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Background: Accurate detection of renal cysts on CT urography requires high diagnostic precision, while manual interpretation by radiologists is susceptible to inter-observer variability and potential delays in clinical decision-making. These challenges underscore the need for a reliable automated detection system to support radiological assessment. Objective: This study aims to develop and evaluate the performance of the Neo-ZasAI application based on the YOLOv8 algorithm for the automatic identification of renal cysts. Methods: Employing a Research and Development design using the ADDIE model, the study encompassed needs analysis, model design, software development, system implementation using 200 CT urography images, and diagnostic performance evaluation. Classification results generated by Neo-ZasAI were compared with radiologist readings through confusion matrix analysis and ROC–AUC assessment. Results: The findings indicate that Neo-ZasAI achieved an accuracy of 97,5%, sensitivity of 96%, specificity of 99%, positive predictive value of 98,9%, and negative predictive value of 96,1%. The ROC analysis yielded an AUC of 0.988 (p < 0.001), demonstrating excellent discriminative capability and high concordance with radiologist interpretations as the diagnostic gold standard. Conclusion: These results suggest that Neo-ZasAI is capable of performing rapid, consistent, and accurate renal cyst detection and is thus feasible for implementation as a clinical decision support system in radiology, with potential integration into PACS workflows and further development to enhance model generalizability.

Land cover is defined as the physical and biological cover of the earth's surface, both those formed naturally such as swamps, hills and rivers and those formed by man-made means such as rice fields, gardens, forests and buildings. As technology develops, conventional methods of satellite image processing are starting to be abandoned. This is because conventional methods require quite a long time to process satellite image data. The presence of Google Earth Engine (GEE), which is a cloud computing-based platform, makes it easier for users to process satellite image data boldly and for free. This research aims to classify satellite image land cover in the Trenggalek Regency area, East Java. The level of accuracy in this study uses a confusion matrix. The accuracy test results show a value of 90.23%.

Scholarships are a form of assistance in the form of educational expenses provided by the government or foundations to students or students who are categorized as from underprivileged families. However, in datermining scholarship recipients, there are still many scholarship recipients who come from wealthy families, while those from less fortunate families do not receive this assistance. This may be due to calculations and data processing that still use manual methods, causing scholarship recipients to not be on target. The purpose of this research is to simplify and minimize calculation errors in determining scholarship recipients for the Smart Indonesia Program (PIP) at SMK Karya Medika. Therefore, for calculating and processing PIP scholarship recipients data, data mining techniques can use the calssification method using the K-NN algprithm. K-Nearest Neighbor is a data classification method that will be used for data objects based on learning data that is closer to the object. In this study using the Confusion Matrix test so as to obtain an accuracy value of 80.00%.     

Di era digital saat ini, kemajuan teknologi informasi mengalami pertumbuhan yang pesat. Salah satu perkembangan yang signifikan terjadi dalam bidang kecerdasan buatan (Artificial Intelligence), yang telah diterapkan luas di berbagai sektor, termasuk analisis data dan pengambilan keputusan. Para peneliti di bidang data mining telah menciptakan beragam algoritma klasifikasi yang meningkatkan proses klasifikasi dengan memanfaatkan atribut numerik dan nominal. Klasifikasi adalah suatu proses analisis data yang menghasilkan model untuk mewakili kelas-kelas dalam data tersebut, seperti yang terjadi pada Decision Tree yang digunakan untuk menganalisis klasifikasi dan pola prediksi data serta menggambarkan hubungan antara variabel atribut  dan variabel target  dalam bentuk struktur pohon. Python, sebagai bahasa pemrograman populer dalam pengembangan kecerdasan buatan, menyediakan pustaka dan framework yang mendukung implementasi algoritma Decision Tree. Dengan menerapkan algoritma Decision Tree untuk klasifikasi data evaluasi mobil menggunakan Python, kita dapat memanfaatkan kekuatan AI untuk memberikan solusi efektif dan efisien dalam pengambilan keputusan terkait evaluasi mobil. Menggunakan dataset Evaluation Car dari UC Irvine Machine Learning Repository, hasil penelitian menunjukkan akurasi sebesar 81%. Confusion Matrix dan laporan klasifikasi menunjukkan performa model yang baik dalam melakukan prediksi.

Technological developments today make it easy for people to use social media as a means of expressing opinions, including Twitter. The case study taken by the researcher is the sentiment towards the performance of the mayor of Medan. The case was taken because it was widely discussed by Indonesian people, especially the city of Medan on Twitter social media. One of the uses of this research is to find out the trend of Twitter user comments on the performance of the mayor of Medan by conducting a sentiment analysis. Sentiment will be classified as positive, negative and neutral. The algorithm used in sentiment analysis is Naïve Bayes. The stages in conducting sentiment analysis in this study are data preprocessing, data processing, classification, and evaluation. The results of this study are using the SMOTE method, the training and testing ratio is 80:20 because it has the highest accuracy, which is 78% compared to other ratios. The prediction results resulting from the classification turned out to be more dominant towards neutral labels. In addition to classifying for sentiment analysis, this study also measures the performance of the model created. The results showed that the Naïve Bayes algorithm has a precision value of 78%, a recall of 78%, and an f1-score of 77%.