PENGGUNAAN MEDIA GAMBAR SERI UNTUK MENINGKATKAN KETERAMPILAN MENULIS KALIMAT SEDERHANA SISWA KELAS II SEKOLAH DASAR

Penelitian ini dilatarbelakangi oleh rendahnya keterampilan siswa dalam menulis kalimat sederhana. Rendahnya keterampilan menulis siswa disebabkan karena dalam menulis kalimat sederhana dengan mengamati gambar, siswa masih binggung memilih kata untuk menuangkan ide yang ada dalam pikiran mereka.Gambar yang ada dalam buku siswa sulit dideskripsikan oleh siswa itu sendiri. Sehubungan dengan permasalahan di atas, maka cara tepat yang digunakan untuk perbaikan mutu pembelajaran Bahasa Indonesia di SDN S3 dalam meningkatkan keterampilan menulis kalimat sederhana dengan menggunakan media gambar seri karena dapat mempermudah siswa dalam menyusun sebuah karangan, paragraf ataupun kalimat sederhana sehingga pembelajaran akan lebih tertantang untuk membuat suatu karya tulis. Dan siswa dapat mudah menyusun kata-kata menjadi sebuah kalimat sederhana yang utuh. Metode yang digunakan dalam penelitian tindakan kelas dari Kemmis & Taggart (Arikunto, 2014, hlm. 74) yang terdiri dari empat tahapan yaitu: perencanaan, pelaksanaan, pengamatan dan refleksi dalam dua siklus penelitian dimana tiap siklus difokuskan pada materi tentang menulis kalimat sederhana dalam teks laporan sederhana dengan menggunakan media gambar seri. Penelitian ini dilaksanakan di SDN S3 kecamatan Sukasari, dengan subjek penelitian kelas II. Tujuan pelaksanaan tindakan kelas adalah untuk mengetahui bagaimana penggunaan media gambar seri dapat meningkatkan keterampilan menulis kalimat sederhana. Perencanaan pembelajaran dengan menggunakan media gambar seri dalam menulis kalimat sederhana pada siswa kelas II SD. Pelaksanaan penerapan dengan menggunakan media gambar seri dalam menulis kalimat sederhana pada siswa kelas II SD. Hasil belajar siswa dalam menulis kalimat sederhana setelah menggunakan media gambar seri pada siswa kelas II SD. Hal ini terlihat dari hasil belajar siswa mulai siklus I sampai siklus II yang mengalami kemajuan, dengan nilai persentasi siklus I 61,76% dan siklus II 97,05% dari KKM yaitu 72. Dengan demikian dapat disimpulkan bahwa penggunaan media gambar seri dapat meningkatkan keterampilan menulis siswa. Maka peneliti merekomendasikan kepada pendidik mengenai penggunaan media gambar seri sebagai media pembelajaran menulis kalimat sederhana, karena dengan menggunakan media gambar seri dapat menuangkan ide dan gagasannya ke dalam bentuk bahasa tulisan. ;--- This research is motivated by the lack of students' skills in writing simple sentences. Lack of writing skills of students due to write simple sentences by observing the images, students are still confused choose words for ideas that exist in their minds. Pictures in the book difficult students described by the students themselves. In connection with the above problems, the right way is used for the improvement of the quality of learning Indonesian in Public Alementary School S3 in improving the skills of writing simple sentences using the media image series because it can facilitate the students in preparing an article, a paragraph or a simple sentence so that learning will be more challenged to make a paper. And students can easily arrange the words into a simple sentence intact. The method used in the classroom action research of Kemmis& Taggart (Arikunto, 2014, p. 74), which consists of four phases: planning, implementation, observation and reflection in two cycles of study in which each cycle is focused on material about writing simple sentences in the text simple report using the media image series. This research was conducted in Public Alementary SchoolS3 Sukasari districts, with a grade II research subjects. The aim of implementing a class action is to know how to use the media image series can increase the skill of writing simple sentences. Planning learning by using media images in the series to write a simple sentence in grade IIAlementary School.Implementation of the application by using the media image series in write simple sentences in grade II AlementaryCchool. The results of students in writing simple sentences after using media image series in grade II AlementaryCchool. It is evident from the results of student learning begin the first cycle to the second cycle is progressing, with a percentage of the value of the first cycle and cycle II 61.76% 97.05% from KKM is 72. It can be concluded that the use of the media image series can improve skills writing students. The researchers recommend to educators regarding the use of media image series as a medium of learning to write simple sentences, because by using the media image series can pour his ideas in the form of written language

Using artificial intelligence to find design errors in the engineering drawings

Artificial intelligence is increasingly becoming important to businesses because many companies have realized the benefits of applying machine learning (ML) and deep learning (DL) in their operations. ML and DL have become attractive technologies for organizations looking to automate repetitive tasks to reduce manual work and free up resources for innovation. Unlike rule-based automation, typically used for standardized and predictable processes, machine learning, especially deep learning, can handle more complex tasks and learn over time, leading to greater accuracy and efficiency improvements. One of such promising applications is to use AI to reduce manual engineering work. This paper discusses a particular case within McDermott where the research team developed a DL model to do a quality check of complex blueprints. We describe the development and the final product of this case—AI-based software for the engineering, procurement, and construction (EPC) industry that helps to find the design mistakes buried inside very complex engineering drawings called piping and instrumentation diagrams (P&IDs). We also present a cost-benefit analysis and potential scale-up of the developed software. Our goal is to share the successful experience of AI-based product development that can substantially reduce the engineering hours and, therefore, reduce the project\u27s overall costs. The developed solution can also be potentially applied to other EPC companies doing a similar design for complex installations with high safety standards like oil and gas or petrochemical plants because the design errors it captures are common within this industry. It also could motivate practitioners and researchers to create similar products for the various fields within engineering industry

Modelling Data Pipelines

Data is the new currency and key to success. However, collecting high-quality data from multiple distributed sources requires much effort. In addition, there are several other challenges involved while transporting data from its source to the destination. Data pipelines are implemented in order to increase the overall efficiency of data-flow from the source to the destination since it is automated and reduces the human involvement which is required otherwise. Despite existing research on ETL (Extract-Transform-Load) and ELT (Extract-Load-Transform) pipelines, the research on this topic is limited. ETL/ELT pipelines are abstract representations of the end-to-end data pipelines. To utilize the full potential of the data pipeline, we should understand the activities in it and how they are connected in an end-to-end data pipeline. This study gives an overview of how to design a conceptual model of data pipeline which can be further used as a language of communication between different data teams. Furthermore, it can be used for automation of monitoring, fault detection, mitigation and alarming at different steps of data pipeline

A taxonomy of software engineering challenges for machine learning systems: An empirical investigation

Artificial intelligence enabled systems have been an inevitable part of everyday life. However, efficient software engineering principles and processes need to be considered and extended when developing AI- enabled systems. The objective of this study is to identify and classify software engineering challenges that are faced by different companies when developing software-intensive systems that incorporate machine learning components. Using case study approach, we explored the development of machine learning systems from six different companies across various domains and identified main software engineering challenges. The challenges are mapped into a proposed taxonomy that depicts the evolution of use of ML components in software-intensive system in industrial settings. Our study provides insights to software engineering community and research to guide discussions and future research into applied machine learning

From Ad-Hoc Data Analytics to DataOps

The collection of high-quality data provides a key competitive advantage to companies in their decision-making process. It helps to understand customer behavior and enables the usage and deployment of new technologies based on machine learning. However, the process from collecting the data, to clean and process it to be used by data scientists and applications is often manual, non-optimized and error-prone. This increases the time that the data takes to deliver value for the business. To reduce this time companies are looking into automation and validation of the data processes. Data processes are the operational side of data analytic workflow.DataOps, a recently coined term by data scientists, data analysts and data engineers refer to a general process aimed to shorten the end-to-end data analytic life-cycle time by introducing automation in the data collection, validation, and verification process. Despite its increasing popularity among practitioners, research on this topic has been limited and does not provide a clear definition for the term or how a data analytic process evolves from ad-hoc data collection to fully automated data analytics as envisioned by DataOps.This research provides three main contributions. First, utilizing multi-vocal literature we provide a definition and a scope for the general process referred to as DataOps. Second, based on a case study with a large mobile telecommunication organization, we analyze how multiple data analytic teams evolve their infrastructure and processes towards DataOps. Also, we provide a stairway showing the different stages of the evolution process. With this evolution model, companies can identify the stage which they belong to and also, can try to move to the next stage by overcoming the challenges they encounter in the current stage

Towards Data-Driven Product Development: A Multiple Case Study on Post-deployment Data Usage in Software-Intensive Embedded Systems

Today, products within telecommunication, transportation, consumer electronics, home automation, security etc. involve an increasing amount of software. As a result, organizations that have a tradition within hardware development are transforming to become software-intensive organizations. This implies products where software constitutes the majority of functionality, costs, future investments, and potential. While this shift poses a number of challenges, it brings with it opportunities as well. One of these opportunities is to collect product data in order to learn about product use, to inform product management decisions, and for improving already deployed products. In this paper, we focus on the opportunity to use post-deployment data, i.e. data that is generated while products are used, as a basis for product improvement and new product development. We do so by studying three software development companies involved in large-scale development of embedded software. In our study, we highlight limitations in post-deployment data usage and we conclude that post-deployment data remains an untapped resource for most companies. The contribution of the paper is two-fold. First, we present key opportunities for more effective product development based on post-deployment data usage. Second, we propose a framework for organizations interested in advancing their use of post-deployment product data

Toward Evidence-Based Organizations Lessons from Embedded Systems, Online Games, and the Internet of Things

Case studies investigated how companies in three domains transition to data-driven development. The results led to a model of the levels that software-intensive companies move through as they evolve from an opinionbased to an evidence-based organization

From Requirements To Continuous Re-prioritization Of Hypotheses

Typically, customer feedback collected in the prestudy, and during the early stages of software development, determines what new features to develop. However, once the decision to develop a new feature is taken, companies stop validating if this feature adds value to its intended customers. Instead, focus is shifted towards developing and implementing the feature. As a result, re-prioritization of feature content is rare, and companies find it difficult to continuously assess and validate feature value. In this paper, we explore the data collection practices in five software development companies. We introduce a model that allows continuous re-prioritization of features. Our model advocates a development approach in which requirements are viewed as hypotheses that need to be continuously validated, and where customer feedback is used to continuously re-prioritize feature content. We identify how the model helps companies transition from early specification of requirements towards continuous re-prioritization of hypotheses

Towards continuous validation of customer value

While close customer collaboration is highlighted as a distinguishing characteristic in agile development, difficulties arise in large-scale agile development where the product owner can no longer represent the different needs of a large customer base. While most companies use the role of a product owner to represent the customer base, experiences show that prioritizations that are made are far from optimal. Also, once the decision to develop a feature has been taken, companies stop to continuously validate if this feature adds value to the large customer base. As experienced in the case companies we work with, re-prioritization of feature content is difficult once development has started, resulting in R&D investments in development of features that have no proven customer value. In this paper, and based on our experiences from working with five B2B software development companies, we present a conceptual model in which qualitative and quantitative customer feedback techniques allow for continuous validation and re-prioritization of feature content. In this way, large-scale software development companies can significantly improve responsiveness to customers throughout the development cycle, while at the same time increase accuracy of their development efforts

So Much Data; So Little Value: A multi-case study on improving the impact of data-driven development practices

The amount of customer and product data that is collected by companies across domains is exploding. Today, connected software-intensive products permeate virtually every aspect of our lives and the actions we take generate data revealing what products we use, when we use them and how we use them. Still, companies struggle with extracting value from the data they collect, and although data collection and analysis techniques exist the impact of data is low. Typically, insights generated from data influence only smaller feature improvements and optimizations at the team level. However, as soon as decisions concern new product development and innovation at a business level, companies fall back on opinions and internal assumptions on what constitutes customer value. In this paper, and based on case study research in six embedded systems and six Software-as-a-Service (SaaS) companies, we identify the key challenges that hinder companies in leveraging the impact of data and we present a systematic approach to value modeling that help companies address these challenges