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All Journal Sistemasi: Jurnal Sistem Informasi Southeast Asia Journal of Business, Accounting, and Entrepreneurship
Imel, Imel
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Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Social Sciences

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The Effect of Service Quality, Price, and Location on Customer Loyalty through Customer Satisfaction Imel, Imel; Nurjannah, Nurjannah; Munawir, Munawir
Southeast Asia Journal of Business, Accounting, and Entrepreneurship Vol. 2 No. 4 (2024): DECEMBER 2024 - SAINS
Publisher : PT Smart Media Makassar

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Abstract

This study aims to determine the effect of service quality, price and location on customer loyalty through customer satisfaction as an intervening variable at Daily Coffee, Palopo, South Sulawesi, Indonesia. The sample in this study amounted to 105 people. The types and sources of data used in this study are qualitative and quantitative data types. Data collection used in this study by conducting observations and questionnaires. Primary data in this study is data obtained from respondents' responses to the statement items he asked in the questionnaire which were obtained either directly or indirectly while secondary data in this study is a source that does not directly provide data to data collectors, for example documents and interviews. The data collected was then analyzed using multiple regression analysis methods with the help of the Smart-PLS program. The results showed that service quality, price and location on customer loyalty through customer satisfaction as an intervening variable at Daily Coffee Palopo, which means that the hypothesis in this study is accepted.
Optimization of the Linear Regression Algorithm using GridSearchCV for Rice Crop Production Prediction Imel, Imel; M.Kom (SCOPUS ID: 57216417658), Norhikmah; Wulandari, Irma Rofni; Mustofa, Ali; Larasati, Niken; subektiningsih, subektiningsih
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5877

Abstract

Rice production in Central Java Province fluctuates annually, affecting food security and agricultural output distribution. Therefore, accurate prediction methods are essential to assist stakeholders in agricultural planning and strategic decision-making. This study applies the Linear Regression algorithm to predict rice production based on historical data from 2014 to 2023 obtained from the official website of the Central Java Provincial Agriculture and Plantation Office. The model is developed using multiple linear regression with variables including planted area, harvested area, and productivity. The novelty of this study lies in the structured application of hyperparameter tuning using GridSearchCV to optimize Linear Regression performance, as well as the integration of a preprocessing pipeline based on data distribution stabilization to improve accuracy and model generalization. The research process includes data collection, preprocessing, modeling, optimization, model evaluation, and deployment as a web-based application using Streamlit Cloud. GridSearchCV optimization results indicate a cross-validation accuracy of 98.26%, confirming the model’s strong predictive capability. Model evaluation shows an R² value of 0.9754, with MAE of 0.0957, MSE of 0.0307, and RMSE of 0.1753, indicating low prediction errors and stable model performance. The optimized model is implemented as a web application via Streamlit Cloud, enabling direct use by end-users. For future research, it is recommended to incorporate additional variables such as rainfall, temperature, and rice variety, or to compare performance with other algorithms such as Random Forest, Support Vector Regression, or Long Short-Term Memory (LSTM) to further enhance prediction accuracy.
Co-Authors Ali Mustopa M.Kom (SCOPUS ID: 57216417658), Norhikmah Munawir Munawir Niken Larasati Nurjannah Nurjannah Subektiningsih Subektiningsih Wulandari, Irma Rofni