The problem of nutrition for children is a health problem that must be solved by the government. Malnutrition is a very important problem in the development of children, especially during the growth period. Lack of nutritional intake in children will have a negative impact on resistance to the virus. This will risk death caused by malnutrition. There is direct monitoring from the government, hospitals, and health offices in looking at the classification of nutrition in children in a system. This study aims to classify the nutritional status of children using a machine learning model, which then the final result can show the classification of nutritional vulnerability in each patient at the North Aceh Hospital. The stages of the research include the identification of theories about nutritional problems. Second, data collection is in the form of symptoms and diagnosis of disease classification in machine learning implementation. The third is to analyze the data using the research and development (R&D) method according to the classification of children's nutrition. Finally, the implementation of the patient classification model with decision tree into machine learning The variables included in the system include JK(X1), U (X2), BB(X3), TB (X4), and BB (X3), which are the variables that have the most influence on malnutrition in children. The results of this study for testing weight 16, height 9.7, age 33 months, nutritional value 54,23772 which the program output results are normal. Patient Syafira Nisman, weight 9, height 72, age 21 months, suffered from malnutrition. The results of the research on the application of machine learning for the classification of malnutrition using the decision tree method make it easier for patients and hospitals to classify children's nutrition.
Small Industry is an industrial company whose workforce consists of 5-19 people. While the Micro Industry is an industrial company whose workforce consists of 1-4 people. The purpose of this study is to estimate the Production Index of Micro and Small Industries according to KBLI for years to come. KBLI is a reference classification used to classify Indonesian economic activities/activities into several business fields/business fields that are distinguished based on the type of economic activity that produces products/outputs in the form of goods and services. In this study, the estimation method used is the Bayesian Regulation Algorithm. This algorithm is one of the Artificial Neural Networks algorithms that can solve many estimation problems by building a trained model and showing good performance. The data to be estimated in this study are data of Micro and Small Industry Production Index according to KBLI processed from the Central Board Statistics of Indonesia. This study uses 3 architectural models, namely: 5-10-20-1, 5-15-30-1 and 5-20-40-1. The best architectural model is 5-10-20-1, resulting in 83% accuracy, MSE 0.0031434897 with a minimum error of 0.001 - 0.05.
Pada saat ini perkembangan teknologi begitu pesat khususnya di bidang informasi. Sistem informasi adalah salah satu hasil dari perkembangan tersebut. dengan adanya sistem informasi, pengelolaan informasi menjadi jauh lebih efisien. Paperless secara umum mengurangi penggunaan kertas dalam berbagai kebutuhan contohnya dalam administrasi. dengan adanya paperless, penggunaan kertas dapat dikurangi sehingga dapat menghemat anggaran dalam manajemen keuangan. Aplikasi yang dibangun pada Fakultas Teknik Universitas Malikussaleh dapat mempermudah dalam proses administrasi persuratan yang sudah tersistem sehingga lebih mudah dalam mengelola persuratan dan dengan adanya sistem ini dapat mengurangi penumpukan kertas. sistem ini akan mengurangi penggunaan kertas yang digunakan dengan cara, mengurangi kertas yang dicetak digantikan dengan dokumen digital. Penulis melakukan wawancara pada bagian administrasi persuratan fakultas dan salah satu jurusan di fakultas teknik serta mencari bahan yang mendukung dalam pendefinisian masalah. Dengan ada sistem ini, diharapkan Dapat memudahkan para staff pada administrasi dalam hal surat menyurat dan dimasa mendatang diharapkan sistem ini dapat dikembangkan agar menghasilkan system yang lebih baik dari yang sebelumnya.
Audit pada SIPD Bappeda Kota Lokseumawe harus ditinjau kembali untuk melihat efesiensi dan integritas yang ada pada perusahaan, untuk itu perlu dilakukan audit Capability level pada SIPD Bappeda Kota Lokseumawe. Audit Capability Level SIPD dilakukan dengan tujuan untuk memetakan Level Capability proses pelayanan dan dukungan I&T di Bappeda Kota Lokseumawe. Audit Capability Level SIPD ini menggunakan framework Cobit 2019 Domain APO (Align, Plan and Organize) dengan perhitungan capability level sehingga berdasarkan hasil akan ditemukan temuan rekomendasi sasaran strategis pada perusahaan untuk pengembangan selanjutnya. Berdasarkan current capability level yang didapatkan dicapai nilai keseluruhan yaitu 2,527 dan berada pada level 2 capability yaitu Managed Process artinya Bappeda Kota Lokseumawe sudah mencapai proses penerapan untuk kemudian nantinya ditetapkan sesuai dengan proses penerapan yang telah diimplementasikan dalam proses ini sesuai dengan proses domain Align, Plan and Organized pada cobit 2019. Model COBIT 2019 juga dinilai mampu mengatasi perubahaan pengelolaan tata kelola dan Manajemen I&T pada perusahaan dengan baik.
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