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Journal : Scientific Journal of Informatics

Test-Driven Development (TDD) for Point of Sale System at Bicycle Shop Subhiyakto, Egia Rosi; Astuti, Yani Parti
Scientific Journal of Informatics Vol 7, No 2 (2020): November 2020
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v7i2.25884

Abstract

The information technology systems are developing faster; one of its developments is a system to process the recording of sales data (Selling System). To support the selling process in Ali Cycle bicycle shop, which previously done manually, it needs a system to record the stock of goods, transaction, supplier, and sales report. There is a lot of model in building the system, one of the model is traditional development model, generally, the phase of this model is never-ending related to system problems or bugs, sometimes bugs are not found in the development but comes after practical use begins. A Point of Sale (POS) system with Test Driven Development (TDD) method has been built in which a test is written before the coding phase in a purpose of the codes, which are created, has passed the test, reducing the bug and it tests the system. The results show that all codes have passed the test; the test consists of 89 functions and 397 statements. Evaluation results of end-users testing showed that the majority of respondents strongly agree and agree with a system with an average rating of 94% for performance, 89% interface and 83% user satisfaction. The conclusion is, building a POS system using TDD method succeeded by producing a useful system as the requirement and expected system quality.
Test-Driven Development (TDD) for Point of Sale System at Bicycle Shop Subhiyakto, Egia Rosi; Astuti, Yani Parti
Scientific Journal of Informatics Vol 7, No 2 (2020): November 2020
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v7i2.25884

Abstract

The information technology systems are developing faster; one of its developments is a system to process the recording of sales data (Selling System). To support the selling process in Ali Cycle bicycle shop, which previously done manually, it needs a system to record the stock of goods, transaction, supplier, and sales report. There is a lot of model in building the system, one of the model is traditional development model, generally, the phase of this model is never-ending related to system problems or bugs, sometimes bugs are not found in the development but comes after practical use begins. A Point of Sale (POS) system with Test Driven Development (TDD) method has been built in which a test is written before the coding phase in a purpose of the codes, which are created, has passed the test, reducing the bug and it tests the system. The results show that all codes have passed the test; the test consists of 89 functions and 397 statements. Evaluation results of end-users testing showed that the majority of respondents strongly agree and agree with a system with an average rating of 94% for performance, 89% interface and 83% user satisfaction. The conclusion is, building a POS system using TDD method succeeded by producing a useful system as the requirement and expected system quality.
High School Major Classification towards University Students Variable of Score Using Naïve Bayes Algorithm Sudibyo, Usman; Astuti, Yani Parti; Kurniawan, Achmad Wahid
Scientific Journal of Informatics Vol 4, No 2 (2017): November 2017
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v4i2.12017

Abstract

Completeness of data in each institution, such as major in a university, is necessary. Data of former school has important role in the need of students data. However, there is no relationship between data of former school and variable of students’ score. The suitable classification used in this research is data mining technique which is naïve bayes algorithm. This algorithm is able to manage massive data with a relative fast timing. By using this algorithm, the data results 64.77% performances in classifying former major in school towards variable of score. Hence, the researchers optimize selection feature by using Backward Elimination and result 71.71% performances data. It concludes that performance increases with selection feature. The increasing shows that not all variable of score affects the former school major.