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Jurnal Aplikasi Statistika & Komputasi Statistik

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Published by Politeknik Statistika STIS

ISSN : 20864132 EISSN : 26151367 DOI : -

Core Subject : Science, Education,

Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

Redaksi menerima karya ilmiah atau artikel penelitian mengenai kajian teori statistika dan komputasi statistik pada bidang ekonomi dan sosial dan kependudukan, serta teknologi informasi. Redaksi berhak menyunting tulisan tanpa mengubah makna subtansi tulisan. Isi jurnal Aplikasi Statistika dan Komputasi Statistik dapat dikutip dengan menyebutkan sumbernya.

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Articles 115 Documents

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Kinerja Modsecurity Technical Report (Studi Kasus: Pencegahan terhadap Serangan SQL Injection) Farid Ridho
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 7 No 1 (2015): Journal of Statistical Application and Computational Statistics
Publisher : Pusat Penelitian dan Pengabdian Masyarakat Politeknik Statistika STIS

Several Measures are impelemented in web application security lifecycle such as Secure Development, Secure Deployment and Secure Operation. In secure operation section, a web application that has been through the stages of development and testing will soon enter production phase. At this stage it will be applied to Web Application Firewall (WAF) that meant to protect application from a malicious request.The purpose of this research is to explore ModSecurity WAF implementation. WAF ModSecurity is a free, open source application that can be used to make the filter to requests which occur on a web application including a request containing SQL Injection commands. Another aim is to see whether the ModSecurity installation on a web server affect the performance of the web server.From the test results concluded that ModSecurity can filter SQL injection and installation of ModSecurity does not significantly affect the performance of the web server.

Klasifikasi Emas Indonesia Sebagai Hedge dan Sage Haven Asset dalam Pasar Sahan Domestik, Pasar Saham Luar Negeri, dan Pasar Dolar AS Tahun 2008-2015 Marini Syafitri; Aisyah Fitri Yuniasih
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 8 No 2 (2016): Journal of Statistical Application and Computational Statistics
Publisher : Pusat Penelitian dan Pengabdian Masyarakat Politeknik Statistika STIS

Gold is supposed to be one of the promising investment instruments because it has good characteristics as a means for investment diversification (O'Byrne and O'Brien (2013)). However, during post-global crisis, especially in 2009, Indonesian gold investment was lower than before. This study aims to identify the classification of Indonesian gold in terms of its strength and its role in the domestic and foreign stock market as well as the US Dollar market, in both normal condition and bullish and bearish conditions in 2008-2015. This study uses the ARDL model in its analysis of hedge and safe haven of Indonesian gold. It indicates that the Indonesian gold, in general, act as a weak hedge asset in the international stock market, a strong hedge asset in the US Dollar market, a strong safe haven asset in the domestic and international stock market and a weak safe haven in the US Dollar market.

Penerapan Model Inferensi Bayesian dengan Variational Bayesian Principal Component Analysis (VBPCA) dalam mengatasi Missing Data Analisis Komponen Utama Ricky Yordani
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 8 No 1 (2016): Journal of Statistical Application & Statistical Computing
Publisher : Pusat Penelitian dan Pengabdian Masyarakat Politeknik Statistika STIS

In standard Principal Component Analysis (PCA) comes one problem in addressing the set of incomplete data. The standard PCA procedure on incomplete data is to eliminate (listwise deletion procedure) or using the mean of the variable, this procedure may result in loss information from these observations. Another method used is to integrate Expectation Maximization (EM) to the method of Probabilistic Principal Component Analysis (PPCA). But PPCA can produce overfitting response prediction. In this study discussed the Variational Bayesian Principal Component Analysis (VBPCA) which is a method of development of PPCA method by incorporating prior information from the distribution of the principal components of the model parameters. From the simulation studies by eliminating the data through the concept of missing at random (MAR), obtained results that the value of the correlation scores principal components complete data with the principal component score predicted results PPCA method is superior when compared with VBPCA, as well as to the value of the correlation scores for the various percentages are generally incomplete data. However, judging from the size of a match between the response to predictions by the size normalized root mean square error of prediction (NRMSEP) VBPCA method produces better than PPCA.

Konsumsi Rokok Masyarakat Kota Bandung Tahun 2015 dengan Model Hurdle Negatif Binomial (Hurdle-Nb) Wulandari Wulandari; Wida Tira Tedra; Irtania Muthia Rizki; Dina Prariesa
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 9 No 2 (2017): Journal of Statistical Application and Computational Statistics
Publisher : Pusat Penelitian dan Pengabdian Masyarakat Politeknik Statistika STIS

Lifestyle that high risk to health is smoking behavior. Smoking behavior has a negative impact on health, both for active smokers and passive smokers. In addition there are also negative impacts in terms of economy. In Bandung, spending on cigarettes ranks second after the food commodity. The number of cigarettes smoked each day is influenced by demographic variables, social environment variables, political variables, and cultural variables. In this study, the Bandung cigarette consumption and the factors suspected to affect, ie sex, age, work status, and education, will be modeled by Hurdle-NB regression. The results showed that Log model, age variable, work status, and education influence to the average of cigarette consumption. While on Logit model, gender variable, age, work status, and education have an effect on the tendency of someone to smoke or not.

Generalized Multilevel Linear Model dengan Pendekatan Bayesian untuk Pemodelan Data Pengeluaran Perkapita Rumah Tangga Azka Ubaidillah; Anang Kurnia; Kusman Sadik
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 9 No 1 (2017): Journal of Statistical Application and Computational Statistics
Publisher : Pusat Penelitian dan Pengabdian Masyarakat Politeknik Statistika STIS

Household per capita expenditure data is one of the important information as an approach to measure the level of prosperity in an area. Such data is needed by the government, both at the central and regional levels in formulating, implementing and evaluating the implementation of development programs. This research is aimed at modeling the household per capita expenditure data which takes into account the specificity of BPS data which has a hierarchical structure, and data distribution pattern which has the right skewed characteristic. The modeling is done by using the three parameters of Log-normal distribution (LN3P) and the three parameters of Log-logistics (LL3P) with a single level (unilevel) and two levels (multilevel) structure. The parameter estimation process is done by Markov Chain Monte Carlo (MCMC) method and Gibbs Sampling algorithm. The results showed that on the unilevel model, the LL3P model is better than the LN3P model. While in multilevel model, LN3P model is better than LL3P model. The results also show that the best model for modeling household per capita expenditure data is the LN3P multilevel model with the smallest Deviance Information Criterion (DIC) value.

Developing Panel Data and Time Series Application (DELTA) : Smoothing Module siti mariyah; Nensi Fitria Deli
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 8 No 2 (2016): Journal of Statistical Application and Computational Statistics
Publisher : Pusat Penelitian dan Pengabdian Masyarakat Politeknik Statistika STIS

Smoothing is commonly used methods to predict time series data. There are many applications that help in the processing of time series data that provide smoothing function such as EViews, Minitab, Zaitun TS, and R. However, these applications have some shortcomings such as the difficulty in comparing several methods. In this study, we build an open source application that provides more complete smoothing method and a facility for comparing several methods, namely smoothing module in DELTA application. Based on the tests, it can be proved that this application is suitable for users and the displayed output is consistent with the theory.

Analisis Multivariate Adaptive Regression Splines (MARS) pada Prediksi Ketertinggalan Kabupaten Tahun 2014 Siskarossa Ika Oktora
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 7 No 2 (2015): Journal of Statistical Aplication and Statistical Computing
Publisher : Pusat Penelitian dan Pengabdian Masyarakat Politeknik Statistika STIS

The purposes of this research are to build underdeveloped regency model and make a prediction in 2014 based on economic categories, Human Resources (HR), infrastructures, fiscal capacity, accessibility, and regional characteristics with MARS method. MARS is a classification method which can handle highdimensional data with unknown pattern in advance, and can be applied to see the interaction between variables. MARS is an alternative method when the data doesn’t fulfil the parametric statistics assumptions. From MARS model, there are three variables that affect underdeveloped regency, they are consumption expenditure per capita, life expectancy, and percentage of household electricity users. The accuracy of MARS model is very high, 97.83 percent and can be used to make a prediction. Based on MARS model, at the end of the National Development Plan 2010-2014 is predicted a significant transitions in regency’s status. This model can also be used to predict the condition of new regency based on empirical data, because in the earlier classification, the status of regency just follows the status of parent region.

Performansi Piecewise Polynomial Smooth Support Vector Machine untuk Klasifikasi Desa Tertinggal di Provinsi Kalimantan Timur Tahun 2011 Ita Wulandari
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 7 No 1 (2015): Journal of Statistical Application and Computational Statistics
Publisher : Pusat Penelitian dan Pengabdian Masyarakat Politeknik Statistika STIS

One of most popular techniques of binary data classification in machine learning is Support Vector Machine (SVM). SVM can be applied extensively in many fields, such as pattern recognition, regression analysis, and probability estimation. SVM uses optimization with quadratic programming which become unefficient when applied in a high dimensional large dataset. Hence, researchers develop a method by changing SVM formulation with a smoothing technique that called Smooth-SVM (SSVM) which converts quadratic into linear programming. The research then continued by modifying that smooth function into polynomial smooth function forms, such as quadratic polynomial function, fourth polynomial function, piecewise polynomial function and spline function. Compared to the other polynomial smooth functions, piecewise polynomial smooth function has a better performance in plus function. When piecewise polynomial smooth function is applied in SSVM model, it will produce piecewise polynomial smooth support vector machine (PPSSVM). PPSSVM has many advantages compared to other SSVM models and its developments such as better efficiency, precision and higher accuracy in generalization. Two PPSSVM model based on piecewise polynomial function are used in this research which found by Luo (PPSSVM1) and Wu and Wang (PPSSVM2). The performance and the convergence of both models then will be examined theoretically, in order to determine the best model for classification of underdeveloped rural in East Kalimantan.PODES data in 2011 will be used in this research. Teoritical analysis showed that PPSSVM2 has a better performance and konvergence than PPSSVM1. Based on the result of this study, PPSSVM2 is not batter than PPSSVM1. It can be seen from the accuracy and AUC values that are not significantly different.

Named Entity Recognition on A Collection of Research Titles Siti Mariyah
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 9 No 1 (2017): Journal of Statistical Application and Computational Statistics
Publisher : Pusat Penelitian dan Pengabdian Masyarakat Politeknik Statistika STIS

The title can help the reader to get the universal point of view of the article as the initial understanding before reading the content as a whole. On technical research papers, the title states essential information. In this study, we aim to develop information extraction techniques to recognize and extract problem, method, and domain of research contained in a title. We apply supervised learning on 671 research titles in computer science from various online journals and international conference proceedings. We conducted some experiments with different schemas to discover the influence of features and the performance of the algorithm. We examined contextual, syntactic, and the bag of words feature sets using Naïve Bayes and Maximum Entropy. The Naïve Bayes classifier learned from the first group of the feature set is successful in predicting category of each token in title dataset. The accuracy and f1-score for each class are more than 0.80 since the first group of feature sets considers the location of a token within a sentence, considers the token and POS tag of some tokens before and after and deliberates the rules of a token. While the Naïve Bayes classifier learned from the second group of the feature set is more appropriate classifying a phrase token than a word token.

Pelacakan Gangguan Kereta Komuter Melalui Twitter Crawling Lya Hulliyyatus Suadaa
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 8 No 1 (2016): Journal of Statistical Application & Statistical Computing
Publisher : Pusat Penelitian dan Pengabdian Masyarakat Politeknik Statistika STIS

Nowadays, Twitter is a very popular social media, especially in Indonesia. Tweets can be used as a data source to explore information. PT KAI (Kereta Api Indonesia) Commuter Jabodetabek (Jakarta, Bogor, Depok, Tangerang, Bekasi) (PT KCJ) has an official account, Twitter @CommuterLine, to disseminate information related to commuter train. One of important information regularly published in @CommuterLine account are information about commuter train intrusion. PT KCJ uses specific tweet format and certain hashtag to inform people about the intrusion. Intrusion information that is usually published is time of the intrusion, name of the station, train number and train line. #InfoLintas and #InfoLanjut hashtag are used to easier tweet searching. Information extraction processes are adopted to automatically extract commuter train intrusion information from @CommuterLine account Twitter. The statistical analysis about commuter train tweets are visualized in tables and graphs. A prototype system in the form of mobile application is developed to track commuter train intrusion based on the result of the information extraction.

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