ABSTRAKCoronavirus Disease-2019 (COVID-19) merupakan coronavirus jenis baru yang menjadi pandemi di berbagai negara. Salahsatu upaya pemerintah dalam mencegah penularan virus ini adalah dengan mewajibkan masyarakat untuk menggunakan masker serta memantau penggunaan maskar oleh masyarakat dalam kehidupan sehari-hari. Ketidak patuhan masyarakat menjadi masalah dalam mencegah penyebaran virus ini sehingg membutuhkan sebuah sistem yang dapat melakukan pengawasan. Pada penelitian ini, dibangun sebuah model dengan dengan memanfaatkan algoritma Convolutional Neural Network (CNN) dan 1000 dataset untuk melakukan pelatihan terhadap sistem deep learning serta melakukan pengujian untuk mendapatkan nilai akurasi dari hasil klasifikasikan terhadap gambar wajah yang menggunakan masker dan tanpa menggunakan maskaer. Hasil penelitian ini menunjukkan skenario kedua yang menggunakan epoch 50 dan rasio dataset 90% data latih dan 10% data uji mendapatkan akurasi terbaik mencapai 96%. Pengujian pada gambar wajah yang menggunakan masker memperoleh nilai precision 98%, recall 94% dan gambar wajah yang tidak menggunakan masker memperoleh nilai precision 94%, recall 98. Skenario satu dan tiga memperolah nilai akurasi terendah yaitu 94% sehingga dapat disimpulkan bahwa jumlah data latih sangat mempengaruhi nilai akurasi.
Indonesia has been known as an agrarian country because of its fertile soil and is very suitable for agricultural land, including rice. Yogyakarta is one of the most significant granary regions in Indonesia, especially in the Sleman region. However, one of the main challenges in rice planting in recent years is the erratic rainfall patterns caused by climate anomalies due to the El Nino and La Nina phenomena. As a result of this phenomenon, farmers have difficulty determining planting time and harvest time and planting other plants. Therefore, we make rainfall predictions to recommend planting varieties with Moving Average and Naive Bayes Methods in Sleman District. The results showed that moving averages well use in predicting rainfall. From these results, we can estimate that in 2020 rice production will below. That can saw from the calculation of the probability of naive Bayes on rice plants being low at 0.999 and 0.923. So that the recommended intercrops planted in 2020 are corn and peanuts. We also find that rainfall prediction with Moving Average using data from several previous years in the same month is more accurate than using data from four past months or periods.
Coronavirus Disease-2019 (COVID-19) is a new type of coronavirus that has become a pandemic in various countries. The large number of people exposed to COVID-19 at the same time makes it difficult for hospitals to accommodate all patients so that they must determine the priority scale which patients should get treatment first. In this study, we designed a decision-making system that was used to determine which patients were prioritized for treatment. In addition, we integrate with AI to quickly detect someone exposed to covid or not from X Ray images. The X Ray image detection model that we use is based on VGG16. Obtained f1-score in testing the Covid-19 and non-Covid-19 label dataset by 99.4%. Then in the normal label dataset, covid-19, and pneumonia obtained f1score 96.5%. In the covid-19 dataset and pneumonia an f1score of 98.7% was obtained. In addition, our model also beats other pretrain models such as densenet, resnet, squeezenet, and inception with a range of around 4%. This study also provides the same results between manual calculations and decision support systems that are built.
Pemanfaatan teknologi informasi diharapkan dapat memudahkan aktifitas sehari-hari tidak terkecuali proses evaluasi hasil belajar mahasiswa olah dosen. Pemeriksaan hasil ujian esai secara manual tentu jauh dari kata efektif dan efisien sehingga penelitian ini memanfaatkan fingerprint yang diperoleh dari nilai hash kumpulan teks menggunakan algoritma Winnowing untuk selanjutnya dihitung nilai kesamaannya menggunakan Jaccard’s Similarity Coeficient. Sebelum proses perhitungan, jawaban dalam bentuk teks bahasa Indonesai melalui pre-prosesing dan menggunakan algoritma Nazief & Andriani sebagai stemmer sehingga dapat memperoleh hasil evaluasi 30 jawaban siswa yang dibandingkan dengan kunci jawaban dalam waktu 1,62 detik dengan nilai rata-rata kesamaan 81,20%.
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