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Detection and identification of plant diseases is critical to the success and efficiency of agricultural production. Plant disease outbreaks are becoming more frequent throughout the world, and the presence of these diseases in cultivated plants has a significant impact on productivity. Therefore, researchers are focusing on developing effective and reliable plant disease detection methods. Thus, farmers can take advantage of early detection of this disease to minimize future losses. This article discusses machine learning approaches as well as decision trees, K-nearest neighbors, naive Bayes, support vector machines (SVM), and random forests for detecting coffee leaf diseases using leaf images. The above-mentioned classifications were researched and compared to determine the most suitable plant disease prediction model with the highest accuracy. Compared with other classification algorithms, the SVM algorithm achieves the highest accuracy of 99.75%. All the models trained above will be used by farmers to quickly identify and classify new diseases in images as a prevention strategy. As a preventive measure, farmers can detect and classify new diseases in images early.

Manual quality assessment of Platelet Concentrate (TC) is highly subjective and inconsistent, necessitating an objective, automated classification system. This study aims to develop a computationally efficient, low-cost model for TC quality classification using Histogram Features extracted from grayscale images combined with the K-Nearest Neighbor (KNN) algorithm. The methodology employed critical preprocessing steps, including StandardScaler for normalization and SMOTE for balancing the training data, followed by optimization across K=1 to K=30. The optimal model achieved a maximum accuracy of 69.23% at K=6, with an F1-Score of 71.43%, confirming robust performance on the imbalanced testing set. The results validate the effectiveness of the Histogram-KNN approach as a consistent and reliable decision support system for rapid TC quality screening in resource-limited settings.

The insurance business within an insurance company offers insurance products owned by the insurance company. In every insurance product there is a premium payment and the premium is the income of an insurance company at the rate of the amount insured. The problem that PT BNI Life Insurance has is that there are many stops in premium payments such as policy redemptions due to errors in the benefits received or incorrect selection of the insurance product, this can reduce the achievement of targets for an insurance company. The aim of this research is to find out the best classification algorithm compared between K-Nearest Neighbor and Naive Bayes to predict the type of insurance product that customers will choose. In this research, data mining methods are applied to compare two different methods, namely the K-Nearest Neighbor method and the Naïve Bayes method. The level of accuracy results for the K-Nearest Neighbor method is 80% and the Naïve Bayes method is 70.53%, which means that the K-Nearest Neighbor method is the best method to apply to an insurance product classification system based on the demographics of prospective customers.

Pada tahun 2024, Indonesia akan menyelenggarakan pemilihan umum serentak yang meliputi pemilihan presiden dan pemilihan wakil rakyat di seluruh Indonesia. Masyarakat menanggapi kejadian ini dengan perasaan campur aduk, membagikan pemikirannya di situs media sosial seperti Twitter. Penelitian analisis sentimen calon presiden Indonesia tahun 2024 dilakukan terkait peristiwa ini. Sebanyak 1458 tweet digunakan dalam penelitian ini. Dengan 40,31% responden menyatakan sikap positif dan 43,46% menyatakan sentimen negatif, temuan analisis menunjukkan keseimbangan antara kedua sentimen tersebut. Menggunakan frasa "calon presiden," program Python di situs web Google Colab mengambil data twitter. Pendekatan K-Nearest Neighbor digunakan dalam proses klasifikasi. Selain itu data latih dibagi 6 : 4. 40% data uji dan 60% data latih. Nilai evaluasi yang diperoleh dari pengujian model dengan teknik K-Nearest Neighbor adalah akurasi sebesar 90,95%, presisi sebesar 62,17%, recall sebesar 62,33%, dan F-Measure sebesar 61,87%.

This research relates to predictions of laptop sales in computer shops in Central Aceh, with a focus on laptop brands Acer, Asus, HP and Lenovo. Over the last three years, sales of these laptops have reached 1,629 units, with a monthly average of between 108 and 150 units. Business owners today prefer brands with the highest percentage of sales, but this can lead to dead stock problems. Therefore, the author proposes using data mining techniques, especially the K-Nearest Neighbor (K-NN) method, to make recommendations for the number of products to be purchased by business owners based on past sales data. The K-NN method requires complete, structured and continuous sales data. It is important to choose an appropriate K value, and other factors such as weather, seasons, promotions, and special events also affect laptop sales. K-NN models may need to be combined with other data to improve prediction accuracy. It is hoped that this research will provide academic benefits in expanding knowledge about the use of the K-NN method in sales prediction, as well as practical benefits for business owners in planning their sales strategies. The research conclusions highlight the importance of good data collection, choosing the right K value, and considering external factors in the laptop sales prediction process.      

With the increasing development of technology the more variety of books circulating on the internet. As is the recommendation system on online book sites that provide books relevantly and as needed with one's preferences. One alternative is GoodReads, a social networking site that specializes in cataloging books and users can share reading book recommendations with each other by rating, reviewing, and commenting. As a large book recommendation site, it has a lot of data that can be processed by applying machine learning methods, but still not known as the most accurate model. By using the right model, we can provide more accurate recommendations. Therefore, this study will analyze the data obtained from the www.kaggle.com namely the goodreads-books dataset. This study proposed a data mining classification model to get the best model in recommending books on GoodReads. The algorithms used are Decision Tree, K-Nearest Neighbor, Naïve Bayes, Random Forest, and Support Vector Classifier, then for model evaluation using accuracy, precision, recall, f1-score, confusion matrix, AUC, and Mean Error Absolute. The test results of several classification algorithms found that Decision Tree has the highest accuracy among the methods presented by 99.95%, precision by 100%, recall by 96%, f1-score of 98% with MAE of 0.05 and AUC of 99.96%. This is proof that decision tree algorithms can be used as book recommendations based on book categories on GoodReads.