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This study compared the performance of SARIMA and Prophet models in forecasting daily close prices of three major Indonesian banking stocks: BBCA, BBRI, and BMRI, using data from January 2020 to March 2026. Data were retrieved via the yfinance library, preprocessed, and split into 80% training and 20% testing sets. SARIMA modeling followed the Box-Jenkins procedure, while Prophet was configured with a Lag-1 regressor, weekly and monthly seasonality, Indonesian public holidays, and log transformation. Model performance was evaluated using MAPE, MSE, and Dstat metrics. Results showed that SARIMA outperformed Prophet in MAPE and MSE across all six stock-variable combinations, with MAPE values ranging from 1.3368% to 1.9386% for SARIMA and 1.5992% to 2.2300% for Prophet. However, Prophet demonstrated marginally higher Dstat values in several series. Both models achieved "Very Good" forecasting accuracy. A web-based forecasting system was also developed using Streamlit to make the models accessible to investors.

This study aims to design, implement, and compare the performance of the Backpropagation algorithm from Artificial Neural Networks and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model in predicting the optimal daily rice requirement at Grillme Restaurant in Pontianak. The main problem faced by the restaurant is the uncertainty in determining the required daily rice stock, which periodically results in either understocking (shortage) or overstocking (wastage), leading to operational losses. To address this, the study utilizes historical daily rice sales data from January 2023 to April 2025 as the database for training and testing both predictive models. The SARIMA approach is employed to capture time series components (trend and seasonality), while Backpropagation is utilized to model non-linear patterns. Comparative test results indicate that the SARIMA model achieved superior accuracy compared to the Backpropagation model. This is confirmed by the Mean Absolute Percentage Error (MAPE) value of the SARIMA algorithm being 17.35%, which is lower than the MAPE value of Backpropagation at 19.62%. The MAPE values obtained by both models demonstrate good predictive capability, but it is concluded that SARIMA is more recommended for a more efficient and planned management of rice stock at Grillme Restaurant in Pontianak.

The instability of the climate is becoming increasingly prominent across Southeast Asia, creating uncertainty in agricultural systems that are highly dependent on seasonal weather patterns. Indonesia, where rice remains the primary staple food, is particularly vulnerable to the effects of rising temperatures and rainfall deficits. This study applies the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to predict rice production while incorporating indicators of extreme climate anomalies. Using publicly available datasets, including FAOSTAT production statistics, NOAA rainfall and temperature anomalies, and climate indices from the World Bank, this model was developed following the Box-Jenkins procedure. Among the configurations tested, the SARIMA model (1,1,1)(0,1,1)₁₂ showed the strongest performance, reflected in a MAPE of 4.62% and low RMSE values. The model indicates that significant El Niño events can reduce annual rice production by 3–7%, while wetter La Niña conditions may support production recovery. These findings highlight the importance of integrating climate-sensitive data into agricultural forecasting. The model presented here could support early warning systems, adaptive farming strategies, and long-term food security planning in Indonesia.

This study aims to develop and evaluate an adaptive hybrid weather prediction model that combines pattern classification techniques with a deep learning approach to improve forecasting accuracy, especially for extreme weather events. Using a quantitative-based Research and Development (R&D) approach, this study utilizes ten years of daily rainfall time series data from the Juanda Meteorological Station. The method developed comprises three main phases: weather pattern classification using K-Means clustering to separate normal and extreme patterns; development of a specialist prediction model using SARIMA for seasonal patterns and LSTM for non-linear patterns; and integration of both models into a single adaptive framework. The results show that the adaptive hybrid model performs significantly better than the single model, with a Mean Absolute Percentage Error (MAPE) of 8.76% and a Root Mean Square Error (RMSE) of 9.13%. The main contribution of this study is the development of an intelligent, accurate prediction framework with strong potential for integration into the national early warning system, thereby supporting more effective disaster mitigation efforts in Indonesia. Further research is recommended to validate the model in various regions and add additional climate variables to improve prediction accuracy.

The development of digital technology and increasingly widespread internet access in recent decades has led to a significant increase in online gambling participation. The ease of access offered by online gambling platforms has attracted a wide range of people regardless of age, gender, or socioeconomic background. However, this phenomenon also brings serious negative impacts, including addiction that can trigger criminal behaviour such as fraud, theft, and money laundering. This research aims to explain the relationship between online gambling and criminal behaviour from a criminal psychology perspective and discuss the social implications and effective prevention strategies. The method used is a literature study, with analysis of various data sources from books, journals, and previous research. The results of the review show that online gambling not only affects the economy, religion and mental health of individuals, but can also damage household harmony and social relationships. This study is expected to provide a deeper understanding of the dynamics of online gambling addiction and criminal behaviour so that more effective intervention approaches can be formulated.

This research aims to determine the feasibility, practicality and effectiveness of Cartoon Puppet Media to improve students' listening skills in Theme 2 Sub-theme 1 for Class III Students at SD N 091407 Sarimatondang. This research is research and development using the ADDIE development model which consists of 5 stages, namely Analysis, Design, Development, Implementation and Evaluation. Based on the research results obtained, validation by media experts obtained 90% in the "Very Appropriate" category, validation by material experts obtained 84% in the "Very Appropriate" category, practicality test results by education practitioner experts obtained 90% in the "Very Practical" category. The results of the students' listening skills test were with an average pre-test score of 60.8 with a completion percentage of 25% in the "Not Effective" category. Meanwhile, from the post-test results, an average score increase of 90.2 was obtained with a completion percentage of 100% in the "Very Effective" category. From these results it can be concluded that Puppet Cartoon media is feasible, practical and effective to use to improve students' listening skills.

Maksud dari riset ini ialah guna mengetahui dampak kompensasi terhadap kepuasan kerja dan turnover intention pada PT. Tri Bakti Sarimas Kuantan Singingi. Populasi penelitian berjumlah 114 orang dan untuk pengambilan sampel dengan mengaplikasikan persamaan Slovin sehingga banyak sampelnya adalah 89 orang. Pendekatan analisa data menerapkankan SEM (Structural Equation Model). Luaran riset menginterpretasikan bila: 1) Kepuasan kerja berdampak negatif pada turnover intention secara signifikan; 2) Kompensasi berdampak positif pada kepuasan kerja secara signifikan; 3) Kompensasi berdampak negatif pada turnover intention secara signifikan; 4) Kompensasi berdampak negatif pada turnover intention secara signifikan melalui kepuasan kerja.

Salah satu cara paling umum untuk mengontrol kecepatan adalah dengan menggunakan sensor enkoder Data yang dihasilkan akan ditampilkan di komputer atau laptop Anda. Saya tahu pengamatan mereka. Semakin banyak motor memiliki biaya perawatan yang lebih rendah selain efisiensi, torsi, kecepatan dan variabilitas. motor DC ini memiliki proses pergantian yang lebih cepat dan biaya perawatan yang lebih rendah, yang menyebabkan sikat menjadi percikan api dan cepat menjadi rusak. Rotor terbuat dari kumparan dan diinduksi oleh medan magnet berputar stator, yang menyebabkan arus listrik di rotor. Kecepatan putaran rotor jauh lebih lambat daripada kecepatan putaran medan magnet stator. Akibatnya motor induksi cenderung kurang efisien dibandingkan motor DC kecepatan tinggi, bisa fluktuatif, dan biaya perawatannya lebih rendah, sehingga digunakan motor DC brushless atau biasa dikenal dengan motor BLDC.