Dalam melakukan analisis penjualan dapat dilakukan dalam beberapa sudut pandang, salah satunya yaitu melakukan analisis terhadap barang yang sering muncul dan dibeli bersamaan oleh pelanggan. Hal ini bertujuan agar penyediaan barang memiliki sifat yang saling berhubungan atau membentuk aturan assosiasi. Setiap barang akan memiliki nilai penunjang dan nilai kepastian. Metode yang digunakan untuk menangani masalah ini adalah Data Mining Asosiasi dengan menerapkan algoritma apriori pada proses pembacaan data transaksi pada database. Untuk membantu mencapai hasil akhir yang optimal, digunakan software Rapid Miner. Hasil dari penerapan assosiation rule ini dapat digunakan sebagi bahan pertimbangan dalam membuat keputusan strategi pemasaran dan penjualan yang efektif, khususnya barang dagang yang seharusnya diadakan.

Bipolar disorder is one of the world's most common mental health disorders. To find out public sentiment regarding bipolar disorder, sentiment analysis is carried out through social media to analyze positive or negative sentiments with the aim of maintaining positive sentiment towards the problem of bipolar disorder. Twitter is a social media that is often used to exchange information, discuss, and even express emotions. The emotions of Twitter users can be called sentiment. Sentiment analysis is also carried out to see opinions or tendencies towards an opinion. Opinion tendencies can be in the form of positive or negative sentiments. The data used in this study uses the bipolar keyword. There are 2177 tweets data that were successfully obtained in the crawling process using API key access from Twitter developers, after which the data will be processed using preprocessing. The comparison of the presentations obtained is 70.92% expressing a negative opinion and 29.08% expressing a favorable opinion. The analysis results in this study using the nave Bayes algorithm is with an accuracy value of 92.110092%.

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The objective of this study is to compare the effectiveness of the Weighted Product (WP) and Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) methods in determining the best customers. Onesnet, the case study service provider, provides discounts and rewards to eligible customers to support this objective. The problem addressed in this study is how to determine the most relevant method for selecting eligible customers for bonuses. To achieve this, sensitivity testing was conducted by altering the weights of each criterion in both methods and observing the percentage changes of the results. The Weighted Product method multiplies the rating of each connected attribute, which is raised to the appropriate attribute weight, to decide. Data for this study was collected through interviews and observations at Onesnet and processed using the Rank Order Centroid (ROC) method for weighting, and the WP and MOORA methods for evaluating and selecting a decision. The WP and MOORA methods produced different total values and rankings, but the modeling with either method can be used equally for selecting the best customers. While there was a 60% similarity in data between the two methods, the WP method is recommended over MOORA, as it prioritizes customers with high loyalty criteria as the best customers.