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Bayesian Model Averaging Dan Geostatistical Output Perturbation Untuk Prakiraan Cuaca Jangka Pendek Terkalibrasi
Get PDFData prakiraan cuaca berbasis numerik, Numerical Weather Prediction (NWP), yang selama ini digunakan untuk membantu para forecaster masih belum mampu untuk menghasilkan prakiraan cuaca dengan akurat, termasuk untuk jangka pendek. Ensembel adalah gabungan beberapa metode prakiraan yang bertujuan untuk memperbaiki akurasi dan presisi prakiraan. Namun, ensembel seringkali masih bersifat underdispersive, yaitu prakiraan cuaca cenderung terpusat pada suatu nilai dengan varians yang rendah. Bayesian Model Averaging (BMA) adalah metode parametrik untuk mengkalibrasi prakiraan ensembel dan membentuk interval prediktif yang lebih reliabel. Namun, BMA tidak mempertimbangkan korelasi spasial dalam menghasilkan prakiraan cuaca terkalibrasi. Selain itu, beberapa parameter BMA tidak dapat diestimasi secara analitik dengan MLE. Tidak seperti BMA, Geostatistical Output Perturbation (GOP) mempertimbangkan korelasi spasial seluruh lokasi secara serentak. GOP memiliki parameter spasial yang berfungsi untuk memodifikasi hasil prakiraan cuaca sedemikian hingga mampu memanfaatkan informasi spasial. Suhu udara menjadi fokus penelitian karena memiliki hubungan yang cukup erat dengan elemen cuaca lainnya. Analisis yang digunakan untuk mengkalibrasi prakiraan suhu udara di delapan stasiun meteorologi Jabotabek adalah BMA dan GOP. Tahap awal dilakukan reduksi dimensi untuk tiap parameter NWP menggunakan Principal Component Analysis (PCA), sehingga prediktor untuk BMA adalah skor komponen utama sementara prediktor GOP adalah luaran NWP pada grid yang bersesuaian dengan stamet. Ensembel terdiri dari tiga metode statistik, yaitu regresi PLS, PCR, dan Ridge. Untuk periode training selama 30 hari, prakiraan suhu udara BMA di 8 stamet termasuk ke dalam kriteria baik-sedang. BMA juga mampu mengkalibrasi prakiraan suhu udara dimana coverage yang dihasilkan sudah mendekati standar 50%. Sementara, GOP masih menghasilkan prakiraan dengan bias yang cukup tinggi, bahkan tidak terkalibrasi karena coverage 75% yang masih bersifat underdispersive. Namun, jika pemodelan GOP tidak melibatkan stamet Citeko, maka akurasi dan presisi prakiraan menjadi lebih tinggi. Dapat disimpulkan bahwa akurasi dan presisi prakiraan BMA lebih tinggi daripada GOP.
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Massive Numerical Weather Prediction (NWP) currently utilized to aid the forecasters has not yet been able to produce the weather forecast accurately, including for the short-range one. Ensemble is a combination of several processing methods to improve the accuracy and precision. However, it still possesses underdispersive nature, that is the forecast tends to concentrate at a point with low variance. Bayesian Model Averaging (BMA) is parametric method to calibrate the ensemble prediction and create more reliable predictive interval. BMA producing the calibrated forecast, though, does not consider spatial correlation. Furthermore, some BMA parameters are not able to be estimated by MLE. Unlike BMA, Geostatistical Output Perturbation (GOP) reckons spatial correlation among many locations altogether. It owns spatial parameters modifying the forecast output such that being able to capture spatial information. Temperature being the interest tends to have relatively strong correlation with the other elements. Analysis applied to calibrate the temperature forecast at eight meteorological sites within Jabotabek are BMA and GOP. Firstly, dimension reduction of each NWP parameter is applied by Principal Component Analysis, after which BMA’s predictors are PC scores while GOP’s ones are NWP output of temperature at nearest grid corresponding to the interest site. The ensemble members consist of prediction of PLS, PCR, and Ridge regression. For training period over 30 days, BMA temperature forecasts at 8 sites classify into good and fair ones. It is able to calibrate the temperature forecast as well, of which the coverage comes closer to the standard 50%. Meanwhile, GOP yet produces the forecasts possessing quite high bias, even uncalibrated due to underdispersive nature indicated by the 75% coverage, less than the standard 90%. The accuracy and precision somehow improve quite significant without involving Citeko site. Based on some common assessment indicators of weather forecast, such as RMSE and CRPS, BMA is better than GOP in terms of accuracy and precision
Non-cash Payment Transaction Projection Using ARIMAX : Efect of Calendar
Get PDFAs the most Moslem country, economic activity in Indonesia is often parallel with the movement of Qamariah (lunar) calendar which is different with Gregorian calendar. Using calender variation, this research attempts to look for modified time series model for non-cash payment projection (forecast) aim. The result shows that calendar variation plays statistically significant role on non-cash payment, evidenced by significant payment in the month in which Eid Fitr occurs. The occurrence of Eid Fitr in the first and second week of the month is evidently characterized by increasing non-cash payment in one month earlier. The best model with highest accuracy for non-cash payment projection is ARIMAX(2,1,1) as it is able to capture the pattern, trend and fluctuation. It also suggests the peak of non-cash payment will be in December
Spatial Regression Models on Factors Influencing Regional Minimum Wages
Get PDFRegional minimum wages might well represent the economic development of a region. The most likely spotlight province regarding the wage determination issue is East Java. This work is intended to obtain the best regression model on factors influencing East Java's regencies/cities' minimum wages in terms of spatial approach. The methods are Spatial Autoregressive (SAR) and Spatial Error Model (SEM). This study aims to obtain the best spatial model based on the factors influencing the regional minimum wage in districts/cities in East Java and the mapping. The data source is secondary data from Statistics Indonesia (BPS) of East Java. It consists of several variables, namely the Regional Minimum Wage, Total Working Population, Gross Regional Domestic Product, Total Population, and percentage of Population with a minimum education of senior high school. It shows that two significant factors are the number of working civilians and the percentage of high school-college graduates, affecting regional minimum wages. It proves that minimum wages among regions in East Java are spatially correlated with a closed area. Spatial regressions are the better ones than classic ones since they have higher R-sq and satisfy assumptions. Meanwhile, the selected model is SAR rather than SEM as it has a smaller AIC and explains variation better in minimum regional wages. It is indicated that some regions need more care due to small regional wages
Regresi Binomial Negatif Bivariat untuk Pemodelan Kasus Konfirmasi dan Kasus Kematian akibat Covid-19 di Kalimantan
Get PDFCoronavirus disease (Covid-19) caused a pandemic severely affecting various sectors and paralyzed health services in Indonesia. As of June 2020, the percentage of Covid-19 confirmed cases in Kalimantan, the second largest island in Indonesia, contributed about 7% of the total national cases. In the same period, the percentage of Covid-19 deaths reached 12% of the national figure. This study used regression models to respond to bi-response count data consisting of Covid-19 confirmed cases and Covid-19 deaths in regencies/cities in Central Kalimantan and South Kalimantan provinces. This study compared the results of bivariate Poisson regression and bivariate negative binomial regression. There were thirteen predictors representing the determinants of health, social, economic, and demography indicators. The results showed that the prevalence of pneumonia had positive effect on Covid-19 confirmed cases and Covid-19 deaths. The percentage of elderly had negative effect on confirmed cases, while it had no significant effect on Covid-19 deaths. Bivariate negative binomial regression showed more satisfying performance on modeling Covid-19 cases and Covid-19 deaths jointly because it produced lower AIC and deviance than that of Poisson one. The negative bivariate model was also better than the Poisson one because it was able to overcome over-dispersion.
Coronavirus disease (Covid-19) menyebabkan pandemi yang berdampak parah pada berbagai sektor dan melumpuhkan pelayanan kesehatan di Indonesia. Pada akhir Juni 2020, persentase kasus konfirmasi Covid-19 di Kalimantan, pulau terbesar kedua di Indonesia, berkontribusi sekitar 7% dari total kasus nasional. Pada periode yang sama, persentase kasus kematian Covid-19 mencapai 12% dari angka nasional. Penelitian ini menggunakan model regresi untuk respon data cacah berganda yang terdiri atas kasus konfirmasi Covid-19 dan kasus kematian Covid-19 di kabupaten/kota pada Provinsi Kalimantan Tengah dan Provinsi Kalimantan Selatan. Penelitian ini membandingkan hasil pemodelan regresi Poisson bivariat dan regresi binomial negatif bivariat. Prediktor yang digunakan sebanyak tiga belas yang mewakili determinan dari indikator kesehatan, sosial, ekonomi, dan kependudukan. Hasil penelitian menunjukkan bahwa prevalensi pneumonia berpengaruh positif pada kasus konfirmasi dan kasus kematian Covid-19. Adapun persentase lansia berpengaruh negatif pada kasus konfirmasi, dan tidak signifikan berpengaruh pada kasus kematian Covid-19. Regresi binomial negatif bivariat menunjukkan kinerja yang lebih memuaskan dalam memodelkan kasus konfirmasi Covid-19 dan kasus kematian Covid-19 secara bersama karena menghasilkan AIC dan devians yang lebih rendah ketimbang regresi Poisson. Model bivariat negatif juga lebih baik daripada model Poisson karena mampu mengatasi over-dispersi
Machine learning survival analysis on couple time-to-divorce data
No full textMarriage life does not always last harmoniously and occasionally can lead to divorce. The trend for the last three years since 2019 shows that divorce cases in Palangka Raya occur with a fluctuating trend that has recently been increasing. This research used a machine learning method called Survival Support Vector Machine on the divorce dataset in Palangka Raya. This research developed a feature selection technique using backward elimination to determine the factors influencing the couple’s decision to have their divorce registered in the religious court. The backward elimination method yielded the variables contributing to divorce: the number of children, the defendant's occupation, the plaintiff's age at marriage, the cause of divorce, and the defendant's education. Based on the comparison of the survival model performance between the Cox proportional hazard and the Survival Support Vector Machine, it was found that the latter was better since it had a higher concordance index and hazard ratio, which were 61.24 and 0.54, respectively. Thus, 61.24% of divorce cases were classified precisely by SUR-SVM in terms of the time sequence of events. Moreover, the hazard ratio of 0.54 indicated that the divorce rate of couples with censored status was 0.54 times than that of couples with failed/endpoint status
PERAMALAN TRANSAKSI PEMBAYARAN NON-TUNAI MENGGUNAKAN ARIMAX-ANN DENGAN KONFIGURASI KALENDER
Get PDFHuge internet usage boosts transactions using non-cash payment. In Indonesia, lots of activities and transactions are influenced by calendar movement, particularly that of the Islamic calendar. This work is to obtain the model and to forecast total non-cash payment with calendar configuration as an exogenous variable. The methods being compared are ARIMA, ARIMAX, and hybrid ARIMAX-ANN. The best model to forecast a total of non-cash payment is ARIMAX-ANN due to the least RMSE, Rp 20,9 trillion. The specification of the best model is ARIMAX (2,1,1) combined with ANN whose input is selected through stepwise regression. Besides satisfying residual assumption, ARIMAX-ANN is quite well in capturing the dynamics and trend of non-cash payment, particularly that in Ied-Fitr month and end of the year
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
EDUKASI PERAN KOMISI PEMILIHAN UMUM KOTA PALANGKA RAYA PADA MASYARAKAT DAN MAHASISWA DALAM MENYELENGGARAKAN PEMILU DEMOKRATIS
Get PDFThe General Election Commission (KPU) of Palangka Raya is the agency tasked with organizing general election activities in Palangka Raya. This community service aims to provide counselling and educate about the role, obstacles, and ways to overcome these obstacles in Palangka Raya City KPU. The implementation team carried out outreach in talk shows. The service method used was Participatory Action Research. The material regarding the role of the Palangka Raya City KPU in organizing democratic elections was about increasing the integrity, neutrality, and independence of Palangka Raya KPU members, providing healthy politics to voters, and increasing voter participation. The results of the implementation of outreach activities, the success of which was measured through tests, showed an increase in the average results on the post-test when compared to the pre-test results; thus, it can be concluded that the goal of service targeting the community and students as new voters has been achieved. --- Komisi Pemilihan Umum (KPU) Kota Palangka Raya adalah instansi yang bertugas menyelenggarakan kegiatan pemilihan umum di Kota Palangka Raya. Tujuan dari pengabdian kepada masyarakat ini adalah untuk memberikan penyuluhan dan mengedukasi tentang peran, hambatan, dan cara mengatasi hambatan tersebut oleh KPU Kota Palangka Raya. Tim pelaksana melaksanakan penyuluhan berupa talkshow. Metode pengabdian yang digunakan adalah Participatory Action Research. Materi tentang peran KPU Kota Palangka Raya dalam mewujudkan pemilu yang demokratis adalah dengan meningkatkan integritas, netralitas dan kemandirian anggota KPU Kota Palangka Raya, memberikan politik yang sehat kepada pemilih dan meningkatkan partisipasi pemilih. Hasil pelaksanaan kegiatan penyuluhan yang keberhasilannya diukur melalui tes menunjukkan terjadi kenaikan rata-rata hasil pada post-test jika dibandingkan hasil pre-test sehingga dapat disimpulkan bahwa tujuan pengabdian dengan sasaran masyarakat dan mahasiswa sebagai pemilih pemula telah tercapai
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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