General elections are an important part of the political process so that many political figures participate in the process. Electability is one of the concerns, various things are done to be able to increase the electability of political figures who participate in general elections. Media has become one of the important tools used to increase electability, one of which is online news media. Reader comments can be used as an assessment of political figures in the form of sentiment analysis. However, it is not easy to analyze sentiments from comments on online news media, because comments contain unstructured text, especially in Indonesian text. Text pre-processing in text mining is an important part of getting the basic information contained in the comments. This research uses Indonesian text pre-processing using the Gata Framework Tetmining. Then proceed with extracting information using the Naïve Bayes classification algorithm and Support Vector Machine which are optimized using Particle Swarm Optimization. Tests carried out with both methods get the results that, Particle Swarm Optimization based on Support Vector Machine is the best method with an accuracy of 78.40% and AUC 0.850. This study found an algorithm that was effective in classifying positive and negative comments related to political figures from online news media.

Saat ini mungkin semua orang berfikir kanker itu ganas dan tidak dapat disembuhkan. Pernyataan itu salah sebab kanker bisa dicegah dan disembuhkan asalkan penanganannya tidak terlambat. Jadi sangat penting bagi semua orang untuk mengetahui gejala awal dan faktor-faktor resiko kanker agar bisa melakukan upaya mencegah pemicu munculnya penyakit dan segera mengambil tindakan yang tepat untuk memberantasnya karena penanganan yang terlambat bisa berakibat fatal. Para Dokter dan peneliti telah menulis banyak buku selama ini, bagaimana cara menangani penyakit kanker payudara. Namun, nasihat dari para dokter seringkali kurang ditopang oleh kemajuan yang telah dibuat oleh para peneliti. Dan para peneliti sering kali gagal menterjemahkan pengetahuan mereka menjadi saran-saran praktis yang dapat digunakan oleh masyarakat. Didalam sistem pakar ini teknik inferensi yang digunakan adalah pelacakan dan pencarian. Teknik pelacakan yang digunakan adalah pelacakan ke depan (Forward Chaining), sedangkan untuk pencarian keputusan dari setiap permasalahan digunakan metode pencarian Best First Search yaitu pencarian yang menggabungkan dua metode pencarian yang ada, yaitu metode Breadth First Search dan Depth First Search. Oleh karena itu, penulis membuat program aplikasi berbasis web untuk membantu masyarakat umum yang ingin mengetahui tentang informasi penyakit kanker payudara dan bagaimana cara menanganinya

Research in the field of Text Mining in general still uses text in English, Arabic, China or others language, while for text in Indonesian is still very limited, so it requires good tools to help Indonesian researchers to conduct research in the field of text mining in Indonesian. Pre-processing is needed for text mining processes such as deleting notation ‘@’, ‘http’ removal, Indonesian stopwords, normalizing acronym, slang words, emoticons, and Indonesian stemming. The GATA Framework Text Mining provided is one of the options for conducting text mining research in Indonesian and has been used by several researchers. There are several known data mining processing methods, including KKD, CRISP-DM, and SEMMA, all three of which are quite reliable methods. CRISP-DM which consists of; Bussiness Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment is a method that is quite widely used in research in the field of text mining which can be combined with text pre-processing. With so much research in the field of Text Mining in Indonesian, the need for pre-processing in Indonesian is very important. GATA Framework is an option for pre-processing devices that can be combined with Repidminer devices, as seen from the results of the excellent FUPRS.

This study aims to model the form of customer profile classification on companies using the C 4.5 and Random Forest algorithms to produce the best profile classification model from customers to sees a pattern of assessments of manual assessments so far. This study uses descriptive analysis method. Through classification of Customer Profiles with the Recency, Frequency, Monetary - Cost (RFM-C) model approach. After process the two models, the results obtained are the C4.5. After testing the two algorithms, the results obtained are the use of the C4.5 algorithm for companies to classify RFM-C which is expected to predict because it has higher accuracy and kappa values compared to the Random Forest algorithm. It can be concluded that the modeling of customer profile forms in companies that use the C 4.5 algorithm and random forest can produce the best profile classification model.