The spiral model is one of the methods used to perform software engineering development and can also be used for development in other fields. This spiral model is the result of a modification from the combination of the waterfall model and prototyping model so that it has many advantages including in each result an evaluation will be carried out, carried out sequentially or systematically, and is more focused in carrying out risk analysis from each stage. Has a function in development to make changes, additions and developments by determining accuracy and speed based on needs. In its implementation the spiral model has been carried out in various fields, but the results of the implementation are not yet known in what scope and how many implementations each year. This study aims to identify the results of the implementation of the spiral model development with data obtained from related papers in the 2012-2022 range. The method used in this study is the Systematic Literature Review (SLR) with the aim of identifying, reviewing, evaluating, and concluding all research on each relevant paper. The results showed that the spiral model development was mostly implemented in software development with a total of 19 papers and in the education sector as many as 17 papers, while the peak of the spiral model development was mostly implemented in 2016 and then increased again in 2021

Forest fires are one of the disasters that cause problems for the environment. Forest fires can cause damage and threats, not only to forest resources but also to the entire ecosystem, both fauna and plants that can damage biodiversity and the environment of an area and can endanger human life. The source of forest fires was initially thought to come from a dry and hot environment, but in some cases, forest fires are triggered by human activities in clearing land for agriculture or other purposes. One of the factors that influence the spread of forest fires is several variables combined with humidity levels, wind speed, and rainfall. In this study, researchers used machine learning algorithms KNN and Naïve Bayes to predict forest fires and compare the results of the performance levels of each method used. The results obtained indicate that the naive Bayes method has an accuracy value of 53.33% and K-NN has an accuracy value of 62.66%

In order to supply the world's protein needs, one of the most crucial industries is the poultry business. The problem that often occurs in chicken farms is disease, and this can have a significant impact on the farm. The availability of large enough amounts of data makes it possible to carry out the process of monitoring chicken diseases using deep learning technology for the classification of chicken diseases. With the availability of large enough data, the dataset has a variety of features that cause problems with data clutter. To overcome the problem of data conflict, an oversampling technique is used to increase the sample data from the minority class so that it has the same value as the other majority classes, and the Inception-V3 algorithm is used to classify chicken diseases based on fecal images. The total number of data used was 8067, which were broken down into the following four categories: Healthy, Salmonella, Coccidiosis, and Newcastle disease. Data balancing was done using oversampling to get the total data to 10500 before the evaluation process was started. The data was distributed by splitting it by 80% of the data will be used for training, 10% for data validation, and 10% for testing. The results of the test, which employed Inception V3 without oversampling, produced the highest possible score of 94.05%.