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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.

With the increasing development of technology the more variety of books circulating on the internet. As is the recommendation system on online book sites that provide books relevantly and as needed with one's preferences. One alternative is GoodReads, a social networking site that specializes in cataloging books and users can share reading book recommendations with each other by rating, reviewing, and commenting. As a large book recommendation site, it has a lot of data that can be processed by applying machine learning methods, but still not known as the most accurate model. By using the right model, we can provide more accurate recommendations. Therefore, this study will analyze the data obtained from the www.kaggle.com namely the goodreads-books dataset. This study proposed a data mining classification model to get the best model in recommending books on GoodReads. The algorithms used are Decision Tree, K-Nearest Neighbor, Naïve Bayes, Random Forest, and Support Vector Classifier, then for model evaluation using accuracy, precision, recall, f1-score, confusion matrix, AUC, and Mean Error Absolute. The test results of several classification algorithms found that Decision Tree has the highest accuracy among the methods presented by 99.95%, precision by 100%, recall by 96%, f1-score of 98% with MAE of 0.05 and AUC of 99.96%. This is proof that decision tree algorithms can be used as book recommendations based on book categories on GoodReads.

This research aims to find out and analyze the factors that are predicted to determine the firm value, the factors are liquidity, debt policy, asset turnover, and profitability. The sample used in the mining sector listed on BEI for the period 2014-2018, the sampling technique using purposive sampling, obtained 118 samples. Data analysis uses multiple regressions using the SPSS program version 23. Hypothesis test results found debt policy and asset turnover positively affected firm value, while it did not prove liquidity and profitability affect firm value.

Abstract In this study that was used as the object of research in classifying red wine based on the quality influenced by each red wine or red wine based on the content of each type of wine, from each attribute containing the composition in the wine seen which attributes most affect the quality of red wine, so that it will be known ingridents that can improve the quality of the wine, in this study was carried out by the application of Machine learning by comparing three algorithms of mining data that is , Decission Tree, Random Forest and Support Vector Machine (SVM), from the results of research that has been done by comparing the three algorithms, Random Forest produced the best accuracy among other algorithms that have been tested. Random Forest with accuracy results of 0.7468 makes this algorithm best used to classify the quality of red wine. And in the second order Decission Tree with accuracy results of 0.7031, while Support Vector Machine (SVM) get an accuracy result of 0.65. So in the research that has been done to classify the quality of red wine based on its composition Random Forest becomes the best algorithm to use..

Study This goal is to ascertain how return on investment affects the share price of PT Astra International. Time frame for the study: this covers the years 2013–2018. population studied This is a mining company that is listed on IDX. Purposive sampling is used in the election sample. In the years 2008 to 2010, there were up to 31 firms miningyang listed on the IDX. utilized data analysis Simple regression analytical techniques are used to test the hypothesis.According to a results study, the results mark significance of 0.935 acquired by results processing statistics is larger than the criteria significance (0.05). The regression model is therefore not significant. Therefore, the linearity criterion is satisfied by linear regression. The R2 result is 0.002 which indicates that the variability variable dependent may be described by the variability variable independent by 0.2% while the remaining 99.8% is explained by other factors not included in the regression model.

In the first quarter of 2017, retail in Indonesia recorded a growth of 2.5%, while in 2018 the growth was only in the range of 1% -1.5%. The cause of the slow growth is the change in the consumption pattern of the people and it will continue at the beginning of 2018. In addition, the decreasing productivity of the community at the lower middle level. As a retailer, Anterah store also faces the same thing, so anticipating a decline in sales requires an analysis of the best-selling products and how to find out the relationship between the products purchased by consumers. The association relationship between these products will be used as the basis for product arrangement, so that the frequency of products that consumers often buy can be arranged closely together so that consumers do not have to look for them longer. Market basket analysis to determine the relationship between products sold simultaneously is used to explore association rules (Association Rule Mining) which will produce products that are purchased simultaneously as a consideration for product arrangement in Anterah Retail storefront. Meanwhile, the best-selling products will be explored using the Frequent Pattern Growth method in order to obtain a ranking list of the most purchased products by consumers. This analysis is used as a basis for considering product promotion. The test results on the sales sample data obtained an average value of minimum support = 0.0025, minimum confidence = 0.610, LaPlace = 0.9985, Gain = -0.006, p-s = 0.003, Lift = 103.82, Convicting 2.5285 with a processing time of 41.456 seconds.

Bisnis retail merupakan bisnis yang keberhasilannya sangat dipengaruhi oleh kemampuan untuk memahami perilaku konsumen dan kesigapan respons dari pemiliknya. Memahami konsumen dapat dilakukan dengan mempelajari data historis dari transaksi konsumen. Metode association rule-mining dalam Machine Learning dapat kita manfaatkan untuk menemukan tren pola perilaku beli konsumen yang menunjukkan keterkaitan antar produk / kategori produk. Penelitian ini bertujuan untuk menemukan tren asosiasi kategori produk serta memberikan rekomendasi penempatan produk (product placement layouting) dengan memaksimalkan exposure pembeli terhadap produk-produk yang terkait saat berbelanja suatu barang dengan harapan akan terjadi peningkatan penjualan. Sebanyak 12.760 data transaski digunakan untuk menemukan pola beli konsumen. Pola beli konsumen ini kemudian dijadikan dasar untuk memberikan rekomendasi penempatan produk untuk meningkatkan penjualan.

Hepatitis merupakan penyakit yang diderita oleh banyak orang, bahkan bisa menyebabkan kematian. Prediksi awal dapat mencegah kematian tersebut yaitu denganmengumpulkan data pasien hepatitis yang dilihat dari faktor - faktornya. Faktor-faktor tersebut antara lain Protime, Alk Phosphat, Albumin, Bilirubin dan Usia. Untuk mengolah datatersebut, dibutuhkan Data Mining. Salah satu metode data mining yang digunakan pada penelitian ini adalah klasifikasi.Tujuan penelitian ini yaitu bagaimana memprediksi hidup atau meninggalnya pasien penyakit hepatitis dengan tingkat akurasi dan mencari atribut paling berpengaruh terhadapprediksi hidup atau meninggalnya pasien penyakit hepatitis dengan menggunakan algoritma Algoritma K-Nearest Neighbor, Naïve Bayes Dan Neural Network dan kemudianmembandingkan ketiga hasil analisis dari ketiga algoritma tersebut.Dari hasil analisis 20 atribut dilakukan 3 kali percobaan dengan algoritma Naïve Bayes didapat model klasifikasi dengan tingkat akurasi yang terbaik yaitu 76.92 %, tingkat error23.01% dan atribut Acites dan Spider merupakan atribut yang berpengaruh terhadap keputusan hidup atau meninggalnya pasien yang terkena penyakit hepatitis.Dengan menggunakanAlgoritma Neural Network didapat model klasifikasi dengan tingkat akurasi yang terbaik yaitu 82,97%, tingkat error 17.03% dan atribut yang paling berpengaruh yaitu anorexia, spiders dan protime. Dengan menggunakan algoritma K-Nearest Neighbor didapat model klasifikasi dengan tingkat akurasi terbaik yaitu 93%, tingkat error 7% dan atribut yang paling berpengaruh terhadap penderita penyakit hepatitis yaitu Albumin.