Football is a sport that has the most fans in the world. What makes sebak patterns so popular are their uncertain and unpredictable results. There are many factors that affect the outcome of a football match, including strategy, skill, and even luck. Therefore, guessing the results of a soccer match is an interesting problem. All shots are grouped into sections on the playing field and theoretical goal scores are applied to each area. The factors analyzed are: distance of shot from goal and angle of shot in relation to goal. When calculating xG, it is recommended that the distance and angle of the shot are important. The combination of the two xG factors is better calculated than each variable only. In addition, this xG check has been able to relatively accurately identify the mid-table teams that score and concede goals.

Abstract Meyer's seven sins have been recognized as types of mistakes that a requirements specialist are often fallen to when specifying requirements. Such mistakes play a significant role in plunging a project into failure. Many researchers were focusing in ambiguity and contradiction type of mistakes. Other types of mistakes have been given less attentions. Those mistakes often happened in reality and may equally costly as the first two mistakes. This paper introduces an approach to detect forward reference. It traverses through a requirements document, extracts, and processes each statement. During the statement extraction, any terms that may reside in the statement is also extracted. Based on certain rules which utilize POS patterns, the statement is classified as a term definition or not. For each term definition, a term is added to a list of defined terms. At the same time, every time a new term is found in a statement, it is check against the list of defined terms. If it is not found, then the requirements statement is classified as statement with forward reference. The experimentation on 30 requirements documents from various domains of software project shows that the approach has considerably almost perfect agreement with domain expert in detecting forward reference, given 0.83 kappa index value.KeywordsForward Reference; Natural Language Processing; Term AbstrakMeyer's seven sins dikenal sebagai jenis kesalahan yang sering dilakukan sistem analis ketika menspesifikasi kebutuhan. Kesalahan-kesalahan tersebut berperan besar sebagai penyebab gagalnya sebuah proyek. Banyak peneliti memfokuskan diri pada kesalahan berjenis kerancuan dan kontradiksi. Jenis kesalahan yang lain kurang mendapat perhatian. Padahal jenis kesalahan tersebut juga pada kenyataannya sama dampak finansialnya disbanding dua jenis pertama. Artikel ini menjelaskan sebuah pendekatan untuk mendeteksi forward reference. Pendekatan ini akan mengekstrak dan memproses setiap pernyataan dalam dokumen kebutuhan Selama proses ekstraksi tersebut, setiap istilah yang ditemukan juga diekstraksi. Berdasarkan aturan tertentu yang memanfaatkan pola POS, pernyataan diklasifikasikan sebagai sebuah definisi istilah atau bukan. Untuk setiap definisi tersebut, sebuah istilah akan ditambahkan ke daftar istilah terdefinisi. Pada saat yang sama, untuk setiap kali sebuah istilah baru ditemukan dalam sebuah pernyataan, pendekatan ini akan mengecek eksistensi definisinya. Jika tidak ditemukan, maka pernyataan tersebut diklasifikasikan sebaga pernyataan yang mengandung forward reference. Hasil pengujian atas 30 dokumen kebutuhan dari berbagai ranah proyek perangkat lunak menunjukkan bahwa pendekatan ini hampir dapat diandalkan sebagaimana seorang ahli dalam mendeteksi forward reference, dengan nilai kappa 0.83.

Background, A/B checking is a regular measure in many marketing procedures for ecommerce companies. Through well-designed A/B research, advertisers can gain insight about when and how marketing efforts can be maximized and active promotions driven. In practical terms, standard A/B experimentation makes less money relative to more advanced machine learning methods. Purposes, in order to examine the current A/B testing state, identify some popular machine learning algorithms (multi-arm bandits) which are used to optimize A/B testing, and then explain the output in some standard marketing cases of these algorithms. Methodology, In this study, the state of A/B testing have been addressed, some typical A/B learning algorithms (Multi-Arms Bandits) like Thompson Sampling, Epsilon Greedy and UCB-1 will be implemented and compared used to optimize A/B testing are described and comparable. As a result, UCB-1 and Thompson Sampling, be an exceptional winner to optimize payouts in this situation. Because it showed more effective results, without losing experimentation and statistical variations, to maximize total payouts. Based on its accuracy and strong tolerance to noise on the results, UCB-1 is the right option for MAB for a low base conversion, a limited impact size scenario.

Both Web-based information infrastructure and marketing activities are dealt with by business-to-consumer electronic commerce. Centered on information systems and marketing literature, this review suggests a research model to explain the effect on consumer loyalty of the dimensions of website quality (system quality, information quality, and service quality). In order to verify the validity of the calculation model, confirmatory factor analysis was performed, and the structural model was also examined to investigate the correlations hypothesized in the study model. In this study, by comparing the Hyperparameter class & Catboost class we can find a number of distributions of individual absolute errors which can be considered as a fairly important factor in the analysis of sales quality on e-commerce websites.

Scholarships are grants in the form of financial assistance given to individuals that aim to be used for the continuation of the education achieved. With this scholarship program, it is hoped that it can help students who have problems in financing. The selection of scholarship recipients at SMK YPM 14 Sumobito still experiences problems in decision making, because the assessment process is not always decided based on definite considerations but the policy of the decision makers who ultimately determine the scholarship recipients. This is because there is no method that can predict prospective scholarship recipients. Eligibility of prospective scholarship recipients is determined by applying the Naïve Bayes method. This method was chosen because it was able to study the previous case data used as test data. After testing with the Naïve Bayes algorithm using the Rapidmaner tool, the results were 90.48% accuracy, 96.88% precision, 83.33% recall each. So that it can be said as a good key to apply for prospective scholarship recipients.