Pola Perilaku Bermasalah Dan Rancangan Intervensi Pada Anak Tunalaras Tipe Gangguan Perilaku (Conduct Disorder) Berdasarkan Fungsctional Behavior Assesment

Anak dengan gangguan perilaku (conduct disorder) sering bermasalah perilaku seperti menentang, melanggar, agresif, berkelahi, dan sebagainya. Asesmen perilaku bermasalah perlu dilakukan sebagai dasar intervensi. Penelitian ini bertujuan untuk memaparkan prosedur Functional Behavior Assessment (FBA) sebagai salah satu pendekatan asesmen perilaku, menemukan pola perilaku bermasalah anak dengan gangguan perilaku hasil FBA, dan merumuskan rancangan intervensi berdasarkan hasil FBA. Penelitian studi kasus ini menggunakan pendekatan kualitatif dengan teknik analisa data diskriptif analitik. Prosedur FBA berdasarkan hasil penelitian adalah (1) mendeskripsikan profil dan karakteristik gangguan perilaku anak, (2) observasi dan analisa ABC perilaku bermasalah, dan (3) pengisian skala motivasi perilaku bermasalah yang menunjukkan perilaku agresif subjek karena tangible dan escape. Perilaku menolak pembelajaran subjek karena escape dan tangible. Pola perilaku bermasalah menunjukkan seluruh subjek sering melakukan agresif fisik dan verbal; pemicunya situasi tidak terstruktur, tidak diperhatikan, dan menginginkan sesuatu; dan konsekuensinya adalah terhindar dari tugas, diperhatikan, dan mendapatkan keinginan. Perilaku melanggar aturan pembelajaran/guru; pemicunya adalah situasi tidak terstruktur dan tidak menarik, serta menginginkan sesuatu; dan konsekuensinya adalah terhindar dari tugas, diperhatikan, dan mendapatkan keinginan. Rancangan intervensi adalah keterampilan sosial, manajemen diri, dan mengatasi masalah di sekolah sebagai target behaviors; strategi antecedents berupa pengaturan perilaku, pengaturan dan konsistensi kegiatan dan aturan di sekolah dan pemberian materi ajar yang kontekstual dan sesuai kemampuan anak; dan strategi consequences berupa penerapan konsekuensi perilaku yang ditetapkan pada strategu antecedents

Pengembangan Panduan Layanan Kesehatan Mental Berbasis Sekolah Bagi Anak Berkebutuhan Khusus

Penelitian ini bertujuan untuk memvalidasi model dan menghasilkan buku panduanlayanan kesehatan mental berbasis sekolah bagi anak berkebutuhan khusus. Penelitianmenggunakan pendekatan Research and Development. Subjek dalam penelitian ini adalahpraktisi kesehatan mental di sekolah, dan praktisi lain yang terlibat dalam layanankesehatan mental (psikolog dan dokter), dan pengguna (guru) di SLB di Daerah IstimewaYogyakarta. Data penelitian akan dikumpulkan melalui kuosioner dan Focus GroupDiscussion (FGD) serta akan dilakukan analisis secara deskriptif kualitatif serta kuantitatif.Rangkaian kegiatan penelitian ini menghasilkan produk akhir berupa buku panduanlayanan kesehatan mental berbasis sekolah bagi anak berkebutuhan khusus yang telahdiuji validasi oleh subjek penelitian. Penelitian selanjutnya diharapkan akan menjadipenelitian ujicoba produk dalam skala yang lebih besa

A comparison of gap-filling algorithms for eddy covariance fluxes and their drivers

The errors and uncertainties associated with gap-filling algorithms of water, carbon, and energy fluxes data have always been one of the main challenges of the global network of microclimatological tower sites that use the eddy covariance (EC) technique. To address these concerns and find more efficient gap-filling algorithms, we reviewed eight algorithms to estimate missing values of environmental drivers and nine algorithms for the three major fluxes typically found in EC time series. We then examined the algorithms' performance for different gap-filling scenarios utilising the data from five EC towers during 2013. This research's objectives were (a) to evaluate the impact of the gap lengths on the performance of each algorithm and (b) to compare the performance of traditional and new gap-filling techniques for the EC data, for fluxes, and separately for their corresponding meteorological drivers. The algorithms' performance was evaluated by generating nine gap windows with different lengths, ranging from a day to 365gd. In each scenario, a gap period was chosen randomly, and the data were removed from the dataset accordingly. After running each scenario, a variety of statistical metrics were used to evaluate the algorithms' performance. The algorithms showed different levels of sensitivity to the gap lengths; the Prophet Forecast Model (FBP) revealed the most sensitivity, whilst the performance of artificial neural networks (ANNs), for instance, did not vary as much by changing the gap length. The algorithms' performance generally decreased with increasing the gap length, yet the differences were not significant for windows smaller than 30gd. No significant differences between the algorithms were recognised for the meteorological and environmental drivers. However, the linear algorithms showed slight superiority over those of machine learning (ML), except the random forest (RF) algorithm estimating the ground heat flux (root mean square errors - RMSEs - of 28.91 and 33.92 for RF and classic linear regression - CLR, respectively). However, for the major fluxes, ML algorithms and the MDS showed superiority over the other algorithms. Even though ANNs, random forest (RF), and eXtreme Gradient Boost (XGB) showed comparable performance in gap-filling of the major fluxes, RF provided more consistent results with slightly less bias against the other ML algorithms. The results indicated no single algorithm that outperforms in all situations, but the RF is a potential alternative for the MDS and ANNs as regards flux gap-filling

Daily soil temperature modeling using ‘panel-data’ concept

<p>The purpose of this research was to predict soil temperature profile using ‘panel-data’ models. Panel-data analysis endows regression analysis with both spatial and temporal dimensions. The spatial dimension pertains to a set of cross-sectional units of observation. The temporal dimension pertains to periodic observations of a set of variables characterizing these cross-sectional units over a particular time-span. This study was conducted in <i>Khorasan-Razavi</i> Province, Iran. Daily mean soil temperatures for 9 years (2001–2009), in 6 different depths (5, 10, 20, 30, 50 and 100 cm) under bare soil surface at 10 meteorological stations were used. The data were divided into two sub-sets for training (parameter training) over the period of 2001–2008, and validation over the period of the year 2009. The panel-data models were developed using the average air temperature and rainfall of the day before (<math><mrow><msub><mi>T</mi><mrow><mi>d</mi><mo>−</mo><mn>1</mn></mrow></msub></mrow></math> and <math><mrow><msub><mi>R</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub></mrow></math>, respectively) and the average air temperature of the past 7 days (<i>T</i>_w) as inputs in order to predict the average soil temperature of the next day. The results showed that the two-way fixed effects models were superior. The performance indicators (<i>R</i>² <i>=</i> 0.94 to 0.99, RMSE = 0.46 to 1.29 and MBE = −0.83 and 0.74) revealed the effectiveness of this model. In addition, these results were compared with the results of classic linear regression models using <i>t</i>-test, which showed the superiority of the panel-data models.</p