Roadmap Implementasi Knowledge Management

Dalam konteks menjaga eksistensi organisasi, manajemen pengetahuan (Knowledge Management-KM) merupakan kebutuhan bagi setiap organisasi. KM sendiri sejatinya dapat diartikan sebagai sebuah tindakan sistematis untuk mengidentifikasi, mendokumentasikan, dan mendistribusikan segenap pengetahuan yang relevan kepada setiap anggota organisasi tersebut, dengan tujuan meningkatkan daya saing organisasi. Ikujiro Nonaka membuat formulasi yang terkenal dengan sebutan SECI atau Knowledge Spiral. Konsepnya bahwa dalam siklus perjalanan kehidupan anggota organisasi, pengetahuan itu mengalami proses yang kalau digambarkan berbentuk spiral, proses itu disebut dengan Socialization - Externalization - Combination – Internalization. KM adalah kegiatan yang kompleks, dan implementasinya tidak dapat membawa dampak positif bagi organisasi jika tanpa perencanaan yang sempurna. Paper ini memperkenalkan metode perencanaan, yaitu 10-langkah knowledge management roadmap. Kesepuluh langkah tersebut adalah: 1) analisis keberadaan infrastruktur; 2). kesejajaran KM dan strategi bisnis; 3). desain infrastruktur KM; 4). audit keberadaan aset pengeathuan dan sistem; 5). desain tim KM; 6). membuat KM blueprint; 7). membangun sistem KM; 8). menggunakan metodologi yang tepat; 9). mengelola Perubahan, budaya dan struktur reward; dan 10).evaluasi kinerja, mengukur return of investment. Kesepuluh langkah tersebut dikategorikan dalam empat fase, yaitu: 1). evaluasi infrastruktur; 2). analisis, desain dan pengembangan KM system; 3). sistem penyebaran dan 4).evaluasi. Diharapkan dengan memahami roadmap ini, organisasi akan menerapkan seluruh proses sebagai panduan tahapan strategi manajemen pengetahuan. Perubahan yang terjadi diharapkan berjalan secara soft sehingga tidak terjadi resistensi dari anggota organisasi, dan secara eksternal mampu meningkatkan keunggulan kompetitif

THE ROLE OF LOCAL GOVERNMENT EXPENDITURE ON ECONOMIC GROWTH: A REVIEW OF PANEL DATA IN INDONESIA

Purpose of the study: This research aims to empirically prove the composition of local government expenditure (education, health, marine and fisheries, agriculture, and general allocation fund) on economic growth in 18 provinces in Indonesia from 2010 to 2015. Methodology: The model used in this research is panel data regression. The use of panel data in regression can provide more information that cannot be provided by cross-section or time-series data, and provides the best solution for inferring dynamic changes than cross-section data. Main Findings: The findings in this study are foreign investment has no influence on economic growth. Fiscal policies that are carried out are not effective in encouraging economic growth, and the use of the General Allocation Fund is not on target. Applications of this study: Foreign investment must be a trigger for the local economy and the national economy by means of foreign investment in Indonesia which is prioritized using raw materials and local labour, so that dependence on imported raw materials can be minimized. To overcome leakage of development budget must implement a budgeting system that is oriented towards organizational output and is very closely related to the organization's vision, mission, and strategic plan. The use of general allocation funds needs to be monitored by certain institutions and prioritizing public interest

Implementation 2D Lidar and Camera for detection object and distance based on RoS

The advanced driver assistance systems (ADAS) are one of the issues to protecting people from vehicle collision. Collision warning system is a very important part of ADAS to protect people from the dangers of accidents caused by fatigue, drowsiness and other human errors. Multi-sensors has been widely used in ADAS for environment perception such as cameras, radar, and light detection and ranging (LiDAR). We propose the relative orientation and translation between the two sensors are things that must be considered in performing fusion. we discuss the real-time collision warning system using 2D LiDAR and Camera sensors for environment perception and estimate the distance (depth) and angle of obstacles. In this paper, we propose a fusion of two sensors that is camera and 2D LiDAR to get the distance and angle of an obstacle in front of the vehicle that implemented on Nvidia Jetson Nano using Robot Operating System (ROS). Hence, a calibration process between the camera and 2D LiDAR is required which will be presented in session III. After that, the integration and testing will be carried out using static and dynamic scenarios in the relevant environment. For fusion, we use the implementation of the conversion from degree to coordinate. Based on the experiment, we result obtained an average of 0.197 meter