Klasifikasi Tingkat Laju Data Covid-19 Untuk Mitigasi Penyebaran Menggunakan Metode Modified K-Nearest Neighbor (MKNN)

<p class="Abstrak">COVID-19 atau <em>Coronavirus Disease</em> 2019 merupakan sebuah penyakit yang disebabkan oleh virus yang dapat menular melalui saluran pernapasan pada hewan atau manusia dan menyebabkan ribuan orang meninggal hampir di seluruh dunia, sehingga dinyatakan sebagai sebuah pandemi di banyak negara, termasuk di Indonesia. Kasus COVID-19 pertama kali ditemukan di Indonesia pada tanggal 2 Maret 2020, dalam menangani pandemi COVID-19 pemerintah menerapkan <em>social distancing </em>dengan menjaga jarak antara satu sama lain sejauh lebih dari 1 meter dan menerapkan protokol kesehatan yang telah diatur saat melakukan aktivitas di luar rumah sesuai anjuran <em>World Health Organization</em> (WHO). Rendahnya kesadaran masyarakat Indonesia dalam menerapkan <em>social distancing</em> dan protokol kesehatan menyebabkan bertambahnya kasus positif COVID-19 di Indonesia secara signifikan sehingga banyak korban yang meninggal, oleh karena itu pada penelitian ini kami membuat sistem klasifikasi tingkat laju data COVID-19 untuk mitigasi penyebaran di seluruh provinsi di Indonesia dengan menggunakan metode <em>Modified K-Nearest Neighbor </em>(MKNN) dengan hasil keluaran berupa kelas laju penyebaran yaitu laju penyebaran rendah yang artinya mitigasi penybarannya tinggi, kemudian kelas laju penyebaran sedang yang artinya mitigasi penyebarannya sedang, dan laju penyebaran tinggi yang berarti mitigasi penyebaran rendah dan dijelaskan lebih lanjut pada bagian metodologi penelitian. Hasil keluaran dari sistem bertujuan untuk meningkatkan kesadaran masyarakat Indonesia dalam mencegah COVID-19 dengan melihat kelas laju penyebaran pada masing-masing provinsi di Indonesia. Alasan penggunaan metode <em>Modified K-Nearest Neighbor </em>pada penelitian ini adalah karena metode <em>Modified K-Nearest Neighbor </em>merupakan salah satu metode klasifikasi yang cukup baik, dimana pada metode ini dilakukan pemvalidasian dan pembobotan yang bobot nya ditentukan dengan menghitung fraksi dari tetangga berlabel yang sama dengan total jumlah tetangga.<em> </em>Parameter yang digunakan dalam proses klasifikasi adalah jumlah kasus positif, jumlah orang yang sembuh, dan jumlah orang yang meninggal akibat COVID-19. Data yang digunakan pada penelitian ini berasal dari situs resmi kementerian kesehatan republik Indonesia yang dapat diakses pada link <a href="https://infeksiemerging.kemkes.go.id/">https://infeksiemerging.kemkes.go.id/</a> dengan jumlah data latih sebanyak 374 data pada tanggal 12 Mei 2020 sampai 22 Mei 2020 dan data uji sebanyak 136 data pada tanggal 23 Mei 2020 sampai tanggal 26 Mei 2020 , hasil akurasi yang dihasilkan adalah 97,79% dengan nilai <em>K</em> = 3.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>COVID-19 or Coronavirus 2019 is a disease caused by a virus that can be transmitted through the respiratory tract to animals or humans and causes more people to die around the world, making it a pandemic in many countries, including Indonesia. COVID-19 cases were first discovered in Indonesia on March 2, 2020. Under the COVID-19 pandemic agreement, the government imposed a social grouping with a grouping of more than 1 meter apart from one another and the transfer of related health protection when carrying out activities outside the home as directed by the World Health Organization(WHO). Considering the Indonesian people in implementing social preservation and protecting health policies increase the positive acquisition of COVID-19 in Indonesia significantly related to the number of victims who died, therefore in this study, we created a COVID-19 data level assessment system for transfer mitigation in all provinces in Indonesia by using the Modified K-Nearest Neighbor (MKNN) method with the output in the form of a spread rate class, namely a low spread rate which means that the spread mitigation is high, then the medium spread rate class which means the spread mitigation is moderate, and the spread rate is high which means low spread mitigation which is further explained in the section on the research methodology. The purpose of the system output is to increase the awareness of the Indonesian people in preventing COVID-19. The parameters used in the classification process are the number of positives, the number of people recovered, and the number of people died by COVID-19 by looking at the class distribution rate in each province in Indonesia. The reason for using the Modified K-Nearest Neighbor method in this research is because the Modified K-Nearest Neighbor method is a fairly good classification method, where this method is validated and weighted whose weight is determined by calculating the fraction of neighbors labeled the same as the total of neighbors number. The data used in this study was released from the official website of the Ministry of Health of the Republic of Indonesia which can be accessed at the link <span style="text-decoration: underline;">https://infection.infemerging.kemkes.go.id/</span> with a total of 374 training data from May 12, 2020 to May 22, 2020 and test data As many as 136 data from 23 May 2020 to 26 May 2020, the resulting accuracy was 97.79% with a K = 3.</em></p>

A Hairpin DNA-Based Piezoelectric E-Nose: Exploring the Performances of Heptamer Loops for the Detection of Volatile Organic Compounds

A hairpin DNA (hpDNA) piezoelectric gas sensors array with heptamer loops as sensing elements was designed, realized, and challenged with pure volatile organic compounds VOCs and real samples (beer). The virtual binding versus five chemical classes (alcohols, aldehydes, esters, hydrocarbons, and ketones) of the entire combinatorial library of heptamer loops (16,384 elements) was studied by molecular modelling. Six heptamer loops, having the largest variance in binding the chemical classes, were selected to build the array. The six gas sensors were realized by immobilizing onto gold nanoparticles (AuNPs) via a thiol spacer the hpDNA constituted by the heptamer loops and the same double helix stem of four base pairs (GAAG at 5′ and CTTC at 3′ end). The HpDNA-AuNP was used to modify the surface of 20 MHz quartz crystal microbalances (QCMs). The realized E-nose was able to clearly discriminate among 15 pure VOCs of different chemical classes, as demonstrated by hierarchical cluster analysis. The analysis of real beer samples during fermentation was also carried out. In such a challenging matrix consisting of 23 different VOCs, the hpDNA E-nose with heptamer loops was able to discriminate among different fermentation times with high success rate. Class assignment using the Bayes theorem gave an excellent 98% correct beer samples classification in cross-validation.

DESKRIPSI KEMAMPUAN MULTIREPRESENTASI PADA MATERI LAJU REAKSI SISWA KELAS XI IPA SMA MUHAMMADIYAH 1 KETAPANG

The purpose of this study was to describe the symbolic, the microscopic, and the macroscopic levels of multi representation skills on Reaction Rate class among Science students of the eleventh grade of SMA Muhammadiyah Ketapang. The subjects of this study were 30 eleventh grade Science students of SMA Muhammadiyah ketapang. The data collection techniques were interview and test. The study reveals a number of findings. First, the symbolic level of multi representation skills of the first question was 86,5% (excellent), the second question was 59,22% (average), and the third question was 66% ( good). In other words, the students’ skill on symbolic level of multi representation is considered good by 70,57%. Second, the students’ skill on macroscopic level of multi representation on question number four is considered excellent by 83,86%, question number five is considered average by 68,57%, and question number six is considered very good by 77,14%. Overall, the students’ skill on macroscopic level of multi representation is very good by 76,52%. Last, the students’ skill on microscopic level of multi representation on question number seven is considered average by 52,86%, question number eight is considered poor by 36,14%, and question number nine is considered poor by 34,71%. In conclusion, the students’ skill on microscopic level of multi representation is below average by 41,25%.Keywords: Description, Multi Representation Skills, Reaction Rate

Determination of the Metabolic Rate and Work Arduousness Class for Workers in Coal Mines – The Results of in Situ Research

Considering different duties and activities among miners working in underground coal mines, their work is connected with variable metabolic rate. Determination of this rate for different workplace was the aim of the research and was the base for set up the work arduousness classes for the workplace (according to the standard PN-EN 27243). The research covered 6 coal mines, 268 workers and 1164 series of measurements. Metabolic rate was established on the base of heart rate obtained from individual pulsometers (according to the standard PN-EN ISO 8996). Measurements were supplemented by poll surveys about worker and thermal environment parameters. The results showed significant variability of average heart rate (from 87 bmp to 100 bpm) with variance coefficient 14%. Mean values of metabolic rate were from 150 W/m2 to 207 W/m2. According to the results, the most common class of work arduousness was at moderate metabolic rate (class 2 - moderate work), however, more intense work was found in headings, especially at “ blind end” workplace.