Judul: CO and PM10 Prediction Model based on Standard Air Quality Index with Meteorological Factors in DKI Jakarta using LSTM
Abstrak: Jakarta is one of the areas in Indonesia that has poor air quality. This makes air quality in Jakarta a problem that deserves serious attention. Currently, the air quality standard index that is officially used in Indonesia is the Air Pollutant Standard Index (ISPU). ISPU calculations are carried out on 7 parameters, namely PM10, PM2.5, NO2, SO2, CO, O3, and HC. Of all ISPU parameters, the parameters that have a negative impact in a relatively small range are CO and PM10. One way to solve the problem of air pollution in DKI Jakarta is to make temporal predictions of air quality using data from the past. This study aims to create PM10 and CO prediction models in DKI Jakarta using LSTM with meteorological parameters as predictors, namely wind speed, solar radiation, humidity and air temperature to see how these variables affect the model. The data used in this study are ISPU data and weather data from January 1, 2017 to March 31, 2021. This study has successfully built a prediction model for CO and PM10 indices in 5 air quality monitoring stations in DKI Jakarta using LSTM. The modeling was carried out using two scenarios: modeling using meteorological predictors and without meteorological predictors in five air quality monitoring stations in DKI Jakarta. The results obtained show that the use of meteorological predictors in the CO prediction model does not affect the model. Still, the use of meteorological predictors has an influence on the PM10 prediction model, where prediction models using meteorological predictors produce a smaller RMSE and a stronger correlation coefficient than modeling. PM10 without using a meteorological predictor. This is because PM10 has a stronger correlation with the meteorological variables used than CO. The prediction model for CO and PM10 obtained in this study has not been able to predict extreme values because of the outlier found in each SPKU and the model can only be used to predict CO and PM10 index on the next day. Therefore, a prediction model for the CO at SPKU DKI 2 and the PM10 at SPKU DKI 1 was made to be compared with the model before adding the number of lags. The RMSE obtained shows that the addition of lags in the model decreases the model’s performance.
Keyword: CO, LSTM, meteorological factors, PM10, standard air quality index

Judul: Influence of Meteorological Factors on PM2.5 and PM10 Concentration Fluctuation (Case Study: Jakarta)
Abstrak: Jakarta sebagai ibukota negara Indonesia merupakan salah satu kota dengan aktivitas penduduk, transportasi dan industri yang tinggi. Hal ini mendorong semakin tingginya pencemaran udara di Jakarta. Terdapat berbagai faktor yang mempengaruhi pencemaran udara, salah satunya faktor meteorologi. Faktor meteorologi dapat mempengaruhi fluktuasi konsentrasi polutan di udara, seperti suhu udara, kelembaban, curah hujan, radiasi, kecepatan dan arah angin. Penelitian ini bertujuan untuk mengetahui fluktuasi konsentrasi PM2.5 dan PM10 di Jakarta serta interaksi antara faktor meteorologi dengan konsentrasi partikulat. Interaksi antara faktor meteorologi dan konsentrasi partikulat ditunjukkan melalui korelasi pearson dan menggunakan package openair di RStudio. Hasil penelitian menunjukkan konsentrasi PM2.5 dan PM10 di Jakarta memiliki puncak pada siang hari, menurun saat menuju sore hari dan kembali meningkat pada saat menuju malam hari. Interaksi antara faktor meteorologi dengan konsentrasi PM2.5 dan PM10 di Jakarta berkorelasi positif dengan suhu dan radiasi, serta berkorelasi negatif dengan kelembaban, curah hujan dan kecepatan angin. Secara bulanan konsentrasi PM di bulan kering lebih tinggi daripada di bulan basah., Jakarta as the capital city of Indonesia is one of the cities with high population, transportation, and industrial activity. This has led to higher air pollution in Jakarta. There are various factors that influence air pollution, one of which is meteorological factors. Meteorological factors will affect the fluctuation of pollutant concentrations in the air, such as air temperature, humidity, rainfall, radiation, wind speed and wind direction. This study aims to see the fluctuation of PM2.5 and PM10 concentrations in Jakarta and the interaction between meteorological factors and PM concentrations. The interaction between meteorological factors and PM concentrations was shown through Pearson correlation and using the openair package in RStudio. The results showed that the concentrations of PM2.5 and PM10 in Jakarta had a peak during the day, decreases towards the afternoon and increases towards the evening. The interaction between meteorological factors and concentrations of PM2.5 and PM10 in Jakarta has a positive correlation with temperature and radiation, and negatively correlates with humidity, rainfall and wind speed. On a monthly basis, PM concentration will increase in dry months and decrease in wet months.
Keyword: concentration, correlation, meteorology, PM2.5, PM10

Judul: Trichomonas Gallinae Penyebab Trichomonosis Pada Unggas
Abstrak: Trichomonas gallinae adalah protozoa yang bergerak aktof dengan keempat flagella yang mempunyai layar dan pada salah satu sisinya terdapat membran seperti sirip ikan yang berombak. Spesies dari genus Trichomonas ini mempunyai arti penting pada unggas, karena dapat menyebabkan infeksi pada alat pencernaan bagian atas.
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