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Dive into the research topics where Atje Setiawan Abdullah is active.

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Featured researches published by Atje Setiawan Abdullah.


Journal of Physics: Conference Series | 2018

Combining Fuzzy Clustering and Hidden Markov Models for Sundanese Speech Recognition

Intan Nurma Yulita; Akik Hidayat; Atje Setiawan Abdullah; Erick Paulus

Sundanese tribe is one of the largest population tribe in Indonesia. However, over time, users of the Sundanese language are declining because of the living languages outside of Sundanese. One way to preserve Sundanese is Sundanese Speech Recognition. In this research, several processes of recognition were done include pre-processing, feature extraction, Fuzzy Clustering, and Hidden Markov Models. Pre-processing aims to separate the recording from the noise and normalize the speech signal, while the feature extraction to obtain the characteristics of the speech signal to distinguish each phoneme from the speech. In particular, the contribution of this research is to combine Fuzzy Clustering and Hidden Markov Models for Sundanese Speech Recognition. Fuzzy Clustering plays a role in finding unique symbols in the speech signal. These symbols are represented as centroid in fuzzy clustering. The next process, each segment of the speech signal calculated the probability of the membership for all centroids. The output of this calculation becomes input to Hidden Markov Models. The test uses a speech corpus derived from 30 people. The results obtained that the combination of Fuzzy Clustering and Hidden Markov Models have a better performance than Hidden Markov Models. Also, the research also analyses the optimal number of clusters of Fuzzy Clustering and states of Hidden Markov Models for the datasets used.


Applied Mathematics & Information Sciences | 2018

Implementation of Generalized Space Time Autoregressive (GSTAR)-Kriging Model for Predicting Rainfall Data at Unobserved Locations in West Java

Atje Setiawan Abdullah; Setiawan Matoha; Deltha Airuzsh Lubis; Annisa Nur Falah; I. G. N. Mindra Jaya; Eddy Hermawan; Budi Nurani Ruchjana

A Generalized Space Time Autoregressive or GSTAR is a specia l model of Vector Autoregressive (VAR) model which is a combination of time series and spatial models which has t e assumption of autoregressive parameter and space time pa rameter having different value for each location of observation. In addition, it assumes stationary time series data at the mean and variance levels and applies to locations with heterogeneous charact eristics. One disadvantage of the GSTAR model is that it can n ot be used to predict at unobserved locations. In this paper we combine the GSTAR model with the Ordinary Kriging (OK) technique, na med GSTAR-Kriging model so that the GSTAR model can be used to pre dict in unobserved locations. GSTAR parameters are estimat ed using the Ordinary Least Squares (OLS) method and these are u sed as inputs for the Kriging technique. Furthermore, by usi ng linear semivariogram we can obtain simulations to predict the GSTA R parameters. For the case study we applied the model to annua l rainf ll data in wet season (Desember, January and February) from sev eral locations in West Java, Indonesia, such as Majalengka, Kuningan and Ciamis Regencies. The GSTAR (1;1) model in observed loca tion have Mean Average Percentage Error (MAPE) value overal l l ss than 15 percent and residual of model have identically indep endent distributed normal. The results of GSTAR-Kriging mo del show that the GSTAR-Kriging parameter at unobserved locations are al most similar to GSTAR parameter at observed locations.


STATISTICS AND ITS APPLICATIONS: Proceedings of the 2nd International Conference on Applied Statistics (ICAS II), 2016 | 2017

Prediction of cadmium pollutant with ordinary point kriging method using Gstat-R

Annisa Nur Falah; Atje Setiawan Abdullah; Kankan Parmikanti; Budi Nurani Ruchjana

Kriging is a method of estimation that provides an unbiased linear prediction of the values of a point or block. Ordinary point kriging is one of the most simple kriging method when the average population is not known which normally applied to the spatial data, for example Meuse river floodplain. On the Meuse river floodplain are contaminating metals such as cadmium, zinc, copper and lead, it is necessary to predict the location that contains cadmium. Calculation of cadmium pollutant can be implemented using the software Gstat-R in order to obtain accurate results. In the calculation of prediction with ordinary point kriging method required gstat library, sp library and some algorithms in GStat-R to be applied to the data of Meuse river floodplain to obtain an index prediction of pollutant in unobserved locations. Calculation of the index prediction of pollutant using GStat-R is easy, fast and accurate because the average kriging variance minimum resultant. GStat-R can also display contours showing the lo...


Journal of Physics: Conference Series | 2017

Development of the statistical ARIMA model: an application for predicting the upcoming of MJO index

Eddy Hermawan; Budi Nurani Ruchjana; Atje Setiawan Abdullah; I. Gede Nyoman Mindra Jaya; Sinta Berliana Sipayung; Shailla Rustiana

This study is mainly concerned in development one of the most important equatorial atmospheric phenomena that we call as the Madden Julian Oscillation (MJO) which having strong impacts to the extreme rainfall anomalies over the Indonesian Maritime Continent (IMC). In this study, we focused to the big floods over Jakarta and surrounded area that suspecting caused by the impacts of MJO. We concentrated to develop the MJO index using the statistical model that we call as Box-Jenkis (ARIMA) ini 1996, 2002, and 2007, respectively. They are the RMM (Real Multivariate MJO) index as represented by RMM1 and RMM2, respectively. There are some steps to develop that model, starting from identification of data, estimated, determined model, before finally we applied that model for investigation some big floods that occurred at Jakarta in 1996, 2002, and 2007 respectively. We found the best of estimated model for the RMM1 and RMM2 prediction is ARIMA (2,1,2). Detailed steps how that model can be extracted and applying to predict the rainfall anomalies over Jakarta for 3 to 6 months later is discussed at this paper.


Journal of Physics: Conference Series | 2017

Naive Bayes as opinion classifier to evaluate students satisfaction based on student sentiment in Twitter Social Media

Fahmi Candra Permana; Yusep Rosmansyah; Atje Setiawan Abdullah

Students activity on social media can provide implicit knowledge and new perspectives for an educational system. Sentiment analysis is a part of text mining that can help to analyze and classify the opinion data. This research uses text mining and naive Bayes method as opinion classifier, to be used as an alternative methods in the process of evaluating studentss satisfaction for educational institution. Based on test results, this system can determine the opinion classification in Bahasa Indonesia using naive Bayes as opinion classifier with accuracy level of 84% correct, and the comparison between the existing system and the proposed system to evaluate students satisfaction in learning process, there is only a difference of 16.49%.


Journal of Physics: Conference Series | 2017

Rainfall prediction of Cimanuk watershed regions with canonical correlation analysis (CCA)

Shailla Rustiana; Budi Nurani Ruchjana; Atje Setiawan Abdullah; Eddy Hermawan; Sinta Berliana Sipayung; I Gede Nyoman Mindra Jaya; Krismianto

Rainfall prediction in Indonesia is very influential on various development sectors, such as agriculture, fisheries, water resources, industry, and other sectors. The inaccurate predictions can lead to negative effects. Cimanuk watershed is one of the main pillar of water resources in West Java. This watersheds divided into three parts, which is a headwater of Cimanuk sub-watershed, Middle of Cimanuk sub-watershed and downstream of Cimanuk sub- watershed. The flow of this watershed will flow through the Jatigede reservoir and will supply water to the north-coast area in the next few years. So, the reliable model of rainfall prediction is very needed in this watershed. Rainfall prediction conducted with Canonical Correlation Analysis (CCA) method using Climate Predictability Tool (CPT) software. The prediction is every 3months on 2016 (after January) based on Climate Hazards group Infrared Precipitation with Stations (CHIRPS) data over West Java. Predictors used in CPT were the monthly data index of Nino3.4, Dipole Mode (DMI), and Monsoon Index (AUSMI-ISMI-WNPMI-WYMI) with initial condition January. The initial condition is chosen by the last data update. While, the predictant were monthly rainfall data CHIRPS region of West Java. The results of prediction rainfall showed by skill map from Pearson Correlation. High correlation of skill map are on MAM (Mar-Apr-May), AMJ (Apr-May-Jun), and JJA (Jun-Jul-Aug) which means the model is reliable to forecast rainfall distribution over Cimanuk watersheds region (over West Java) on those seasons. CCA score over those season prediction mostly over 0.7. The accuracy of the model CPT also indicated by the Relative Operating Characteristic (ROC) curve of the results of Pearson correlation 3 representative point of sub-watershed (Sumedang, Majalengka, and Cirebon), were mostly located in the top line of non-skill, and evidenced by the same of rainfall patterns between observation and forecast. So, the model of CPT with CCA method is reliable to use.


Journal of Physics: Conference Series | 2017

The role of ethnomathematics in West Java (a preliminary analysis of case study in Cipatujah)

Dianne Amor Kusuma; Stanley P. Dewanto; Budi Nurani Ruchjana; Atje Setiawan Abdullah

Indonesia is a multicultural country of which people usually do anything influenced by their culture. The culture contain of many aspects, one of them is ethnomathematics. It is a study about the connection between culture and mathematics concepts. It also reveals mathematical practices of day life. These mathematical practices could be seen at Cipatujah, West Java, Indonesia. It has several ethnics which implemented of ethnomathematics in their life, for example they implement traditional mathematical concept in the way they determine the time to head seaward for fishing, and the way they construct their houses. The exploration will describe about how deep is the role of ethnomathematics in Cipatujah and state any problem found according to the exploration result. The objectives of this study is to show that ethnomathematics holds an important role in our day life, with the case study of primary school students and for the people of Cipatujah. The method which implemented in this study is exploratory. The result is the people of Cipatujah have been implemented ethnomathematics in their life for many years and believe that ethnomathematics is part of their life, but the teachers of primary school there have not implemented ethnomathematics approach yet in learning process of mathematics. Conclusion of this study is ethnomathematics as the root of culture life in West Java.


2016 International Workshop on Big Data and Information Security (IWBIS) | 2016

Spatial data mining for predicting of unobserved zinc pollutant using ordinary point Kriging

Alexander Agung Santoso Gunawan; Annisa Nur Falah; Alfensi Faruk; Destiny S. Lutero; Budi Nurani Ruchjana; Atje Setiawan Abdullah

Due to pollution over many years, large amounts of heavy metal pollutant can be accumulated in the rivers. In the research, we would like to predict the dangerous region around the river. For study case, we use the Meuse river floodplains which are contaminated with zinc (Zn). Large zinc concentrations can cause many health problems, for example vomiting, skin irritations, stomach cramps, and anaemia. However there is only few sample data about the zinc concentration of Meuse river, thus the missing data in unknown regions need to be generated. The aim of this research is to study and to apply spatial data mining to predict unobserved zinc pollutant by using ordinary point Kriging. By mean of semivariogram, the variability pattern of zinc can be captured. This captured model will be interpolated to predict the unknown regions by using Kriging method. In our experiments, we propose ordinary point Kriging and employ several semivariogram: Gaussian, Exponential and Spherical models. The experimental results show that: (i) by calculating the minimum error sum of squares, the fittest theoretical semivariogram models is exponential model (ii) the accuracy of the predictions can be confirmed visually by projecting the results to the map.


PADJADJARAN INTERNATIONAL PHYSICS SYMPOSIUM 2013 (PIPS-2013): Contribution of Physics on Environmental and Energy Conservations | 2013

Clustering spatial on the GSTAR model for replacement new oil well

Budi Nurani Ruchjana; Atje Setiawan Abdullah; Toni Toharudin; I. Gede Nyoman Mindra Jaya

Space time models such as the Generalized Space Time Autoregressive (GSTAR) is an important theoretical research topics with many applications in large number of areas ranging from the spread of diseases, mining, economic growth, ecology, agriculture and population development. In this paper we introduce a clustering spatial for determining a group of locations for weight matrix of the GSTAR model. We use Ordinary Least Squares method to estimate the parameters of model and apply the result to develop a GSTAR-Kriging model for replacement new oil wells at volcanic field in Jatibarang, West Java-Indonesia.


Model Assisted Statistics and Applications | 2018

A bayesian spatial autoregressive model with k-NN optimization for modeling the learning outcome of the junior high schools in West Java

I. Gede Nyoman Mindra Jaya; Toni Toharudin; Atje Setiawan Abdullah

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Shailla Rustiana

Bogor Agricultural University

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