Analisis Perbandingan Akurasi Algoritma Naïve Bayes dan C4.5 untuk Klasifikasi Diabtes

Diabetes is a metabolic disease in which blood sugar rises high. If blood sugar is not controlled properly, it can cause a variety of critical diseases, one of which is diabetes. The purpose of this study was to find out the results of comparing the performance values of Naïve Bayes and C4.5 algorithms with 7 different scenarios in the classification of diabetes that will be tested for accuracy, precision, and recall performance. The method used in this study is descriptive, and the source of skunder data obtained from the data of diabetic patients available on Kaggle with the format .csv issued by Ishan Dutta as many as 520 data and 17 fields. The tool used for data analysis is Rapidminer for the process of classification and performance testing of Naïve Bayes algorithm and C4.5 Algorithm. Our results showed that the C4.5 algorithm (scenario 4) had good results in the classification of diabetes compared to Naïve Bayes' algorithm (scenario 2) where the performance of the C4.5 algorithm had an accuracy of 99.03%, precision 100%, and recall 98.18%.Diabetes is a metabolic disease in which glucose increases high. Glucose is an important source of energy for cells and tissues of the human body. If glucose is not controlled properly, it can cause various kinds of critical diseases, one of which is diabetes. According to WHO, nearly 350 million people suffer from diabetes, WHO also predicts that by 2030 diabetes will be one of the main causes of death. This research was conducted to determine the comparison of which algorithm is good for the classification of diabetes, the two algorithms are Naïve Bayes and C4.5. By doing this research it is hoped that it will help in diagnosing diabetes. This research uses Rapidminer tools for the classification and testing of the Naïve Bayes and the C4.5. From the results of the research that has been done, it is found that Naïve Bayes results have an accuracy of 87.38%, precision (+) 90.38%, precision (-) 84.31%, recall (+) 85.45%, and recall (-) 89.58% while the classification algorithm C4.5 has accuracy 95.15%, precision (+) 96.30%, precision (-) 93.88%, recall (+) 94.55%, and recall (-) 95.83%. It can be concluded that the C4.5 has a better performance than Naïve Bayes in classifying diabetes

Systematic Literature Review of Waste Classification Using Machine Learning

The development of the global economy has caused people's living standards to increase and the production of domestic waste has also increased from year to year. The population of big cities that have limited environmental carrying capacity, causing the waste problem requires serious handling. Manual waste sorting is hazardous to health and wastes time, money and effort. If waste is not handled properly, environmental problems will increase in the long run. Machine learning works by combining features such as textures and colors to complement junk image recognition. Today's machine learning technology continues to develop, not only methods, types of waste, and features but also identify and analyze datasets used in waste management by gathering all scientific evidence. Collecting existing research and then identifying, assessing, and interpreting requires a systematic literature review. Until the end of 2021, the research topic of waste classification using machine learning was found with various types of waste, algorithms, datasets, and others. However, the dataset used by the algorithm in image recognition is relatively single, the types of garbage classified and the relative accuracy results can still be improved

Systematic Literature Review Of Particle Swarm Optimization Implementation For Time-Dependent Vehicle Routing Problem

Time-dependent VRP (TDVRP) is one of the three VRP variants that have not been widely explored in research in the field of operational research, while Particle Swarm Optimization (PSO) is an optimization algorithm in the field of operational research that uses many variables in its application. There is much research conducted about TDVRP, but few of them discuss PSO's implementation. This article presented as a literature review which aimed to find a research gap about implementation of PSO to resolve TDVRP cases. The research was conducted in five stages. The first stage, a review protocol defined in the form of research questions and methods to perform the review. The second stage is references searching. The third stage is screening the search result. The fourth stage is extracting data from references based on research questions. The fifth stage is reporting the study literature results. The results obtained from the screening process were 37 eligible reference articles, from 172 search results articles. The results of extraction and analysis of 37 reference articles show that research on TDVRP discusses the duration of travel time between 2 locations. The route optimization parameter is determined from the cost of the trip, including the total distance traveled, the total travel time, the number of routes, and the number used vehicles. The datasets that are used in research consist of 2 types, real-world datasets and simulation datasets. Solomon Benchmark is a simulation dataset that is widely used in the case of TDVRP. Research on PSO in the TDVRP case is dominated by the discussion of modifications to determine random values of PSO variables

Analisis Sentimen Masyarakat Tentang Tambang Di Indonesia Pada Twitter Menggunakan Data Mining

Penelitian ini menyelidiki sentimen masyarakat terhadap pertambangan emas di Indonesia melalui Twitter, menggunakan clustering K-Means dan Naïve Bayes Classifier untuk analisis sentimen. Mengingat pertambangan batu bara menjadi isu yang kontroversial, mengukur opini publik sangatlah penting untuk memahami dampak sosial dan mendorong dialog antar pemangku kepentingan. Penelitian ini melibatkan pengumpulan data Twitter, diikuti dengan pra-pemrosesan untuk mempersiapkan analisis. Algoritme K-Means mengidentifikasi tiga kelompok sentimen: netral (1561 tweet), positif (202 tweet), dan negatif (631 tweet). Selanjutnya, Pengklasifikasi Naïve Bayes, yang diterapkan pada set pelatihan yang terdiri dari 1.348 tweet dan set pengujian yang terdiri dari 1.046 tweet, selanjutnya mengkategorikan sentimen menjadi 324 tweet negatif, 40 netral, dan 682 tweet positif. Metodologi tersebut mencapai akurasi gabungan sebesar 99%, yang menunjukkan

Identification of Condition of Corn Plant Based on Leaf Image Features Using Gray Level Co-Occurrence Matrix and Backpropagation Neural Network

This study aims to identify the condition of corn plants based on imagery leaf using the gray level co-occurrence matrix (GLCM) method and artificial neural network (ANN) backpropagation. The GLCM method is used for extracting features from image leaf corn, whereas ANN backpropagation is used for classification condition plant corn based on features. The classification was done using a dataset of corn leaves with four conditions: healthy, leaf spot, blight, and leaf rust. Next, the leaf features are extracted using method GLCM and training on model ANN backpropagation to classify conditions of corn plants. After training on the model, the next step is model evaluation using the confusion matrix method. The research results show that the technique can produce accuracy, which is tall enough to identify condition corn plants, with an accuracy of 95%. This indicates that the use of GLCM and ANN backpropagation can be a good alternative in determining the condition of corn plants. This research provides benefits in facilitating the identification of the state of corn plants quickly and accurately

Analisis Kelulusan Mahasiswa Jurusan Sistem Informasi Stmik Amikom Yogyakarta

Quantitative association rule adalah metode yang sangat komplek untuk mencari hubungan antar item secara kuantitatif.Metode ini terdiri dari tiga proses, yaitu proses pengelompokan data yang jenis itemsetnya sangat banyak menjadikan ke beberapa interval, proses pencarian frequent itemset dan proses pencarian aturan yang mengandung nilai kuantitatif. Pada penelitian ini medote quantitative association rule digunakan untuk mencari informasi tentang faktor-faktor yang terkait dengan kelulusan mahasiswa.Tujuan penelitian ini untuk mengetahui faktor apa saja yang berpengaruh terhadap kelulusan mahasiswa STMIK AMIKOM Yogyakarta, sehingga pihak akademik dapat mengambil kebijakan. Penelitian ini fokus untuk menganalisis keterkaitan antara data mahasiswa dan data keaktifan mahasiswa dalam berorganisasi dan pekerjaan. Pada penelitian ini mencoba membandingkan hasil dari metode yang digunakan dengan cara pengujian manual dengan menggunakan sampel 15 data mahasiswa, agar aplikasi yang di bangun sesuai dengan metode yang digunakan. Dari hasil pengujian didapatkan 3 nilai interval dari data IPK dan ditemukan 11 rule akhir. Dari hasil analisis dapat disimpulkan bahwa dengan menggunakan metode Quantitative association rule aturan yang diperoleh dapat membantu menentukan kebijakan dari pihak STMIK AMIKOM. &nbsp

ANALISIS PEMBOBOTAN KATA PADA KLASIFIKASI TEXT MINING

Abstract - In this era, we need to extract the text needed to visualize or need knowledge from a large collection of document texts. Text mining is the process of obtaining high-quality information from text. High-quality information obtained because of attention to patterns and trends by reading statistical patterns. In the process of extracting the text, we need to pay for the words offered to give value/weight to the terms provided in a document. The weight given to the term depends on the method used. In weighting many words such as algorithms for example such as TF, IDF, RF, TF-IDF, TF.RF, TF.CHI, WIDF. This research will be analyzed and compared with the TF-IDF, TF.RF, and WIDF algorithms. For the test method, the naïve Bayes classification method will be used and the valuation analysis using the confusion matrix. With a dataset used as many as 130 documents in which 100 data transfer and 30 test data. Based on the analysis of the results of the classification that has been done, it can determine the weighting of TF.RF with naif classification is better than weighting TF.IDF and WIDF with Accuracy values of 98.67%, Precision 93.81%, and Recall 96.67%.Keywords - Text Mining, TF-IDF, TF-RF, WIDF, Classification, Naïve Bayes. Abstract - Pada era sekarang ini pemanfaatan text mining sangatlah diperlukan untuk mevisualkan atau mengevaluasi pengetahuan dari kumpulan besar dari teks dokumen. Text mining adalah proses untuk memperoleh informasi berkualitas tinggi dari teks. Informasi berkualitas tinggi biasanya didapatkan karena memperhatikan pola dan tren dengan cara mempelajari pola statistik. Pada proses teks mining terdapat pembobobtan kata yang bertujuan untuk memberikan nilai/bobot pada term yang terdapat pada suatu dokumen. Bobot yang diberikan pada term tergantung kepada metode yang digunakan. Dalam pembobotan kata banyak sekali terdapat algoritma-algoritma contohnya seperti TF, Idf, RF, TF-IDF, TF.RF, TF.CHI, WIDF. Pada penelitian ini akan dianalisis dan dibandingkan algoritma  TF-IDF, TF.RF, dan WIDF. Untuk metode pengujiannya akan digunakan metode klasifikasi naïve bayes  dan analisis perbandingannya menggunakan confussion matrix. Dengan dataset yang digunakan sebanyak 130 dokumen yang mana 100 data traning dan 30 data uji. Berdasarkan analisa pada hasil klasifikasi yang telah dilakukan, dapat disimpulkan bahwa pembobotan TF.RF dengan klasifikasi Naïve bayes lebih baik dari pembobotan TF.IDF dan WIDF dengan nilai Accuracy 98,67%, Precision 93,81%, dan Recall 96,67%.   Kata Kunci - Text Mining, TF-IDF, TF-RF, WIDF, Klasifikasi, Naïve Bayes

PENERAPAN FUZZY LOGIC DALAM PENENTUAN KELAYAKAN PEMBERIAN KREDIT

Cooperatives provide financing assistance in the form of credit or installment payments and have multiple systems, procedures andrequirements to be met by the prospective customer. Nonetheless, there is the problem that many cases lending arrears return ofcredit. For this reason the study was conducted in an effort to develop a decision support system that can help the credit in taking adecision. This decision support system using Fuzzy Logic method application. This study will produce a prototype system whichfunctions which resulted in the decision creditworthiness and alternative decisions based on nominal submission. On the other handwith this system can also assist the cooperative in minimizing non-performing loans with an effort to provide loans based on thelowest value

Systematic Literature Review on Auditing Information Technology Risk Management Using the COBIT Framework

Information technology has an important role in carrying out company management activities. It is important that information technology is managed properly so that no risks arise that could endanger the company. Companies can implement information technology risk management through risk management audits. An audit on information technology risk management can help evaluate companies by identifying information technology risks and minimizing information technology risks. Such audits can be carried out with the help of the COBIT framework. This study intends to conduct a systematic literature review on risk management audits related to information technology using the COBIT framework. Literature search from IEEXplore, ScienceDirect and Garuda Kemdikbud database sources. Papers were selected based on inclusion criteria. Inclusion criteria include paper language is Indonesian and English, paper is published between 2019-2023, the paper describes COBIT in IT risk management audits, and paper is available as full text. The results obtained were 24 papers. There are two criteria for assessing paper quality, namely the paper contains the COBIT framework used for IT risk management audits and the paper contains the COBIT domain used. The results of the analysis of research questions indicate that COBIT 5 is a guide used by many researchers in information technology audits for risk management. COBIT 5 provides a complete and comprehensive risk governance guide for measuring enterprise IT risk management. Implementation of COBIT 5 in IT risk management audits to assist in risk assessment and risk management in order to minimize and prevent IT risks that may occur. Domain APO12 (Manage Risk) and EDM03 (Ensure Risk Optimization) as a reference in conducting IT risk management

KLASIFIKASI JENIS REMPAH-REMPAH BERDASARKAN FITUR WARNA RGB DAN TEKSTUR MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR

 Indonesia is a country famous for its spices wealth, spices have many benefits such as cooking and can also be used as medicine, but nowadays there are many Indonesian people who cannot distinguish each type of spices especially the rhizomes that will be used due to their shape quite similar, even though the selection of the right type of spices in accordance with the needs is very important because the spices used for cooking or medicine have different taste and efficacy, therefore the use of computer technology needs to be used to facilitate and accelerate humans in conducting classifications, this research classifies spices based on RGB and Texture colors using K-Nearest Neighbor Algorithm and distance measurement using Euclidean Distance, from 30 times the test experiment gets the result that the level of truth with K = 1 is 76%, K = 3 is equal to 67% and K = 5 by 63%. From these results it is known that based on GE colors and computer textures can classify spices but with a fairly low accuracy so that further development is needed such as adding form features. Keywords: classification, spices, K-Nearest Neighbor