Mitigation solutions are often evaluated in terms of costs and greenhouse gas reduction potentials, missing out on the consideration of direct effects on human well-being. Here, we systematically assess the mitigation potential of demand-side options categorized into avoid, shift and improve, and their human well-being links. We show that these options, bridging socio-behavioural, infrastructural and technological domains, can reduce counterfactual sectoral emissions by 40-80% in end-use sectors. Based on expert judgement and an extensive literature database, we evaluate 306 combinations of well-being outcomes and demand-side options, finding largely beneficial effects in improvement in well-being (79% positive, 18% neutral and 3% negative), even though we find low confidence on the social dimensions of well-being. Implementing such nuanced solutions is based axiomatically on an understanding of malleable rather than fixed preferences, and procedurally on changing infrastructures and choice architectures. Results demonstrate the high mitigation potential of demand-side mitigation options that are synergistic with well-being.

Transaction data is a set of recording data result in connections with sales-purchase activities at a particular company. In these recent years, transaction data have been prevalently used as research objects in means of discovering new information. One of the possible attempts is to design an application that can be used to analyze the existing transaction data. That application has the quality of market basket analysis. In addition, the application is designed to be desktop-based whose components are able to process as well as re-log the existing transaction data. The used method in designing this application is by way of following the existing steps on data mining technique. The trial result showed that the development and the implementation of market basket analysis application through association rule method using apriori algorithm could work well. With the means of confidence value of 46.69% and support value of 1.78%, and the amount of the generated rule was 30 rules.

Over the past three years, Indonesia has tasked provincial governments with defining sub-national actions to help implement the nationally appropriate mitigation action (NAMA) it pledged to the United Nations Framework Convention (UNFCCC) in 2009. This paper assesses provincial plans in Indonesia's Sustainable Urban Transport Initiative (SUTI) -a set of transport plans that developed in parallel to its NAMA -focusing on three key questions distilled from sustainable low carbon transport and multi-level governance literature. The questions seek to shed light into whether provincial governments: (1) prioritize actions that avoid unnecessary travel and shift passengers to more efficient modes; (2) carefully consider funding and costs estimates; and (3) clearly define institutional roles and responsibilities. The assessment suggests that most provincial governments prioritize measures designed to improve vehicle efficiencies and shift to more efficient modes (rather than avoid and shift). Furthermore, there is little evidence of due diligence being performed in relation to estimating costs and seeking creative financing. Lastly, observations point toward low levels of cross-agency collaboration and weakly defined institutional responsibilities at sub-provincial levels. The results imply that Indonesia may encounter some of the same problems confronting developed countries such as the United Kingdom as they translate national climate targets into local transport actions. Three focused case studies suggest that provinces with greater population density, economic diversity, and experience with international organizations outperform other provinces. They may therefore serve as a useful model for sharing with other provinces that are further behind the learning curve.

One thing that tourists need to plan their tourism activities is a recommendation system. The tourism destinations recommendation system in this study has three primary nodes, namely user, server, and sensor. Each node requires the ability to share data to produce recommendations that the user expects through their mobile devices. In this paper, we propose the data-sharing system scheme uses a blockchain-based decentralized network that each node can be connected directly to each other, to support the exchange of data between them. The block architecture used in the blockchain network has three main parts, namely block information, hashes, and data. Each type of node has a different structure and direction of data communication. Where the user node sends destination assessment data to the server node, then the server node sends data from the machine learning process to the user node. The sensor sends dynamic data about popularity, traffic, and weather to the user node as consideration for finalizing the generating recommendations process. In the process of sending data, each node in the blockchain network goes through several functions, including hashing, block validation, chaining block, and broadcast. We conduct web-based experiments and analysis of the data-sharing system to illustrate the system works. The experimental results show that the system handles data circulation with an average time of mine is 84.5 ms in sending multi-criteria assessment data from the user and 119.1 ms in sending data of machine learning result from the server.

Climate mitigation solutions are often evaluated in terms of their costs and potentials. This accounting, however, shortcuts a comprehensive evaluation of how climate solutions affect human well-being, which, at best, may only be crudely related to cost considerations. Here, we systematically list key sectoral mitigation options on the demand side, and categorize them into avoid, shift and improve categories. We show that these options, bridging socio-behavioral, infrastructural and technological domains, can reduce counterfactual sectoral emissions by 50-80% in end use sectors. Based on expert judgement and literature survey, we then evaluate 324 combinations of wellbeing outcomes and demand side options. We find that these are largely beneficial in improving wellbeing across all measures combined (76% have positive, 22% neutral, and 2.4% have negative effects), even though confidence level is low in the social dimensions of wellbeing. Implementing demand-side solution requires i) an understanding of malleable not fixed preferences, ii) consistently measuring and evaluating constituents of wellbeing, and iii) addressing concerns of incumbents in supply-side industries. Our results shift the emphasis in the climate mitigation solution space from supply-side technologies to demand-side service provision.

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Abstract-Determining the right class on student achievement is important in an evaluation process, because placing students in the right class helps lecturer in reflecting the successfullness of learning process. This problem relates to the performance of classification method which is measured by the classifier metrics. High performance is indicated by the optimality of these classifier's metrics. Besides, meta-algorithm adaptive boosting has been proven to be able to improve the performance of classifier in various fields. Therefore, this paper employs adaptive boosting to reduce the number of incorrect student placement in a class. The experimental results of implementing adaptive boosting in classifying student achievement shows that there is an increase of performance of all classification metrics, i.e., Kappa, Precision, Recall, F-Measure, ROC, and MAE. In terms of accuracy, J-48 is able to rise about 3.09%, which means this method reduces three misclassified students. Additionally, decisionStump increases 12.37% of accuracy. This also means this method is able to decrease 12 misclassified students. Finally, Simple Cart reaches the highest accuracy of about 23.71%, while the number of misclassified students is reduced to 24 students. However, there is no improvement in Random Forest method by using this adaptive boosting. Intisari-Penetapan kelas yang tepat pada prestasi belajar mahasiswa adalah suatu hal yang penting dalam proses evaluasi.Hal ini disebabkan keberadaan mahasiswa pada kelas yang tepat dapat membantu dosen untuk merefleksikan berhasil tidaknya proses belajar mengajar yang telah dilakukan. Masalah ketepatan kelas ini berkaitan dengan kinerja metode klasifikasi yang diukur dengan ukuran-ukuran classifier. Kinerja yang tinggi diindikasikan dengan optimalnya ukuran-ukuran classifier tersebut. Di sisi lain, meta-algoritme adaptive boosting telah terbukti mampu meningkatkan kinerja classifier pada berbagai bidang. Oleh karena itu, makalah ini menerapkan adaptive boosting untuk meningkatkan kinerja classifier agar keberadaan mahasiswa pada kelas yang tidak sesuai bisa direduksi. Hasil uji coba penerapan adaptive boosting pada klasifikasi prestasi belajar mahasiswa ini menunjukkan peningkatan kinerja pada semua ukuran classifier, Penggunaan sistem e-learning dan sistem evaluasi ini akan menghasilkan tumpukan data pada tempat penyimpanan. Tumpukan data ini dapat digunakan untuk pengoptimalan pada proses-proses tersebut dengan penerapan data mining. Hal ini dapat dilakukan karena penerapan ini menghasilkan pengetahuan yang sangat berguna. Pengetahuan ini diperoleh dari beberapa aktivitas dari data mining, antara lain klasifikasi, klaster, dan analisis asosiasi. Beberapa penelitian tentang klasifikasi antara lain prediksi mahasiswa nonaktif dengan menggunakan beberapa metode klasifikasi [6], klasifikasi untuk memprediksi prestasi belajar siswa dengan menggunakan Naïve Bayesian [7], dan klasifikasi dengan decision tree untuk mengetahui hubungan kinerja akademik dan bidang minat [8]. Untuk penelitian pen...

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