The development of technology that is advancing rapidly today encourages the emergence of buying and selling processes that can be done online. The many conveniences that are felt in the process have made many people switch from buying and selling conventionally to buying and selling online. Clothing is one of the primary human needs that do not escape online sales. However, the obstacle experienced was when choosing the size, because when buying online the buyer could not try on the clothes, thus creating doubts in choosing the size of the clothes that matched the buyer's body size. Therefore, this study develops a smartphone-based application that is used to recommend clothing sizes. The stage that is passed to get the results, namely, the data training process will be carried out on the dataset used. Furthermore, it takes determinant variables that affect the size of a person's clothes, namely gender, weight, height, and body shape to be able to make predictions using previously trained datasets. The result of this study is a smartphone-based application that is useful for recommending clothing sizes. The test results using the Confusion Matrix for 21 test data taken randomly from 207 training data, showed an accuracy rate of 67%.

ABSTRAKSebagai penyakit menular yang dipicu oleh Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), kode genetika virus terus berevolusi hingga sampai pada varian dominan yang diketahui adalahvtipe Delta yang memperpanjang pandemi di seluruh dunia. Dibandingkan dengan varianlain seperti Alfa, Beta dan Omicron yang lebih baru, varian Delta adalah salah satu yang paling parah menyerang sistem imun tubuh manusia, seperti yang dapat dilihat di situs Worldometer di mana jumlah kematian harian yang tinggi di banyak negara. Kondisi ini mendorong pemerintah untuk mengambil keputusan yang sangat sulit melalui sejumlah kebijakan pembatasan mobilitas penduduk yang sangat ketat. Penelitian ini dimaksudkan untuk mengukur pengaruh jumlah tes harian, pengaruh varian delta COVID-19 dan penerapan pembatasan aktivitas parsial dan mobilitas masyarakat terhadap pertumbuhan harian kasus positif di Provinsi DKI Jakarta. Dalam penelitian ini, kasus positif harian dimodelkan sebagai fungsi pertumbuhan eksponensial basis 3yang estimasi parameternya dilakukan dengan menggunakan teknik regresi linierberganda. Hasil estimasi menunjukkan adanya pengaruh signifikan dengan taraf nyata 5 % dari varian Delta berdasarkan jumlah uji COVID-19 harian terhadap jumlah kasus baru harian dengan faktor perkalian sekitar 2,6. Sedangkan pengaruh pembatasan sebagian aktivitas dan mobilitas masyarakat, secara kasar mampu secara signifikan dapat menekan hingga setengah dari potensi kasus positif harian yang mungkin terjadi.Kata kunci: virus varian delta, pembatasan aktivitas komunitas, jumlah kasus positif harian, jumlah tes harian

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The study discusses problems related to the formation of a decision tree based on a collection of evaluation data records obtained from a number of car buyers. This secondary data was obtained from the UCL machine learning website. The purpose of this research is to produce a prototype algorithm for obtaining an inductive decision tree based on Chi-square statistics. An inductive decision tree formation method based on the Chi-square contingency test was compared with a decision tree obtained using a machine learning algorithm which was done using RapidMiner5 software. The work to produce an inductive decision tree was carried out by first processing data using Microsoft excel and next processed using SPSS software, on the crosstabs descriptive menu. The results of the two methods provide some kind of similar rules, in terms of the order of priority of the variables that most influencing people's decision to accept an automotive product. The formation of the decision tree uses a random sampling of size 300 data records among 1729 respondent data records in the car evaluation database. The resulting decision tree should have a minimal structure like a binary tree. This is possible because its formation is based on the statistical inferential method, so it does not require a separate pruning process as an addition step in the C4.5 algorithm, which actually this algorithm also considers aspects of the statistical significance.

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