Judul: Prediksi Kadar Particulate Matter (PM10) di Kota Jakarta dengan Menggunakan Metode Jaringan Saraf Tiruan
Abstrak: Pembangunan di Jakarta berkembang dengan pesat, namun kualitas udara di kota Jakarta tidak membaik. Nilai dari Air Quality Index di Jakarta pernah menempatkan kota tersebut sebagai kota dengan kualitas udara terburuk di dunia pada tanggal 25 Juli 2018 sampai 26 Juli 2018. Kegiatan lalu lintas di Jakarta merupakan salah satu kontributor dari buruknya kualitas udara di Jakarta. Emisi dari kendaraan bermotor yang terus-menerus terpapar di udara menghasilkan berbagai macam polutan, salah satunya adalah particulate matter (PM10). Penelitian ini membuat model prediksi kadar PM10 di kota Jakarta berdasarkan data iklim dan kadar PM10 di Jakarta pada tahun 2018. Model prediksi kadar PM10 dibangun dengan metode jaringan saraf tiruan menggunakan bahasa pemrograman R. Model terbaik dibuat dengan pembagian dataset dengan rasio 70:30 dan satu hidden layer. Pemberian nilai learning rate sebesar 0.01 atau 0.05 memberikan hasil yang terbaik. Salah satu model terbaik menghasilkan MSE sebesar 290.707 dan korelasi sebesar 0.414. Berdasarkan hasil evaluasi tersebut model yang dibangun masih belum dapat memprediksi kadar PM10 di kota Jakarta dengan akurasi tinggi menggunakan data yang tersedia.
Keyword: jaringan saraf tiruan, kualitas udara, PM10, prediksi

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: The Exploration of Discrete Logarithm over Finite Field GF(3m)
Abstrak: The security of many public~key algorithms is based on the problem of finding discrete logarithms. The generalized discrete logarithm problem is the following: given a finite cyclic group G of order n, a generator a of G, and an element fl E G, find the integer x, 0 ~ x ~ n - 1, such that 0: = fl. Algorithm for discrete logarithm problem focused on Menezes et al. (1997) that consist of exhaustive search algorithm, the baby-step giant-step algorithm, Pollard's rho algorithm, Pohlig-Hellman algorithm, and index-calculus algorithm. These algorithms are explorated to be used in discrete logarithm problem over finite field GF(3m ). The exploration also produces some algorithms, i.e. naif negative algorithm, baby-step mother-step algorithm, baby-step mother free-step algorithm, and baby-step free-step algorithm. All algorithms implemented using Maple 11. The Pohlig-Hellman and baby-step giant-step algorithms are efficient enough to be used in discrete logarithm problem over finite field GF(3m ) for m < 20.
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