The application of a structural equation modeling (SEM) assumes that all data follow only one model. This assumption may be inaccurate in certain cases because individuals tend to differ in their responses, and failure to consider heterogeneity may threaten the validity of the SEM results. This study focuses on unobservable heterogeneity, where the difference between two or more data sets does not depend on observable characteristics. In this study, we propose a new method for estimating SEM parameters containing unobserved heterogeneity within the data and assume that the heterogeneity arises from the outer model and inner model. The method combines partial least squares (PLS) and modified fuzzy clustering. Initially, each observation was randomly assigned weights in each selected segment. These weights continued to be iteratively updated using a specific objective function. The sum of the weighted residual squares resulting from the outer and inner models of PLS-SEM is an objective function that must be minimized. We then conducted a simulation study to evaluate the performance of the method by considering various factors, including the number of segments, model specifications, residual variance of endogenous latent variables, residual variance of indicators, population size, and distribution of latent variables. From the simulation study and its application to the actual data, we conclude that the proposed method can classify observations into correct segments and precisely predict SEM parameters in each segment.

The retail industry continues to grow and develop in Indonesia. The retail sector as a provider of goods used in everyday life has long started digital transformation in its business. Digital technology helps the retail industry collect valuable customer data. Business analytic is the use of data, information technology and statistical analysis, which is used to obtain information about a business and make decisions based on facts. Business analytic turns data into steps or actions in the context of making business decisions. Consumer needs and purchasing behavior can be predicted with big data-based technology. Association Rule is a technique in data mining to find the relationship between items in an item set combination. One of the utilizations of the association rule method is market basket analysis. Algorithms that can be used to analyze consumer purchasing patterns include the Apriori algorithm, Frequent Pattern Growth (FP-Growth) which represents a database structure in a horizontal format, and the Equivalence Class Transformation (ECLAT) algorithm which represents a vertical data format. In addition, this research will first analyze the complexity of the algorithm based on the time complexity in running the algorithm. This analysis uses these three algorithms, which are applied to Supermarket "X" transaction data in 2021, namely 136,202 transactions. The measure of goodness that is used to find out the best algorithm uses support and confidence values. The results show that the ECLAT algorithm is the most superior algorithm compared to the others based on the execution time required by the algorithm. The support value used in forming associations in the ECLAT algorithm is 1%, resulting in 19 rules. From the results of these rules, the highest support value was generated by the purchase of Indomie goreng special and Indomie ayam bawang, where as many as 1,362 shopping transactions bought these two items together or 2.71% of the total transactions.

The purpose of the study is to forecast the outflow and inflow of currency at Indonesian Central Bank or Bank Indonesia (BI) in Sulawesi Region. The currency outflow and inflow data tend to have a trend pattern which is influenced by calendar variation effects. Therefore, this research focuses to apply some forecasting methods that could handle calendar variation effects, i.e. Time Series Regression (TSR) and ARIMAX models, and compare the forecast accuracy with ARIMA model. The best model is selected based on the lowest of Root Mean Squares Errors (RMSE) at out-sample dataset. The results show that ARIMA is the best model for forecasting the currency outflow and inflow at South Sulawesi. Whereas, the best model for forecasting the currency outflow at Central Sulawesi and Southeast Sulawesi, and for forecasting the currency inflow at South Sulawesi and North Sulawesi is TSR. Additionally, ARIMAX is the best model for forecasting the currency outflow at North Sulawesi. Hence, the results show that more complex models do not neccessary yield more accurate forecast than the simpler one.

The fixed effect meta-analytic structural equation modeling (MASEM) model assumes that the population effect is homogeneous across studies. It was first developed analytically using Generalized Least Squares (GLS) and computationally using Weighted Least Square (WLS) methods. The MASEM fixed effect was not estimated analytically using the estimation method based on moment. One of the classic estimation methods based on moment is the Generalized Method of Moments (GMM), whereas GMM can possibly estimate the data whose studies has parameter uncertainty problems, it also has a high accuracy on data heterogeneity. Therefore, this study estimates the fixed effect MASEM model using GMM. The symmetry of this research is based on the proof goodness of the estimator and the performance that it is analytical and numerical. The estimation results were proven to be the goodness of the estimator, unbiased and consistent. To show the performance of the obtained estimator, a comparison was carried out on the same data as the MASEM using GLS. The results show that the estimation of MASEM using GMM yields the SE value in each coefficient is smaller than the estimation of MASEM using GLS. Interactive GMM for the determination of the optimal weight on GMM in this study gave better results and therefore needs to be developed in order to obtain a Random Model MASEM estimator using GMM that is much more reliable and accurate in performance.

PT Japfa Comfeed Indonesia, Tbk. Unit Margomulyo merupakan perusahaan yang berfokus pada kebutuhan pakan ternak khususnya ternak ayam broiler. Proses produksi pakan ternak dibantu oleh beberapa mesin salah satunya adalah mesin Pellet Mill. Mesin Pellet Mill merupakan mesin yang berfungsi untuk pembentukan pellet pada pakan ternak. Beberapakali mesin Pellet Mill mengalami kerusakan ditengah proses produksi, sehingga mengganggu proses produksi. Perbaikan pada mesin tidak selamanya efisien karena semakin sering mesin mengalami perbaikan maka keandalan mesin akan semakin menurun dan laju kerusakan semakin meningkat seiring bertambahnya waktu. Geometric Process merupakan metode yang sesuai untuk memodelkan permasalahan tersebut karena mesin setelah mengalami perbaikan maka dianggap memiliki kinerja dan keandalan yang menurun secara linier. Pada penelitian ini bertujuan untuk menentukan waktu optimum perusahaan melakukan tindakan pemeliharaan pada mesin Pellet Mill 5.12 dan 8.12 berupa perbaikan, pengecekan seluruh komponen, dan melakukan penggantian komponen. Hasil penelitian menunjukkan waktu optimum untuk pemeliharaan mesin Pellet Mill 5.12 adalah pada kerusakan ke-8 dengan estimasi rata-rata biaya jangka panjang sebesar Rp 3.812.096 perjam dan pada mesin Pellet Mill 8.12 pada kerusakan ke-7 dengan estimasi rata-rata biaya jangka panjang Rp 7.344.398 perjam

Coal has an important role in country growth. Coal production in Indonesia decreased 53.63 milion tons in 2020 because of Covid-19 Pandemic, declining coal consumption and low coal prices. Indonesia has many coal producers, one of them is “B” Company. “B” Company has not been able to meet its daily production target due to ups and downs in operation problem. So, predictive analysis of coal production is needed to help companies prepare for coal demand in the market. In this study, predictive analysis of coal production, especially actual coal getting “B” Company will be carried out in the daily production period in March 2021 - March 2022 using ARIMA-Asymmetric GARCH. The ARIMA method can be used on all data models, the data are not stationary, and have high accuracy for short-term predictions. Meanwhile the GARCH model can be used on data that has a heteroscedasticity effect on the residuals and Asymmetric GARCH can handle the asymmetric effects of the GARCH model. The goodness of the model and the level of accuracy will be seen through the values of AIC, SBC, RMSE and, MAD. The result of this study can also assist companies in making decision for preparation of coal production. Based on analysis, ARIMA([1,6],1,[1,13,14])-EGARCH(1,1) model is better than ARIMA([1,6],0,[1,13,14]) model, because it has an smaller AIC, SBC, dan RMSE values that is equal to 5,174.28, 5,188.82, and 2,500.98. Meanwhile, the MAD values for ARIMA([1,6],1,[1,13,14])-EGARCH(1,1) and ARIMA([1,6],0,[1,13,14]) models tend to be same that is equal to 2,381.58.