Judul: Predicting Ground Water Level on Peat Land using Facebook Prophet and Gated Recurrent Unit
Abstrak: Lahan gambut merupakan akumulasi dari bahan organik yang mengandung karbon lebih dari 12%. Keringnya lahan gambut membuat rentan terjadinya kebakaran hutan dan lahan (karhutla) yang dapat diidentifikasi melalui tinggi muka airnya (TMA). Indonesia telah membangun Sistem Pemantau Air Lahan Gambut (Sipalaga) pada laman https://sipalaga.brg.go.id. Data TMA, curah hujan, dan kelembaban tanah dari Sipalaga stasiun Rimba Panjang digunakan untuk prediksi TMA guna deteksi dini dari terjadinya karhutla. Namun, pada data banyak ditemukan missing value yang harus ditangani. Penelitian ini memiliki tujuan untuk membangun sekaligus membandingkan model prediksi TMA menggunakan Facebook Prophet dan Gated Recurrent Unit (GRU). Nilai hilang ditangani menggunakan teknik interpolasi dan moving average. Model terbaik diperoleh menggunakan dimensi input multivariat metode GRU dengan imputasi interpolasi linier. Evaluasi menggunakan R2 , MAE, dan MSE memiliki hasil 0,96469, 0,01135, dan 0,00023. Prediksi TMA menggunakan hasil penelitian ini diharapkan dapat membantu sistem peringatan dini karhutla di Indonesia., Peatlands are accumulations of organic matter containing more than 12% carbon. Dry peatlands make them vulnerable to forest and land fires which can be identified by their groundwater level (GWL). Indonesia has built a Peatland Water Monitoring System on the https://sipalaga.brg.go.id page. TMA data, rainfall, and soil moisture from Sipalaga Rimba Panjang station are used for TMA predictions to detect early forest and land fires. However, in the data there are many missing values that must be addressed. This study aims to build and compare the TMA prediction model using Facebook Prophet and Gated Recurrent Unit (GRU). Missing values are handled using interpolation techniques and moving averages. The best model is obtained using multivariate input dimensions GRU method with linear interpolation imputation. Evaluation using R2, MAE, and MSE has results of 0.96469, 0.01135, and 0.00023. TMA predictions using the results of this study are expected to help the land and forest fire early warning system in Indonesia.
Keyword: Facebook Prophet, GRU, karhutla, lahan gambut, TMA

Judul: Temporal Data Imputation Model for Peatland Groundwater Level
Abstrak: Lahan gambut adalah akumulasi tumbuhan mati yang terdekomposisi penuh selama lebih dari ribuan tahun dan dapat menyimpan karbon, memproduksi oksigen serta mengatur tingkat air. Tinggi muka air (TMA) menjadi indikator penting dalam peringatan kebakaran lahan dan hutan (karhutla). Sistem Pemantau Air Lahan Gambut (Sipalaga) sudah menjadi sistem yang digunakan untuk merekam TMA kelembaban gambut, dan tingkat curah hujan. Penelitian ini menggunakan data Sipalaga berstasiun di Rimba Panjang. Namun data yang digunakan masih mengandungi banyak missing value yang harus ditangani. Penelitian ini berfokus untuk membandingkan metode rekonstruksi data TMA lahan gambut menggunakan Last Observation Carried Forward, Next Observation Carried Forward, Interpolation, Weighted Moving Average, Kalman filter, K-nearest Neighbor Imputation, dan Simple Averaging Ensemble. Data yang sudah direkonstruksi dievaluasi menggunakan Long-Short Term Memory. Hasil terbaik yang diperoleh adalah menggunakan Stacked LSTM dengan unit 64 dan 128, dengan metode Spline Interpolation menghasilkan MSE 0.00012, MAE 0.00822 dan R2 0.95316. Hasil penelitian ini diharapkan dapat membantu prediksi TMA lahan gambut sebagai peringatan dini karhutla di Indonesia., Peatlands are accumulations of dead plants that have fully decomposed over thousands of years that can store carbon, produce oxygen and regulate water levels. The groundwater level (GWL) is an important indicator for forest fires. The Sistem Pemantau Air Lahan Gambut (Sipalaga) has become a system used to record GWL, peatland moisture, and rainfall levels at peatland locations. This study will use data from Sipalaga stationed at Rimba Panjang. However, the data used still contains many missing values that must be addressed. This research will focus on comparing methods of reconstructing peatlands’ GWA data using Last Observation Carried Forward, Next Observation Carried Forward, Weighted Moving Average, Interpolation, Kalman filter, K-nearest Neighbor Imputation and Simple Averaging Ensemble. Reconstructed data will be evaluated with Long-Short Term Memory predictions. The best model made in this study is using Stacked LSTM with 64 and 128 units using Spline Interpolation resulting MSE 0.00012, MAE 0.00822 dan R2 0.95316. The results of this study are expected to help predict the TMA of peatlands as an early warning of forest and land fires in Indonesia.
Keyword: LSTM, forest fire, groundwater level, peatland, reconstruction

Judul: Pemurnian dan karakterisasi protein inhibitor RNA helikase virus hepatitis C
Abstrak: Hepatitis C virus (HCV) infection is the second most common chronic viral infection in the world with global prevalence. This infection is contribution to chronic hepatitis and liver cirrhosis, often leading to hepatocellular carcinoma. There is no effective drug and vaccine available against HCV. The current standard treatment, a combination of pegylated interferon-α with ribavirin, is limited by its suboptimal response rate in a significant patient population, side effects, and affordability. Therefore, development of effective drug is needed. One approach to find out the candidate of antiviral drug is to discover the inhibitor of enzyme that essential for virus replication, such as RNA helicase. Streptomyces chartreusis 5-095 produce the inhibitor of HCV RNA helicase. The aim of this study was to purify the protein inhibitor which has inhibition activity against ATPase activity of RNA helicase HCV from S. chartreusis 5-095. The supernatant of culture S. chartreusis 5-095 exhibited high inhibiton activity against ATPase activity of RNA helicase HCV (approximately 72%) at 10 days of incubation. The extracellular protein inhibitor was partially purified by procedures of precipitation with ammonium sulfate to 65% saturation and gel filtration (Sephadex G50) using methanol water solution (4:6) as an eluen. The purity of protein inhibitor was monitored by SDS PAGE. The protein inhibitor showed a relative molecular mass were estimated to be 14 kDa and increased the purity 23,08 fold than those of the crude protein. The specific activity and IC50 after purification were 13,1338 U/mg and 0,0767 mg/mL, respectively. The activity of purified protein inhibitor showed its stability over pH range from 5-8 and optimum at pH 7,4. The inhibition activity showed its stability up to 70% at temperature 100 oC. The purified protein inhibitor relatively stable its activity at storage in the range of -20 oC to room temperature for 35 days.
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