ABSTRAK Imbas Covid-19 dalam dunia pendidikan memaksa pendidik untuk berinovasi dalam mengembangkan dan mengalihkan proses pembelajaran konvensional ke pembelajaran jarak jauh. Salah satu platform pembelajaran jarak jauh adalah google classroom . Tujuan penelitian ini mendeskripsikan keefektifan pembelajaran daring melalui google classroom yang ditinjau dari hasil belajar siswa. Penelitian ini berjenis quasi experiment yang dirancang memakai one group pretest-postest design. Sampel penelitian ini menggunakan seluruh siswa di kelas 11 yang berjumlah 31 orang pada salah satu sekolah di Kabupaten Purwakarta, kemudian data diolah dengan uji paired sample t test serta N-Gain . Hasil penelitian menggambarkan bahwa pembelajaran jarak jauh menggunakan google classroom apabila ditinjau dari hasil belajar siswa mencapai persentase 58.3% (cukup efektif menurut tafsiran N-Gain ). Hal ini berkaitan dengan pelaksanaan pembelajaran dibandingkan sebelum menggunakan google classroom mengalami peningkatan rata-rata hasil belajar sebesar 22.6%. Namun, di sisi lain ditemukan beberapa kelemahan dalam pembelajaran tersebut seperti penguasaan fitur, siswa tidak memiliki paket data, serta jangkauan sinyal yang tidak merata. Kesimpulan penelitian menunjukkan bahwa penggunaan Google classroom cukup efektif dalam pembelajaran sejarah kebudayaan Islam ditinjau dari hasil belajar siswa. Kata kunci : google classroom , hasil belajar, sejarah kebudayaan Islam. ABSTRACT The impact of current covid-19 has succeeded in changing various educational aspect, thus forcing educators to develop and diverting conventional learning processes into distance learning. One of the distance learning platforms is google classroom. During the Covid-19 pandemic, this application became of its own urgency because of the many features presented to support learning. This research employed a quasi-type experiment design by using a one-group pretest-postest design, which aims to determine the google classroom's effectiveness on Islamic Cultural History learning from the perspective of student learning outcomes. This research employed all students in grade 11 with total of 31 people at one of the Madrasah Aliyah in Purwakarta Regency. The the data were processed by using the paired-sample t-test and N-Gain. The results of the research illustrate that distance learning by using google classroom when viewed from student learning outcomes, reaches a percentage of 58.3% (quite effective according to the interpretation of N-Gain). The result related to the implementation of learning compared to before using Google Classroom, showed an increase in average learning outcomes of 22.6%. However, several weaknesses were found in the learning process, such as mastery of features, students not having data packages, and uneven signal coverage. The conclusion of this research showed that the use of Google classroom is quite effective in learning the history of Islamic culture in terms of student learning outcomes. Keywords: google classroom, history of Islamic culture, learning outcomes

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Efektifitas pembelajaran sejarah kebudayaan Islam melalui google classroom ditinjau dari hasil belajar siswa

Rainfall forecasting model using data Global Circular Model (GCM) with Statistical Downscaling technique has a fairly high accuracy. However, missing local climate information poses a constraint in data analysis and forecasting. Missing value imputation is one solution that can be used. Kalman Filter Imputation and State Space Model Arima are imputation methods that operate recursively where there is an update of prediction values when data updates occur. This study aimed to find the best model to use for missing value imputation with small imputation errors. The results of the missing value imputation were used to obtain the best statistical downscaling model on a 3 × 3 to 12 × 12 grid. The research was conducted on the daily rainfall data of Kupang City with 17% missing values and 8% unusual data at the Eltari Meteorological observation station, Kupang city. The average daily rainfall data in East Nusa Tenggara Province were utilized as a reference for the characteristics of rainfall data at the Kupang City observation station. The best missing value imputation was obtained by using the Arima State-Space Model (2,1,1) with a Root Mean Square Error (RMSE) of 0.930 and the model was statistical downscaling best obtained on a grid 6 × 6 with a Mean Absolute Percentage Error (MAPE) of 1.3 % and the number of PCs 11.

Handling Missing Values and Unusual Observations in Statistical Downscaling Using Kalman Filter

\n The intercomparison of streamflow simulation and the prediction of discharge using various renowned machine learning techniques were performed. The daily streamflow discharge model was developed for 35 observation stations located in a large-scale river basin named Cauvery. Various hydrological indices were calculated for observed and predicted discharges for comparing and evaluating the replicability of local hydrological conditions. The model variance and bias observed from the proposed extreme gradient boosting decision tree model were less than 15%, which is compared with other machine learning techniques considered in this study. The model Nash–Sutcliffe efficiency and coefficient of determination values are above 0.7 for both the training and testing phases which demonstrate the effectiveness of model performance. The comparison of monthly observed and model-predicted discharges during the validation period illustrates the model's ability in representing the peaks and fall in high-, medium-, and low-flow zones. The assessment and comparison of hydrological indices between observed and predicted discharges illustrate the model's ability in representing the baseflow, high-spell, and low-spell statistics. Simulating streamflow and predicting discharge are essential for water resource planning and management, especially in large-scale river basins. The proposed machine learning technique demonstrates significant improvement in model efficiency by dropping variance and bias which, in turn, improves the replicability of local-scale hydrology.

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Intercomparing the robustness of machine learning models in simulation and forecasting of streamflow

Contribution of ordinal variables to short-term global solar irradiation forecasting for sites with low variabilities

Statistical downscaling (SD) is a statistical technique used to predict local scale rainfall based on global atmospheric circulation. The global scale climate variable used is precipitation from GCM (Global Circulation Model). However, the precipitation data of GCM outputs have a large dimension, giving rise to multicollinearity in the data. This problem is handled by the Principal Component Regression (PCR) method. In addition, the SD models have heterogeneous error variances. The dummy variable is added to the PCR models to solve the problem. Hierarchical (k-means) and non-hierarchical cluster techniques (average linkage, median linkage, and ward linkage) are used in modeling to determine rainfall data groups. Furthermore, the group formed is the basis of the formation of dummy variables. This study aims to estimate local rainfall data in Pangkep district as a salt-producing area in South Sulawesi. There are 4 dummy variables based on the 5 groups formed. Dummy variables are able to improve predictions from the PCR models. R2 values of the PCR-dummy models (ranging from 89.89% to 95.58%) are relatively higher than the PCR models (ranging from 55.87% to 57.61%). This result is also consistent with the model validation stage. The PCR-dummy models based on non-hierarchical cluster techniques (k-means) are better than the PCR-dummy models based on cluster hierarchy techniques. In general, the best model is the PCR-dummy model of the non-hierarchical cluster technique (k-means ) and involves 4 main components.

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Pemodelan statistical downscaling dengan peubah dummy berdasarkan teknik cluster hierarki dan non- hierarki untuk pendugaan curah hujan

Statistical downscaling (SD) analyzes relationship between local-scale response and global-scale predictors. The SD model can be used to forecast rainfall (local-scale) using global-scale precipitation from global circulation model output (GCM). The objectives of this research were to determine the time lag of GCM data and build SD model using PCR method with time lag of the GCM precipitation data. The observations of rainfall data in Indramayu were taken from 1979 to 2007 showing similar patterns with GCM data on 1st grid to 64th grid after time shift (time lag). The time lag was determined using the cross-correlation function. However, GCM data of 64 grids showed multicollinearity problem. This problem was solved by principal component regression (PCR), but the PCR model resulted heterogeneous errors. PCR model was modified to overcome the errors with adding dummy variables to the model. Dummy variables were determined based on partial least squares regression (PLSR). The PCR model with dummy variables improved the rainfall prediction. The SD model with lag-GCM predictors was also better than SD model without lag-GCM.

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Application of Principal Component Regression with Dummy Variable in Statistical Downscaling to Forecast Rainfall

The involvement of teachers in Bone Regency in using information and communication technology (ICT) to prepare teaching materials is very little or even never said, even though computer facilities and infrastructure are available in the computing lab. This activity aims to provide knowledge to Mathematics teachers about online learning Google Classroom and Geogebra. The use of Google Classroom will make learning more effective for teachers and students because learning is no longer limited by space and time, student can explore learning resources easily and utilize information technology properly. Likewise, Geogebra training is expected to overcome the difficulties of teachers in visualizing concept charts in mathematics dynamically. The target audience for community service is mathematics teachers who are members of the Mathematics MGMP in Bone Regency.

Peningkatan Kualitas Pembelajaran Matematika Bagi Guru SMA Melalui Media Google Classroom dan Geogebra

The port has become an important part of people's lives. On certain days there is an increase in the number of ship passengers which can slow down operational activities from the port, thus causing a buildup of passengers at the port. therefore, the port must be prepared to deal with the buildup of passengers at the port. Based on this, the researchers made a prediction or forecasting the number of ship passengers at Makassar Soekarno Hatta Port in the coming period to find out how much the estimated number of passengers at Makassar Soekarno Hatta Port. The results of these studies can be input to the PT. Pelabuhan Indonesia IV (Persero ) Makassar if there will be a surge in passengers in the future period. researchers used the fuzzy method of high order chen time series in forecasting or prediction in this study . The researcher divides the data onto training and testing data . The results of the study using fuzzy time series with the best high order chen are that the second order produces MAPE error size of 0,143 , MSE 13470993,9 and MAE of 9478,52 . The result of prediction of testing data onto one period in the future is 52.608.

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Peramalan Jumlah Penumpang Kapal Laut Menggunakan Metode Fuzzy Runtun Waktu Chen Orde Tinggi: Forecasting the Number of Marine Ship Passengers Using the High Order Chen Time Series Fuzzy Method

Indonesia is a tropical country, which only has two seasons throughout the year, namely the dry season and the rainy season. Thus, it is likely that rain will continue to fall during the dry season, which has a serious impact on various sectors of life. General Circulation Model (GCM) is used to deal with climate change, but the GCM cannot conduct simulations well for local scale climate variables. Therefore, Statistical Downscaling (SD) is used to predict local scale rainfall in the district of Pangkep based on square GCM (CMIP5) 8 × 8 grid data. Modified jackknife ridge regression is used to overcome multicollinearity problems that occur in GCM-lag data. Three dummy variables were added as predictor variables for the model to overcome the heterogeneity of the various forms. SD model MJR dummy regression gives good results based on the coefficient of determination and high correlation with lower root mean square error and root mean square error prediction.

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Sitti Sahriman

Papers

Application of Principal Component Regression with Dummy Variable in Statistical Downscaling to Forecast Rainfall

Pemodelan statistical downscaling dengan peubah dummy berdasarkan teknik cluster hierarki dan non- hierarki untuk pendugaan curah hujan

Peningkatan Kualitas Pembelajaran Matematika Bagi Guru SMA Melalui Media Google Classroom dan Geogebra

Peramalan Jumlah Penumpang Kapal Laut Menggunakan Metode Fuzzy Runtun Waktu Chen Orde Tinggi: Forecasting the Number of Marine Ship Passengers Using the High Order Chen Time Series Fuzzy Method

Pemodelan Statistical Downscaling dengan Regresi Modifikasi Jackknife Ridge Dummy Berbasis K-means untuk Pendugaan Curah Hujan