3 research outputs found
Analysis To Predict The Quality Of Toddler Growth By Implementing The KNN And Naïve Bayes Methods
No full textIn particular, stunting and being under the Red Line (BGM) are significant issues for society and the healthcare system. This research utilizes machine learning, particularly the K-Nearest Neighbor (KNN) and Naïve Bayes algorithms, for classifying the health of children experiencing stunting or BGM. The training data used comes from the Indonesian Posyandu website, serving as the foundation for classifying new data. This research not only identifies patterns in the data through KNN but also compares the prediction results between KNN and Naïve Bayes in assessing the probability of stunting or BGM in children. This issue reflects nutritional deficiencies and has the potential to cause developmental delays and long-term health impacts. This approach allows for the comparison of predictive outcomes, enhancing the accuracy of children's health assessments. By using the RapidMiner application, the accuracy result for KNN is 70.62% and for Naïve Bayes is 99.47%, providing a deeper understanding of the effectiveness of each algorithm in addressing child health challenges. The aim of this research is to classify new toddler data using the KNN and Naïve Bayes methods, implemented in the form of a Visual Basic application. It is hoped that this will help monitor children's health more effectively and be more easily accessible to interested parties
Analisis Emisi Karbon Pada Kendaraan Dan Peralatan Listrik Rumah Tangga Untuk Mengantisipasi GWP (Global Warming Potential)
No full textPenelitian ini bertujuan untuk mengembangkan perhitungan jejak karbon berbasis aplikasi pada kendaraan dan peralatan listrik untuk memahami emisi gas rumah kaca. Analisis yang dilakukan meliputi penghitungan GWP gas rumah kaca, faktor emisi, dan konversi energi. Saran yang mungkin diberikan antara lain meningkatkan akurasi perhitungan emisi, mengintegrasikan data konsumsi bahan bakar dan listrik, serta meningkatkan akurasi perhitungan emisi karbon. Batasan KarbonDioksida(CO2), metana(CH4), dan dinitrogen oksida(N2O) di udara berdampak negatif jika melebihi batas peraturan. Emisi karbon dari aktivitas manusia berkontribusi terhadap pemanasan global dan perubahan iklim. Sistem ini dirancang dan diimplementasikan untuk menghitung emisi karbon dari kendaraan dan peralatan listrik, serta memberikan informasi mengenai konsentrasi emisi dan riwayat penghitungan. Analisis emisi karbon dari kendaraan dan peralatan rumah tangga dilakukan untuk memprediksi GWP (Potensi Pemanasan Global). Tujuan analisis ini adalah untuk mengidentifikasi dan mengurangi emisi Gas Rumah Kaca, sehingga dapat mengurangi dampak pemanasan Global. Metode penelitian ini menggunakan algoritma yang menghitung emisi CO2, CH4, dan N2O berdasarkan penggunaan kendaraan dan peralatan listrik pada rumah tangga. Penelitian ini sangat penting agar dapat memahami faktor yang mempengaruhi terhadap emisi karbon dan meningkatkan kesadaran tentang pentingnya perlindungan lingkungan.
Kata kunci: Gas Rumah Kaca, GWP, Perubahan Iklim, Emisi Karbo
The SIB Swiss Institute of Bioinformatics' resources: focus on curated databases
No full textThe SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article
