Waste-to-energy (WTE) is one of the Indonesian government’s programs aiming to meet the target of achieving a new and renewable energy (NRE) mix, as well as one of the solutions proposed to overcome the problem of waste. One of the products of WTE is energy derived from raw material waste (refuse-derived fuel/RDF). Using the formula y = 0.00003 x⁵ − 0.0069 x⁴ + 0.6298 x³ − 24.3245 x² + 432.8401 x + 55.7448 with R² = 0.9963, which was obtained by comparing a scatter plot diagram from the RDF calorie test dataset produced through a bio-drying process, the potential RDF calories produced using the waste composition dataset taken from each region in Indonesia can be calculated. The results of the calculations using the determined equations produce a list of provinces with RDF calorie potential, ordered from the largest to the smallest, using which the government can determine which areas are the main priority for processing waste into energy. Thus, through this method, the target of 5.1% renewable energy sourced from waste can be achieved by 2025.

Rizqy Agung merupakan organisasi bisnis layanan katering yang mengutamakan kepuasan pelanggan. Rizqy Agung dituntut untuk mengirimkan pesanan tepat waktu dengan kualitas makanan yang baik. Namun, staf bagian persediaan mengalami kesulitan dalam manajemen data persediaan bahan makanan dan mengakibatkan tidak terkontrolnya penyimpanan sehingga beberapa diantaranya membusuk. Sistem manajemen persediaan bahan baku makanan yang masih dilakukan secara manual menggunakan kertas mengakibatkan proses berjalan dengan tidak efisien dan akurat. Manajemen rantai pasok merupakan semua aktivitas yang dibutukan dalam mengelola bahan baku untuk menghasilkan produk yang berkualitas bagi pelanggan. Dalam manajemen rantai pasok, terdapat beberapa komponen yang terlibat dalam menciptakan nilai bagi organisasi atau perusahaan. Dengan manajemen rantai pasok, persediaan bahan baku dapat terkelola dengan baik. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem informasi berbasis web dengan menggunakan konsep manajemen rantai pasok yang dapat membantu Rizqy Agung dalam mengelola data persediaan bahan baku makanan. Model pengembangan sistem yang digunakan adalah waterfall. Berdasarkan hasil pengujian penerimaan, sistem layak untuk dioperasikan serta mampu meningkatkan keakuratan dan efiesiensi.

Artificial Neural Networks (ANN) with the Backpropagation algorithm have been widely applied across various domains, including data prediction tasks. However, one of the primary challenges in implementing Backpropagation is the selection of an optimal learning rate. A learning rate that is too high can lead to unstable convergence, while one that is too low can significantly slow down the training process. To address this issue, this study proposes an optimization of Backpropagation using an Adaptive Learning Rate through the implementation of the Adam optimizer. The objective of this research is to analyze the performance comparison between Standard Backpropagation and Backpropagation with the Adam optimizer in predicting rice harvest yields based on rainfall, temperature, and humidity variables. The dataset consists of 100 synthetic samples generated based on a normal distribution to resemble real-world data. The results show that the use of the Adam optimizer improves the performance of the ANN model compared to the Standard Backpropagation method. Model accuracy increased from 92.04% to 92.99%, while the values of loss, Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) decreased significantly, indicating that the model optimized with Adam is more stable and yields lower prediction errors. Therefore, Adaptive Learning Rate optimization using the Adam optimizer is proven to be effective in enhancing both the accuracy and efficiency of ANN in data prediction tasks.

A signature is a sign in written form, a person's identity for whether a document is correct or not, commonly known as a Biometric system. The Biometric system is the most basic, crucial and considered a superb process for a signature in detecting a person's identification and security. Signature forgery is a fraud that often occurs, causing bigger and longer expenses. For reasons like these, a signature detection system must be able to quickly and accurately recognize genuine and dummy signatures. The purpose of this study was to present the original and dummy signature pattern recognition by grouping the original signature data. In this study, Image Segmentation was used to divide the image into several parts, the K-Means Clustering algorithm to group several parts according to the properties of each object, and Feature Extraction of Texture Patterns and Shape Patterns with Gray Level Co-Occurrence Matrix (GLCM) to obtain feature values such as Entropy, Energy, Homogeneity, Correlation, and Contrast which has resulted in a study to detect genuine and counterfeit signatures. Preliminary results show that the percentage of identification of the signature biometric system developed using Feature Extraction with signature shapes on texture patterns got an average similarity rate of: 92.74%, and signature shapes on shape patterns attained an average similarity rate of: 79.20%. Therefore, the texture extraction pattern can detect the degree of similarity between the original signature and the dummy signature with a higher percentage value compared to the shape extraction pattern. The proposed method can produce better accuracy

Improvement of system management in public services needs special attention in an era of increasing population growth. Crowd Counting is proposed to ensure that the detection system for crowd objects in public facilities can run optimally. This study aims to develop Crowd Counting in a crowd object detection system in public facilities. This development is carried out to improve the performance of the You Only Look Once (YOLO) algorithm based on the Streamlit Framework. The performance of the YOLO algorithm can provide maximum results by combining the streamlit framework based on the image of the captured object at the train station. The test results of the development of Crowd Counting presented provide output with an mAP value of 90%, Recall 95%, and Precision 93.6%. Blackbox testing has also shown that the performance of Crowd Counting has provided quite significant detection accuracy. This research can contribute to the renewal of the detection system and be used as a form of solution in handling crowd problems in public facilities