Gold, as a valuable commodity, has been a primary focus in the global financial market. It is often utilized as an investment instrument due to the belief in its potential price appreciation. However, the unpredictable and complex movement of gold prices poses a significant challenge in investment decision-making. Therefore, this research aims to address this issue by proposing the use of the Long Short-Term Memory (LSTM) model in time series analysis. LSTM is a robust approach to understanding patterns and trends in gold price data over time. In the context of time series analysis, historical gold price data includes daily, weekly, and monthly datasets. Each model with its respective dataset is useful for identifying patterns in gold prices. The daily model achieves an MSE of 452.2284140627481 and an RMSE of 21.26566279387379. The weekly model achieves an MSE of 1346.1816584357384 and an RMSE of 36.69034830082345. The monthly model achieves an MSE of 11649.597907584808 and an RMSE of 107.93330305139747. With these RMSE results, the LSTM model can predict gold prices effectively. Based on the trained models, it can also be concluded that gold prices exhibit long-term temporal dependence.

The study aims to implement Data Mining with Apriori Algorithm and Association Methods (shop cart analysis) to analyze the sales pattern of Kaffa Beauty Shop stores as a case study. Sales information obtained from stores is used to find out the repeated buying habits of cosmetic products. This analysis provides store owners with valuable information to make more useful decisions about product inventory management, marketing strategies, and other aspects of their business. The Apriori Algorithm implementation follows steps including data preprocessing, subsetting, frequent dataset search, and strong association rules (strong Association Rules). The results of the analysis show that there are important purchasing patterns among some cosmetic products that can be the basis of a more effective sales strategy. The study helps understand how data mining and Apriori Algorithms can be applied in business contexts such as Kaffa Beauty Shop stores. Therefore, the results of this analysis are expected to contribute greatly to improving business efficiency and optimizing marketing strategies for store owners and stakeholders. The research is also expected to show the enormous potential of data analysis to support optimal business decision making.

Sekarang ini keadaan udara di daerah perkotaan  sudah sangat tercemar oleh polusi. Semakin banyak gas CO2 yang mulai menyebar ke udara dapat menyebabkan meningkatnya suhu udara.. Hal ini dikarenakan bahwa CO2 dapat meningkatkan suhu di permukaan bumi. CO2 mempunyai peran yang dapat menyebabkan pemanasan global karena gas CO2 mempunyai  di udara bebas dan dapat menyerap panas Matahari sehinggs suhu Bumi meningkat dampak pencemaran udara seperti asap kendaraan, asap rokok, asap dari pembakaran pabrik, dan kontribusi terbesar dalam pemanasan global mempunyai pengaruh sebesar 50% dan mempunyai lama hidup 50 200 tahun di atmosfer. Peningkatan suhu udara dan konsentrasi CO2 merupakan masalah yang sering terjadi pada daerah perkotaan dimana salah satunya adalah meningkatnya jumlah kendaraan bermotor sehingga konsentrasi CO2 juga ikut meningkat. Dengan melihat korelasi CO2 Terhadap Suhu dan kelembapan Dengan mengunakan Multivariate Linear Regression, kita dapat melihat bagaiman korelasi antara suhu serta kelembapan. Regresi linier multivariat merupakan model regresi linier dengan lebih dari satu variabel respon Y berkorelasi dan satu atau lebih variabel prediktor X. Hasil penelitian menunjukkan bahwa  Dalam penelitian tersebut dikatakan bahwa terdapat korelasi antara CO2 suhu serta cahaya.  

In this comprehensive study, a meticulous analysis of the application of Convolutional Neural Network (CNN) methodologies in the classification of Covid-19 and non-Covid-19 cases was conducted. Leveraging diverse optimization techniques such as RMS, SGD, and Adam, the research systematically evaluated the performance of the CNN model in accurately discerning intricate patterns and distinct features associated with Covid-19 pathology. the implementation of the RMS and Adam optimization methods resulted in the highest accuracy levels, with both models achieving an impressive 98% accuracy in the classification of Covid-19 and non-Covid-19 cases. Leveraging the robust capabilities of these optimization techniques, the study successfully demonstrated the effectiveness of the RMS and Adam models in enhancing the precision and reliability of the Convolutional Neural Network (CNN) for the accurate identification and differentiation of Covid-19 patterns within the medical imaging datasets. The notable achievement of 98% accuracy further emphasizes the potential of these optimization methods in advancing the capabilities of CNN-based diagnostic tools, thus contributing significantly to the ongoing efforts in Covid-19 diagnosis and management.  

Poverty is a complex problem because it relates to various aspects of human life. In Indonesia, there is one province that has a very high percentage of poverty, namely East Java Province. Although from year to year the poverty rate has decreased, when viewed from the national level it is still very far from the government's expectations of reducing the poverty rate. Cases of poverty can be modeled by Econometrics. Econometric models are often applied to problems involving one or more related equations. One method that can be used to solve several interrelated equations because there is a correlation error regression between one another, namely Seemingly Unrelated Regression which is usually abbreviated as SUR, in this case Spatial Seemingly Unrelated Regression (SUR-Spatial) is development that takes into account the spatial influence between locations. From the results of tests conducted in the SUR-Spatial Lagrange Multiplier model, the poverty data generated by the East Java Province is the SUR-Spatial Autoregressive Model (SUR-SAR). So with the SUR-SAR model it can be seen that the variable that has a significant effect on the percentage of poor people is the growth rate of Gross Regional Domestic Product based on the constant price of the minimum wage for each district, as well as the average length of school years. Meanwhile, the Poverty Depth Index has an effect because of the growth rate of Gross Regional Domestic Product on the basis of constant prices and the average length of schooling. The Poverty Severity Index is influenced by the growth rate of Gross Regional Domestic Product at constant prices and average years of schooling.

Saat ini, peranan desain grafis sangat penting. Hal ini karena adanya pengaruh tren media sosial dalam dunia komunikasi teknologi, yang menjadikan media komunikasi lebih personal. Kuantitas pengguna desain grafis menjadi luas. Tujuan kegiatan ini adalah memberikan pengetahuan dan pelatihan desain grafis, yang saat ini di butuhkan. untuk kepentingan organisasi, promosi dan wirausaha lain. Metode yang digunakan adalah pengenalan, pelaksanaan dan evaluasi. Pengenalan dilakukan untuk mengetahui kebutuhan yang diperlukan para pemuda IPNU/IPPNU desa Ngadiluwih, pelaksanaan dilakukan dengan memberikan pelatihan desain grafis menggunakan aplikasi pixellab dan canva dengan media handphone. Setelah pelatihan ini para peserta yang sebelumnya belum memahami dan belum mengetahui cara mendesain dengan media handphone, menjadi paham dan mampu mendesain hanya dengan media handphone dan peserta dapat meningkatkan kreativitas. inovasi mereka dalam mendesain gambar tanpa harus menggunakan laptop/computer.