Evaluating Naïve Bayes Automated Classification for GBAORD

The Indonesian Government Budget Appropriations or Outlays for Research and Government (GBAORD) has been analyzed manually every year to measure the government expenditures in research and development. The analysis process involved several experts in making the budget classification. This method, commonly known as manual classification, has its downsides, which are time consumption and inconsistent result. Therefore, a study about implementing the machine learning method in GBAORD budget classification to avoid inconsistency is proposed in the previous research. For further analysis, this paper evaluates the performance of the Naïve Bayes algorithm for the GBAORD budget classification. This paper aims to measure the robustness of the Naïve Bayes to classify GBAORD data taken from 2017 until 2019. This paper uses three models of Naive Bayes with different preprocessing methods and features. This paper concludes that using the Naïve Bayes algorithm in Indonesian GBAORD budget classification is suitable since the robustness of the algorithm is proved to be high with 96.788+-0.185% average accuracy

SOCIAL NETWORK ANALYSIS OF MANGOSTEEN TECHNOLOGY DEVELOPMENT CLUSTER IN INDONESIA BASED ON PATENT DOCUMENT APPLICATION

Functional food consumption is on the rise and has a significant market value. Indonesia is one of the largest mangosteens (a functional food source commodity) exporting countries globally. Unfortunately, the mangosteen export is still in fresh fruit condition, not in other forms that have a higher value. Policymakers need to identify critical technologies in the development of mangosteen commodities. This study uses a patent-based technology document analysis method to map the potential of technology. The data used is patent data that has been registered with the Indonesian Patent Office and the WIPO Patentscope database. The analysis was carried out using computational methods, namely a Social Network Analysis with the Girvan-Newman algorithm. According to the study's findings based on global patent data, there are three major technology clusters used in mangosteen patents: 1) 24 percent for technology related to developing preparations for medical, dental, or toilet purposes (A61K). 2) 20% for food and food ingredient technology or non-alcoholic beverages (A23L). The remaining 43 percent is spread across many other IPC technology codes, including technology related to drug preparations (A61P). It is in line with the results of patent data analysis in Indonesia, which also shows that there are three dominant technology groups applied to mangosteen in Indonesia, namely 1) Technology related to the development of medical, dental, and toilet technology (A61K) of 47 percent; 2) Technology related to food and food ingredients or non-alcoholic drinks (A23L) by 18 percent, and 3) Technology related to drug preparations (A61P) by 13 percent and the remaining 22 percent spread over several other IPC technology codes. According to Social Network Analysis, the world's dominant technology cluster for mangosteen is technology related to the development of food and food ingredients or non-alcoholic beverages (A23L). The technology associated with medical, dental, and toilet technology is the most important mangosteen technology cluster in Indonesia (A61K)

Indonesian Scientists’ Behavior Relative to Research Data Governance in Preventing WMD-Applicable Technology Transfer

Performing research data governance is critical for preventing the transfer of technologies related to weapons of mass destruction (WMD). While research data governance is common in developed countries, it is still often considered less necessary by research organizations in developing countries such as Indonesia. An investigation of research data governance behavior for Indonesian scientists was conducted in this study. The theories of planned behavior (TPB) and protection motivation (PMT) were used to explain the relationships between different factors influencing scientists’ behavior. The theories have been widely used in the information security domain, and the approach was adopted to build the research model of this study. The obtained data were analyzed using partial least-squares structural equation modeling (PLS-SEM) to answer the main research question: “what factors determine the likelihood of practicing research data governance by Indonesian scientists to prevent WMD-applicable technology transfer?” By learning what motivates scientists to adopt research data governance practices, organizations can design relevant strategies that are directed explicitly at stimulating positive responses. The results of this study can also be applied in other developing countries that have similar situations, such as Indonesia

Indonesian Scientists’ Behavior Relative to Research Data Governance in Preventing WMD-Applicable Technology Transfer

Performing research data governance is critical for preventing the transfer of technologies related to weapons of mass destruction (WMD). While research data governance is common in developed countries, it is still often considered less necessary by research organizations in developing countries such as Indonesia. An investigation of research data governance behavior for Indonesian scientists was conducted in this study. The theories of planned behavior (TPB) and protection motivation (PMT) were used to explain the relationships between different factors influencing scientists’ behavior. The theories have been widely used in the information security domain, and the approach was adopted to build the research model of this study. The obtained data were analyzed using partial least-squares structural equation modeling (PLS-SEM) to answer the main research question: “what factors determine the likelihood of practicing research data governance by Indonesian scientists to prevent WMD-applicable technology transfer?” By learning what motivates scientists to adopt research data governance practices, organizations can design relevant strategies that are directed explicitly at stimulating positive responses. The results of this study can also be applied in other developing countries that have similar situations, such as Indonesia

Pelatihan Penggunaan Aplikasi Administrasi RT/RW Berbasis Website Pada PKK RW 06 Tegal Parang Mampang

Kehidupan bermasyarakat pada tingkatan paling bawah diatur melalui Permendagri nomor 5 tahun 2007 yang mengatur tentang pembentukan Rukun Warga dan Rukun Tetangga. Untuk dapat menjalankan fungsi dan perannya dengan baik pada revolusi 4.0 ini, pemerintah diharapkan dapat beradaptasi dengan perkembangan teknologi untuk menyelesaikan dan memenuhi kebutuhan masyarakat melalui penggunaan aplikasi berbasis teknologi pada pelayanan publik. Dalam rangka melaksanakan kegiatan tri dharma perguruan tinggi yaitu pengabdian kepada masyarakat, Fakultas Teknologi Universitas Nusa Mandiri menyelenggarakan pelatihan penggunaan aplikasi administrasi RT RW berbasis website yang bertujuan guna memberikan cara dan langkah-langkah penggunaan aplikasi administrasi RT RW yang dapat digunakan oleh pengurus RW 06 dan RT yang berada dibawah RW 06. Pelatihan ini diselenggarakan bekerjasama dengan mitra yaitu PKK RW 06 Kelurahan Tegal Parang Kecamatan Mampang Prapatan. Dengan adanya aplikasi, dapat memberikan pelayanan administrasi kepada masyarakat dengan lebih cepat dan tepat

An Ontology-Driven Personalized Faceted Search for Exploring Knowledge Bases of Capsicum

Capsicum is a genus of flowering plants in the Solanaceae family in which the members are well known to have a high economic value. The Capsicum fruits, which are popularly known as peppers or chili, have been widely used by people worldwide. It serves as a spice and raw material for many products such as sauce, food coloring, and medicine. For many years, scientists have studied this plant to optimize its production. A tremendous amount of knowledge has been obtained and shared, as reflected in multiple knowledge-based systems, databases, or information systems. An approach to knowledge-sharing is through the adoption of a common ontology to eliminate knowledge understanding discrepancy. Unfortunately, most of the knowledge-sharing solutions are intended for scientists who are familiar with the subject. On the other hand, there are groups of potential users that could benefit from such systems but have minimal knowledge of the subject. For these non-expert users, finding relevant information from a less familiar knowledge base would be daunting. More than that, users have various degrees of understanding of the available content in the knowledge base. This understanding discrepancy raises a personalization problem. In this paper, we introduce a solution to overcome this challenge. First, we developed an ontology to facilitate knowledge-sharing about Capsicum to non-expert users. Second, we developed a personalized faceted search algorithm that provides multiple structured ways to explore the knowledge base. The algorithm addresses the personalization problem by identifying the degree of understanding about the subject from each user. In this way, non-expert users could explore a knowledge base of Capsicum efficiently. Our solution characterized users into four groups. As a result, our faceted search algorithm defines four types of matching mechanisms, including three ranking mechanisms as the core of our solution. In order to evaluate the proposed method, we measured the predictability degree of produced list of facets. Our findings indicated that the proposed matching mechanisms could tolerate various query types, and a high degree of predictability can be achieved by combining multiple ranking mechanisms. Furthermore, it demonstrates that our approach has a high potential contribution to biodiversity science in general, where many knowledge-based systems have been developed with limited access to users outside of the domain

Usability and acceptance of crowd-based early warning of harmful algal blooms

Crowdsensing has become an alternative solution to physical sensors and apparatuses. Utilizing citizen science communities is undoubtedly a much cheaper solution. However, similar to other participatory-based applications, the willingness of community members to be actively involved is paramount to the success of implementation. This research investigated factors that affect the continual use intention of a crowd-based early warning system (CBEWS) to mitigate harmful algal blooms (HABs). This study applied the partial least square-structural equation modeling (PLS-SEM) using an augmented technology acceptance model (TAM). In addition to the native TAM variables, such as perceived ease of use and usefulness as well as attitude, other factors, including awareness, social influence, and reward, were also studied. Furthermore, the usability factor was examined, specifically using the System Usability Scale (SUS) score as a determinant. Results showed that usability positively affected the perceived ease of use. Moreover, perceived usefulness and awareness influenced users’ attitudes toward using CBEWS. Meanwhile, the reward had no significant effects on continual use intention