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: Development of Time Series Data Preprocessing Model and Peat Groundwater Level Prediction Model with LSTM Algorithm
Abstrak: Gambut Indonesia dengan luas 13,43 juta hektar merupakan ekosistem basah yang sangat penting yang perlu dikelola dengan baik. Parameter yang sangat penting dalam pengelolaan gambut adalah tinggi muka air (TMA) sehingga informasi secara waktu nyata dengan sistem telemetri maupun pengukuran langsung di lapangan wajib dilakukan. Pengukuran telemetri TMA dilakukan dengan memasang alat ukur TMA berupa sensor. Data TMA gambut hasil perekaman sensor rentan terhadap missing value dan outlier. Masalah ini terjadi akibat kesalahan interpretasi sensor, kerusakan alat, kegagalan perekaman data, dan pengiriman data. Penelitian ini mengusulkan modul praproses data untuk mengatasi masalah missing value dengan enam pendekatan imputasi berdasarkan interpolasi dan Moving Average (MA), menangani outlier dengan Median Absolute Deviation (MAD), dan mengembangkan model prediksi TMA dengan LSTM. Model LSTM dirancang dengan dua skenario: satu layer LSTM (Skenario 1) dan dua layer LSTM (Skenario 2); dua jenis dimensi input (univariat dan multivariat); dan enam dataset hasil praproses. Data TMA yang digunakan berasal dari sistem telemetri dari web SIPALAGA periode 2019 hingga 2021. Atribut data meliputi TMA, curah hujan, dan kelembaban tanah. Tahap penelitian dimulai dengan pembuatan modul praproses data kemudian pembuatan model dan evaluasi. Ketersediaan data TMA tiap jam berkisar 76-93%, kecuali data pukul 06.00 sebesar 6%. Ditemukan 17% mengandung missing value. Kejadian missing value sebanyak 1135 kali dan jeda terpanjang dengan durasi 10 hari. Perbaikan missing value menghasilkan enam dataset baru yang lengkap. Selanjutnya keenam dataset dilakukan deteksi dan pembersihan data. Penanganan outlier menunjukkan 2% data TMA dan kelembapan tanah serta 14% data curah hujan terdeteksi sebagai outlier. Hasil prediksi TMA dengan metode LSTM dengan 24 kombinasi skenario menunjukkan model kumulatif terbaik untuk aspek dataset yaitu Dataset Simple Moving Average dan Dataset Linear Moving Average; aspek dimensi input multivariat; dan aspek arsitektur LSTM Model lengkap. Skor terbaik R2 0,96388; MAE 0,01159; dan MSE 0,00024 didapatkan dari kombinasi Dataset Simple Moving Average, dimensi input univariat dan arsitektur LSTM Model Skenario 2. Hasil penelitian ini diharapkan dapat memberikan informasi prediksi TMA gambut yang akurat dengan jangka waktu bulanan untuk menyusun strategi pencegahan kebakaran lahan dan pengelolaan ekosistem gambut., Indonesia's 13.43 million hectares of peat is a very important wet ecosystem. A very important parameter in peat management is the groundwater level (TMA), so real-time information using a telemetry system or direct measurement in the field is mandatory. Telemetry measurements are carried out by installing a groundwater level measuring instrument in the form of a sensor. Peat TMA data recorded by sensors are prone to missing values and outliers. These problems occur due to sensor misinterpretation, equipment damage, data recording failure, and data transmission. This study proposes a data preprocessing module with interpolation and moving average methods to overcome the missing value problem; the Median Absolute Deviation (MAD) method to overcome the outlier problem, and develop a long-term TMA prediction model using LSTM. The LSTM model is designed with two scenarios: one layer LSTM (Scenario 1) and two layers LSTM (Scenario 2); two types of dimensions (univariate and multivariate); and six pre-processed datasets. The TMA data used in this study come from the telemetry system from the SIPALAGA web for the period 2019 to
Keyword: groundwater level, long short-term memory, peat, preprocessing, prediction

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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