Journal Of Data Insights

Analysis Autocorrelation Spatial on Amount Fundraising at LAZISMU Semarang City Using Moran's Index

Hasna, Khansa', M. Al Haris, Fatkhurokhman Fauzi — Wed, 31 Dec 2025 00:00:00 +0000

Institution Zakat and Infaq Collectors And Sed e kah Muhammadiyah (LAZISMU) , has role important in gather And distribute funds activity social use help communities in need . L AZISMU Semarang City in general special focus on management funds at the level city , with not quite enough answer gather And allocate funds from public to humanitarian programs like help education , health , and help social research This aim For increase effectiveness collection funds Institution Zakat, Infaq , and Charity Collectors Alms Muhammadiyah in Semarang City. With apply approach spatial , research This analyze pattern distribution geographical donors , potential donations , and characteristics economy as well as demographics in each sub-district . Methodology study involving spatial data collection and analysis statistics . Results study This expected can give contribution on understanding scientific related zakat- based management spatial And become guidelines for institution similar in optimize collection And allocation funds .

Forecasting Starbucks Indonesia Share Prices with Methods ARIMA

Fellya Naza Nurcahyani, Septiana Putri Milasari, Indah Manfaati Nur — Wed, 31 Dec 2025 00:00:00 +0000

Starbucks is the largest coffee shop company in the world from the United States. This increase has become a trend in drinking coffee consumption among young people in a lifestyle while discussing. This indicates that the increase in the number of Starbucks stores is one of the drivers of Starbucks share prices among investors. Starbucks shares have the code SBUX as the issuer code. Starbucks Corporation is a coffee company and global coffeehouse chain. Satrbucks is an international company (MNCs) that anticipates various risks. The ARIMA forecasting method is different from other forecasting methods. This method uses an iterative approach to identify the most appropriate model from all possible existing models and this model can use all types of data. The ARIMA method was chosen for this research because this method is very suitable for short-term forecasting, where the products produced by the PT have a short expiration date. The result of the MAPE value is 3.218%, which means the accuracy is good because it is less than 10%.

Analysis of Data Mining in Predicting Poverty Levels in Indonesia Using the Decision Tree Method

Ahsin Ilallah, Zaihol Fatah — Wed, 31 Dec 2025 00:00:00 +0000

This study aims to examine the application of the Decision Tree method in predicting poverty levels in Indonesia using the RapidMiner software. Poverty is a complex issue influenced by social, economic, and educational factors. Through a data mining approach, this research seeks to identify patterns within poverty data to support more accurate decision-making. The research data were obtained from the public platform Kaggle and include key variables such as individual expenditure, the Human Development Index (HDI), average study time, access to proper sanitation and safe drinking water, as well as the open unemployment rate. The results show that the Decision Tree model achieved an accuracy of 94.90%, with a precision of 95.24% and a recall of 93.75%, based on the confusion matrix. The use of RapidMiner also facilitates the analysis, as the results are presented visually and are easy to understand. This model is recommended for implementation in government information

Geographically Weighted Regression Modeling Using Fixed and Adaptive Kernel Weights for the Human Development Index Case in West Java Province

Karin Karin, Alwan Fadlurohman, Dannu Purwanto — Wed, 31 Dec 2025 00:00:00 +0000

This study aims to analyze the factors influencing the Human Development Index (HDI) in West Java Province using the Geographically Weighted Regression (GWR) approach. The independent variables used in this study are the Open Unemployment Rate (TPT), School Participation Rate for ages 16–18 (APS_16_18), Population Density, and Gross Regional Domestic Product per Capita (PPK). The modeling was carried out by comparing various kernel functions, namely Gaussian, Bisquare, and Tricube, as well as two bandwidth approaches: fixed and adaptive. The results indicate that the GWR model with a Gaussian kernel and a fixed bandwidth approach provides the best performance based on the lowest AIC value. Compared to the classical Ordinary Least Squares (OLS) model, the GWR model offers a better explanation of spatial variation in HDI across the study area. Although the GWR model was not statistically significant overall based on the ANOVA test, local analysis showed that the variables TPT and PPK had significant effects in all districts and cities, while APS_16_18 and Population Density were not significant in any region. These findings demonstrate that the GWR model is capable of capturing spatial heterogeneity that is not detected by the global regression model.

Forecasting the Price of Curly Red Chilies in Malang Regency With Using the ARIMA Method

Nur Hanifah Ibrahim, Burhanuddin Izzul Salam, Indah Mafaati Nur — Wed, 31 Dec 2025 00:00:00 +0000

CChile is one of the hultikura plants that grows abundantly in Indonesia. In Indonesia, chilies are widely used as a cooking spice, making them a household staple. The increasing need for chilies (during the holidays) causes the demand for chilies to also increase. The increase in chile prices which is not directly proportional to chile production causes price changes. To maintain optimal availability of chilies, forecasting is needed to help make decisions and develop policies. One method that can be used for forecasting is the Autoregressive Integrated Moving Average (ARIMA) method. Based on the analysis results obtained, the best ARIMA model used in this research is the ARIMA model (0, 1, 0) which produces the smallest AIC value and MAPE of 2.664656%, the accuracy value is less than 10% which means that the forecasting ability with the ARIMA (0, 1, 0) model is very good.

Sentiment Analysis of YouTube User Comments on Government Policies Using the Naïve Bayes Method

Trisnawadi Ismardani, Zaihol Fatah — Wed, 31 Dec 2025 00:00:00 +0000

This research endeavors to analyze public sentiment expressed in YouTube user comments regarding the government's policy pertaining to the confiscation of undeveloped land after a two-year period of non-utilization. The methodology employed leverages the Naïve Bayes algorithm for classification, implemented within the Google Colaboratory environment. Data were systematically collected from specific YouTube videos discussing the aforementioned land confiscation policy. The research workflow encompassed comprehensive stages: data acquisition, rigorous text preprocessing, feature weighting utilizing the Term Frequency-Inverse Document Frequency (TF-IDF) technique, and final classification using the Naïve Bayes algorithm. Evaluation results demonstrate that the proposed model achieved a high accuracy level of 90%, with the highest F1-score recorded within the neutral sentiment class. However, an imbalance in the dataset's class distribution led to comparatively lower precision and recall values for both the positive and negative classes. Overall, this study confirms the high efficacy of the Naïve Bayes algorithm in analyzing Indonesian-language text data from social media platforms, specifically YouTube comments, and provides a crucial foundation for the future development of more balanced sentiment models.

Stock Price Forecasting of PT. Bank Rakyat Indonesia (Persero) Tbk. Using Long Short-Term Memory (LSTM) Method

Wed, 31 Dec 2025 00:00:00 +0000

Stock price forecasting is a major challenge in financial market analysis due to the volatility and unpredictability of price movements. The limitations of traditional statistical methods in capturing nonlinear patterns and long-term temporal dependencies have encouraged the adoption of deep learning–based approaches. This research aims to predict the stock price of PT Bank Rakyat Indonesia (Persero) Tbk. (BBRI) using the Long Short-Term Memory (LSTM) method, which is effective at handling problems with fading information and identifying long-term trends in time series data. The dataset comprises historical BBRI share prices from April 16, 2015, to April 16, 2025, with 80% of the data used for training and 20% for testing. LSTM’s model was trained for 10 epochs with a batch size of 32 using the Adam optimizer. The results prove that the LSTM model can effectively capture stock price movement patterns, achieving a mean absolute error (MAE) of 8.42 and a mean absolute percentage error (MAPE) of 1.50%, indicating a high level of accuracy. The visualization of the prediction results reveals a trend that closely aligns with the actual values. These findings reinforce LSTM’s position as a reliable approach to stock price forecasting and highlight its potential as a strategic tool for investors and policymakers in managing market risk.