Perbedaan Hasil Belajar Siswa Yang Menggunakanmodel Pembelajaran Kooperatif Tipe Team-assisted Individualization Dengan Pembelajaran Konvensional Pada Mata Pelajaran Ekonomi Siswamtsn Model Padang

Penelitian ini dilatarbelakangi oleh rendahnya hasil belajar siswa kelas VIII MTsN Model Padang,dalam mata pelajaranIPS Terpadu (Ekonomi). Hal ini terlihat dari rendahnya nilai rata-rata ujian mid semester genapsiswa kelas VII yaitu56.47. Rendahnya hasil belajar ekonomi disebabkan oleh siswa kurang aktif dan kurang berpartisipasi dalam proses pembelajaran, siswa kurang berani bertanya dan menjawab pertanyaan guru. Salah satu usaha yang dapat dilakukan untuk meningkatkan hasil belajar ekonomi adalah dengan menerapkanmodel pembelajaran kooperatif tipe Teasm-Assisted Individualization. Penelitian ini bertujuan untuk mengetahui perbedaan hasil belajar antara penggunaan model pembelajaran kooperatif tipe Team-Assisted Individualizationdengan model pembelajaran konvensional terhadap hasil belajar ekonomi siswa kelas VIII MTsN Model Padang. Hipotesis penelitian adalah terdapat perbedaan hasil belajar siswa MTsN Model Padang, dalam mata pelajaran ekonomi yang diajarkan dengan model kooperatif tipe team-assisted individualization dengan model konvensional.Jenis penelitian yang digunakan adalah penelitian eksperimen. Populasi penelitian adalah seluruh siswa/i kelas VIII MTsN Model Padang, yang berjumlah 427 orang, dari 12 kelas yang terdaftar pada tahun ajaran 2012/2013. Pengambilan sampel dilakukan secara purpoosive sampling, dan terpilihkelas VIII4 sebagai kelas eksperimen dan kelas VIII11 sebagai kelas kontrol.Teknik yang digunakan untuk menguji hipotesis adalah uji kesamaan dua rata-rata dua arah melalui uji-t. Uji t dilakukan karena data terdistribusi normal dan kelompok data mempunyai varians yang homogen.Rata- rata hasil belajar aspek kognitif pada kelas eksperimen adalah 86.33 dan pada kelas kontrol adalah 71.71. Hasil analisis data melalui uji t diperoleh, thitung = 5,103 dan ttabel = 1,67. Nilai thitung >ttabel, yaitu 5,103>1,67 dengan = 0.05. Keputusannya adalah H0 ditolak dan Ha diterima. Model pembelajaran kooperatif tipeteam-assisted individualization dapat meningkatkan hasil belajar ekonomidi kelas VIII MTsN Model Padang pada taraf kepercayaan 95 %. Maka dapat disimpulkan bahwa terdapat perbedaan hasil belajar ekonomi siswa yang menggunakan model pembelajaran kooperatif tipe team-assisted individualization dengan hasil belajar ekonomi siswa yang menggunakan model pembelajaran konvensional

Optimal Model for Path Loss Predictions using Feed-Forward Neural Networks

[EN] In this paper, an optimal model is developed for path loss predictions using the Feed-Forward Neural Network (FFNN) algorithm. Drive test measurements were carried out in Canaanland Ota, Nigeria and Ilorin, Nigeria to obtain path loss data at varying distances from 11 different 1,800 MHz base station transmitters. Single-layered FFNNs were trained with normalized terrain profile data (longitude, latitude, elevation, altitude, clutter height) and normalized distances to produce the corresponding path loss values based on the Levenberg-Marquardt algorithm. The number of neurons in the hidden layer was varied (1-50) to determine the Artificial Neural Network (ANN) model with the best prediction accuracy. The performance of the ANN models was evaluated based on different metrics: Mean Absolute error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), standard deviation, and regression coefficient (R). Results of the machine learning processes show that the FNN architecture adopting a tangent activation function and 48 hidden neurons produced the least prediction error, with MAE, MSE, RMSE, standard deviation, and R values of 4.21 dB, 30.99 dB, 5.56 dB, 5.56 dB, and 0.89, respectively. Regarding generalization ability, the predictions of the optimal ANN model yielded MAE, MSE, RMSE, standard deviation, and R values of 4.74 dB, 39.38 dB, 6.27 dB, 6.27 dB, and 0.86, respectively, when tested with new data not previously included in the training process. Compared to the Hata, COST 231, ECC-33, and Egli models, the developed ANN model performed better in terms of prediction accuracy and generalization ability.This work was supported by Covenant University [grant number CUCRID-SMARTCU-000343].Popoola, SI.; Adetiba, E.; Atayero, AA.; Faruk, N.; Tavares De Araujo Cesariny Calafate, CM. (2018). Optimal Model for Path Loss Predictions using Feed-Forward Neural Networks. 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(2017, July 5–7). C-BRIG: A network architecture for real-time information exchange in smart and connected campuses. In Lecture notes in engineering and computer science: Proceedings of the world congress on engineering 2017 (pp. 398–401). London.Medeisis, A., & Kajackas, A. (s. f.). On the use of the universal Okumura-Hata propagation prediction model in rural areas. VTC2000-Spring. 2000 IEEE 51st Vehicular Technology Conference Proceedings (Cat. No.00CH37026). doi:10.1109/vetecs.2000.851585Mohtashami, V., & Shishegar, A. A. (2012). Modified wavefront decomposition method for fast and accurate ray-tracing simulation. IET Microwaves, Antennas & Propagation, 6(3), 295. doi:10.1049/iet-map.2011.0264Nimavat, V. D., & Kulkarni, G. (2012). Simulation and performance evaluation of GSM propagation channel under the urban, suburban and rural environments.Paper presented at the communication, information & computing technology (ICCICT), 2012 international conference on.. O. F. O. (2014). RADIO FREQUENCY OPTIMIZATION OF MOBILE NETWORKS IN ABEOKUTA, NIGERIA FOR IMPROVED QUALITY OF SERVICE. International Journal of Research in Engineering and Technology, 03(08), 174-180. doi:10.15623/ijret.2014.0308027Phillips, C., Sicker, D., & Grunwald, D. (2013). A Survey of Wireless Path Loss Prediction and Coverage Mapping Methods. IEEE Communications Surveys & Tutorials, 15(1), 255-270. doi:10.1109/surv.2012.022412.00172Popoola, S. I., Atayero, A. A., Badejo, J. A., John, T. M., Odukoya, J. A., & Omole, D. O. (2018). Learning analytics for smart campus: Data on academic performances of engineering undergraduates in Nigerian private university. Data in Brief, 17, 76-94. doi:10.1016/j.dib.2017.12.059Popoola, S. I., Atayero, A. A., & Faruk, N. (2018). Received signal strength and local terrain profile data for radio network planning and optimization at GSM frequency bands. Data in Brief, 16, 972-981. doi:10.1016/j.dib.2017.12.036Popoola, S. I., Atayero, A. A., Faruk, N., & Badejo, J. 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Analysis and Improvement of Indoor Multiple-Input–Multiple-Output Path Loss Prediction Model for 2.4-Ghz 802.11n Network

In telecommunication systems, indoor propagation models have been addressed by many researchers in different approaches for signal prediction purposes. However, the use of those models is limited at different environments and conditions. Enhancement of the models is needed since the environment and mobile technologies are increasing drastically and need to suit the current generation and satisfy mobile users. The 802.11n network features multiple-input-multiple-output, which is not presented in any previous wireless local area network standards; it is considered imperative to figure out a suitable prediction model for the 802.11n network. In this article, development of indoor path loss prediction model for the IEEE 802.11n network at 2.4 GHz is presented. The work based on simulation and measurement. Validation of the developed model has been tested at different office buildings, and it showed good accuracy and consistency with a mean error of 2.21 dB and a standard deviation of 2.02 dB. This new model is found to be suitable for predicting indoor signal loss in an 802.11n wireless local area network system and is useful for telecommunication providers to improve their service for better capacity and mobile user satisfaction

Microwave and millimeter wave technology, path loss model development for indoor signal loss

This study focuses on developing an indoor empirical path loss prediction model for 802.11n network at 2.4 GHz. As 802.11n features Multiple-Input Multiple-Output (MIMO) which is not present in any previous wireless local area network (WLAN) standards, it is considered imperative to figure out a suitable prediction model for 802.11n network. Path loss exponent n values were calculated using regression fitting method based on data collected on site. Measurement comparisons between several established prediction models with the actual measurements are taken at an academic building to identify which model gives the best estimation result. The best model produced a mean error of 6.46 dB and would then be further optimized through formula modifications to increase signal prediction accuracy. Prediction results of the optimized model showed that the mean error is reduced to 3.27 dB. Validation of the optimized model has been conducted in a different office building and the prediction results still showed good accuracy, with a mean error of 3.38 dB. This new optimized model is named as Solah's Model and is recommended for predicting indoor signal loss in 802.11n WLAN, especially in assisting network deployment, migration and management

Pemberi Makan Otomatis pada Kucing Menggunakan Raspberry Pi Berbasis Android

Keep a pet at home takes time and effort. For people who have very dense flurry of activity certainly keep a pet such as a cat would be very hard to do. A Raspberry Pi microcontroller is designed for the purpose of automatic feeding so it is easy to use. The workings of the tool are automatic scheduling using an Android-based smartphone so that the servo motor will open and close so that the cat food is taken out into the food container that has been provided. By using an Android-based smartphone, the feeding schedule can be set by the hour for each funnel. Equipped with a buzzer as a reminder of cat owners if the available food stock is low and must be immediately refilled. The programming language used is Python language. Based on testing and performance of "Automatic Cat Feeding Using Raspberry Pi Android Based" has shown results in accordance with the design that is able to open and close the funnel that fills the cat food container with a servo motor automatically by setting a predetermined time

Optimised COST-231 Hata Models for WiMAX path loss prediction in suburban and open urban environments

In Malaysia, the incumbent WiMAX operator utilises the bands of 2360-2390MHz to provide broadband services. Like all Radio Frequency (RF), WiMAX is susceptible to path loss. In this paper, field strength data collected in Cyberjaya, Malaysia is used to calculate the path loss suffered by the WiMAX signals. The measured path loss is compared with the theoretical path loss values estimated by the COST-231 Hata model, the Stanford University Interim (SUI) model and the Egli model. The best model to estimate the path loss based on the path loss exponents was determined to be the COST-231 Hata model. From this observation, an optimised model based on COST-231 Hata parameters is developed to predict path loss for suburban and open urban environments in the 2360-2390MHz band. The optimised model is validated using standard deviation error analysis, and the results indicate that the new optimised model predicts path loss in both suburban and open urban environments with very low standard deviation errors of less than 4.3dB and 1.9dB respectively. These values show that the model optimisation was done successfully and that the new optimised models will be able to determine the path loss suffered by the WiMAX signals more accurately. The optimised model may be used by telecommunication providers to improve their service

Path loss model optimization for urban outdoor coverage using Code Division Multiple Access (CDMA) system at 822MHZ

The study of this work is to develop an optimized path loss model for urban coverage in Code Division Multiple Access (CDMA) system based on the existing models and empirical measurements. The Okumura's model is chosen as a reference for optimized path loss model development based on the smallest mean relative error compared to the measured path loss. A new empirical model is developed from Okumura's model and empirical measurements by regression fitting method. Okumura's model will be optimized by using this new empirical model to achieve the smallest mean relative error. The optimized Okumura's model is implemented in the path loss calculation during the validation process. It is found to be more accurate with up to 6.67% smaller mean relative error obtained. Thus, this optimized Okumura's model is successfully improved and would be more reliable to be applied in the Malaysia CDMA system for urban path loss calculation

Road pavement density analysis using a new non-destructive ground penetrating radar system

Density is an important parameter to determine the strength of road, and it will ensure the safety of the use as well as maintaining the quality of road pavement. In this paper, the validation of GPR mixture model based on the microwave nondestructive free space method to determine the density of road pavement typed Hot Mix Asphalt (HMA) will be presented. The frequency range of operation used is 1.7-2.6 GHz. The attenuation is a major factor for gathering the density of road pavement predictably. The existing mixture model has been used to produce simulation data for determining the predicted complex permittivity and attenuation due to various densities of road pavement. The GPR laboratory measurement is performed where the measured attenuation due to various densities was obtained. The comparison results between measurement and simulation were investigated, and the relative errors in between were calculated to see the performance of the model. The best performance of mixture model was selected in the optimization technique due to the smallest mean error. An improved attenuation formula or optimized mixture model was obtained from the optimization technique to produce the better model. The finding from the optimization process suggested that three additional constant parameters which are volume factor, permittivity factor and attenuation factor need to be included to improve the existing mixture model. The optimized mixture model is introduced as GPR mixture model in this work. The validation process at field test had been conducted to evaluate the performance of optimized GPR model and produce the error range from 3.3% and 4.7%. At the end of this project, the GPR mixture model can be used as a calibration curve where the values of predicted density of a given real road pavement can be read directly once the attenuation values are known

Soil density prediction tool using microwave ground penetrating radar

In this paper, a microwave surface reflection method is proposed to analyze the effect of soil density with its electrical properties using ground penetrating radar (GPR) principal. Three types of soil samples are chosen for the analysis of this project, namely sandy, loamy and clay. The work is based on measurement, simulation and model development. In the analysis, it is found that the average error percentages from the three developed models are 0.04%, 0.21% and 0.74% for sandy, loamy and clay soil, respectively. The effect of soil density with its electrical characteristics in terms of permittivity, propagation velocity and two-way wave travel time are also discussed. At the end of this paper, a soil density prediction tool is developed using the empirical models introduced consisting the density and attenuation for each soil sample at frequency range of 1.7 GHz to 2.6 GHz