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"Looking for new ideas to enhance performance in your organization? Consider the possibilities of learning communities within your organization. In this issue, you will learn how these communities can open up the borders to thinking, generate better decisions, and improve learning programs and implementation of these programs in your organization. Discover how to build and sustain an effective learning community. Plus, learn how to measure the results of this new approach to problem solving and learning."

"Menurut EEB Laboratory Jakarta, pada tahun 2016 sektor bangunan memiliki mengkonsumsi 18-20% dari total penggunaan energi di Indonesia, dan terus menerus meningkat seiring perkembangan teknologi yang membutuhkan sumber energi dalam upaya peningkatan kualitas hidup penghuni bangunan. Bangunan pintar merupakan sebuah konsep pemanfaatan teknologi yang tidak hanya bertujuan meningkatkan kenyamanan penghuni, tetapi juga dapat membantu dalam upaya efisiensi energi pada operasional bangunan. Maka dari itu, penelitian ini akan membantu upaya perancangan efisiensi energi pada sebuah bangunan dengan meninjau fitur dan karakteristik yang berpotensi dalam mendukung efisiensi energi dengan penerapan konsep bangunan pintar. Selain itu, akan dibuat sebuah model dengan pemanfaatan machine learning yang mampu memberikan prediksi tingkat penggunaan energi berdasarkan fitur-fitur yang diberikan. Model machine learning yang dihasilkan memiliki rata-rata nilai kesalahan relatif sebesar 17,76%, serta didapatkan tingkat efisiensi dengan penerapan seluruh fitur yang diidentifikasi pada rentang 34,5% hingga 45,3% tergantung pada lantai yang ditinjau.

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According to EEB Laboratory Jakarta, Indonesian building sector accounts for 18- 20% energy consumption in 2016, and this trend will continuously increase as technology needed to increase housing residents' quality keeps advancing. Smart building is a concept to utilise technology that does not only help increase occupants' comfort inside the building, but it can also help increase energy usage efficiency in building operations. This research aims to help the effort in designing energy efficiency planning for a building by reviewing potential features and characteristics that could help improves energy efficiency with implementation of the smart building concept. A model based on machine learning that could give prediction on the level of energy consumption based on given features will also be discussed here. This model of machine learning has a 17,76% average of relative error, as well as 34,5% until 45,3% efficienct level that includes implementation of all features, depending on analysed floor."

"Seiring dengan kemajuan teknologi, fase konstruksi dan pembangunan mengalami perkembangan yang cukup pesat secara signifikan dalam mempersingkat waktu desain konstruksi. Proses desain bangunan hijau yang memakan waktu lebih lama daripada bangunan konvensional membuat industri konstruksi diharapkan mampu menemukan cara alternatif dengan alat desain terkomputerisasi yang efisien. Salah satunya dengan penerapan teknologi dengan menggunakan pembelajaran mesin. Tujuan dari penelitian ini adalah untuk menyelidiki persiapan data bangunan hijau untuk digunakan dalam alat desain berbasis pembelajaran mesin yang diusulkan agar lebih akurat dalam memprediksi variabel bangunan hijau khususnya kualitas lingkungan dalam ruangan daripada metode desain konvensional. Tinjauan literatur tentang bangunan hijau, serta pembelajaran mesin dalam konstruksi dilakukan untuk mengidentifikasi topik penelitian yang sedang dilakukan. Data umum tersebut dikuantifikasi, dianalisis, dan diproses untuk digunakan dalam model desain bangunan hijau yang dikembangkan menggunakan algoritma dan pemrograman artificial neural network dan support vector regression. Konsep pengembangan model ini menunjukkan bahwa alat desain akan membantu mengambil keputusan dalam memprediksi variabel bangunan hijau secara kuantitatif dengan akurasi yang memadai. Metode yang diambil sebagai model terbaik dalam penelitian ini adalah model dengan algoritma support vector regression.

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Along with technological advances, the construction and development phases have developed quite rapidly, significantly shortening the construction design time. The green building design process which takes longer than conventional buildings makes the construction industry expected to be able to find alternative ways with efficient computerized design tools. One of them is by applying technology using machine learning. The aim of this study is to investigate the preparation of green building data for use in a machine learning-based design tool which is proposed to be more accurate in predicting green building variables especially indoor environmental quality than conventional design methods. A review of the literature on green building, as well as machine learning in construction was conducted to identify the topic of the research being carried out. The general data is quantified, analyzed, and processed to be used in a green building design model developed using algorithms and programming artificial neural networks and support vector regression. The concept of developing this model shows that design tools will help make decisions in predicting green building variables quantitatively with sufficient accuracy. The method taken as the best model in this study is a model with a support vector regression algorithm."

"A comprehensive approach focused on sustainable change Asset Building and Community Development, Fourth Edition examines the promise and limits of community development by showing students and practitioners how asset-based developments can improve the sustainability and quality of life. Authors Gary Paul Green and Anna Haines provide an engaging, thought-provoking, and comprehensive approach to asset building by focusing on the role of different forms of community capital in the development process. Updated throughout, this text explores how communities are building on their key assets—physical, human, social, financial, environmental, political, and cultural capital— to generate positive change. With a focus on community outcomes, the authors illustrate how development controlled by community-based organizations provides a better match between assets and the needs of the community."

"This Infoline defines action learning as a group effort that involves solving real world problems through the use of acquired learning and implementing systems-wide solutions. It discusses why this process works and the benefits to organizations that use this technique. It features brief case studies from Whirlpool, National Semiconductor, General Electric, and Cigna International Property and Casualty Corporation."

"Dalam mencapai tujuan proyek yang efektif, efisien, serta aman diperlukan perencanaan metode konstruksi yang matang. Metode konstruksi pada proyek bisa berbeda-beda tergantung deskripsi dan desain dari proyek tersebut. Perencanaan metode konstruksi pada umumnya tidak sebentar, melibatkan banyak pihak, serta perlu pertimbangan dari segi biaya, sumber daya, dan safety. Salah satu proses berkelanjutan dan berpengaruh dalam pendefinisian metode konstruksi adalah penentuan sumberdaya, alat, tahapan kerja, serta penyesuaian teknis alat berat. Ditambah perlunya karakteristik perencanaan konstruksi untuk gedung pintar pada masa ini. Machine-learning yang dikombinasikan dengan sistem blockchain merupakan salah satu instrumen yang dapat mengakomodir proses dalam metode konstruksi terutama dalam pendefinisian schedule alat dan sumberdaya, serta tahapan konstruksi. Mulai dari perencanaan sampai integrasi-evaluasi dengan basis historical data. Digunakannya instrumen tersebut dalam penelitian hasilnya mampumendefinisikan proses perencanaan konstruksi yang sangat cepat dengan menggunakan data spesifikasi dan biaya bangunan, sehingga dapat meningkatkanproduktiftas proses perencanaan konstruksi, serta dibandingkan metode perencanaan manual dapat memberikan hasil yang lebih efisien dari segipenjadwalan sumberdaya dan alat, serta biayanya.

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In achieving project objectives that are effective, efficient, and safe, careful planning of construction methods is required. Construction methods on projects may vary depending on the description and design of the project. Construction method planning is generally not short, involves many parties, and requires consideration in terms of costs, resources, and safety. One of the continuous and influential processes in defining construction methods is the determination of resources, tools, work stages, and technical adjustments of heavy equipment. Plus the need for construction planning characteristics for smart buildings at this time. Machine-learning combined with a blockchain system is one instrument that can

accommodate the process of construction methods, especially in defining tool and resource schedules, as well as construction stages. Starting from planning to integration-evaluation with the historical database. The use of these instruments in research results in being able to define fast construction planning process using building specifications data and budget, to increase the productivity of the construction planning process, and compared to manual planning methods can provide more efficient results in terms of scheduling resources and tools, as well as costs."