PENGARUH METODE DEMONSTRASI TERHADAP HASIL BELAJAR PEMELIHARAAN SASIS DAN PEMINDAH TENAGA KENDARAAN RINGAN KELAS XI TKR SMK NEGERI 1 MOTOLING

THE EFFECT OF DEMONSTRATION METHODS ON LEARNING RESULTS OF SASIS AND MANAGEMENT MAINTENANCE LIGHTWEIGHT VEHICLE CLASS XI TKR SMK NEGERI 1 MOTOLING By: FRANDO RUNTUNUWU NIM 14 212 036 Supervisor : Dr. H. M. Sumual, ST, M.Eng Ir. D. J. I. Manongko, M.Eng ABSTRACT This study aims to determine and analyze the effect of demonstration methods on learning outcomes applying manual transmission maintenance at SMK Negeri 1 Motoling. This research method uses a quantitative approach, using the experimental method. Namely a method that is directed at solving problems by describing or describing what the research results are. The results showed that the effect of the demonstration method can significantly improve student learning outcomes in the Subject of Light Vehicle Chassis Maintenance at SMK Negeri 1 Motoling. Effect of learning Demonstration method through statistical tests using t-test, it turns out that the value of t is greater than t table or t = 3.071> t table = 1.684 at α = 0.05 dk = n - 2.Thus this study accepts the alternative hypothesis (Ha) and rejecting the normal hypothesis (H0) means that there is an effect of Demonstration Method Learning on student learning outcomes in the subject of Light Vehicle Chassis Maintenance at SMK Negeri 1 Motoling. . Keywords: Demonstration Method, Learning Outcomes and SPT TKR.

PENGARUH MODEL PEMBELAJARAN SNOWBALL THROWING TERHADAP PENINGKATAN HASIL BELAJAR SISWA DI SMK NEGERI 3 TONDANO

PENGARUH MODEL PEMBELAJARAN SNOWBALL THROWING TERHADAP PENINGKATAN HASIL BELAJAR SISWA DI SMK NEGERI 3 TONDANO Oleh : David G. Sangari 14212081 Dosen Pembimbing : Drs. H. J. R. Sumarauw, M.Pd Dra. Lenie Ratag, M.Pd ABSTRAK David G. Sangari. 2019. The Influence of the Snowball Throwing Learning Model on the Improvement of Student Learning Outcomes at SMK Negeri 3 Tondano. Thesis, PTM S-1 Study Program, Faculty of Engineering. University Manado State. Advisors (1) Drs. H. J. R. Sumarauw, M.Pd, (II) Dra.Lenie Ratag, M, Pd Keywords: Snowball Throwing Model, Competency Learning of Light Vehicle Engineering Skills Based on the observations of researchers in the field, students in the learning process centered only on teachers and students only memorized concepts. The results at this reflection stage would be the basis for making improvements in this study. This study used 2 classes, namely the experimental class and the control class. The research was conducted in 12 meetings in each class. At the first meeting, a preliminary test was given which aims to see to what extent the students' ability limits. The second meeting until the 11th meeting was given a learning process using the Snowball Throwing learning model in the experimental class and conventional learning models in the control class. In the end, at the 12th meeting, each class was given a posttest to see if there was an improvement from the first meeting. Based on the research hypothesis testing at SMK N 3 Tondano on basic automotive engineering subjects with jacking and blocking material, it can be seen that Snowball Throwing learning affects students' mastery of concepts.

THE INFLUENCE OF PROBLEM BASED LEARNING MODEL AND LEARNING MOTIVATION ON LEARNING OUTCOMES OF LIGHT VEHICLE ENGINE MAINTENANCE LESSONS

Abstract:. The purpose of this study is to test 1) The existence of learning outcomes of Light Vehicle Engine Maintenance (PMKR) between students who get the Problem Based Learning (PBL) learning model and learning motivation on sent learning outcomes with conventional learning models, 2) There is an interaction between the PBL learning model and learning motivation on learning outcomes in PMKR lessons. 3) There are differences in the PMKR learning outcomes of students who have high learning motivation who receive the PBL learning model treatment. 4) There are differences in learning outcomes of light vehicle engine maintenance students who have low learning motivation who get the PBL learning model. This study used an experimental method with a 2 x 2 factorial design. The population was 80 students, the sample to be used was 66 students taken using smple random sampling technique. Then from the 66 students into 2 groups where Group 1 gathered 33 students into the Experiment Class which was taught with the PBL learning model and Group 2 returned 33 students from the Control class who were taught by the conventional model. The results showed that there were differences in learning outcomes between students who received treatment Problem Based Learning learning model and students who received conventional learning model treatment. There is an interaction between the learning model and learning motivation. Students who have learning motivation are more suited to the PBL model, while students who have low learning motivation are more suited to using the conventional model.

Pricing indirect emissions accelerates low—carbon transition of US light vehicle sector

Large–scale electric vehicle adoption can greatly reduce emissions from vehicle tailpipes. However, analysts have cautioned that it can come with increased indirect emissions from electricity and battery production that are not commonly regulated by transport policies. We combine integrated energy modeling and life cycle assessment to compare optimal policy scenarios that price emissions at the tailpipe only, versus both tailpipe and indirect emissions. Surprisingly, scenarios that also price indirect emissions exhibit higher, rather than reduced, sales of electric vehicles, while yielding lower cumulative tailpipe and indirect emissions. Expected technological change ensures that emissions from electricity and battery production are more than offset by reduced emissions of gasoline production. Given continued decarbonization of electricity supply, results show that a large–scale adoption of electric vehicles is able to reduce CO2 emissions through more channels than previously expected. Further, carbon pricing of stationary sources will also favor electric vehicles.

Enhanced MSRCR optical frequency segmented filter algorithm for alow-light vehicle environment

To solve the problem of difficult face detection in a low illumination vehicle environment, a novel multi-scale retinex color restoration (MSRCR) approach exploiting the RGB three-channel decomposition and guided filtering (MSRCR-3CGF) is proposed. The MSRCR algorithm is employed to remove the artifacts and interference of low-light in the image based on the face detector using a multi-task cascaded convolutional neural network (MTCNN). The enhanced face image is decomposed into RGB, and GF is applied to each channel. The proposed method is tested on three widely used datasets: Dark Face, large-scale CelebFaces attributes (CelebA) and WIDER FACE, and an actual low-light scene in vehicles. The experimental results show that the proposed method suppresses the high-frequency noise of MSRCR, whilst improving the image enhancement and accuracy in the face detection in a low-light vehicle environment.

Sitem Pendukung Keputusan Penentuan Jurusan Pada Tingkat SMK Menggunakan Metode SMART (Studi Kasus:SMK Negeri2 Binjai)

Programs at the high school level makes it difficult students make choicesthatsuitcapabilities.Someofthefactorsthatinfluencesuchamistakeinchoosing majors went along with friends or majors trends of today and parentreferences, so that they appear remorse for students who have determined thechoice if it is not in accordance with their interests, abilities or talents. Thereforeit takes a decision support system that can perform the calculation criteria of thestudentstohelpdetermineappropriateschooldepartment.Thissystemimplements the SMART method based on the criteria of each alternative, namelytheIndonesiaNationalexamscores,theEnglishNationalexamscores,theMathematics National exam scores, the Natural SciencesNational exam scores.The result of this system is the value of knowledge for determining the directionbased on predetermined criteria. Of the 10 alternative or candidate is taken, thereis4eligible student majoring in technical light vehicle,4 eligible student majoringin bodyrepairtechniques and 2 eligiblestudentmajoring in welding technique.

Identifying Future Vehicle Safety Priority Areas in Australia for the Light Vehicle Fleet

Formulating priorities for future road safety strategies requires supporting analysis to predict what the future crash population will look like and to assess how the countermeasures either already in place or planned will address the crash problems forecast. This analysis aimed to identify future priority action areas for light vehicle safety by identifying crash types that will not be fully addressed in the future by projected improvements in active and passive safety in the Australian light vehicle fleet. The future crash profile was modelled from 2017 to 2030 using crash data from 5 Australian jurisdictions overlayed with available evidence on vehicle safety feature fitment and effectiveness. The methodology can be applied to larger sets of safety technologies when sufficient evidence and supporting crash data become available. Three future vehicle safety priority areas were identified from the analysis: (i) fatal pedestrian crashes, (ii) single vehicle frontal crashes with objects, and (iii) front-to-front vehicle crashes both at intersections and midblocks, and front-to-side impacts at intersections including straight crossing path and right turn across path crash types. These crash types were projected to be the largest contributors to fatalities by 2030. Projections showed that remaining crash types in 2030 will be poorly addressed by current vehicle safety technologies such as autonomous emergency braking, lane departure warning and electronic stability control. Future vehicle safety policy priorities should address these crash types through the development of additional or enhanced vehicle safety technologies and where vehicle safety technology proves inadequate other countermeasures such as road infrastructure treatments and appropriate speed limit setting for high risk environments that address the key crash types remaining in the system.