Using Social Network Analysis For Analysing The Acceptance of Islamic Mobile Banking In Indonesia

The purpose of the research is to find the positive and negative sentiment influencing the acceptance of Islamic Mobile Banking using Social Network Analysis. The research uses the reviews from Google Store from January 2017 to December 2018 related to the Bank Syariah Mandiri (BSM) and the Bank Muamalat. The total reviews for the research are 4472 (2489 for BSM and 1983 for Bank Muamalat). The result shows the positive sentiment for BSM mobile banking has 1407 nodes and 61537 edges. The positive sentiment of BSM mobile banking correlates with the feature of the transfer feature. For the negative sentiment, BSM mobile banking has 1811 nodes and 46926 edges which is related to an error when the customer checks the balance and transfer money. The positive sentiment of Muamalat mobile banking has 1020 nodes and 30492 nodes which correlate with the ease of use when customers use mobile banking. For the negative sentiment, Muamalat mobile banking has 531 nodes and 13691 edges which correlate with login and registration features

Mapping Multi Stakeholder Roles on Fire Management in Conservation Areas of Kuningan Regency

Forest fire was a persistent concern management in conservation areas of Mount Ciremai National Park (MCNP) and Kuningan Botanical Garden (KBG). Many of the forest fire was sparked by anthropogenic ignitions like careless fire use for extracting forest honey. This study aims to map multi stakeholder roles on fire management in conservation areas. Twenty-seven actors were interviewed to learn who are the fire actors and network. These multi stakeholders included government officials, local businessmen, non-governmental organizations and community members. Study site and data collection were carried out in seven villages around conservation areas from July to September 2019. The relationships between the actors were analyzed with the software Node XL Basic and Gephi 9.0.2 using the Social Network Analysis. Our results identify close relationships and strong connections to all actors of more than half (63.2%) but social or personal approach between all actors were still required. Head of MCNP, Head of KBG and Head of AKAR (Aktivitas Anak Rimba) acted as the important actors. To prevent the area from further fire occurrences, management authorities should establish mutual confidence and make other actors believe that heads of conservation areas are a solid team to prevent conservation areas from burning

Smart library model based on big data technologies

Предмет истраживања докторске дисертације је развој модела памет не библиотеке заснованог на big data технологијама и сервисима. Централни истраживачки проблем разматран у раду је развој big data инфраструктуре и сервиса паметне библиотеке који омогућавају интелигентну претрагу и препоруку библиотечког садржаја. Посебан циљ рада је да испита могућност интеграције развијеног модела са паметним образовним окружењима у циљу унапређе ња квалитета образовног процеса. У докторској дисертацији је представљен модел паметне библиотеке као интегралног дела образовног система који може да побољша квалитет и свеобухватност наставних ресурса и повећа мотивацију у процесу учења препоручивањем садржаја од интереса. Модел описан у раду омогућава примену big data система за анализу, обраду и визуализацију података прикупљених из различитих извора и обухвата њихову интеграцију у паметну библиотеку. Циљ развоја паметних библиотека је да се унапреде библиотечки пословни процеси и да се корисницима пруже иновативни сервиси за претрагу и коришћење садржаја. У дисертацији се разматрају различите перспективе имплементације big data решења за паметне библиотеке као део континуираног образовног процеса, са посебним фокусом на интеграцију традиционалних система и big data технологија. Поред наведених компонената система, модел обухвата инфраструктуру и интеграцију система препоруке колаборативног филтрирања извора различитих података са big data технологијама. Модел је евалуиран кроз тестирање и мерење релевантних параметара перформанси који утичу на ефикасност предложеног модела.The subject of this doctoral dissertation research is the development of a smart library model based on big data technologies and services . The central research problem discussed in the thesis is the development of big data infrastr ucture and smart library services that enable intelligent searches and recommendations from the library content. A particular focus of the paper is an examination of the possibility of integrating the developed model into a smart educational environment in order to improve the quality of the educational process. The thesis presents a model of the smart library as an integral part of the educational system that would improve quality level and comprehesivness of learning resources and increase the motivation of its users through content aware recommendations. The model described in the thesis considers the possibilities of applying a big data system for the collection, analysis, processing and visualization of data from multiple sources, and the integration of data into the smart library . The goal of developing a smart library is to improve the library’s business process and to offer users innovative metho ds to search and content use. The thesis discusses the perspective of the implementation of a big data solu tion for smart libraries as a part of a continuous learning process with the aim of improving the results of library operations by integrating traditional systems with big data technology. In addition to the above system components, the model includes the infrastructure and integration of a recommender system for collaborative filtering by incorporating multiple sources of differential data with big data technologies. Within the evaluation of the model, testing and measurement of the relevant performance p arameters which influence the efficiency of the proposed model were carried out