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: Inducible and constitutive synthesis of penicillinase by staphylococcus aureus
Abstrak: When tested quantitatively for the production of penicillinase, eighteen of one hundred and twenty-two naturally occurring penicillin-resistant staphylococci were classified as constitutive or microconstitutive. Fourteen of the constitutive strains were not typable, the other four were lysed by group III phage. The inducible and constitutive strains of Staphylococcus aureus were resistant to concentrations of mercuric chloride that inhibited strains sensitive to penicillin. Transfer of the penicillinase determinant by transduction from a microconstitutive strain to a sensitive strain resulted in recombinants that produced inducible penicillinase.
Keyword: Staphylococcus aureus, Staphylococcal penicillinase, Bacteriophage