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Dunia bisnis khususnya dalam industri penjualan dimana-mana tidak di ambil kemungkinan banyak resiko yang di hadapi pembisnis untuk bisa melangsungkan usaha yang telah di dirikan akan selalu ada dan mendapatkan konsumen yang tetap membeli barang yang telah disediakan maka dari itu seorang entrepreneur dituntut untuk memiliki strategi dalam membaca peluang. Untuk menyiasati hal tersebut, tentunya pihak manajemen harus mampu menganalisa data yang ada untuk dijadikan bahan acuan untuk strategi diperlukan untuk komputerisasi. Pencarian judul penelitian dan abstraknya dipermudah dengan kata-kata kunci tersebut. berbisnis selanjutnya. Meubel Master borneo merupakan salah satu perusahaan yang memiliki resiko mendapatkan konsumen yang tetap dan harus memberikan atau meyediakan barang yang memiiki kualitas tinggi dan memberikan pelayanan yang akan diberikan kepada pelanggan yang setia membeli produk yang telah disediakan. Dengan menggunakan data mining yang merupakan knowledge discovery dikarenakan bidang yang berupaya untuk menemukan informasi yang memiliki arti yang berguna dari jumlah data yang besar, untuk menemukan pola (pattern) data dan memprediksi kelakuan (trend) dimasa mendatang [7]. Untuk mengetahui produk yang sering terjual dalam periode bulan Januari sampai bulan Mei 2019 diperlukan algoritma apriori yang ada di data mining. Dengan melakukan analisa keranjang belanja menggunakan metode asosiasi dengan Algoritma Apriori, dimana kombinasi itemset transaksi penjualan barang pada meubel master borneo menghasilkan 6 rules dimana minimum confidence sebesar 41,6 % dan minimum support sebesar 0,08% berdasarkan 35 transaksi penjualan dari 63 jenis barang pada meubel Master Borneo.

Legal knowledge is very necessary for the creation of legal understanding and awareness of legal awareness for the community. There are several factors that can affect public legal awareness. This community service is expected to provide knowledge and understanding to the community, especially students. With knowledge and understanding of the prevailing legal rules, they will maintain their legal awareness to a higher level.

The recruitment of new mechanics at Mataram Sakti is no longer through the training process at the head office, but new mechanics are placed at the dealership accompanied by the chief mechanic as a supervisor, with the aim of providing training to new mechanics in order to be able to service or periodically repair Yamaha products especially the latest motorbikes namely Yamaha NMAX, because of the flurry sometimes the mechanical head is not in place so that when the mechanic is experiencing problems it cannot directly ask the mechanical head causing the handling of repairs to be less effective and efficient. One way to overcome this is to create a web-based expert system application with a forward chaining method so that the chief mechanic can transfer the knowledge he has into the computer. Forward chaining method is a search technique that starts with known facts and then matches these facts with the IF part of the IF-Then rules, so that conclusions can be drawn. This expert system was built using supporting software including, Macromedia Dreamweaver MX, PHP script language, MySQL database and WAMP server. The expert system for diagnosing damage to Yamaha NMAX motorcycles using the forward chaining method can be used as a new mechanical learning media in diagnosing damage to NMAX motorcycles and as a means of chief mechanics transferring their knowledge. 

Classification is one of the most widely used techniques in machine learning. Text classification is the process of classifying data according to pre-determined groups or classes. Where in most cases, text classification uses labeled training data to obtain the rules used to classify test data into predefined groups. In this study, it is proposed to use CountVectorizer for Indonesian text classification which will be compared with TF-IDF Term Weighting and its three feature levels, namely Character Level, Word Level and N-gram Level as feature extraction which is implemented together with Naive Bayes classification and the BPPPTIndToEngCorpusHalfM dataset. To compare the classification performance, this study uses 10-Fold Cross Validation and Split Data using a ratio of 90:10, while to evaluate the accuracy of the authors using the F1-Score and AUC with the hope that this study will get good accuracy results so that it can be used as a reference to be developed using another method. The F1-Score accuracy obtained in this study was 0.93 and the AUC score was 0.95.