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Anonymity data for multiple sensitive attributes in microdata publishing is a growing field at present. This field has several models for anonymizing such as k-anonymity and l-diversity. Generalization and suppression became a common technique in anonymize data. But, the real problem in multiple sensitive attributes is sensitive value distribution. If sensitive values do not distribute evenly to each quasi identifier group, it is potentially revealed to sensitive value holder. This research investigated on how the high-sensitive values are distributed evenly into each group. We proposed a novel method/algorithm for distributing high-sensitive values when it forms groups. This method distributes high-sensitive values evenly and varies high-sensitive values in a group. We called our method as extended systematic clustering since it is an extension of systematic clustering method. Diversity metrics was used for evaluating our method. Experiment result showed our method outperformed systematic clustering with average diversity value 0.9719 while systematic clustering 0.3316.

We can recognize a person by his voice alone. In principle, the sound has a tone (pitch) that is different for each person. This study aims to measure a Deep Neural Network (DNN) performance with static and dynamic prosodic features. Prosodic is information about sound related to tone, intonation, pressure, duration, and rhythm of a person's pronunciation. The data used is dictated and spontaneous voice data that taken from YouTube. The data used consists of three male voices and one female voice. The data is segmented into various duration, 3 seconds, 5 seconds, and 10 seconds. After the data has been segmented, the static prosodic features with 103 dimensions will be extracted and the dynamic prosodic features with 13 dimensions will be extracted too. Each feature and feature combination will be trained and tested using DNN with a ratio of 90:10. The result shows that the 10 seconds segmented data has higher accuracy than the others. Accuracy of static prosodic features is better than dynamic prosodic features. The average accuracy of DNN for static prosodic features is 87.02%. The average accuracy of DNN for dynamic prosodic features is 72.97%. The average accuracy of DNN for combined static and dynamic prosodic features is 87.72%.

High rise building, especially in Jakarta faces a serious threat from fires which can cause substantial losses. An effective fire safety management is critical to prevent those losses. This study aims to propose a conceptual framework for fire safety management by evaluating the existing strategy in term of policy, stakeholders, building occupancy, building components/WBS and Building Information Modeling (BIM) on high-rise buildings. To this end, a considerable amount of data collected through literature studies, in-depth interviews, focus group discussions, research instrument trials, surveys and case studies. In here, we present the preliminary result of the study from literature studies. The result shows that there are sufficient number of policies can be strategically implemented to determine fire insurance premium costs. However, many stakeholders have minimum strategy to prevent fires, guarantee people safety, monitor fire risks and review fire safety system. This can increase the fire premium costs for the stakeholders. To overcome this, we propose to implement fire safety design for high-rise building. Another strategy can be implemented is building occupancy management. Fire safety design should consider the occupancy of the high-rise building to minimize victims during fires. In addition, we found that BIM is a very useful tool to model building fire safety management to minimize losses due to fire such that it can be used to determine realistic fire insurance premium costs.

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.

DKI Jakarta Provincial Government has implemented Citizen Relationship Management in the form of Jakarta Smart City. There are 8 channels provided as a door or ways to submit complaints, of which 7 doors use Information and Communication Technology and 1 door of offline. Until now there has no evaluation of an offline channel, even though this channel requires personnel who must work full time on every Saturday. The data collection method for this research was an interview with 250 DKI Jakarta residents using a questionnaire with semi-closed questions. The result of this study indicates that the most widely used canals of DKI Jakarta residents in channeling complaints are offline channels that are conducted by face-to-face. While information about programs in the DKI Jakarta Provincial Government that is most influential is information from relatives or closest people that is usually done face to face.

Automation with artificial intelligence (AI) has widely implemented in robotics, transportation and manufacture. AI has become a powerful technology that change human life and help human more flexible doing something. In this paper, it will show a result of simulation from an autonomous car using the evolutionary neural network algorithm which combines genetic algorithm and neural network. The purpose of the simulation is to test the model that we develop to know the right direction based on the track, so the evolutionary neural network that implemented to the autonomous car be able to deliver the best solution before it implements in the real machine or car technology. Genetic algorithm combines with a neural network to reach an evolution condition. The evolution process is achieved through crossover, mutation and selection process, so the algorithm will give the best result from the iteration of the experiment. The result of our experiment shows that evolutionary neural network algorithm give the best result within 3 layer architecture, with iteration average is 14.5 reach finish point (check point) 3 in the track simulation. Based on the simulation, our car model can find out the right direction.

The Information and Communication Network Center (BJIK) is one of the centers in the Agency for the Assessment and Application of Technology (BPPT). BJIK develops a network monitoring information system called Simontik to protect the BPPT system from threats where antivirus softwares and firewalls fail to give the level of protection needed. The random nature of threats makes it difficult to develop a rule-based system to predict the existence of intrusion. In this research, we apply a deep learning model to predict network intrusion. We found that our deep learning model using deep neural network and random forest algorithm can produce 99.91% accuracy compared to 98.11% using support vector machine algorithm.

Jakarta as the capital city is viewed as the barometer of every city in Indonesia. The city preparedness to face VUCA (Volatility, Uncertainty, Complexity and Ambiguity) caused by Covid-19 pandemic is also in the national spotlight. One of the main solutions to face VUCA is Computational Thinking (CT). CT provides the ability to approach VUCA characteristics by systematically ingraining abstraction, algorithmic thinking, decomposition, and pattern recognition in mind. For this reason, Jakarta plans to disseminate this concept to students as early as possible, namely by embedding CT to every subject in primary and secondary schools. The activity began with online CT socialization, which was attended by 1,761 teachers, of which 238 via Google Meet and 1,523 via live streaming. In this activity we also distributed questionnaires to 258 elementary and junior high school teachers spread across 41 sub-districts of 44 sub-districts in Jakarta. The result is that 93.8% of teachers in Jakarta do not understand CT, excitingly 69.4% of teachers wish to teach CT in the subjects they can afford even though it is not compulsory, but if it is obligatory then all teachers will implement it.

Knowledge management is one of organizational strategy to improve the organizational competitive value. Align with presidential regulation No. 81 Year 2010 about bureaucratic reform and PERMENPAN&RB No. 20 year 2010 about bureaucratic reform roadmap declare that one bureaucratic reform objective are human capital development. This program is important to increase the public service. Three government ministries based on the UU No. 5 Year 2014 about Government Human Capital assigned to manage them. This research aims to develop the model of knowledge management for government human capital management. Research object implicate State Ministry for State Apparatus Reform (KEMENPAN & RB), National Civil Service Agencies (BKN) and National Institute of Public Administration (LAN). While the research stages are identify the strategic issue, develop the theoretical model, evaluate the theoretical model, identify the element model and knowledge and the last develop the model. Data analysis uses multiple methods in qualitative and quantitative technique. This model was developed using hybrid and synthesis method from the theoretical model of Indonesian knowledge management, knowledge management solution and foundation and the APO framework KMP. The model of government human capital knowledge management of Republic Indonesia (NUSANTARA) consist of eight component there are vision and mission, CSF, KM Mechanisms and Technologies, KMS, KM cycle, KM process, organizational core knowledge and outcome (government public services).

The real research strength of an institution is important for establishing research priorities and developing an institutional research plan. It can also be used as a basis for SWOT analysis, developing vision-mission of an institution and also useful for research funder in distributing their research grants. Unfortunately, there was only a small number of researches found. In this article, we proposed a new method to quantify the quality of research outputs. The method is suitable for measuring research strength. We applied the method to an institution using research articles downloaded from a reputable document database/index. The results were research strength map in 27 subject areas and 337 subject categories.

WorldCat had recorded that there were roughly about 1.89 million titles of Russian bibliography. Unfortunately, there were no scientific articles that conversing about the Russian bibliography. Should there were analysis, it can be used to reveal: a) the distribution of Russian bibliography based on its disciplines and subject areas, b) the dominance disciplines and subject areas, and c) subject areas that were the most copied. By using web crawler, we could harvest ¼ million records of Russian bibliography. We can analyze it by using some data mining methods. The results: a) about 85% (based on DDC) or 76% (based on LLC) of the bibliography were related to social sciences, b) the most used subject areas/headings were Soviet Union, Russia, Russia (Federation), Russian literature, and Economic history respectively, and c) three of the most copied books were about Operas, Russian language and Ballets—Scores.

The Indonesian Government has announced several policies to mandate lecturers and students, the vast majority of Indonesian researchers, to publish scientific articles. The policies are considered very effective; it increased the quantity of Indonesian scientific articles tremendously. Unfortunately, we did not know the quality of the articles. The objective of this research is to assess the quality of Indonesian scientific articles. For comparison purpose, we also assess the quality of Indonesia neighboring countries scientific articles i.e. Malaysia, Singapore, Thailand, and Vietnam. This research used three methods to assess the quality of scientific papers, they were data mining, bibliometrics, and scientometrics. The dataset used was mined from Scopus web database. The results of this research are: a) the quality of Indonesian scientific articles is the lowest, b) efficiency and effectivity of Indonesian scientific articles is low, and c) research fund is directly proportional to research quality.

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.

Privacy preserving data publishing is a growing research in computer science. Many of study on this research focus on single sensitive attribute. While many of multiple sensitive attributes researches do not set the sensitive attribute distribution. It is necessary for ensuring p-sensitive property since multiple sensitive attributes not just apply the model in single sensitive attribute. It needs more setting. This research discusses a distribution model to set sensitive attribute values when p-sensitive is applied on multiple sensitive attributes. We build a rule to distribute sensitive values. The result shows that this distribution model satisfies privacy guarantee that is provided by p-sensitive.

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 system for supply chain processes is known as e-SCM (electronic supply chain management). The system is expected to provide benefits toward the organization. The benefits of the e-SCM can be represented as its effectiveness towards the organization. The purpose of this study is to analyze factors that influence the effectiveness of the use of e-SCM. The proposed model in this study adopts DeLone-McLean model and thus investigates the relationship between information quality, system quality, service quality to use, user satisfaction, and the net benefit. A case of XYZ in Indonesia is used to give context to the study. XYZ is a company that runs its business within the telecommunications industry. In providing services to its consumers, XYZ uses a computer-based information system for SCM (e-SCM), namely Sitehandler. Data from 149 respondents were collected via questionnaires. The data were processed using PLS-SEM. The results showed that there is positive correlation between information quality toward user satisfaction and usage. Likewise, system quality influences positively the user satisfaction and usage. However, service quality in this study does not positively influence user satisfaction and usage. Usage has a positive influence on user satisfaction and net benefit. Also, user satisfaction has a positive influence on the net benefit.

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.

In our research, we found that there were no extensive study about library’s collection in Indonesian language due to the lack of library of things metadata. As the consequence, the Government of Republic of Indonesia has no depiction of the output oflndonesian authors. The aim of this paper is to describe Indonesian bibliography by tools of visualization. In absence of the needed metadata of Indonesian book, we must gather those data ourselves. The method we employ for collecting data is using focused-web crawler. The crawler has collected about 1.48 billion items (holdings) in 25 million distinct titles in which 71 thousands titles were in Indonesian language. Novelty of this research is visualization of Indonesian bibliographic records by its subject headings and call number. By using scientometrics approach, we reveal that more than 90% of Indonesian publication are in social sciences mostly in the subject of Politics and Government, Economic History, Social Conditions, Indonesian Fiction, and Islam. The portion of exact sciences is about 8% which consists of Natural Sciences (1.26%), Medicine (1.26%), Agriculture (3.69%) and Technology (2.00%).

Organizational culture believed as an enabler's of knowledge management implementation. Others assumed that one of critical success factor of knowledge management can be seen from its culture. Generally organizational culture has positive impact to knowledge management. Type of organizational culture can be a guide to encourage the knowledge management (KM) implementation strategy and programs. Culture can direct organization member to behave in certain manners, rules, procedures and policies as an organizational mechanism. This paper describes the process to determine the current organizational culture and suggestion future culture for implement knowledge management in government. OCAI (Organizational Culture Assessment Instrument) and its framework (Competing Value Framework) were used for identifying type of organizational culture in some government ministries/agencies which duties to manage government capital. Result of this study will be used as references for implementing knowledge management for Indonesian Government Human Capital Management for achieve their goals and competitive advantages.

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.

Government Human Capital Knowledge Management of Republic Indonesia (NUSANTARA) is KM model that developed to solve the government human capital management problem. NUSANTARA also can enhance the bureaucratic reform program by improved the government human capital knowledge. The outcome of NUSANTARA is individual and organizational knowledge improvement which enhanced the government service performance. Implementation of NUSANTARA model can be done by assessment of each NUSANTARA layer. This research purposes to define the implementation readiness index of each ministry in order to implement NUSANTARA. Four government ministries as case study of implementation assessment, there are Ministry of Cooperatives Small and Medium Enterprises, Ministry of Transport, Ministry of Law and Human Rights and Ministry of Health. Content analysis is used to define the assessment instruments. While, Pearson Correlation conducted to determine the implementation readiness of each NUSANTARA layer. Result show that four case studies ministries already implement KM. The Ministry of Cooperatives Small and Medium Enterprises and Ministry of Health implementation readiness index is eligible enough. Hence, for the Ministry of Transport and Ministry of Law and Human Rights implementation readiness index is eligible.

Availability of vast amount of digital documents that have surpassed human processing capability calls for an automatic information extraction method from any text document regardless of their domain. Unfortunately, open domain information extraction (open IE) systems are language-specific and there is no published system for Indonesian language. This paper introduces a system to extract entity relations from Indonesian text in triple format using an NLP pipeline, rule-based candidates generator, rule-based token expander and machine-learning-based triple selector. We cross-validate four candidates: logistic regression, SVM, MLP, Random Forest using our dataset to discover that Random Forest is the best classifier for the triple selector achieving 0.60 F1 score (0.62 precision and 0.58 recall). The low score is largely due to the simplistic candidate generation rules and the coverage of dataset.

K-Means is a well known algorithms of clusteing. It generates some groups based on degree of similarity. Simplicity of implementation, ease of interpretation, adaptability to sparse data, linear complexity, speed of convergence, and versatile in almost every aspect are noble characteristics of this algorithm. However, this algorithm is very sensitive on defining initial centroids process. Giving a bad initial centroid always produces a bad quality output. Due to this weakness, it is recommended to make some runs with different initial centroids and select the initial centroid that produces cluster with minimum error. However, this procedure is hard to achieve a satisfying result. This paper introduces a new approach to minimize the initial centroid problem of K-Means algorithm. This approach focus on centroid updating stage in K-Means algorithm by applying minimum forest graph to produce better new centroids. Based on gain information and Dunn index values, this approach provided a better result than Forgy method when this approach tested on both well distributed and noisy dataset. Moreover, from the experiments with two dimentional data, the proposed approach produced consisten members of each cluster in every run, where it could not be found in Forgy method.

Mining data from a web database becomes more challenging in recent years due to the exploding size of data, the rising of dynamic web, and the increasing performance of web security. Mining data from a web database differs from mining data from web sites because it is intended to collect specific data from a single web site. Collecting a very large data in a limited time tends to be detected as a cyber attack and will be banned from connecting into the web server. To avoid the problem, this paper proposes a crawling method to mine web database faster and cheaper than conventional web crawlers. The method used is to run hundreds of threads from a single web crawler in a single computer and to distribute the threads into hundreds or thousands publicly available proxy servers. This web crawler strategy highly increases the speed of mining and is more secure than using single thread of web crawler.

Government Human Capital Management is activities to manage government human capital based on their performance and competency. These processes were managed by three government institution (MENPAN, LAN, and BKN). These institutions should coordinate and collaborate to perform their duties and function. Knowledge management (KM) can enhance collaboration among individual and institution with sharing and transferring their organizational knowledge. KM is not easy to implement, some strategic issues, problems and challenges must be considered in KM implementation for government human capital management. This research aims to determine preliminary studies for KM implementation of government human capital management. It aims to identify strategic issue and problems in government ministries. Deep interview and observation are conducted to collect the data from three government institution. Qualitative research used to discover some findings align with respondent experiences. While, content analysis and fishbone analysis are used to analyze the data. The strategic issues are aim to developed the professional human resource and organization. While, findings problem consist of sixteen problems which represent in fishbone diagram into four categories there are people, organization, policies and technologies. Those problems cause a main effect that is unprofessional people and organization.

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.

Government Human Capital Management (GHCM) is activities to manage civil servant as government resources and assets. These assets are used as much as possible to improve the achievement of government objectives. In 2014, the government appointed legally three ministries to manage all government human capital in Indonesia. They are KEMENPAN&RB, LAN, BKN and each of them has a duty respectively in GHCM. Therefore, collaboration, coordination, and teamwork are necessary to achieve the professional management of human capital among the three ministries. Knowledge management believed can enhance sharing and transferring organizational knowledge among individual and institutions in order to create new knowledge in the field of GHCM. The knowledge is essential for the completion of tasks and problems that arise in GHCM. Some continuous activities to create, manage and disseminate knowledge are known as knowledge management cycle. This research aims to determine the KM cycle of GHCM. Deep interview conducted to collect the data related to knowledge management cycle. Mixed methods used to analyze the data which sequentially done with qualitative method and then the quantitative method. Content analysis used for analyze the transcribe data and correspondence analysis is used to analyze the contingency value of each activities. Then, the findings verified by expert judgment. Result shown that knowledge management cycle of GHCM are acquire, verified, store, share and utilize knowledge wherein each stage has a different activity.