Internasional Journal of Data Science, Engineering, and Anaylitics最新文献

{"title":"Water Availability Forecasting Using Univariate and Multivariate Prophet Time Series Model for ACEA\u0000 (European Automobile Manufacturers Association)","authors":"P. Riyantoko, Tresna Maulana Fahrudin, K. M. Hindrayani, A. Muhaimin, Trimono","doi":"10.33005/ijdasea.v1i2.12","DOIUrl":"https://doi.org/10.33005/ijdasea.v1i2.12","url":null,"abstract":"Time series is one of method to forecasting the data. The ACEA company has competition with opened the\u0000 data in the Water Availability and uses the data to forecast. The dataset namely, Aquifers-Petrignano in\u0000 Italy in water resources field has five parameters e.g. rainfall, temperature, depth to groundwater,\u0000 drainage volume, and river hydrometry. In our research will be forecast the depth to groundwater data using\u0000 univariate and multivariate approach of time series using Prophet Method. Prophet method is one of library\u0000 which develop by Facebook team. We also use the other approach to making the data clean, or the data ready\u0000 to forecast. We use handle missing data, transforming, differencing, decomposition time series, determine\u0000 lag, stationary approach, and Augmented Dickey-Fuller (ADF). The all approach will be uses to make sure that\u0000 the data not appearing the problem while we tried to forecast. In the other describe, we already get the\u0000 results using univariate and multivariate Prophet method. The multivariate approach has presented the value\u0000 of MAE 0.82 and RMSE 0.99, it’s better than while we forecast using univariate Prophet.","PeriodicalId":220622,"journal":{"name":"Internasional Journal of Data Science, Engineering, and Anaylitics","volume":"43 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134552276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Visitor Forecasting Wisata Bahari Lamongan (WBL) Using Hybrid Particle Swarm Optimization (PSO) and\u0000 Seasonal ARIMA","authors":"D. Rahmalia","doi":"10.33005/ijdasea.v1i2.7","DOIUrl":"https://doi.org/10.33005/ijdasea.v1i2.7","url":null,"abstract":"The revenue of city is determined by some factors, one of them is tourism sector. A problem of tourism\u0000 sector is forecasting visitors Wisata Bahari Lamongan (WBL). Because data of the number of visitors WBL are\u0000 fluctuating and seasonal, then it is required Seasonal ARIMA method. In the Seasonal ARIMA method, there are\u0000 some parameters that should be optimized for producing forecasting with small mean square error (MSE). In\u0000 this research, Seasonal ARIMA parameters will be optimized by Particle Swarm Optimization (PSO). PSO is\u0000 optimization algorithm inspired by behavior of birds group in searching food. Based on simulation results,\u0000 PSO algorithm can optimize Seasonal ARIMA parameter which is optimal and it can produce forecasting result\u0000 with small MSE.","PeriodicalId":220622,"journal":{"name":"Internasional Journal of Data Science, Engineering, and Anaylitics","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122314719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated System for Evaluation of Implementation of Internal Quality Audits and ISO 9001; 2015 Case\u0000 Study : Universitas Pembangunan Nasional “Veteran” Jawa Timur","authors":"M. Idhom, Jojok Dwiridotjahjono, I. G. S. Mas Diyasa, Rheza Rizqi Ahmadi, Munoto","doi":"10.33005/ijdasea.v1i2.15","DOIUrl":"https://doi.org/10.33005/ijdasea.v1i2.15","url":null,"abstract":"The Internal Quality Assurance System (SPMI) is a system to ensure quality in the process of providing\u0000 education. All components in the process of providing education support the achievement of aspects of SPMI.\u0000 An important role in SPMI is the scope of the study program (Prodi), faculties, Institute for Learning\u0000 Development and Quality Assurance (LP3M), and reviewers. Study program/faculty as SPMI document compiler.\u0000 LP3M acts as system manager and decision maker at SPMI. Reviewers as assessors who assess the results of the\u0000 SPMI study program documents. In SPMI activities, study programs / faculties fill out the required form\u0000 files. Then the reviewer can evaluate the completed file with a score from 1 to 4. However, the evaluation\u0000 process which is also called Internal Quality Audit (AMI) is still manual. This makes it less easy for LP3M\u0000 managers to monitor evaluation values ​​and make decisions. From the description above, this proposal\u0000 proposes a system that can perform an integrated evaluation of AMI online. Not only focusing on AMI,\u0000 SITEPAMIS can also conduct evaluations to meet ISO 9000;2015. ISO 9000;2015 is a standard for quality\u0000 management. This research is divided into two years. In the first year, the creation of a web\u0000 technology-based system with evaluation features of AMI and ISO 9000;2015 values ​​until the implementation\u0000 process. The output of this stage is a SITEPAMIS web application, reputable national journals, national\u0000 seminars, and copyrights. In the second year, the mobile version of SITEPAMIS was started. The output of\u0000 this stage is a SITEPAMIS mobile application, international journals, international seminars, and textbooks,\u0000 so that at the end of the research results in an Integrated System Application for Evaluation of Internal\u0000 Quality Audit Implementation and an ISO version of SITEPAMIS which is purely Web-based.","PeriodicalId":220622,"journal":{"name":"Internasional Journal of Data Science, Engineering, and Anaylitics","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123277096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Negative Binomial Time Series Regression – Random Forest Ensemble in Intermittent Data","authors":"A. Muhaimin, Prisma Hardi Aji Riyantoko, H. Prabowo, Trimono Trimono","doi":"10.33005/ijdasea.v1i2.10","DOIUrl":"https://doi.org/10.33005/ijdasea.v1i2.10","url":null,"abstract":"Intermittent dataset is a unique data that will be challenging to forecast. Because the data is\u0000 containing a lot of zeros. The kind of intermittent data can be sales data and rainfall data. Because both\u0000 sometimes no data recorded in a certain period. In this research, the model is created to overcome the\u0000 problem. The approach that is used in this research is the ensemble method. Mostly the intermittent data\u0000 comes from the Negative Binomial because the variance is over the mean. We use two datasets, which are\u0000 rainfall and sales data. So, our approach is creating the base model from the time series regression with\u0000 Negative Binomial based, and then we augmented the base model with a tree-based model which is random\u0000 forest. Furthermore, we compare the result with the benchmark method which is The Croston method and Single\u0000 Exponential Smoothing (SES). As the result, our approach can overcome the benchmark based on metric value by\u0000 1.79 and 7.18.","PeriodicalId":220622,"journal":{"name":"Internasional Journal of Data Science, Engineering, and Anaylitics","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124104765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Inflation Forecasting of Major Cities In East Kalimantan: A Comparison Of Holt-Winters And SARIMA\u0000 Model","authors":"Regi Muzio Ponziani","doi":"10.33005/ijdasea.v1i2.8","DOIUrl":"https://doi.org/10.33005/ijdasea.v1i2.8","url":null,"abstract":"This research aims to compare the performance of Holt Winters and Seasonal Autoregressive Integrate\u0000 Moving Average (SARIMA) models in predicting inflation in Balikpapan and Samarinda, two biggest cities in\u0000 East Kalimantan province. The importance of East Kalimantan province cannot be overstated since it has been\u0000 declared as the venue for the capital of Indonesia. Hence, inflation prediction of the two cities will give\u0000 valuable insights about the economic nature of the province for the country’s new capital. The data used in\u0000 this study extended from January 2015 to September 2021. The data were divided into training and test data.\u0000 The training data were used to model the time series equation using Holt winters and SARIMA models. Later,\u0000 the models derived from training data were employed to produce forecasts. The forecasts were compared to the\u0000 actual inflation data to determine the appropriate model for forecasting. Test data were from January 2015\u0000 to December 2020 and test data extended from January 2021 to September 2021. The result showed that\u0000 Holt-Winters performed better than SARIMA in prediction inflation. The Root Mean Squared Error (RMSE) values\u0000 are lower for Holt-Winters Exponential Smoothing for both cities. It also predicts better timing of\u0000 cyclicality than SARIMA model.","PeriodicalId":220622,"journal":{"name":"Internasional Journal of Data Science, Engineering, and Anaylitics","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116035130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Word Search on the \"SITUK\" Application Using the Levenshtein Distance Algorithm","authors":"I. G. S. Mas Diyasa, Kraugusteeliana, Gideon Setya Budiwitjaksono, Alfiatun Masrifah, Muhammad Rif'an Dzulqornain","doi":"10.33005/ijdasea.v1i2.13","DOIUrl":"https://doi.org/10.33005/ijdasea.v1i2.13","url":null,"abstract":"Integrated System for Online Competency Certification Test (SITUK) is an application used to carry out\u0000 the assessment process (competency certification) at LSP (Lembaga Sertifikasi Profesional) UPN (University\u0000 of Pembangunan Nasional) “Veteran” Jawa Timur, each of which is followed by approximately five hundred (500)\u0000 assessments. Thus the data stored is quite a lot, so to find data using a search system. Often, errors occur\u0000 in entering keywords that are not standard spelling or typos. For example, the keyword \"simple,\" even though\u0000 the default spelling is \"simple.\" Of course, the admin will get incomplete information, and even the admin\u0000 fails to get information that matches the entered keywords. To overcome the problems experienced in\u0000 conducting data searches on the SITUK application, we need a string search approach method to maximize the\u0000 search results. One of the algorithms used is Levenshtein which can calculate the distance of difference\u0000 between two strings. Implementation of the Levenshtein algorithm on the data search system in the SITUK\u0000 application has been able to overcome the problem of misspelling keywords with the mechanism of adding,\u0000 inserting, and deleting characters.","PeriodicalId":220622,"journal":{"name":"Internasional Journal of Data Science, Engineering, and Anaylitics","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124184707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Selection of Notification Based on Priority Scale with Fuzzy Algorithm","authors":"Mohammad Faisal Riftiarrasyid, Sherli Nur Diana, Aulia Istiqomah, Sumiati Ratna Sari","doi":"10.33005/ijdasea.v1i1.6","DOIUrl":"https://doi.org/10.33005/ijdasea.v1i1.6","url":null,"abstract":"Notification is one method that works as a marker that there is information waiting to be read. But\u0000 along with the times, notifications are increasingly filled with information that is considered less\u0000 important for device users. So there needs to be a breakthrough to overcome this. This study aims to design\u0000 a system that can help users sort out notifications that are considered important. It is proven that the\u0000 system can sort notifications based on the given metrics.","PeriodicalId":220622,"journal":{"name":"Internasional Journal of Data Science, Engineering, and Anaylitics","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134354583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Model Selection For Forecasting Rainfall Dataset","authors":"A. Muhaimin, H. Prabowo, Suhartono","doi":"10.33005/ijdasea.v1i1.2","DOIUrl":"https://doi.org/10.33005/ijdasea.v1i1.2","url":null,"abstract":"The objective of this research is to obtain the best method for forecasting rainfall in the Wonorejo\u0000 reservoir in Surabaya. Time series and causal approaches using statistical methods and machine learning will\u0000 be compared to forecast rainfall. Time series regression (TSR), autoregressive integrated moving average\u0000 (ARIMA), linear regression (LR), and transfer function (TF) are used as a statistical method. Feedforward\u0000 neural network (FFNN) and deep feed-forward neural network (DFFNN) is used as a machine learning method.\u0000 Statistical methods are used to capture linear patterns, whereas the machine learning method is used to\u0000 capture nonlinear patterns. Data about hourly rainfall in the Wonorejo reservoir is used as a case study.\u0000 The data has a seasonal pattern, i.e. monthly seasonality. Based on the cross-validation and information\u0000 criteria, the results showed that DFFNN using the time series approach has a more accurate forecast than\u0000 other methods. In general, machine learning methods have better accuracy than statistical methods.\u0000 Furthermore, additional information is obtained, through this research the parameter that best to make a\u0000 neural network model is known. Moreover, these results are also not in line with the results of M3 and M4\u0000 competition, i.e. more complex methods do not necessarily produce better forecasts than simpler methods.\u0000","PeriodicalId":220622,"journal":{"name":"Internasional Journal of Data Science, Engineering, and Anaylitics","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115264743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnosis of Diabetes Using Naïve Bayes Classifier Method","authors":"Tasya Ardhian Nisaa, Shavira Maya Ningrum, Berlianda Adha Haque","doi":"10.33005/ijdasea.v1i1.4","DOIUrl":"https://doi.org/10.33005/ijdasea.v1i1.4","url":null,"abstract":"Not a few people suffer from diabetes, diabetes is usually caused by genetic inheritance from parents\u0000 and grandparents. Not only from heredity but many criteria or characteristics can determine a person has\u0000 diabetes. This research was conducted by looking for a dataset on Kaggle that contains criteria for someone\u0000 diagnosed or undiagnosed with diabetes such as age, gender, weakness, polyuria, polydipsia, and others.\u0000 Furthermore, from these criteria, predictions are calculated using the Naive Bayes classification method\u0000 where this method is one of the data mining techniques. This prediction calculation uses the Python\u0000 programming language. From these criteria, each criterion is grouped with similarities and the results of\u0000 the program that have been made can diagnose someone with diabetes. The prediction calculations that have\u0000 been carried out have resulted in 90% accuracy, 93% precision, 89% recall, 92% specificity, and 91%\u0000 F1-Score.","PeriodicalId":220622,"journal":{"name":"Internasional Journal of Data Science, Engineering, and Anaylitics","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123509658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geometric Brownian Motion and Value at Risk For Analysis Stock Price Of Bumi Serpong Damai Ltd","authors":"Trimono Trimono, Di Asih I Maruddani, Prisma Hardi Aji Riyantoko, I. G. S. Mas Diyasa","doi":"10.33005/ijdasea.v1i1.3","DOIUrl":"https://doi.org/10.33005/ijdasea.v1i1.3","url":null,"abstract":"Investment is one of the activities that last actually attractive to the people of Indonesia. One of\u0000 the most widely traded financial assets in the capital market is stocks. Stock prices frequently experience\u0000 challenges to predict changes, so they can increase or decrease at any time. One method that can be applied\u0000 to predict stock prices is GBM. Then, the risk can be measured using the VaR risk measure. The GBM model is\u0000 determined to be accurate in predicting the stock price of BSDE.JK, with a MAPE value of 5.17%. By using\u0000 VaR-HS and VaR CFE, the prediction of risk of loss at the 95% confidence level for the period 06/07/21 is\u0000 -0.0597 and -0.0623","PeriodicalId":220622,"journal":{"name":"Internasional Journal of Data Science, Engineering, and Anaylitics","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132375311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}