Monitoring Information System of Covid-19 Cases in Dago, Bandung, Indonesia

The Orang Dalam Pengawasan (ODP) and Pasien Dalam Pegawasan (PDP) Covid-19 monitoring system in the Health Service task force in Dago area is a system that monitors and records people who are indicated to be affected by the Covid-19 disease. Orang dalam Pengawasan means “people under monitoring” in Bahasa Indonesia while Pasien dalam Pegawasan means “patients under surveillance”. To support the ODP and PDP Covid-19 monitoring system, an application is needed that can assist in the monitoring process in the Health Office task force in the Dago area through the utilization of Android-based technology. This research aims to help the task force not be late in reporting the number of ODP and PDP to the Government of West Java Province. Moreover, it also aims to avoid differences for data between the public health center and the Health Service task force in the Dago area so that the results of this study are in the form of information about the total findings and monitoring of ODP and PDP Covid-19 every day through the Android application. This research used a descriptive qualitative method. The results show that the application could help the task force in reporting the number of ODP and PDP to West Java Province. In the ongoing system, when carrying out the monitoring process, the task force always contacts the health centers in Dago via telephone or WhatsApp for the data collection and reporting process to not be well computerized. It causes delays in reporting data to West Java Province. Problems also occurred with the amount of data due to the absence of a data closing system, which resulted in reports on the number of daily ODP and PDP Covid-19 data that were always differences between the public health center and the Health Office. In conclusion, this information system could help the reporting process.

Director independence: Going beyond misaligned incentives to resource dependence

Board director independence is critical to achieving and maintaining control to address the agency theory–based issue of interest misalignment between the principal (the organization) and the executives (agent). However, theoretical and empirical research and strategic risk considerations have brought into question the role or relevance that director independence plays in these control task and agency theory domains. We ask, using a quantitative survey method, whether board activity–based applications of independence may be associated with the service task of the board, namely its resource dependence mission. Our findings suggest that the resource dependence duty of the board may be positively associated with some autonomous activities, and yet other activities might be driven primarily by normative practices. Based on this, we suggest that a theoretical scope beyond and greater than agency theory may be needed when reassessing the role of director independence. JEL Classification: M1, O3

Deliberative and Conceptual Inference in Service Robots

Service robots need to reason to support people in daily life situations. Reasoning is an expensive resource that should be used on demand whenever the expectations of the robot do not match the situation of the world and the execution of the task is broken down; in such scenarios, the robot must perform the common sense daily life inference cycle consisting on diagnosing what happened, deciding what to do about it, and inducing and executing a plan, recurring in such behavior until the service task can be resumed. Here, we examine two strategies to implement this cycle: (1) a pipe-line strategy involving abduction, decision-making, and planning, which we call deliberative inference and (2) the use of the knowledge and preferences stored in the robot’s knowledge-base, which we call conceptual inference. The former involves an explicit definition of a problem-space that is explored through heuristic search, and the latter is based on conceptual knowledge, including the human user preferences, and its representation requires a non-monotonic knowledge-based system. We compare the strengths and limitations of both approaches. We also describe a service robot conceptual model and architecture capable of supporting the daily life inference cycle during the execution of a robotics service task. The model is centered in the declarative specification and interpretation of robot’s communication and task structure. We also show the implementation of this framework in the fully autonomous robot Golem-III. The framework is illustrated with two demonstration scenarios.

The service task of automotive troubleshooting process in collaborative environment using knowledge support approach

The automotive troubleshooting unfolds from the system phase to the multiple-domain phase. It is necessary that different field members, for example, system and domain engineers, cooperate tightly toward an integrated product service process. Therefore, the purpose of this research is to generate a knowledge management platform for the overall automotive troubleshooting service. Besides, general ontologies, automotive troubleshooting service ontology (ATSO), is also created as a helpful tool for the communication of heterogeneous systems. This proposed approach of automotive collaborative troubleshooting is explained with the corresponding meta-model. The main contribution of this research is to assure the traceability in both system and domain phases by structuring knowledge. Eventually, an application is illustrated in enhancing re-usability in the region of automotive braking system in collaborative environment.

Perancangan Arsitektur Integrasi Sistem Informasi Menggunakan Metode Service Oriented Analysis And Design (SOAD) (Studi Kasus : Politeknik LPKIA Bandung)

Seiring dengan perkembangan teknologi informasi, aplikasi dan platform yang digunakan oleh departemen-departemen dan unit pendukung pada perguruan tinggi yaitu Politeknik LPKIA Bandung menjadi beragam.Oleh karena itu diperlukan sebuah metode yang digunakan untuk melakukan integrasi sistem informasi yang berjalan, metode tersebut adalah metode Service Oriented Analysis and Design (SOAD). Merupakan sebuah metode perancangan sistem yang mengelompokan proses bisnis menjadi sekumpulan service melalui dua tahapan utama yaitu analisis service dan desain service. Untuk memperoleh service yang responsive terhadap proses bisnis yang berlangsung, digunakan juga pendekatan Object Oriented Analysis and Design (OOAD). Yaitu use case diagram untuk memetakan kandidat service entity-centric business service dan kandidat service task-centric business service dan menggunakan class diagram untuk menentukan arsitektur data dari kandidat service-service yang terpilih menjadi desain service final. Dari desain service final yang ada kemudian dibagi menjadi 3 layer, yaitu 1 service pada orchestration service layer yang berperan mengatur service-service yang diperlukan setiap aplikasi, 29 service pada business service layer yang berperan untuk menyediakan dan mempublikasikan service untuk orchestration service layer dan 3 service pada application service layer yang berperan mengakses database. Selain itu agar desain service final yang sudah dihasilkan memiliki operasi yang sesuai dengan proses bisnis yang berlangsung maka dilakukan identifikasi sevice operation berdasarkan hasil Business Process Modeling Notation (BPMN). Hasil akhir yang diperoleh dari tahapan analisis service dan desain service tersebut yaitu berupa arsitektur sistem informasi dan arsitektur data berorientasi service yang menjadi dasar dalam menentukan rancangan arsitektur integrasi sistem informasi di Politeknik LPKIA Bandung

Engaged to a Robot? The Role of AI in Service

This article develops a strategic framework for using artificial intelligence (AI) to engage customers for different service benefits. This framework lays out guidelines of how to use different AIs to engage customers based on considerations of nature of service task, service offering, service strategy, and service process. AI develops from mechanical, to thinking, and to feeling. As AI advances to a higher intelligence level, more human service employees and human intelligence (HI) at the intelligence levels lower than that level should be used less. Thus, at the current level of AI development, mechanical service should be performed mostly by mechanical AI, thinking service by both thinking AI and HI, and feeling service mostly by HI. Mechanical AI should be used for standardization when service is routine and transactional, for cost leadership, and mostly at the service delivery stage. Thinking AI should be used for personalization when service is data-rich and utilitarian, for quality leadership, and mostly at the service creation stage. Feeling AI should be used for relationalization when service is relational and high touch, for relationship leadership, and mostly at the service interaction stage. We illustrate various AI applications for the three major AI benefits, providing managerial guidelines for service providers to leverage the advantages of AI as well as future research implications for service researchers to investigate AI in service from modeling, consumer, and policy perspectives.