Eksperimentasi Model Pembelajaran Kooperatif Tipe Teams Games Tournament (Tgt) Berbantu Media Audio-visual Ditinjau Dari Kemampuan Komunikasi Matematis Pada Materi Segiempat

. The objective of this research was to investigate the effect of the learning models on the learning achievement in Mathematics viewed from the mathematical communication of the students. The learning models compared were the cooperative learning model of the TGT type with audio-visual media, the cooperative learning model of the TGT type, and the direct learning model. This research was the quasi experimental. Its population was all of the students in Grade VII of State Junior Secondary Schools of Pati regency in Academic Year 2013/2014. The taken samples used technique stratified cluster random samples consisted of 283 students, consisted 3 class, namely 97 students in Experimental Class I, 94 students in Experimental Class II, and 92 students in Control Class. The instruments to gather the data were test of achievement in Mathematics on the learning material of Quadrangle, and test of mathematical communication. The proposed hypotheses of the research were analyzed by using the two way analysis of variance with unbalanced cells. The results of the research were as follows: 1) The TGT type with audio-visual media results in a better learning achievement than the TGT type and the direct learning model, and the TGT provided a better learning achievement of direct learning model, 2) The students with the high mathematical communication was a better learning achievement than those with the moderate and low mathematical communication, while the students with moderate mathematical communication was the same learning achievement as those with the low mathematical communication, 3) a) In the students with the high mathematical communication, the TGT type with audio-visual media results in the same learning achievement as the cooperative learning model of the TGT type, the TGT type with audio-visual media or the TGT type results in a better learning achievement than the direct learning model, 3) b) the category of moderate and low mathematical communication, the TGT type with audio-visual media provided equal learning performance both with models the TGT type and direct learning model, 4) a) In the TGT type with audio-visual media, students who was a high mathematical communication was the same learning achievement as those with the moderate mathematical communication, the students with the moderate mathematical communication was the same learning achievement as those with the low mathematical communication, and students who with the high mathematical communication was a better learning achievement than those with the low mathematical communication, 4) b) In the TGT type, students with the high mathematical communication was a better learning achievement than those with the moderate or low mathematical communication, and students with the moderate mathematical communication was the same learning achievement as those with the low mathematical communication, 4) c) In the direct learning model, students with the high mathematical communication was the same learning achievement as those with the moderate or low mathematical communication

Prevalence of diabetes distress and associated factors among patients with diabetes using antihypertensive medications in community health centres in Bandung City, Indonesia

Diabetes distress is common among patients with type 2 diabetes mellitus (T2DM), which remains unrecognized in primary care settings. A higher level of diabetes distress was found among T2DM patients with comorbidities. The objectives of this study are to assess the prevalence rate of diabetes distress and its association with sociodemographic factors among T2DM patients using antihypertensive medication in Bandung City, Indonesia. An observational cross-sectional survey was performed in six community health centres in Bandung City, Indonesia, among T2DM patients aged at least 18 years who were using antihypertensive medications. Diabetes distress subscales (emotional, regimen, interpersonal, and physician-related distress) were evaluated using the validated Diabetes Distress Scale. Pearson χ2 and Mann–Whitney tests were performed to assess the associations of patients’ sociodemographic factors (age, gender, insurance type, education, and duration since diagnosed with diabetes and hypertension) with diabetes distress. Of 105 patients who participated and completed the survey (response rate 93.8%), most of them were female and were aged 60-69 years. A total of 38 patients (36.2%) had moderate-high diabetes distress with emotional (56.2%) and regimen (53.3%) distress as the most commonly reported distress. Moderate-high emotional and regimen diabetes distress were significantly higher among the elderly (p 0.014) and patients who could not afford to pay the health insurance premium (p 0.012). Emotional and regimen distress as dominant forms of diabetes distress was observed among T2DM patients using antihypertensive medications. A routine diabetes distress assessment is needed in T2DM patients with comorbidity in primary care settings

Prevalence of diabetes distress and associated factors among patients with diabetes using antihypertensive medications in community health centres in Bandung City, Indonesia

Diabetes distress is common among patients with type 2 diabetes mellitus (T2DM), which remains unrecognized in primary care settings. A higher level of diabetes distress was found among T2DM patients with comorbidities. The objectives of this study are to assess the prevalence rate of diabetes distress and its association with sociodemographic factors among T2DM patients using antihypertensive medication in Bandung City, Indonesia. An observational cross-sectional survey was performed in six community health centres in Bandung City, Indonesia, among T2DM patients aged at least 18 years who were using antihypertensive medications. Diabetes distress subscales (emotional, regimen, interpersonal, and physician-related distress) were evaluated using the validated Diabetes Distress Scale. Pearson χ2 and Mann–Whitney tests were performed to assess the associations of patients’ sociodemographic factors (age, gender, insurance type, education, and duration since diagnosed with diabetes and hypertension) with diabetes distress. Of 105 patients who participated and completed the survey (response rate 93.8%), most of them were female and were aged 60-69 years. A total of 38 patients (36.2%) had moderate-high diabetes distress with emotional (56.2%) and regimen (53.3%) distress as the most commonly reported distress. Moderate-high emotional and regimen diabetes distress were significantly higher among the elderly (p 0.014) and patients who could not afford to pay the health insurance premium (p 0.012). Emotional and regimen distress as dominant forms of diabetes distress was observed among T2DM patients using antihypertensive medications. A routine diabetes distress assessment is needed in T2DM patients with comorbidity in primary care settings

Principal component analysis-based data clustering for labeling of level damage sector in post-natural disasters

Post-disaster sector damage data is data that has features or criteria in each case the level of damage to the post-natural disaster sector data. These criteria data are building conditions, building structures, building physicals, building functions, and other supporting conditions. Data on the level of damage to the post-natural disaster sector used in this study amounted to 216 data, each of which has 5 criteria for damage to the post-natural disaster sector. Then the 216 post-disaster sector damage data were processed using Principal Component Analysis (PCA) to look for labels in each data. The results of these labels will be used to cluster data based on the value scale of the results of data normalization in the PCA process. In the data normalization process at PCA, the data is divided into 2 components, namely PC1 and PC2. Each component has a variance ratio and eigenvalue generated in the PCA process. For PC1 it has a variance ratio of 85.17% and an eigenvalue of 4.28%, while PC2 has a variance ratio of 9.36% and an eigenvalue of 0.47%. The results of the data normalization are then made into a 2-dimensional graph to see the visualization of the PCA results data. The result is that there is 3 data cluster using a value scale based on the PCA results chart. The coordinate value (n) of each cluster is cluster 1 (n<0), cluster 2 (0 ≤n <2), and cluster 3 (n≥2). To test these 3 groups of data, it is necessary to conduct trials by comparing the original target data, there are two experiments, namely testing the PC1 results with the original target data, and the PC2 results with the original target data. The result is that there are 2 updates, the first is that the distribution of PC1 data is very good in grouping the data when comparing the distribution of data with PC2, because the variance ratio and eigenvalue values of PC1 are greater than PC2. While second, the results of testing the PC1 data with the original target data produce good data grouping, because the original target data which has a value of 1 (slightly damaged) occupies the coordinates of cluster 1 (n<0), while the original target data which has a value of 2 (damaged moderately) occupies cluster 2 coordinates (0 ≤n <2), and for the original target data the value 3 (heavily damaged) occupies cluster 3 coordinates (n≥2). Therefore, it can be concluded that PCA, which so far has been used by many studies as feature reduction, this study uses PCA for labeling unsupervised data so that it has an appropriate data label for further processing

Penerapan Teknik SURF Pada Forensik Citra Untuk Analisa Rekayasa Foto Digital(Application of SURF Technique in Image Forensic for Digital Photo Engineering Analysis)

Perkembangan teknologi citra digital yang semakin maju membuat mudahnya merekayasa suatu citra. Perubahan pada citra membuat informasi yang disampaikan menjadi berubah dan rawan dimanfaatkan menjadi aksi kejahatan digital. Salah satu cara menyelesaikan kasus kejahatan digital ini menggunakan forensik citra. Penelitian ini menggunakan metode teknik pendeteksian rekayasa foto digital Speeded Up Robust Features (SURF). Tahapan pertama melakukan mengakuisisi data kemudian melakukan proses ekstraksi dengan hasil akuisisi tersebut. Hasil yang sudah didapat dianalisis menggunakan algoritma SURF, algoritma ini mendeteksi adanya manipulasi pada foto dengan tidak adanya keypoint pada beberapa objek yang tidak terhubung. Rekayasa foto digital dapat dipastikan menggunakan perbandingkan kualitas citra pada setiap foto dengan perhitungan MSE, RMSE dan PSNR. Hasil perbandingan nilai kualitas didapat perbedaan antara nilai kualitas pada foto asli dan foto manipulasi, hal tersebut dapat membuktikan bahwa foto tersebut sudah dimanipulasi

Findings from the Indonesian family life survey on patterns and factors associated with multimorbidity

Abstract The prevalence of multimorbidity tends to increase with age, but it is now also reported in the middle-aged population, which has a negative impact on healthcare systems and health outcomes. This study aims to analyze the patterns and factors associated with multimorbidity in Indonesia. This national cross-sectional population-based survey used publicly available data from the Indonesian Family Life Survey (IFLS-5) for 2014 among middle-aged (40–59 years old) and elderly (≥ 60 years old) respondents. Information on all chronic diseases was assessed using a self-reported questionnaire. Sociodemographic and health-related behavioral factors were obtained from self-reported data. Binary logistic regression analysis was used to identify the factors associated with multimorbidity. Adjusted odds ratios (AORs) with 95% confidence intervals (CIs) were reported. The study recruited 11,867 respondents. The prevalence of multimorbidity was 18.6% (95% CI 17.9–19.3) with which 15.6% among middle age (95% CI 14.95–16.25) and 24.9% among the elderly (95% CI 24.12–25.68). Hypertension was the most commonly reported disease (23.2%) in all combinations of multimorbidity and among all age groups. Socio-demographic factors: elderly (AOR: 1.66; 95% CI 1.46–1.89), female (AOR: 1.42; 95% CI 1.20–1.69), living in the urban area (AOR: 1.22; 95% CI 1.09–1.38), higher educational level (AOR: 2.49; 95% CI 1.91–3.26), unemployed (AOR: 1.63; 95% CI 1.44–1.84), and higher economic level (AOR: 1.41; 95% CI 1.18–1.68) were associated with multimorbidity. Poor health behavior factors: being former smokers (AOR: 2.03; 95% CI 1.65–2.51) and obesity (AOR: 1.53; 95% CI 1.35–1.75) were also associated with multimorbidity. The prevalence of multimorbidity in the middle-aged and elderly population in Indonesia is relatively high, particularly in populations with poor health behaviors. Therefore, healthcare professionals should integrate more patient-specific factors when designing and implementing tailored interventions to manage multimorbidity in Indonesia

SASSD: A smart assessment system for sector damage post-natural disaster using artificial neural networks

Smart Assessment System Sector Damage (SASSD) is an intelligent system for assessing the level of sector damage after natural disasters based on Machine Learning (ML) by applying the Artificial Neural Network (ANN) method. SASSD uses forward propagation in ANN. To measure the level of accuracy of the forward propagation algorithm, it is necessary to have a trial method using data pattern modelling. The optimal accrual level value can be achieved by applying 15 data pattern models and changing the structural values of the forward propagation, namely the hidden layer, and epoch. We used 100 training data and 50 testing data at the experimental stage. The training data is the processed data from Decision Support System (DSS), while the training data contains the level of damage to the sector after natural disasters collected by surveyors. The trial results demonstrate the E5 data pattern model's ideal accuracy rate of 97 percent with a Mean Squared Error (MSE) value of 0.06 and a Mean Absolute Percentage error (MAPE) of 3 percent. This model uses five hidden layers and 125 epochs. The trial results demonstrate the E5 data pattern model's ideal accuracy rate of 97 % with an MSE value of 0.06 and a MAPE of 3 %. This model uses five hidden layers and 125 epochs. Thus, the SASSD can use the 15th data pattern model (E5) to obtain optimal and accurate results

SASSD: A smart assessment system for sector damage post-natural disaster using artificial neural networks

Smart Assessment System Sector Damage (SASSD) is an intelligent system for assessing the level of sector damage after natural disasters based on Machine Learning (ML) by applying the Artificial Neural Network (ANN) method. SASSD uses forward propagation in ANN. To measure the level of accuracy of the forward propagation algorithm, it is necessary to have a trial method using data pattern modelling. The optimal accrual level value can be achieved by applying 15 data pattern models and changing the structural values of the forward propagation, namely the hidden layer, and epoch. We used 100 training data and 50 testing data at the experimental stage. The training data is the processed data from Decision Support System (DSS), while the training data contains the level of damage to the sector after natural disasters collected by surveyors. The trial results demonstrate the E5 data pattern model's ideal accuracy rate of 97 percent with a Mean Squared Error (MSE) value of 0.06 and a Mean Absolute Percentage error (MAPE) of 3 percent. This model uses five hidden layers and 125 epochs. The trial results demonstrate the E5 data pattern model's ideal accuracy rate of 97 % with an MSE value of 0.06 and a MAPE of 3 %. This model uses five hidden layers and 125 epochs. Thus, the SASSD can use the 15th data pattern model (E5) to obtain optimal and accurate results