Social and economic disparities between regions in Indonesia are still a serious problem, reflected in the high Gini Ratio and disparities in purchasing power. The Consumer Price Index (CPI) is an important indicator in measuring consumption patterns and price pressures between regions. This study aims to cluster 150 districts/cities based on consumption patterns through the CPI using the Agglomerative Hierarchical Clustering (AHC) and Spectral Clustering methods combined with Principal Component Analysis (PCA). The innovation lies in the comparison of the two clustering methods as well as the application of PCA to clarify the data structure. Evaluation with Silhouette Score and Davies-Bouldin Index showed that AHC gave the best results with four representative clusters (Silhouette: 0.76; DBI: 0.32), compared to Spectral Clustering with three clusters (Silhouette: 0.75; DBI: 0.54). Each cluster has different expenditure characteristics, useful for data-driven policy making. These results show that AHC is more effective in capturing interregional variations in consumption.

Coronary heart disease (CHD) remains a leading cause of mortality worldwide. Early detection is essential to reduce complications and improve patient outcomes. This study aims to develop a classification model using machine learning algorithms to predict CHD risk based on clinical symptoms. The dataset used is the Cleveland Heart Disease dataset from the UCI Machine Learning Repository, consisting of 303 patient records with 14 clinical features. The preprocessing stage involved handling missing values, normalizing features, and transforming categorical variables. Four classification algorithms were applied: K-Nearest Neighbors (K-NN), Decision Tree, Random Forest, and Support Vector Machine (SVM). Each model was trained using stratified 10-fold cross-validation to ensure generalizability. Evaluation using accuracy, precision, recall, F1-score, and ROC-AUC metrics showed that the Random Forest algorithm achieved the highest performance with 87.2% accuracy. Feature importance analysis indicated that chest pain type, resting blood pressure, cholesterol, and ST depression were the most influential indicators. These results demonstrate that machine learning, particularly Random Forest, can effectively support early diagnosis of CHD in clinical settings and has the potential to be integrated into clinical decision support systems (CDSS).

This study aims to qualitatively analyze the coupon-setting strategy employed by the Directorate General of Budget Financing and Risk Management (DJPPR) in response to fluctuations in Bank Indonesia's benchmark interest rate, specifically the BI 7-Day Reverse Repo Rate (BI7DRR), during the 2021–2024 period. Using a descriptive-analytical approach based on a literature review, the research examines official policy documents, historical BI7DRR data, and relevant academic studies. The findings indicate that DJPPR implemented a floating with floor coupon mechanism as an adaptive strategy to address interest rate volatility. This mechanism has proven effective in maintaining the appeal of Savings Bond Ritel (SBR) among retail investors, while also reflecting the synergy between fiscal and monetary policy in managing national debt instruments

Crime generates insecurities, which limit tourism in several ways: reduced tourist destination appeal, increased perceptions of the risk of being victimized as a tourist, harm to destination image, increased transaction costs, and reduced institutional quality. This study employed a panel data approach to investigate the impact of criminal acts on tourist arrivals in 35 Central Javanese districts/cities from 2020 to 2024. Control variables included tourist attractions, accommodation, restaurants, vehicles, and the COVID-19 pandemic. Findings revealed a significant negative relationship between criminal cases and tourist visits, underscoring security's crucial role. Conversely, tourist attractions, accommodation, and restaurants positively affected arrivals. As expected, the COVID-19 pandemic negatively influenced tourism. Notably, the study found no significant impact from the prevalence of violence against children and women or the ratio of vehicles. Comparative analysis (between tourism-based and non-tourism-based regions) shows that crime consistently deters and attractions consistently promote tourism in all areas. Still, accommodation positively impacts tourism-based economies, while restaurants promote tourism in both regions. These results highlight the necessity of enhancing security measures at tourist destinations, fostering the development of sustainable attractions, and ensuring adequate supporting infrastructure to stimulate tourism sector growth in Central Java.

The rapid advancement of information technology has had a significant impact in various fields, particularly in pattern recognition and image processing. One of the ongoing challenges is accurately recognizing handwritten digits, which plays a crucial role in document digitization, automated form reading, and other intelligent systems. This study aims to implement and evaluate the K-Nearest Neighbors (KNN) algorithm, a simple yet effective classification technique, in recognizing handwritten digit images. The data used comes from the public dataset load_digits from Scikit-learn, which contains 1,797 grayscale images of handwritten digits sized 8x8 pixels. Each image is represented as a 64-dimensional feature vector. The dataset is split into training and testing data with an 80:20 ratio, and the model is trained using KNN with k=3. The experimental results show a classification accuracy of 96.94%, with minimal prediction errors that typically occur in digits with similar visual shapes, such as 5 and 9. This study demonstrates that KNN, despite its simplicity, can provide high accuracy in handwritten digit recognition when supported by proper preprocessing and parameter selection. The implications of this research highlight the potential for developing intelligent applications in education, data entry automation, and identity verification.

Indonesia is a developing country located at the convergence of four tectonic plates, making it highly prone to natural disasters such as earthquakes, tsunamis, landslides, and volcanic eruptions. These frequent disasters highlight the critical need for reliable electricity during emergencies. However, disaster-affected areas often struggle to restore power due to accessibility issues. To address this, alternative energy sources are needed, and Solar Power Plants (PLTS) offer a practical solution. PLTS are easy to implement, depend only on sunlight, and provide clean energy with low carbon emissions. Under clear skies, solar radiation can reach 1,000 Watts per square meter, making it a powerful energy source. Additionally, PLTS systems are adaptable and can be deployed in various formats, including mobile units. This study focuses on designing a Mobile PLTS to support BASARNAS operations in disaster zones. Data collection was conducted using resources from BNPB, BMKG, BASARNAS, and NASA. The analysis includes the geographical characteristics of the site, solar radiation intensity, and the operational dimensions of the BASARNAS Mobile Truck. The study aims to determine the suitable system specifications and estimate the energy production capacity of the Mobile PLTS. The proposed design features 20 solar panels, each with a capacity of 300 Wp, producing an average of 27.70 kWh per day. It also includes 16 batteries for energy storage. The remaining space in the truck can be used for transporting logistics or essential tools, making it a multifunctional unit ideal for disaster response scenarios.

The aim of this research is to evaluate the influence of macroeconomic factors such as interest rates, inflation and exchange rates on the movement of Bank Rakyat Indonesia (BBRI) share prices in the period 2020 to 2024. The analysis used is a quantitative method taking a multiple linear regression approach. The data used is secondary, obtained from other sources, namely the Indonesian Stock Exchange (BEI) and Bank Indonesia (BI). The analysis was carried out using multiple linear regression using EViews 12 software. The findings from the analysis show that the three independent variables do not have a significant impact on BBRI share price fluctuations, both in simultaneous and partial analysis. From the results of the analysis, it can be concluded that there is a relationship where interest rates and exchange rates have a significant positive influence on stock prices, while inflation shows a negative impact on share prices.

The number of pesantren in Indonesia is vast and has contributed to economic development and growth. The development of pesantren today is about providing entrepreneurial provisions for students (santri) so that they will be ready to enter the community by having the ability to act creatively and innovatively. This research intends to strengthen students' social entrepreneurship intentions by applying motivational training based on developing self-potential. The research to be carried out is experimental research with quasi-experimental research techniques (quasi-experimental design). The research was conducted at the Pondok Pesantren as Salafy Al Asror, Semarang City, Central Java, Indonesia. The data collection techniques used are tests, observations, and interviews. The results indicated that managerial ability, motivational ability, creativity, and communication significantly affect the intention of the student to pursue social entrepreneurship. This research also develops an entrepreneurial model for students by utilizing the abilities possessed by students, namely managerial abilities, motivation, creativity, and communication. The model developed was tested differently and, based on the model developed, was effective in increasing the intention of students to pursue social entrepreneurship. Thus, this research has contributed to creating social enterprise intentions in students through the students' self-potential.

Energi adalah aspek krusial dalam pembangunan dan keberlangsungan hidup manusia. Dalam konteks ini, pemahaman yang mendalam tentang jenis energi dan ketersediaannya menjadi penting. Tulisan ini memberikan tinjauan menyeluruh tentang berbagai jenis energi yang digunakan secara luas di seluruh dunia, seperti tenaga surya, angin, hidro, biomassa, dan nuklir. Setiap jenis energi memiliki karakteristik, kelebihan, dan tantangan tersendiri dalam penggunaannya. Selain itu, ketersediaan energi menjadi faktor penentu dalam keberlanjutan ekonomi dan sosial suatu negara. Penelitian ini juga mengulas tentang tantangan global terkait ketersediaan energi, termasuk perubahan iklim, polusi, dan ketergantungan terhadap sumber energi yang tidak terbarukan. Dengan mempertimbangkan kompleksitas ini, upaya-upaya untuk meningkatkan ketersediaan energi, mempromosikan energi bersih dan berkelanjutan, serta mengembangkan teknologi dan kebijakan yang mendukung, menjadi sangat penting untuk menghadapi tantangan energi global di masa depan.

Linguistics plays a crucial role in studying and understanding the structure of language, including in the context of Arabic. The Arabic language, with its complex system of morphology, syntax, and phonology, requires an in-depth linguistic approach to analyze its grammatical structure. This study aims to explore how linguistic theories, such as phonology, morphology, syntax, and semantics, contribute to understanding the basic structure of Arabic. Through linguistic approaches, a clearer and more structured understanding of sentence structure, word patterns, and the relationships between language elements can be achieved. This paper also discusses the challenges faced in applying linguistic theories to Arabic, as well as the importance of a linguistic approach in teaching Arabic to non-native speakers. The findings of this study are expected to provide broader insights into the role of linguistics in learning and teaching Arabic more effectively and systematically.