Integrasi Model dan Konsep Learning Factory untuk meningkatkan produktivitas siswa SMK di Kota Makassar

MIFTA ZULFAHMI MUASSAR. 2018 (Dibimbing oleh ). Semakin pesatnya peningkatan persaingan kebutuhan kerja di dunia industri sekarang ini sangat membutuhkan pemahaman tentang dunia industri, Sekolah Menengah Kejuruan yang merupakan representative dari sebuah sekolah yang menitik beratkan kepada skill harus banyak bersaung dan meningkatkan pemahaman tentang dunia industry.Peningkatan pemahaman siswa dalam dunia industri sangat di butuhkan sebagai bekal siswa untuk masuk ke dunia industri yang sebenarnya setelah menyelesaikan pendidikan di bangku sekolah. Adanya program prakerin (praktek kerja industri belum membarikan pemahaman menyeluruh tentang dunia industri, begitupun dengan adanya program teaching factory belum memberikan pemahaman yang lebih terhadap dunia industri. Tujuan Penelitian ini adalah (i) memberi gambaran konsep learning factory yang dapat diterima baik oleh siswa SMK di Kota Makassar (ii) menganalisis produktivitas siswa SMK Kota Makassar melalui learning factory (iii) merancang konsep leraning factory yang sesuai dengan kondisi dan kebutuhan sehingga dapat diterima oleh siswa SMK di Kota Makassar. Populasi dalam penelitian ini adalah seluruh SMK Negeri yang ada di Kota Makassar, sampel di ambil dengan menggunakan random sampling, yang dimana sampel dari penelitian ini adalah SMK yang melaksanakan program teaching factory, instrument penelitian ini menggunakan angket,. Dianalisis secara kauntitatif, uji normalitas, linearitas, homogenitas dan hipotesis dengan menggunakan software minitab 18, dari hasil analisis (i) siswa SMK memliki gambaran terhadap dunia industri, yang didalamnya membahas tentang sistem produksi, sistem pemasaran, dan quality control, (ii) adanya peningkatan produktivitas siswa melalui learning factory (iii) memberikan hasil rancangan konsep learning factory yang sesuai dengan kondisi kota Makassar. Kata kunci: learning factory dan produktivitas sisw

Design Factory New Zealand: A co-creation space where students work in multidisciplinary teams with industry partners to solve complex problems

This workshop provided an opportunity for participants to work with Design Factory New Zealand (DFNZ) principles to experience learning as a multidisciplinary team, working on a complex problem. Participants had a chance to see how DFNZ acts as a transformation agent within students, staff, institution and the wider community. DFNZ as a curriculum allows students to explore new themes that challenge the usual paradigms. We encourage students to take ownership of their learning, to be open minded, and to have the freedom to respond to solutions without the shackles of a traditional design process driven by cost. By partnering with industry and exposing students to create solutions for real world problems, DFNZ has the aim of producing global citizens who have a better chance of succeeding in the workplaces of the future. Currently DFNZ has facilitated learning to students from Civil and Mechanical Engineering, Communication, Information Technology, Business, Design and Sports Science. Industry partners working with the DFNZ team tap into a pool of carefully selected and motivated students who are supported to come up with innovative and holistic solutions to their problems. Industry can use DFNZ as an opportunity to solve specific and existing needs of the company, or utilise fresh thinking to approach complex and wider issues. Involvement with the Design Factory can provide industry with critical strategic insight

Science led vs design led teaching approaches in materials science and engineering for aeronautical engineering students

A comparison on teaching styles has been conducted by analysing behavioural, cognitive, developmental, social cognitive and constructivist perspectives of 26 students (higher engineering apprentices). All of those students are in their full-time employment at Broughton factory (Airbus UK) and were comprehensively surveyed at the end of module (ENGF405: Composites and Aeronautical Materials) to quantify their learning experiences. It is generally assumed that design led, in comparison to science led, approach is the most appropriate method for these hands-on engineering professionals. However, presented results are quite interesting because majority of the high achievers have opted for science led approach for their improved learning experiences during the module

Intermediate Palomar Transient Factory: Realtime Image Subtraction Pipeline

A fast-turnaround pipeline for realtime data reduction plays an essential role in discovering and permitting follow-up observations to young supernovae and fast-evolving transients in modern time-domain surveys. In this paper, we present the realtime image subtraction pipeline in the intermediate Palomar Transient Factory. By using high-performance computing, efficient database, and machine learning algorithms, this pipeline manages to reliably deliver transient candidates within ten minutes of images being taken. Our experience in using high performance computing resources to process big data in astronomy serves as a trailblazer to dealing with data from large-scale time-domain facilities in near future.Comment: 18 pages, 6 figures, accepted for publication in PAS

Reactive with tags classifier system applied to real robot navigation

7th IEEE International Conference on Emerging Technologies and Factory Automation. Barcelona, 18-21 October 1999.A reactive with tags classifier system (RTCS) is a special classifier system. This system combines the execution capabilities of symbolic systems and the learning capabilities of genetic algorithms. A RTCS is able to learn symbolic rules that allow to generate sequence of actions, chaining rules among different time instants, and react to new environmental situations, considering the last environmental situation to take a decision. The capacity of RTCS to learn good rules has been prove in robotics navigation problem. Results show the suitability of this approximation to the navigation problem and the coherence of extracted rules

Problem-based learning in facilities planning: a pilot implementation

In Universiti Teknologi Malaysia, Problem Based Learning (PBL) is proposed as an alternative to lectures in moulding engineering graduates to acquire attributes that are required to excel in today’s k-economy. To investigate if PBL is viable for undergraduates in the Faculty of Mechanical Engineering, a pilot implementation of PBL in Facilities Planning, a subject required for final year Mechanical Engineering undergraduates with specialization in Industrial Engineering was executed. With 60 students in the class, the whole syllabus of the subject was covered using three main PBL problems. PBL was conducted with the help of industrial partners: a semiconductor company, and a furniture factory. The outcome of the implementation was highly encouraging. Students were able to illustrate good understanding of the content, while progressively exhibiting maturity in their generic skills, such as communication, team-working, self-directed learning and problem-solving. However, several aspects of the execution can be further improved

From the Prison Track to the College Track: Pathways to Postsecondary Success for Out-of-School Youth

Many young people learn a discouraging set of lessons between the ages of 16 and 24. They come to see secondary school as irrelevant, available jobs as demeaning, and their prospects and choices as diminishing. Some continue to "drop in" to school long enough to get a diploma, but leave lacking the skills or interest to pursue further education. Others drop out of school altogether. Seen in this context, the ambitious promise implied in the federal law to "leave no child behind" will require moving expeditiously beyond the "one-size-fits-all," factory-model high school to a far richer diversity of learning environments. This paper focuses on four types of learning environments that appear to hold particular promise for vulnerable and potentially disconnected youth: reinvented high schools, secondary/postsecondary blends, education/employment blends, and extended learning opportunities beyond the school day, year, and building. The first section paints a statistical portrait of the substantial number of urban youth who could potentially benefit from these new programmatic options. The second section describes the authors' process for identifying and investigating emerging, powerful learning environments, then profiles four programs that show evidence of effectiveness. The report concludes with a discussion of the policy opportunities today for creating multiple avenues for young people to achieve to higher standards, along with four specific policy recommendations to meet this goal

The Influence of Teaching Factory Learning Model Implementation to the Students\u27 Occupational Readiness

This study examines the significance level of teaching factory learning model to the occupational readiness of the students. Using the research design of a quantitative approach with causal comparative, the study included the following variables: teaching factory learning model (X) and occupational readiness (Y).The population consisted of 30 students of the study program of Automotive Engineering Education, Faculty of Engineering, State University of Semarang. They were selected using proportional random sampling technique. The data was analysed using descriptive and inferential statistical analysis. The findings shows the implemetation of teaching factory learning models (X) contributes significantly with the percentage of 22.80% to the occupational-readiness (Y). These results suggest that the implementation of teaching factory in the learning process is strongly recommended for the vocational educators both lecturers and teachers. The implementation can be adjusted to suit the conditions and the learning resources available in the educational institution

An Artificial Neural Network Approach to Learning from Factory Performance in a Kanban-Based System

Many Just-In-Time (JIT) manufacturing environments generate operational data reflecting both efficient and inefficient factory performance. Frequently data for inefficient performance is lost or discarded for fear of replicating poor performance. The purpose of this paper is two fold. First, historical JIT shop data is analyzed using a genetic algorithm (GA) to determine which shop factors are important determinants offactory performance. Second, subsequent to these important factors being identified by a GA, an artificial neural network (ANN) is used to learn the relationships between these factors and factory performance. The ANN can then be used to predict factory performance for future shop conditions and enhance shop performance. While ANN learning techniques have previously been applied to JIT production systems (Wray, Rakes, and Rees, 1997) (Markham, Mathieu, and Wray, 2000), these techniques have only been trained on data sets that reflect an efficient factory. Mathieu, Wray, and Markham (2002) investigated inefficient and efficient JIT factory performance but did not deploy either ANNs or a GA. In this paper an example application is presented using a GA to specify important shop factors and to predict saturated, starved or efficient factory performance based on dynamic shop floor data