Forecasting of air passenger and cargo have a major influence on the master plan of the airport infrastructure development and investment by the civil airline. This research aims to obtain the most accurate predictive value of the air passenger and cargo at three international airports Indonesia, namely, Soekarno Hatta, I Gusti Ngurah Rai, and Juanda Airport. Those international airports are the three largest contributors to the number of air passengers and cargo volumes in Indonesia. This research uses a hybrid forecasting method that combines linear and nonlinear models. The combination of two linear and nonlinear models is able to obtain accurate predictions. The first phase is linear modeling with time series regression model (TSR) and Autoregressive Integrated Moving Average with Exogenous Factor (ARIMAX). In the second phase, the error of the linear model is analyzed by using machine learning methods such as Neural Network (NN) and Support Vector Regression (SVR) to capture nonlinear patterns. There are four hybrid models that be applied and compared, i.e. TSR-NN, TSR-SVR, ARIMAX-NN, and ARIMAX-SVR based on the Mean Absolute Percentage Error (MAPE). The results show that hybrid ARIMAX-NN and TSR-NN give more accurate prediction than hybrid TSR-SVR and ARIMAX-SVR.
Water supply management effectively becomes challenging due to the human population and their needs have been growing rapidly. The aim of this research is to propose hybrid methods based on Singular Spectrum Analysis (SSA) decomposition, Time Series Regression (TSR), and Automatic Autoregressive Integrated Moving Average (ARIMA), known as hybrid SSA-TSR-ARIMA, for water demand forecasting. Monthly water demand data frequently contain trend and seasonal patterns. In this research, two groups of different hybrid methods were developed and proposed, i.e. hybrid methods for individual SSA components and for aggregate SSA components. TSR was used for modeling aggregate trend component and Automatic ARIMA for modeling aggregate seasonal and noise components separately. Firstly, simulation study was conducted for evaluating the performance of the proposed methods. Then, the best hybrid method was applied to real data sample. The simulation showed that hybrid SSA-TSR-ARIMA for aggregate components yielded more accurate forecast than other hybrid methods. Moreover, the comparison of forecast accuracy in real data also showed that hybrid SSA-TSR-ARIMA for aggregate components could improve the forecast accuracy of ARIMA model and yielded better forecast than other hybrid methods. In general, it could be concluded that the hybrid model tends to give more accurate forecast than the individual methods. Thus, this research in line with the third result of the M3 competition that stated the accuracy of hybrid method outperformed, on average, the individual methods being combined and did very well in comparison to other methods.
Proses identifikasi terhadap fenomena Long Memory tidaklah mudah. Berbagai alat identifikasi seperti plot ACF dan berbagai statistik uji lain masih sangat lemah. Beberapa penelitian mengungkapkan bahwa beberapa model nonlinear dapat dengan mudah teridentifikasi sebagai Long Memory yang sering dikenal sebagai Spurious Long Memory . Oleh karena itu, dalam tugas akhir ini akan disimulasikan pengaruh flow aggregation dan stock aggregation sebagai alternatif cara untuk mendeteksi Long Memory . Saham digunakan sebagai studi kasus karena proses pencatatannya sama dengan penerapan dari stock aggregation dan beberapa penelitian menyatakan bahwa harga mutlak dari return saham sering tertangkap sebagai fenomena Long Memory , namun tidak sedikit penelitian yang memodelkan return saham dengan model nonlinear, contohnya seperti ESTAR, sehingga simulasi dibangun dengan membangkitkan data Long Memory dan ESTAR sebagai Spurious Model dengan ukuran sampel 2000 dan 5000, lalu diaggregasi masing-masing dengan kedua jenis aggregasi hingga 10 level aggregasi. Hasil simulasi menunjukkan bahwa temporal aggregation terbukti dapat mendeteksi Long Memory dan membedakannya dengan ESTAR dari pola parameter integrasinya. Pada data ESTAR, kedua aggregasi menunjukkan bahwa nilai parameternya tidak berpola atau random seiring naiknya level aggregasi, sedangkan untuk Long Memory memiliki pola khusus untuk setiap jenis aggregasi. Tiga saham yang dijadikan studi kasus yaitu BMRI, BBNI, dan BBRI lebih baik dimodelkan dengan ARFIMA daripada ESTAR karena menghasilkan forecast yang akurasinya lebih baik
Sekolah Dasar Negeri SDN Purworejo 02 Kecamatan Pilangkenceng merupakan sekolah dasar yang terletak di wilayah Madiun. Proses kegiatan belajar mengajar di SD ini masih menggunakan metode sederhana. Guru mengajar menyampaikan rumus-rumus dan soal, lalu murid-murid mengerjakan soal. Sedangkan rata-rata nilai akhir matematika murid-muris sebesar 5,9 selama 5 tahun terakhir. Untuk mengatasi permasalah tersebut peneliti menuangkanya dalam penelitian tindakan kelas (PTK) yang dilaksanakan dalam dua siklus. Sedangkan tahapan yang harus dilalui adalah perencanaan, pelaksanaan implementasi tindakan, pengamatan dan refleksi dengan kriteria keberhasilan rata-rata aktivitas siswa 70%, aktivitas guru 80%, ketuntasan belajar siswa 80%. Pada siklus I aktivitas siswa sebesar 56%, aktivitas guru 76% sedangkan hasil ketuntasan belajar sebesar 65%. Dari hasil siklus I diketahui bahwa indikator keberhasilan masih belum tercapai, sehingga dilanjutkan dalam siklus II, dan hasilnya aktifitas siswa meningkat menjadi 81%, aktifitas guru 95% dan siswa yang tuntas belajar 95%.
Salah satu penelitian Upstream Technology Center (UTC) Direktorat hulu PT. Pertamina (Persero) adalah pada Lokasi X Papua Barat. Analisis Geologi pada penelitian di Lokasi X menerapkan metode Geostatistika pada petrophy-sical modelling. Penelitian ini mengaplikasikan metode Uni-versal Kriging pada Petrophysical Modelling. Metode ini dapat memberikan analisis yang baik secara geologi karena interpolasi properti reservoir primer dilakukan dengan memasukkan trend jenis batuan (facies) sebagai kontrol sehingga penyebaran yang dilakukan memiliki interpretasi yang kuat secara geologi. Pro-perti reservoir yang digunakan adalah porositas dan Net to Gross (NTG). Analisis semivariogram eksperimental dilakukan agar didapatkan semivariogram teoritis porositas dan NTG untuk masing-masing zona. Kesimpulan yang didapatkan adalah Zona 1A dan 1B merupakan target reservoir yang prospektif karena berdasarkan analisas statistika deskriptif dan univeral kriging didapatkan hasil penyebaran porositas dan NTG tertinggi daripada lokasi zona yang lainnya.
This paper proposes enhanced studies on a model consisting of a finite mixture framework of generalized linear models (GLMs) with gamma-distributed responses estimated using the Bayesian approach coupled with the Markov Chain Monte Carlo (MCMC) method. The log-link function, which relates the mean and linear predictors of the model, is implemented to ensure non-negative values of the predicted gamma-distributed responses. The simulation-based inferential processes related to the Bayesian-MCMC method is carried out using the Gibbs sampler algorithm. The performance of proposed model is conducted through two real data applications on the gross domestic product per capita at purchasing power parity and the annual household income per capita. Graphical posterior predictive checks are carried out to verify the adequacy of the fitted model for the observed data. The predictive accuracy of this model is compared with other Bayesian models using the widely applicable information criterion (WAIC). We find that the Bayesian mixture of GLMs with gamma-distributed responses performs properly when the appropriate prior distributions are applied and has better predictive accuracy than the Bayesian mixture of linear regression model and the Bayesian gamma regression model.
The United Nations Sustainable Development Goals (SDGs) have had a considerable impact on Indonesia’s national development policies for the period 2015 to 2030. The agricultural industry is one of the world’s most important industries, and it is critical to the achievement of the SDGs. The second major aspect of the SDGs, i.e., zero hunger, addresses food security (SDG 2). To measure the status of food security, accurate statistics on paddy production must be accessible. Paddy phenological classification is a way to determine a food plant’s growth phase. Imbalanced data are a common occurrence in agricultural data, and machine learning is frequently utilized as a technique for classification issues. The current trend in agriculture is to use remote sensing data to classify crops. This paper proposes a new approach—one that uses two phases in the bootstrap stage of the random forest method—called a two-phase stratified random forest (TPSRF). The simulation scenario shows that the proposed TPSRF outperforms CART, SVM, and RF. Furthermore, in its application to paddy growth phase data for 2019 in Lamongan Regency, East Java, Indonesia, the proposed TPSRF showed higher overall accuracy (OA) than the compared methods.
Indonesian Islamic banking market share projected by Bank Indonesia is an integral part in developing the industry in the country. By setting a projection which will then be used as a benchmark / target, Islamic banks can make a necessary program to attract new customers which eventually increase its asset. If the increase of the asset is significant,the Islamic bank market share may increase. The problem is that the current projection by Bank Indonesia seems to be off target. It means that the projection is pretty much above the actual value. This paper attempts to utilize two projection methods namely Spline and Auto-ARIMA which we think can provide a better result. This study uses the monthly data covering period since January 2006 until December 2012. The result shows that our projections, especially using Spline method, are closer to the actual value of the Islamic banking industry market share. It means that the gap between the projection and the actual value of market share is lesser than the gap on the Bank Indonesia calculation. Moreover, this study argue that, the projection of the Islamic banking market share made by BI will not be achieved unless with government support. So far, government has not made any policy which deposit some of the national budget in the Islamic bank. This study calculates that if government regularly depositing 1% of total National Government Budget in Islamic banks, the projection of Islamic banking market share made by BI will be acheived. As a conclusion, the role of government is very significant in developing the Islamic banking industry in Indonesia.Keywords: Market share Islamic Bank, Spline, Auto-ArimaJEL Classification: E44, E47
