In this COVID-19 pandemic period, the majority of people do their activities indoors. A high number of activities could increase indoor pollution. Some of the pollutants easily found in the house include particulate matter with sizes of <2.5 micrometers (PM2.5) and carbon dioxide (CO2). Both types of these pollutants can enter the body and interfere with health. This study aims to measure the concentration of pollutants in the house and estimate daily exposure and risk level. This study measured the concentration of PM2.5 and CO2 in two houses, house A located in a housing complex in Bandung Regency, and house B, located on the side of Garut City main road. The measuring instruments are placed in 3 points: kitchen, family room, and outdoors. The concentration of PM2.5 in the neighborhood of house A is higher than the concentration of PM2.5 in the neighborhood of house B due to the high construction activity. Large ventilation in house A causes the PM2.5 concentration to follow the outdoor concentration pattern. As for house B, the concentration of PM2.5 is much influenced by the source from the kitchen. The activities much influence the concentration of CO2 for both houses in each room. The relative risk of PM2.5 for cardiovascular and cardiopulmonary disease in each house differed depending on the duration of exposure. Calculation of the relative risk of PM2.5 was conducted on normal people in house A and house B, and the chef in house B. The highest relative risk was obtained by the chef in house B, followed by normal people in house B and house A. The level of relative risk for the chef at house B is 30% for cardiovascular disease and 34% for cardiopulmonary disease. ABSTRAK Di masa pandemi COVID-19, mayoritas masyarakat melakukan kegiatannya di dalam rumah. Aktivitas yang tinggi dapat menyebabkan polutan dalam rumah meningkat. Beberapa jenis polutan dapat dengan mudah ditemukan di dalam rumah di antaranya adalah partikulat berukuran <2,5 mikrometer (PM2,5) dan karbon dioksida (CO2). Kedua jenis polutan tersebut dapat masuk ke dalam tubuh dan mengganggu kesehatan. Penelitian ini bertujuan untuk mengukur konsentrasi polutan dalam rumah dan mengestimasi paparan harian dan tingkat risikonya. Penelitian ini mengukur konsentrasi PM2,5 dan CO2 di dua tipe rumah, yaitu rumah A yang terletak di perumahan Kota Bandung, dan rumah B, terletak di samping jalan utama kota Garut. Alat ukur diletakan pada tiga ruangan, yaitu dapur, ruang keluarga, dan luar ruang. Lingkungan rumah A memiliki konsentrasi polutan PM2,5 yang lebih tinggi dari rumah B karena tingginya aktivitas pembangunan permukiman dan jalur kereta cepat. Ventilasi yang besar pada rumah A menyebabkan konsentrasi PM2,5 cenderung mengikuti pola luar ruang. Sementara itu untuk rumah B, tingginya aktivitas di dapur mempengaruhi konsentrasi polutan PM2,5 dalam rumah. Konsentrasi CO2 untuk kedua rumah pun berbeda untuk tiap ruang. Rata-rata sumber CO2 pada tiap ruang di masing-masing dipengaruhi oleh aktivitas dari tiap ruangan tersebut. Tingkat risiko PM2,5 terhadap penyakit kardiovaskular dan kardiopulmoner pada masing-masing rumah berbeda tergantung dari durasi paparannya. Perhitungan tingkat risiko PM2,5 dilakukan pada orang normal di rumah A dan B, dan juru masak di rumah B. Tingkat risiko tertinggi dihasilkan oleh juru masak di rumah B, diikuti dengan orang normal di rumah B dan A. Tingkat risiko pada juru masak di rumah B sebesar 30% untuk penyakit kardiovaskular dan 34% untuk penyakit kardiopulmoner.

Indoor air quality is crucial to observe because most people spend 90% of their time in the room. Indoor air quality is influenced by various parameters, especially PM 2,5, from a mixture of air outside and inside the room itself. If the occupants are exposed to this parameter continuously, it will affect the occupant’s health significantly. Hence, it is necessary to control indoor air quality if this parameter exceeds the specified quality standards. One technology to reduce PM 2,5 is an air purifier. Air purifiers are generally composed of an exhaust fan, HEPA filter, and pre-filter. One of the air purifiers’ evolution is the smart-air-purifier. Smart-air-purifier can automatically adjust the speeds of the fan so as can minimizes electricity costs. The designed smart system can classify PM 2,5 concentration based on fuzzy logic to flow rate settings using pulse width modulation (PWM). In addition, to analyze the performance of the smart air purifier, we test it in a chamber. The test results show the performance of the smart air purifier in reducing PM 2,5, the clean air delivery rate measurement of the smart air purifier, and its power consumption which can minimize 67.42% of electricity use than commercial air purifiers.

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Indoor air pollution is found to be twice more dangerous as air pollution in the environment, especially 80-90% of people when they are indoors. Children are more susceptible to diseases caused by poor indoor air quality. Typically, students spend 60-90% of their time indoors, with most of the time at school. The targeted study areas are Telkom Education Areas such as VHS, SHS, JHS, and Tourism-VHS. The assessment procedure used a standard protocol developed by the US EPA (United States Environmental Protection Agency). The measuring parameters are CO2, CO, PM2.5, RH, and T. There are four systems at potential points with a height of 1-1.5 meters above the floor surface (human breathing zone). The indoor air quality assessment results show that almost all rooms have CO2 concentrations exceeding the standard (>1000 ppm). All rooms in Tourism-VHS exceed the PM2.5 concentration standard (>35 μg/m3), and each room has open ventilation, except for room type C (kitchen). Only three rooms exceed the CO concentration standard (>9 ppm), Tourism-VHS type A to C. It is necessary to conduct further research with normal daily conditions, and the measurements also need to be conducted for approximately three days for more data.

This study aims to make a device prototype for identifying vocal cord abnormalities based on Raspberry Pi. This prototype could also classify the abnormalities into seven classes, i.e., cysts, granulomas, nodules, normal, papilloma, paralysis, and no vocal cords. The applied method to classify is a deep learning algorithm, mainly using Convolutional Neural Network (CNN). In building the CNN model, we used a statistical method to form a model training scenario, also modified the AlexNet architecture model by optimizing the parameters. The optimized parameters in the test scenario obtained 95.35% for accuracy. The CNN model implemented on the Raspberry Pi, and the test results obtained 79.75% for accuracy.