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Many document indexing has been developed since the emergence of the Internet for local, regional, or global authors. Some local document indexing has also been developed for Indonesian authors like Garuda, Neliti, IPI (Indonesian Publication Index), and Sinta (Indonesian Science and Technology Index). The most phenomenal among Indonesian document indexing is Sinta because it is used to score authors that are taken into account when the researcher apply for research grants and promotion. It is also to rank authors, institutions, and journals. Unfortunately, there was no research about Sinta to convince authors that Sinta database is reliable. The purposes of this research are to propose a technique to evaluate document indexing services such as Sinta and to propose some improvements. We used focused web crawler for data gathering. The data then processed by using bibliometrics and information retrievals methods. The results show that there were noises in Sinta’s database like duplicate accounts, foreign authors, foreign articles, invalid Scopus articles, difference citation count, and retracted & corrigendum articles. This research also revealed a side effect of Sinta that is unethical ways of publication by Indonesian authors. We also showed that some part of the design of Sinta was inappropriate.

Web mining is an important techniques for it enables extraction of data and information from the web for further needs. Although there are a lot of web databases that holds bibliography data, The Online Computer Library Center (OCLC) owns the largest web databases on bibliography in the world. A large number of bibliography data, surely needs more than manual downloading. In this research we conduct an experimentation to harvest bibliography data using multi-threading process that is fast, safe and ethical. Using C# programming language and Visual Studio IDE, We were successful in harvesting five million bibliography data, without being penalized by the source site.

An accurate wind information forecasting plays the significant role for wind power system. However; the intermittent characteristic wind speed in nature over the time and from one location to another makes it hard to estimate the usage factor of wind farms. Therefore, actual long and short duration forecasting of wind speed is necessary for wind power generation system efficiency. In this research, we propose the method to forecast the wind speed data based on weather parameters including, temperature, sea level pressure, dew point, visibility, station pressure, rain intensity, optimum wind speed, maximum temperature, minimum temperature, hail intensity and thunder intensity data. Au parameters were predicted using time series model, then the result of predicted data was implemented to predict the wind speed data. This research implemented radial basis function neural network (RBF NN) to predict the wind speed and the results were compared to univariate time series forecasting and Least Square Support Vector Machine (LS SVM) algorithm. The result experimentally express better forecasting using RBF NN compared to two other models on the measures of MAPE, MSE and correlation coefficient

Since coined by a German researcher in 2011, Industry 4.0 has piqued the interests of many researchers. The number of scientific publications related to Industry 4.0 or Fourth Industrial Revolution increases tremendously. Unfortunately, 90% of those publications have not been reviewed thus hindering many to track the progress and trends of research in this field. This article aims to address the most subject areas, top productive countries, the most influential authors, the most productive and influential journals/proceedings, the most used/influential keywords, and research trends related to Industry 4.0 based on documents indexed by Scopus. The method used is scientometrics and linear regression. It is revealed that Germany, China and Italy are the most prolific countries, but US, Portugal and UK are the most impactful countries. Researchers from China also dominate the top ten of most influential authors. The study prediction shows that Cyber Physical Systems (CPS), Internet of Things (IoT), Intelligent/Smart Manufacturing, Automation, and Big Data will dominate researches in 2018 and the next two years.

Information explosion that can be generated by anyone may lead to the spread of fake news not only at the news channel, but also at social media, and so forth. Detection of fake news has become an urgent need on the society because of fake news spread of unrest in the society. Several related studies have been conducted in the news classification with the aim of providing a decision whether a news is included in fake news or original news. In the related research, a vector representation of documents is used. This vector representation is then given to the algorithm for further processing. This study aims to model vectors that can accommodate the characteristics of fake news before further processed by language algorithms using the Indonesian language. In this research, fake news and original news are represented according to the vector space model. Vector model combination of frequency term, inverse document frequency and frequency reversed with 10-fold cross validation using support vector machine algorithm classifier. Variations of phrase detection as well as name recognition entities (entity recognition names) are also used in vector representation. A vector representation that uses the term frequency shows promising performance. It can recognize news characteristics correctly 96.74% of 2516 documents across phrase detection and named entity recognition process.

This paper aimed to present a formal representation design or classification concept to design relationships among real-world description into image description on a land cover domain using ontology approach. This paper focused on airport domain in Indonesia since detection of airport imagery features was an open challenge due to various features were used to describe the visual existence of an airport. Main characteristics of real-world definition for airport domain were identified by using the reference from Klasifikasi Penutup Lahan SNI 7645 Badan Standardisasi Nasional (Land Cover Classification SNI 7645 National Standardization Body) and Ministry of the Transportation Republic of Indonesia. Subsequently, image description of airport domain was defined based on the real-world description of the airport. This description used image features consideration namely spatial, spectral (color and texture) which was organized as ontological properties itself. At the end, the formal representation of airport domain was designed by mapping real-world description of an object into image description. This paper showed ontology approach could be used to design the formal representation of land cover by mapping real-world description of an object into image description. Ontology itself would be used for image identification/classification/clustering at airport domain or other image domains.

Investigation into privacy preserving data publishing with multiple sensitive attributes is performed to reduce probability of adversaries to guess the sensitive values. Masking the sensitive values is usually performed by anonymizing data by using generalization and suppression techniques. A successful anonymization technique should reduce information loss due to the generalization and suppression. This research attempts to solve both problems in microdata with multiple sensitive attributes. We propose a novel overlapped slicing method for privacy preserving data publishing with multiple sensitive attributes. We used discernibility metrics to measure information loss. The experiment result shows that our method obtained a lower discernibility value than other methods.

An ontology-based system can currently logically reason through the Web Ontology Language Description Logic (OWL DL). To perform probabilistic reasoning, the system must use a separate knowledge base, separate processing, or third-party applications. Previous studies mainly focus on how to represent probabilistic information in ontologies and perform reasoning through them. These approaches are not suitable for systems that already have running ontologies and Bayesian network (BN) knowledge bases because users must rewrite the probabilistic information contained in a BN into an ontology. We present a framework called ByNowLife, which is a novel approach for integrating BN with OWL by providing an interface for retrieving probabilistic information through SPARQL queries. ByNowLife catalyzes the integration process by transforming logical information contained in an ontology into a BN and probabilistic information contained in a BN into an ontology. This produces a system with a complete knowledge base. Using ByNowLife, a system that already has separate ontologies and BN knowledge bases can integrate them into a single knowledge base and perform both logical and probabilistic reasoning through it. The integration not only facilitates the unity of reasoning but also has several other advantages, such as ontology enrichment and BN structural adjustment through structural and parameter learning.

Government Human Capital Knowledge Management of Republic Indonesia (NUSANTARA) is a technology for support the government knowledge management. This technology also well known as knowledge management system which is manage the process of knowledge creation among organization. NUSANTARA was developed by identifying some features of activity in managing knowledge on the government knowledge management. Those features were identified from survey using a questionnaire in three government ministries that manage government human capital. Then the author does the analysis and design for the NUSANTARA. Quantitative method is used to analyze the survey result. Data analysis is used Pearson Correlation Analysis and the design are use the use case diagram, activity diagram and class diagram. Expert judgment is used to verified and validated the research result that is the KMS features. In this study some priority of KMS features was determined there are electronic discussion group, best practice database, email, work performance system and video conference. Those features are represented in NUSANTARA prototype as the current important requirement for the government human capital management stakeholder.