Publication Search

As many as 7.3 million people worldwide die from heart disease. This indicates that heart disease is one of the diseases that cause the most deaths. As a preventive effort in handling heart disease, it is necessary to predict heart disease in patients. The classification process to predict heart disease is done using a decision tree. This decision tree is interesting because it is more flexible in providing the advantage of visualizing the advice so that the prediction can be observed. This study uses Heart Disease Prediction Dataset data with a total of 303 data. Then predictions are made using Decision tree so that the accuracy results are 83.60%, precision 89.28%, recall 78.12% and F1 score of 83.33%.

The use of the Decision Tree method in smartphone price classification is the focus of this study. By using the 10 most relevant features and data normalization to achieve scale consistency, the Decision Tree algorithm delivers an average accuracy of 81%. Although some false positives and false negatives occur, the model is able to classify smartphone prices well, especially in identifying low and high prices. These results provide important insights into the features that affect smartphone prices. While there is still room for improvement, this model provides a solid foundation for the smartphone industry to determine prices based on certain specifications. The importance of relevant feature selection and data normalization was revealed in this study. Despite the accuracy reaching 81%, improvements in the classification of medium and high price classes are still possible to reduce prediction errors. This method provides an important basis for the smartphone industry to set prices based on specifications, and data mining techniques such as Decision Tree can be improved to improve the accuracy of future price predictions.

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.

The goal of this data mining C4.5 implementation is to improve student performance in academic coursework in the computer science department at Teknik Fakultas and Pancasakti University in Tegal. Use a limited number of dimensions to assess the following: nyata, jaminan, keandalan, empatia, dan bukti nyata. It is difficult to determine which quality standard has to be raised because the aforementioned kelima aspek cannot be changed in an objective manner. Utilizing the algorithm C4.5 method, the authors consider reducing the sample size to the point where the keputusan is reduced. After manual perhitungan, pembuktian is also carried out using an application called RapidMiner.The analysis's conclusions show that the most important factor in determining the mahasiswa's tingkat kepuasan is the style of teaching.

Data mining is a technology that plays an important role in supporting data-driven decision making, especially in complex and dynamic higher education environments. In the context of education management, the ability to predict student graduation is an essential aspect because it can help institutions plan strategic steps, intervene earlier, and optimize academic resources. This study aims to apply the C4.5 decision tree algorithm to build a student graduation prediction model based on academic data. The research dataset includes key variables such as Grade Point Average (GPA), total Semester Credit Units (SKS) taken, and student attendance rates during lectures. The analysis was conducted using the C4.5 algorithm, which is known for its high level of interpretability, making the model results easy to understand by policy makers. The test results showed an accuracy of 84.6%, indicating that this method has the potential to support data-based academic management systems. These findings are expected to serve as a basis for educational institutions to improve the effectiveness of monitoring and evaluating the student learning process.

. In the shipbuilding industry, the repair process is a series of jobs that require a relatively short time. Delays in repairs can occur due to weak management and also caused by less than optimal empowerment of human resources. This study aims to identify the risks found in four divisions, namely the Production Division, Warehouse Division, Finance Division and Purchasing Division. The study found 31 risk events. Determining the value of each risk is carried out using the Failure Mode and Effect Analysis method. There were 13 risks that had the highest Risk Priority Number, namely the length of approval for requests for goods (RPN = 522.88), delays in payment processes by customers (RPN = 504.64), requests for additional work from the owner (RPN = 477.128), fluctuations in the number of manpower (RPN = 454.08), Changes in material use related to the availability of materials in the warehouse (RPN = 411.768), Changes in material calculations related to design (RPN = 389.017), Length of decision making by the owner (RPN = 388.36), Long material delivery process (RPN = 388.36), Insufficient stock material (RPN = 357,588), Writing the amount on the Request for Goods Bill is not detailed (RPN = 357.71), Lack of availability of stock material (RPN = 349,524), Making and submitting late payment requests (RPN = 316.8), Placement of materials that are less efficient (RPN = 296.8) Risk mitigation is carried out using the Fault Tree Analysis method to find the main cause / basic event of each risk. And the mitigation step that needs to be done is by making changes to the warehouse layout. If the layout design of the warehouse is changed to be more efficient it will speed up the material retrieval process which has an impact on the ship repair process time.

The Mediterranean region, in particular Algeria, is experiencing serious challenges due to the increased opportunities for forest fires. Since the mid-1970s, there has been a 50% reduction in rainfall over northwestern Algeria, making northern Algeria particularly vulnerable to the problem for many years. More than 37,000 hectares of sensitive forest are lost every year due to this extreme drought. The findings of this study, which assessed the hazard of forest fires from 2006 to 2019, agree with those of Bentchakal,Chibane (2022), who examined the problems caused by forest fires in the region. The aim of this investigation is to gain a better understanding of the problems caused by local forest fires and to use that expertise to provide insight for the authors and readers of this report. The report was written by presenting the findings of observations made using the Rapid Miner classification approach, which includes the categorization of areas affected by forest fires. Data is collected using a variety of algorithmic techniques, including Naive Bayes, KNN, and decision trees, which are used as tests of data to identify the most accurate results. The findings show that the Decision Tree technique has the best accuracy of 86.49% and provides a thorough explanation of the data.