PURWARUPA SISTEM PEMANTAUAN POLUSI UDARA DI RUANG TERTUTUP MENGGUNAKAN PLATFORM THINGSPEAK

Smoking is an act of negligence that a person commits intentionally and causes personal harm. The habit of smoking has spread to children and adolescents. One of the health impacts of smoking is the smoke that is released. Therefore, cigarette smoke is categorized as one of the causes of air pollution. A bad habit that smokers do is smoking in a closed room with minimal ventilation. As a result, the air in the room is contaminated by harmful substances from cigarette smoke. This study aims to monitor the quality of air exposed to cigarette smoke in a prototype closed room and measure the effectiveness of sansevieria plants placed in the room to absorb cigarette smoke in real-time. Air quality is displayed in graphical form using the Thingspeak Platform. The stages carried out in this research are air quality detected using an MQ-7 sensor integrated with the MCU8266 WiFi Node, converting sensor data into smoke density values in units of PPM (parts per million), displaying air PPM graphs in real-time and displaying the absorption ability of sansevieria against air contaminated with cigarette smoke. The results prove that one pot of sansevieria plants (5 leaves) placed in a prototype room with a size of 70cm x 30cm x 45cm can absorb cigarette smoke within 1 hour 39 minutes. While for two pots of sansevieria plants (10 leaves), it takes 1 hour and 11 minutes. Visualization of the absorption graph and normalization of air in the room can also be monitored in real-time through the Thingspeak platform based on the smoke density value against time

Analisis Sistem Pengendalian Intern Pemerintah dalam Pengelolaan Bantuan Operasional Sekolah

This study aims to analyze the implementation of the government's internal control system in the management of school operational assistance at SDN 060864. The research method is a descriptive type of research with a qualitative approach, namely solving the problem under investigation by describing the state of the institution that is running the government internal control system based on the facts. through interviews and direct observations in the field. The results of this study indicate that SDN 060864 has implemented the five elements of government internal control which include the control environment, risk assessment, control activities, information and communication, and monitoring so that the financial management of school operational assistance funds (BOS) in SDN 060864 can be accounted for in a transparent and transparent manner. accountability in accordance with the principles of financial management according to Law Number 20 of 2003

Klasifikasi Arritmia pada Sinyal EKG Menggunakan Deep Neural Network

Penelitian yang dikembangkan saat ini memfokuskan klasifikasi sinyal Electrokardiogram (EKG) pada gangguan arritmia detak jantung. Monitoring ini bertujuan agar dapat menjadi penanganan dini terhadap berbagai jenis gangguan arritmia. Klasifikasi yang diajukan dapat mengklasifikasi 9 jenis gangguan arritmia dengan menggunakan metode Deep Neural Network (DNN). Teknik preprosessing data pada sinyal EKG sebelum proses klasifikasi, yaitu segmentasi, normalisasi menggunakan normalize bound, dan fitur extraction dengan menggunakan autoencoder. Hasil menunjukkan bahwa metode yang digunakan mendapatkan nilai akurasi yang sangat baik sebesar 99.62% dan sensitivity about 97.18%. Kata kunci—EKG, Arritmia, Klasifikasi, Deep Neural Networ