tree, for co-reference resolution.

Robust consensus clustering for identification of expressed genes linked to malignancy of human colorectal carcinoma

Previous studies have been conducted in gene expression profiling to identify groups of genes that characterize the colorectal carcinoma disease. Despite the success of previous attempts to identify groups of genes in the progression of the colorectal carcinoma disease, their methods either require subjective interpretation of the number of clusters, or lack stability during different runs of the algorithms. All of which limits the usefulness of these methods. In this study, we propose an enhanced algorithm that provides stability and robustness in identifying differentially expressed genes in an expression profile analysis. Our proposed algorithm uses multiple clustering algorithms under the consensus clustering framework. The results of the experiment show that the robustness of our method provides a consistent structure of clusters, similar to the structure found in the previous study. Furthermore, our algorithm outperforms any single clustering algorithms in terms of the cluster quality score.

User acceptance factors of hospital information systems and related technologies: Systematic review

ABSTRACT This study reviews the literature on the most important acceptance factors associated with Hospital Information Systems (HIS) and related technologies based on user groups’ perspectives (medical staff, hospital management, administrative personnel, patient, medical student, and IT staff), which can assist researchers and hospital management to develop suitable acceptance models to improve the quality of HIS. We conducted searches in online databases with large repositories of academic studies, written in English and fully accessible by the authors. The articles being reviewed are related to health information technology (HIT), clinical information systems (CIS), HIS, electronic medical records (EMR), telemedicine or telehealth, picture archiving and communication systems (PACS), radio frequency identification (RFID), and computerized physician order entry (CPOE), where the use of most of those applications and technologies is highly integrated. A predefined string was used to extract 1,005 articles, and the results were reviewed and checked. The results of this study found 15 user acceptance factors related to HIS and related technologies that were frequently identified by a minimum of five previous studies. These factors were related to individual, technological, and organizational factors. In addition, HIS and related technologies’ user acceptance factors in each user group describe different results.

A comparative analysis of memory-based and model-based collaborative filtering on the implementation of recommender system for E-commerce in Indonesia: A case study PT X

The increasing growth of e-commerce industry in Indonesia motivates e-commerce sites to provide better services to its customer. One of the strategies to improves e-commerce services is by providing personal recommendation, which can be done using recommender systems. However, there is still lack of studies exploring the best technique to implement recommender systems for e-commerce in Indonesia. This study compares the performance of two implementation approaches of collaborative filtering, which are memory-based and model-based, using data sample of PT X e-commerce. The performance of each approach was evaluated using offline testing and user-based testing. The result of this study indicates that the model-based recommender system is better than memory-based recommender system in three aspects: a) the accuracy of recommendation, b) computation time, and c) the relevance of recommendation. For number of transaction less than 300,000 in database, respondents perceived that the computation time of memory-based recommender system is tolerable, even though the computational time is longer than model-based.

Combination of time series forecasts using neural network

Forecast combination, which is a method to combine the result of several predictors, offers a way to improve the forecast result. Several methods have been proposed to combine the forecasting results into single forecast, namely the simple averaging, weighted average on validation performance, or non-parametric combination schemas. Recent literature uses dimensional reduction method for individual prediction and employs ordinary least squares for forecast combination. Other literature combines prediction results from neural networks using dimensional reduction techniques. Thus, those previous combination schemas can be categorized into linear combination methods. This paper aims to explore the use of non-linear combination method to perform the ensemble of individual predictors. We believe that the non-linear combination method may capture the non linear relationship among predictors, thus, may enhance the result of final prediction. The Neural Network (NN), which is widely used in literature for time series tasks, is used to perform such combination. The dataset used in the experiment is the time series data designated for NN5 Competition. The experimental result shows that forecast combination using NN performs better than the best individual predictors, provided that the predictors selected for combination have fairly good performance.

Hospital information system user acceptance factors: User group perspectives

ABSTRACT This study aimed to identify and rank user acceptance factors regarding the implementation of hospital information systems (HIS) based on the views of the following user groups: doctors, nurses, hospital management, and administrative staff (operators). The results should provide guidance for hospital management and HIS developers on meeting user requirements. The study was carried out using both qualitative and quantitative methods. The authors conducted interviews and distributed questionnaires to doctors, nurses, hospital management, and administrative staff in a government-owned Indonesian public hospital. Entropy measurements were used to analyze the questionnaire data. The study identified 15 important HIS user acceptance factors, which were ranked differently by each user group. The results show that non-technological dimensions, such as human and organizational dimensions, influence HIS user acceptance to a greater extent than technological dimensions. More work should be carried out to gain a better understanding of the relationship between user acceptance factors in order to increase the success of HIS implementations.

A Systematic Review of Recommender System for e-Portfolio Domain

The aim of this study was to improve the state of the art recommendation techniques in thee-Portfolio domain.A Systematic Review method is used inthis literature review, with the following steps: (a) Identification of recommendation focus that influences the implementation of e-Portfolio; (b) Identification of recommendation techniques in e-Portfolio environment; (c) Synthesizing the literature; (d)Make comparing in the recommendation focus andtechniques for the implementation of the e- portfolio; and (e) Discuss the result and future research. The process of inclusion and exclude literature obtained from 5 electronic databases, with the amount of literature that met the initial stages as many as 5805 articles, up to 107 papers obtained are eligible. Then from 28 the papers, performed synthesize to produce recommendation focus and techniquesin the e-portfolio of the most widely implemented The results of this study is to find that recommender system may suitable for thee-Portfolio domain is a focus on personalization based on hybrid technique and collaborative filtering.