Judul: Analysis of the Distribution of Sulfur Dioxide (SO2) Pollutants in Bali for the 2011-2020 Period
Abstrak: Tingginya aktivitas pariwisata di Bali berimplikasi terhadap tingginya mobilitas wisatawan dan aktivitas perdagangan yang terjadi. Oleh karena itu, perlu dilakukan peninjauan lebih lanjut terkait kualitas udara khususnya akibat polutan sulfur dioksida (SO2) di Bali dengan menggunakan pendekatan yang ilmiah dan terukur. Tujuan penelitian ini adalah menganalisis perubahan konsentrasi dan pola distribusi spasial polutan SO2 di Pulau Bali periode 2011-2020. Data yang digunakan pada penelitian berasal dari Ozone Monitoring Instrument (OMI) SO2 dan data curah hujan satelit Tropical Rainfall Measuring Mission (TRMM). Prosedur penelitian yang dijalankan yaitu pengumpulan dan pengolahan data sekunder, selanjutnya pembagian periode analisis berdasarkan curah hujan bulanan, analisis perubahan konsentrasi dan spasial polutan SO2, kemudian visualisasi menggunakan aplikasi ArcGis. Nilai konsentrasi polutan SO2 di Pulau Bali mengalami fluktuasi dan cenderung memiliki hubungan berbanding terbalik dengan pola curah hujan rata-rata bulanan yang terjadi. Konsentrasi polutan SO2 selama 10 tahun di Pulau Bali bernilai antara 3 dan 253 μg/m3 dengan konsentrasi rata-rata bulanan berada dalam rentang 34-83 μg/m3. Terdapat konsentrasi polutan SO2 yang melebihi baku mutu berdasarkan PP No. 22 tahun 2021 yaitu sebesar 75 μg/m3 terjadi pada 22 dari 120 bulan pengamatan. Pola distribusi spasial SO2 selama 10 tahun di Pulau Bali dipengaruhi oleh faktor alamiah dan faktor antropogenik.
Keyword: Kualitas udara, pariwisata, Pulau Bali, spasial, sulfur dioksida, Air quality, Bali Island, spatial, sulfur dioxide, tourism

Judul: Pola Distribusi Spasial dan Temporal Polutan SO2 di Wilayah Kabupaten Bogor dan Sekitarnya Menggunakan Data Ozone Monitoring Instrument.
Abstrak: Bogor and surrounding areas currently entering a period of fairly rapid development. However, the development process can also give a bad impact on the environment in the area. The purpose of this research was to determine and describe the level and pattern of distribution of SO2 pollutants in Bogor and surrounding areas using data from OMI in the AURA satellite’s remote sensing process with Giovanni software. The study was conducted using data for 5 years (2006–2010), and found that the highest levels of SO2 in the PBL (Planetary Boundary Layer) section of Bogor surroundings occurred in November 2010 was 14.82 DU, while the lowest occurred on March 2007 was -5.63 DU. The season was classified based on by the changing seasons in Indonesia, which consists of 4 seasons, there was DJF season (December-January-February), MAM (March April-May), JJA (June-July-August), and SON (September-October-November). The distribution pattern of SO2 in DJF (rainy season) and MAM (transitional season) resulted the smallest value of the total column SO2 compared with the obtained values during JJA and SON (dry season)
Keyword: SO2., remote sensing, OMI, Bogor

Judul: Pengenalan kata berbasiskan fonem dengan pemodelan resilient backpropagation
Abstrak: The aim of this research is to know the performance of Neural Network as a model for word recognition. The research uses Resilient Backpropagation for modeling and Mel-Frequency Cepstral Coefficient (MFCC) for feature extraction. The voice data used comes from one speaker. Total words used are 70 words that consist of 50 words as a dictionary of words and 20 words, that each word repeated 10 times. Dictionary words consist of 50 words from a combination of phonemes used in the research. Phonemes used in this research are 10 phonemes consist of 4 vowels and 6 consonants phonemes. From the 20 words, each word is repeated 10 times, 7 times used as training data and 3 times are used as test data. The output from testing process are word transcription. The convertion process from the word transcription into word is done manually by 5 person. This research produces two models. Average accuracy obtained from model with 100 hidden neuron is 75% for test data and 61% for the dictionary of words. The best average accuracy obtained is 93% for test data and 62 % for the dictionary of words generated by testing the model with 1000 hidden neurons. Overfitting occurs in the second model with 1000 hidden neurons. It causes the model can only produce good output for data that has been trained.
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