The process of identifying the ripeness level of cayenne peppers is an important step in cultivation and post-harvest handling. Dependence on the quality factors of farmers, such as visual diversity and differences in ripeness perception, results in subjective harvest outcomes. This manual process is also prone to inconsistent results, as humans have time limitations, fatigue, and sometimes lack concentration when sorting for long periods. To minimize these issues, technological intervention is needed to mechanically classify the ripeness level of cayenne peppers. This research aims to develop a classification model for the maturity level of cayenne pepper plants. This research proposes the use of the CNN method for feature extraction and KNN for data classification based on the features extracted by CNN. From the test scenarios carried out, the classification carried out by KNN based on CNN feature extraction got the best accuracy of 99.33%, while the CNN classification model got the best accuracy of 87.33%.

Penelitian ini menyoroti tantangan dalam perhitungan panen ikan molly secara manual, yang menghambat efisiensi dan profitabilitas peternakan ikan. Kurangnya penelitian komprehensif tentang penerapan teknologi pemrosesan citra digital di Indonesia mendorong eksplorasi algoritma BLOB dan filter HSV. Hasil pengujian menunjukkan tingkat akurasi yang memuaskan hingga 98,14%, meskipun dengan tantangan terkait identifikasi objek yang bertumpuk. Diperlukan penyesuaian parameter yang cermat untuk meningkatkan efektivitas deteksi.

In Indonesia, tuberculosis is ranked third in terms of prevalence among countries with the highest tuberculosis burden. Radiological examination, such as X-rays or X-rays, is a method generally used to detect tuberculosis. Chest X-ray examination is one method used to detect tuberculosis. To achieve these goals, the research will combine two powerful data processing techniques. First, the K-Means algorithm will be used to group x-ray image data based on similar characteristics, making it easier to identify typical patterns from images infected with tuberculosis. The research results show the highest accuracy of 93% using data division with a ratio of 80 : 20 with parameter K = 1. These results show that the combined model of the two algorithms can be applied to identify tuberculosis in the lungs.
 
 

This research explores the classification of brass instrument sounds using Convolutional Neural Network (CNN) combined with Mel-Frequency Cepstrum Coefficient (MFCC) feature extraction. This research aims to improve the accuracy of brass instrument sound recognition by utilizing CNN's ability to process audio data. Through experiments conducted with different audio durations and variations in CNN model architecture, this study evaluates the impact of dataset separation and model design on classification performance. The results show that dataset duration and CNN model architecture significantly affect classification accuracy, with the highest accuracy achieved in the scenario using 30 seconds of audio duration with an accuracy value of 84%. In addition, experiments varying the number of convolution layers in the CNN model show that the selection of the model architecture plays an important role in classification performance. Overall, this research contributes to advancing the field of audio classification by providing insight into the optimal dataset duration and model architecture for wind instrument speech recognition using CNNs.

In the current era of technological advancement, deep learning has become a widely discussed and utilized topic, particularly in image classification, object detection, and natural language processing. A significant development in deep learning is the Convolutional Neural Network (CNN), which is enhanced with various optimizations such as Adam, RMSProp, and SGD. This thesis implements the Inception v3 architecture for the deep learning model, utilizing these three optimization methods to classify malaria disease. The study aims to evaluate performance and determine the best optimization based on classification accuracy. The results indicate that the SGD optimization with a learning rate of 0.001 achieved an accuracy of 94%, RMSProp with learning rates of 0.001 and 0.0001 achieved an accuracy of 96%, and Adam with learning rates of 0.001 and 0.0001 achieved an accuracy of 95%.

Internet of Things (IoT) is a concept that connects electronic devices to the internet and enables the exchange of data between these devices. In this context, this study aims to develop an automatic door security control system using an ESP32 microcontroller-based e-KTP. The proposed system uses e-KTP as a substitute for a physical key on the door of the house. e-KTP will be connected to the ESP32 microcontroller which acts as the brain of the system. Personal data from the e-KTP, such as identity numbers, will be stored securely and used for user authentication. The ESP32 microcontroller will communicate with the server using the WiFi protocol to send and receive data. Users will be able to access the door of the house wirelessly via a mobile application connected to the server. This mobile application will provide an intuitive user interface to control door access and view security status. This system is also equipped with various security features. In addition, users can also monitor home security in real-time through a mobile application, even when they are not at home.

Consumers will make purchasing decisions about a product influenced by several things, namely, price and consumers can make purchasing decisions. This is the company's main goal in making a profit. Apart from price, what influences purchasing decisions is brand image, where brand image plays an important role in a decision to purchase a Vivo cellphone. The formulation of the problem in this research is 1. Does price influence the decision to purchase Vivo cellphones at Griya Satria Cibeber Housing, 2. Does brand image influence the decision to purchase Vivo cellphones at Griya Satria Cibeber Housing, 3. Do price and brand image influence purchasing decisions Vivo cellphone at Griya Satria Cibeber Housing Complex. This research aims to find out how influential price and brand image are on purchasing decisions for Vivo cellphones. The analysis method used is quantitative descriptive analysis and uses a survey method of 60 respondents. The data collection technique uses an instrument in the form of a questionnaire. Data analysis uses classical assumption tests, multiple linear regression and coefficient of determination analysis. Based on the results of the hypothesis test, the value tcount > ttable (3,455 > 2,542) and the significant value 0.001 < ? (0.05), then H0 is rejected and Ha is accepted. Based on the significance test, it was found that there is a significant influence between the price variable (X1) on purchasing decisions (Y). And the value of tcount > ttable (4,593 > 2,542) and the significant value is 0.000 < ? (0.05), then H0 is rejected and Ha is accepted. Based on the significance test, it was found that there is a significant influence between the brand image variable (X2) on purchasing decisions (Y).
 
 

Peningkatan pertumbuhan penduduk di Semarang berbanding lurus dengan peningkatan kebutuhan sampah dan listrik. Persoalannya, sampah hanya berpindah dari tempat pembuangan sampah ke tempat pembuangan akhir. Hal ini menyebabkan munculnya dampak buruk terhadap lingkungan kota yang kotor. Di sisi lain, permintaan kebutuhan listrik yang tinggi setiap tahunnya meningkat. Untuk mengatasi masalah ini adalah pemborosan telah dimanfaatkan bahan pembangkit listrik. Dua parameter telah diusulkan untuk memprediksi potensi pembangkit listrik tenaga sampah di kota Semarang seperti populasi dan sampah. Algoritma backpropagation dari JST telah digunakan untuk memprediksi pembangkit listrik tenaga sampah untuk tahun 2020 hingga 2022. Variabel yang digunakan dalam peramalan meliputi ukuran populasi dan volume sampah. Hasil penelitian menunjukkan bahwa produksi listrik WPP adalah 8,8 MWH untuk peramalan 3 tahun. sedangkan pertumbuhan orang ditunjukkan sebagai 1,7 juta selama 3 tahun. Potensi pembangkit listrik sampah PLN telah diberikan 0,29% dari total kebutuhan listrik di Jawa Tengah.