1,721,006 research outputs found
Perencanaan dan pembuatan alat bantu tuna rungu dan netra dengan ultra sonik/ Syafaah
No full textvii, 61 hal.: ill.; 29 cm
Penerapan Sistem Informasi Manajemen untuk Pembuatan Jadwal Induk Produksi dengan Metode Linier Programming
No full textGenerally, factories always want to give bidden to consumer, therefore ne€d the best production of plain and schedule by programming linier method in order that can be eff€ctive and efficient. Reality main problem flom PT. Serasa Puma Cipta Sidoarjo was pile up of sort ploduct and little others product to next times and to threaten to the first skipsi about difficulty of factories to make QS program for actuality production ofexcellent schedule. Therefore how to actuality management of information system in ordsr that process prepamtion data in tll€ factories was not difficultly for the actuality. The meaning for based on and experienc€ last time, the factories to do that program was difficultly to program operation so production of excellent schedule realize difiiculty. To that answer need actually Management of information system for production of excellent schedule to every group product to every period
Mekanisme model prediksi fluktuasi penderita DM (diabetes millitus) berdasarkan nilai MAGE (mean ampitude of glycemic excursion)/ Syafaah
No full textvii, 56 hal.: ilus.: tab.: lamp.; 29 c
PEMBELAJARAN MENGGAMBAR ILUSTRASI DENGAN KRAYON PADA EKSTRAKURIKULER DI SMP SEMEN GRESIK
Full text linkPenelitian ini dilatarbelakangi oleh keinginan untuk membantu proses pembelajaran pada ekstrakurikuler lukis di SMP Semen Gresik khususnya dalam bidang mewarnai dengan bahan krayon. Tujuan penelitian ini yakni mendeskripsikan (1) proses pelaksanaan menggambar ilustrasi dengan krayon; (2) hasil karya peserta didik menggambar ilustrasi dengan krayon; (3) kesulitan yang dihadapi dalam menggunakan krayon. Metode penelitian yang digunakan berupa penelitian kualitatif deskriptif. Pengumpulan data melalui wawancara, angket, tes praktik berupa pre test dan post test dan dokumentasi, sedangkan analisis data menggunakan reduksi data, penyajian data dan penarikan kesimpulan. Hasil penelitian ini yaitu (1) proses pembelajaran menggambar ilustrasi dengan krayon dapat membantu peserta didik untuk meningkatkan kemampuan dalam bidang menggambar dengan adanya pemberian demonstrasi dan materi teori untuk mengenal bahan dan alat; (2) hasil karya peserta didik pada pre test, mengalami perkembangan kemampuan dalam menggunakan krayon pada post test. Pada pre test 50% peserta didik masuk kategori baik dan 50% masuk dalam kategori cukup. Sedangkan pada post test 12,5% peserta didik naik ke kategori sangat baik dan 87,5% peserta didik masuk dalam kategori baik. Dari persentase ini, peserta didik mengalami peningkatan kemampuan dengan adanya pembelajaran tentang pemahaman teknik dalam suatu bahan untuk menggambar; (3) beberapa kesulitan yang dialami peserta didik dalam menggunakan krayon sebagai bahan menggambar.
Kata kunci: pembelajaran, ilustrasi, krayon, ekstrakurikule
INTERNET OF THINGS (IoT) DALAM PENGEMBANGAN PEMBELAJARAN DI UNIVERSITAS MUHAMMADIYAH MALANG
No full textTeknologi Internet of Things (IoT), membuat segala sesuatu lebih mudah, dalam bidang pendidikan, IoT sangat diperlukan untuk melakukan segala aktifitas dengan menggunakan sistem tertanam. Kurikulum Jurusan Teknik Elektro Fakultas Teknik Universitas Muhammadiyah Malang secara khusus belum ada materi/modul yang berkaitan dengan teknologi IoT. Oleh karenanya, permasalahannya adalah bagaimana membangun sistem mikrokontroler berbasis IoT dan bagaimana membuat pheriperal-pheriperal pendukung dalam sistem yang akan dibuat. Tujuan penelitian ini adalah membuat system IoT yang berbasis mikrokontroler sebagai tambahan materi dan memberi model pembelajaran baru dan warna lain untuk mata kuliah mikrokontroler. Metode/tahapan yang akan dilakukan dalam penelitian ini adalah identifikasi, perencanaan, pembuatan/perakitan dan pengujian. Hasil penelitian ini adalah sistem yang dirancang dan dibuat telah berjalan sesuai rencana dengan mengontrol sebuah beban via jaringan internet
Quality of Service pada Jaringan Multi Protocol Label Switching
No full textMPLS is a work plan that was given by IETF to repair work performance of IP network. Label switching label using forwarding mechanism which has connection oriented characteristic make this technology become routing technology for future guarantec QoS with better service level. This is supporting using a protocol signals for TE-MPLS to maximize network performance balancing the network stressing with distribution that fixed on classes, queuing, and traffic schedule. This research is an analysis of TE-MPLS mechanism to design MPLS network with RSVP-TE daemon that is called emulator MPLS. This analysis used generating 2 different kinds of protocol traffic, UDP, and TCP. The measurement could be concluded that there was an efficiency of MPLS network in group joint of protocol UDP network about 18,6% for channel that is similar with traffic 2 KBRS, 3,4%, 1,5%, and 5,6% for channel similar with traffic 4,8 and 16 KBRS for flow variation of MPLS network data increase 464,2%, 479,5%, 518%, and 2638,5% for each channel. The time that was needed for TCP protocol transmition of MPLS network showed efficiency 2,9% and bit rate of MPLS increase about 0,66%
PENERAPAN DEEP LEARNING UNTUK PREDIKSI KASUS AKTIF COVID-19
No full textCoronavirus disease (Covid-19) is increasingly spreading in Indonesia, so it requires an approach to predict its spread. One approach method that is often used is the Deep Learning (DL) method. DL is a branch of Machine Learning (ML) which is modeled based on the human nervous system. In this study, the prediction of active Covid-19 cases was resolved using the DL method. The dataset used is 260 data with 10 parameters. DL is able to provide an accurate prediction of active cases of Covid-19 with an MSE of 0.032 and an accuracy of 81.333%
Klasifikasi Golongan Darah Menggunakan Artificial Neural Networks Berdasarkan Histogram Citra
No full textlood type in the medical world can be divided into 4 groups, namely A, B, AB and O. To be able to find out the blood type, a blood type test must be done. So far, human blood type detection is still done manually to observe the agglutination process. This research applies a blood type identification process using image processing. This system works by reading the blood type card image that has been filled with blood samples, then it will be processed through a histogram process to get the minimum and maximum RGB values and pixel locations which are then classified by Artificial Neural Networks (ANN) to determine the blood type from the training results and data matching. From the test results using 12 samples, it was found that the average error in blood type identification was 16.67%
Sentiment Analysis of Covid-19 Vaccine Tweets Utilizing Naïve Bayes
No full textCOVID-19 is acknowledged as a transmitted from one person to another through contact, coughing, and
sneezing. Twitter has served as one of the media outlets to raise awareness regarding COVID-19 problems. One of the
government's objectives, based on the rising distribution, is pursued to preserve immunizations in stock. Hence, the vaccine
information has become adequately available. However, immunization has sparked a range of reactions, including support
and objection for vaccination. Attempts require a mechanism to distinguish tweets addressing immunization-related
information. One notable method includes sentiment analysis, expressing a statement's negative, neutral, and positive
feelings. A total of 5200 datasets were employed, with 4000 datasets classified as neutral, 300 datasets as negative, and
900 datasets as positive. The Naïve Bayes method and the TF-IDF (Term Frequency Inverse Document Frequency) word
weighting strategy are proposed to model the COVID-19 vaccine dataset, by comparing the three models of: Gaussian,
Multinomial, and TF-IDF (Term Frequency Inverse Document Frequency). According to study employing Naïve Bayes,
the best model employing Bernoulli Naive Bayes is 80% with a data splitting of 30%
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