https://iptek.its.ac.id/index.php/inferensi/issue/feedInferensi2024-03-26T10:06:12+07:00Dr. Kartika Fithriasari, M.Siinferensi.statistika@its.ac.idOpen Journal Systems<p>Authors who publish with this journal agree to the following terms:</p><ol><li>Authors retain the copyright and full publishing rights without restrictions under a <a href="http://creativecommons.org/licenses/by-sa/4.0/" rel="license">Creative Commons Attribution-ShareAlike 4.0 International License</a><span>.</span></li><li>Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.</li><li>Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (<a href="http://opcit.eprints.org/oacitation-biblio.html">See The Effect of Open Access)</a>.</li></ol><!-- Default Statcounter code for Inferensi https://iptek.its.ac.id/index.php/inferensi/ --><script type="text/javascript">// <![CDATA[ var sc_project=12705886; var sc_invisible=1; var sc_security="278fe25c"; // ]]></script><script type="text/javascript" src="https://www.statcounter.com/counter/counter.js"></script><noscript>&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;div class="statcounter"&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;a title="free hit counter" href="https://statcounter.com/" target="_blank"&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;img class="statcounter" src="https://c.statcounter.com/12705886/0/278fe25c/1/" alt="free hit counter" referrerPolicy="no-referrer-when-downgrade"&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;/a&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;/div&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;</noscript><!-- End of Statcounter Code --><p><strong>Print ISSN: 0216-308X e-ISSN: 2721-3862</strong></p><p>Inferensi is an open-access and peer-reviewed journal publishing advanced results in the fields of statistics and data science, as mentioned explicitly in the scope of the journal. The journal is geared toward the dissemination of original research and practical contributions by both scientists and engineers, from both academia and industry.</p><p>Inferensi is published by the Department of Statistics ITS in cooperation with <a href="https://forstat.org/jurnal/" target="_blank">Forum Pendidikan Tinggi Statistika (FORSTAT)</a>. This journal publishes <strong>three</strong> issues a year in <strong>March</strong>, <strong>July</strong>, and <strong>November</strong>. Inferensi has been accredited by the Ministry of Education Culture, Research, and Technology, Republic of Indonesia based on Decree <a href="https://storage.googleapis.com/arjuna-files/file/info/SK_Akreditasi_Jurnal_Ilmiah_Periode_II_Tahun_2023.pdf" target="_blank">152/E/KPT/2023</a> as <a href="https://sinta.kemdikbud.go.id/journals/profile/7854" target="_blank">Sinta 2</a> started from Vol.6 No. 1 (2023).</p><p>Abstracted & Indexed in:</p><ul><li><a href="https://sinta.kemdikbud.go.id/journals/detail?id=7854" target="_blank">Sinta</a></li><li><a href="https://search.crossref.org/?type-name=Journal+Article&container-title=Inferensi&q=inferensi&sort=year&from_ui=yes" target="_blank">Crossref</a></li><li><a href="https://scholar.google.com/citations?hl=id&authuser=4&user=YxaKupMAAAAJ" target="_blank">Google Scholar</a></li><li><a href="https://garuda.kemdikbud.go.id/journal/view/18008" target="_blank">Garuda</a></li><li><a href="https://onesearch.id/Search/Results?filter[]=repoId:IOS13847" target="_blank">IOS</a></li><li><a href="https://www.base-search.net/Search/Results?lookfor=%22Department+of+Statistics+ITS%22&type=allus&page=1&l=en&oaboost=1&refid=dcpageen" target="_blank">Base</a></li><li><a href="https://www.neliti.com/journals/inferensi" target="_blank">Neliti</a></li><li><a href="https://portal.issn.org/resource/ISSN/2721-3862" target="_blank">Road</a></li><li><a href="https://www.worldcat.org/search?q=0216-308X&qt=results_page" target="_blank">WorldCat</a></li><li><a href="https://journals.indexcopernicus.com/search/journal/issue?issueId=all&journalId=66928" target="_blank">Copernicus</a></li><li><a href="http://journalseeker.researchbib.com/view/issn/0216-308X" target="_blank">ResearchBib</a></li><li><a href="https://www.citefactor.org/journal/index/26342/#.X2v2-2gzZPY" target="_blank">CiteFactor</a></li><li><a href="http://www.infobaseindex.com/" target="_blank">InfoBase</a></li><li><a href="https://app.dimensions.ai/discover/publication?search_mode=content&or_facet_source_title=jour.1159260&order=date" target="_blank">Dimensions</a></li></ul><div><a href="https://sinta.kemdikbud.go.id/journals/detail?id=7854" target="_blank"><img src="https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSHMHi3Obc9aD86jmBPdl3RVFOctibUrZgD8gWiLp2z_Z7DI1P_b_mc4IqBmVcZzfhE2Jo&usqp=CAU alt=" alt="" width="115" height="35" /></a> <a href="https://search.crossref.org/?type-name=Journal+Article&container-title=Inferensi&q=inferensi&sort=year&from_ui=yes" target="_blank"><img src="/public/site/images/caakali/Untitled1.jpg" alt="" /></a><a href="https://scholar.google.com/citations?hl=id&authuser=4&user=YxaKupMAAAAJ"><img src="/public/site/images/caakali/google_scholar.png" alt="" width="100" /></a><a href="https://garuda.kemdikbud.go.id/journal/view/18008" target="_blank"><img src="/public/site/images/ahsan4th/garuda21.png" alt="" /></a> <a href="https://onesearch.id/Search/Results?filter[]=repoId:IOS13847" target="_blank"><img src="/public/site/images/ahsan4th/one_search.png" alt="" /></a><a href="https://www.base-search.net/Search/Results?lookfor=%22Department+of+Statistics+ITS%22&type=allus&page=1&l=en&oaboost=1&refid=dcpageen" target="_blank"><img src="/public/site/images/ahsan4th/base_ok.png" alt="" /></a> <a href="https://www.neliti.com/journals/inferensi" target="_blank"><img src="/public/site/images/ahsan4th/neliti_inf1.png" alt="" /></a> <a href="https://portal.issn.org/resource/ISSN/2721-3862" target="_blank"><img src="/public/site/images/ahsan4th/road_inf.jpg" alt="" /></a> <a href="https://journals.indexcopernicus.com/search/journal/issue?issueId=all&journalId=66928" target="_blank"><img src="/public/site/images/ahsan4th/cop2.png" alt="" /></a> <a href="https://www.worldcat.org/search?q=0216-308X&qt=results_page" target="_blank"><img src="/public/site/images/ahsan4th/wc2.png" alt="" /></a> <a href="http://journalseeker.researchbib.com/view/issn/0216-308X" target="_blank"><img src="/public/site/images/ahsan4th/Rbib2.png" alt="" /></a> <a href="https://www.citefactor.org/journal/index/26342/#.X2v2-2gzZPY" target="_blank"><img src="/public/site/images/ahsan4th/citefactor4.png" alt="" /></a> <a href="https://app.dimensions.ai/discover/publication?search_mode=content&or_facet_source_title=jour.1159260&order=date" target="_blank"><img src="/public/site/images/ahsan4th/infobase_inf.png" alt="" /></a></div><div> </div><div><a href="https://app.dimensions.ai/discover/publication?search_mode=content&or_facet_source_title=jour.1159260&order=date" target="_blank"><img src="/public/site/images/ahsan4th/dimensions.png" alt="" /></a></div><p><br />Peer Review Policy:<br /><br />All submitted manuscripts are checked first by the Editor-in-chief whether the manuscript falls under the scope of the journal. If the manuscript fits within the scope, then the formal peer review process is conducted by the editorial board members. The manuscript will be forwarded to be reviewed by at least two reviewers. The authors and reviewers are encouraged to use the electronic submission and peer-review system.<br /><br />Article Submission:<br /><br />All papers submitted to the journal must be written in English and formatted in the 1-column format using the Microsoft Word template below:</p><p><a href="https://drive.google.com/uc?id=1OAL4202D39OAIGuMF1yd1Ea9Wwa2yqAd&export=download" target="_blank">Layout Template</a></p><p>ISSN: <a href="http://u.lipi.go.id/1180434943">0216-308X</a> e-ISSN: <a href="http://issn.pdii.lipi.go.id/issn.cgi?daftar&1583481508&1&&" target="_blank">2721-3862</a></p><p><span><br /></span></p><p><span><br /></span></p>https://iptek.its.ac.id/index.php/inferensi/article/view/18751Intrusion Detection Systems (IDSs) using Multivariate Control Chart Hotelling’s T2 with Dimensional Reduction of Factorial Analysis of Mixed Data (FAMD) and Autoencoder2024-03-26T10:06:10+07:00Kevin Agung Fernanda Rifkifernandakevin02@gmail.comNiam Rosyadiniam_r@gmail.comAmanatullah Pandu Zenklinovam_pandu@gmail.comNovri Suherminovri@statistika.its.ac.idTraditional multivariate control charts for network intrusion detection encounter significant challenges including false alarms due to non-conforming network data traffic distributions, limitations in identifying outlier intrusions caused by masking effects, and handling diverse data types. This paper introduces a T<sup>2</sup>-based multivariate control chart that leverages dimensional reduction techniques using Factor Analysis of Mixed Data (FAMD) and Autoencoder to address these issues. FAMD reduces data with both quantitative and qualitative variables, while Autoencoder focuses on dimensionality reduction for quantitative variables, enhancing multivariate control chart performance. The proposed chart, a modified T<sup>2</sup>, is compared to conventional T<sup>2</sup> with dimensionality reduction through FAMD and Autoencoder. Results from simulating data using UNSW-NB 15 demonstrate T<sup>2</sup>'s superior performance with dimensionality reduction compared to conventional T<sup>2</sup>. Under various conditions, conventional control chart T achieves 64% accuracy, T<sup>2</sup> with FAMD achieves 74%, and T<sup>2</sup> with Autoencoder reaches 76%. Notably, T<sup>2</sup> with FAMD excels in detecting normal activity as intrusion compared to Autoencoder. This approach holds promise for improving network intrusion detection accuracy, especially in mixed-data environments.2024-03-25T00:00:00+07:00https://iptek.its.ac.id/index.php/inferensi/article/view/18755Comparing the Performance of Multivariate Hotelling’s T2 Control Chart and Naive Bayes Classifier for Credit Card Fraud Detection2024-03-26T10:06:10+07:00Ichwanul kahfi Prasetyaichwankahfi@gmail.comDevi Putri Isnawartydevi_pi@gmail.comAbdullah Fahmia_fahmi@gmail.comSalman Alfarizi Pradana Andikaputrasalman@yahoo.comWibawati Wibawatiwibawati@its.ac.idCredit card is a transaction tool using a card which is a substitute for legitimate cash in transactions. The use of computer technology is needed for various kinds of electronic transactions. In the world of technology, the term machine learning is not new and technological developments are increasingly rapid in recent years. Statistical process control method (SPC) is one of the measuring instruments used to improve the performance of public services. Hotelling T^2 control chart is a method in SPC that can be used to control the process. Methods that are widely used in the detection and classification of documents one of them is Naive Bayes Classifier (NBC) which has several advantages, among others, simple, fast and high accuracy. Those two methods will be used to detecting o2utlier of this dataset. The study used the credit card fraud registry with some PCA as independent variables. The size of fraud transaction is very small which represented only 0.172% of the 284,807 transactions. This research will use Area Under Curve (AUC) as the performance goodness test. A comparison of the accuracy of NBC and Hotelling's T2 predictions shows that the performance of the T2 Hotelling method is better in detecting outliers than the NBC method2024-03-25T00:00:00+07:00https://iptek.its.ac.id/index.php/inferensi/article/view/18738Penerapan Metode Hybrid Dekomposisi-Arima dalam Peramalan Jumlah Wisatawan Mancanegara2024-03-26T10:06:11+07:00Aswi Aswiaswi@unm.ac.idIna Rahmainarahman09@gmail.comMuhammad Fahmuddinmfahmuddin@unm.ac.idThe Decomposition-ARIMA hybrid method is a combination of two methods used to predict future events in time series data. This method separates the data into three components: the seasonal component, the trend component, and the random component. The decomposition method is employed to forecast the seasonal and the trend components in a data series, while the ARIMA method is utilized to predict the random component within the data series. A tourist is an individual who visits an area for a specific period, making use of its facilities and infrastructure. In order to ascertain the growth of the number of foreign tourists, this study employs the decomposition-ARIMA hybrid method. The aim is to derive forecasting results from the data on the count of foreign tourists from January 2022 to December 2022. The research finding indicates that the best ARIMA model is ARIMA (0, 1, 1) with a Mean Absolute Percentage Error (MAPE) of 8.5% signifying a very high forecast accuracy.2024-03-25T09:54:43+07:00https://iptek.its.ac.id/index.php/inferensi/article/view/19843Determinants of PM2.5 Concentration in DKI Jakarta Province: A VAR Model Approach2024-03-26T10:06:11+07:00Bertolomeus Laksana Jayadri222111955@stis.ac.idMafitroh Pangastuti222112169@stis.ac.idMuh Farhan222112195@stis.ac.idFitri Kartiasihfkartiasih@stis.ac.id<p>Air pollution in the DKI Jakarta Province is a serious issue as it is related to public health and environmental concerns. Therefore, this research aims to analyze the causality of PM2.5 concentration with meteorological factors such as air temperature, humidity, rainfall, and wind speed. The data source used is from the MERRA-2 satellite, which provides information at a spatial resolution of 0.5° × 0.625°. The data covers the period from January 1, 1980, to November 1, 2023, with hourly time intervals. The research variables involve PM2.5 concentration as the response variable, as well as predictor variables such as air temperature, humidity, rainfall, and wind speed. The analytical method employed is the Vector Autoregressive (VAR) approach, as all variables are stationary at the level. The constructed VAR model tends to indicate that meteorological variables have a low explanatory power for PM2.5 concentration, while changes in PM2.5 concentration itself have sustained impacts in both the short and long term. This suggests that the fluctuations in PM2.5 concentration in DKI Jakarta Province are not significantly influenced by meteorological factors.</p>2024-03-25T00:00:00+07:00https://iptek.its.ac.id/index.php/inferensi/article/view/16247Pengendalian Kualitas Semen PCC di PT Semen Bosowa Banyuwangi Menggunakan Maximum Half-Normal Multivariate Control Chart (Max-Half-Mchart)2024-03-26T10:06:11+07:00I Melda Puspita Lokaimeldapuspitaloka@gmail.comHidayatul Khusnahidayatul@its.ac.idDiaz Fitra Aksiomadiaz_fa@statistika.its.ac.idCement is a building adhesive material produced from clinker and has the main ingredients in the form of calcium silicate and additional gypsum. The Indonesian Cement Association (ASI) states that there will be an increase in domestic cement consumption by 5.5% in 2021. Competition in the industrial sector is quite tight, causing PT Semen Bosowa Banyuwangi maintain and improve the quality of its products continuously. One of the steps taken is to check for blaine, residual, and free lime through the laboratory before the cement is distributed. Since there are more than one quality characteristic of Portland Composite Cement (PCC) and each quality characteristic is monitored every shift, the control chart used is a multivariate control chart for individual observations in the form of a Max-Half-Mchart. The Max-Half-Mchart for individual observation can effectively monitor mean and process variability simultaneously. PCC cement quality control using the Max-Half-Mchart in phase I showed that the process was statistically controlled. In phase II, there were out of control observations identified as a shift in the average process. The multivariate process capability measurement results obtained a 〖MC〗_pk value of 1.053, which means that the overall production of PCC cement complies with company regulations.2024-03-26T09:18:52+07:00https://iptek.its.ac.id/index.php/inferensi/article/view/20148Modeling Life Expectancy Index in West Nusa Tenggara Province with Panel Regression Method2024-03-26T10:06:11+07:00Alfira Mulya Astutialfiramulyastuti@uinmataram.ac.idErina Salsabila Ashrierina@gmail.comSabri Sabrisabri@unm.ac.id<p>Health is a condition of total physical, mental, and social well-being, rather than simply the lack of disease or weakness. One way to assess health indicators in a region is by enhancing the development of the health sector, which may be quantified using the life expectancy index (LEI). This study seeks to analyze the impact of average years of schooling, the adjusted per capita expenditure, and the number of poor people on life expectancy in NTB province from 2011 to 2020. The study's individual observation units consist of 10 regencies/cities in NTB Province. The data were obtained from BPS NTB in a panel data format and processed using the panel regression method. The panel model selection indicates that the Random Effect Model is the most suitable to predict the life expectancy in NTB province. The average years of schooling and the adjusted per capita expenditure have a notable impact on the life expectancy in NTB province. The effect provided is a beneficial impact. The number of poor people has a limited impact on life expectancy. Simultaneously, the average years of schooling, the adjusted per capita expenditure, and the number of poor people in the province of NTB have a substantial impact on the life expectancy. </p>2024-03-26T09:54:16+07:00