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Journal : International Journal of Mathematics, Statistics, and Computing

Analysis Testing Black Box and White Box on Application To-Do List Based Web Pirdaus, Dede Irman; Hidayana, Rizki Apriva
International Journal of Mathematics, Statistics, and Computing Vol. 2 No. 2 (2024): International Journal of Mathematics, Statistics, and Computing
Publisher : Communication In Research And Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijmsc.v2i2.95

Abstract

The rapid development of information technology has led to the creation of numerous web-based applications designed to assist human activities and work. One such application is the To-Do List, which helps users manage their tasks and increase productivity. This study aims to analyze the quality of web-based To-Do List applications through black box and white box testing. The research focuses on the login and main pages of the application, where various scenarios are tested to ensure that the system functions as intended. The testing process includes designing test scenarios, creating test cases, executing the test cases, and collecting and processing test result data. The study also includes an analysis of the program's source code using flowcharts and flowgraphs to identify the number of independent logic execution paths and design test cases for white box testing. The results of the testing will help identify errors and weaknesses in the application, ensuring that the final product is of high quality.
Optimization of Stock Portfolio in Indonesian Health Sector using Markowitz Modern Portfolio Theory Kalfin; Hidayana, Rizki Apriva
International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 1 (2025): International Journal of Mathematics, Statistics, and Computing
Publisher : Communication In Research And Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijmsc.v3i1.182

Abstract

This study analyzes the optimization of the health sector stock portfolio on the Indonesia Stock Exchange using the Markowitz Modern Portfolio Theory method. The data used are the daily closing prices of health sector stocks over the last three years obtained through web scraping techniques from Yahoo Finance. The analysis includes the calculation of daily returns, daily risks, and covariance matrices between stocks. The results of the portfolio optimization show that out of the ten stocks analyzed, the optimal portfolio consists of four stocks, namely MIKA.JK (62.82%), KLBF.JK (15.58%), CARE.JK (15.37%), and SAME.JK (6.23%). This portfolio generates a daily return of 0.216% with a risk level of 1.996%. MIKA.JK contributes the highest return of 0.02063% with a risk of 1.52601%. This study provides guidance for investors in optimizing fund allocation in the health sector stock portfolio in Indonesia.
Matching Riders to Drivers Under Uncertain Wait Times in Ride-Hailing Systems: A Robust Optimization Approach with Box Uncertainty Megantara, Tubagus Robbi; Hidayana, Rizki Apriva
International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 2 (2025): International Journal of Mathematics, Statistics, and Computing
Publisher : Communication In Research And Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijmsc.v3i2.202

Abstract

The advent of ride-hailing systems has revolutionized urban mobility, yet efficient vehicle assignment remains challenging due to inherent uncertainties in passenger waiting times. This study addresses the ride-hailing matching problem under uncertain wait times, proposing a robust optimization model with a box uncertainty set to mitigate the impact of variability in service delivery. We first contextualize the problem by examining the evolution of transportation systems, emphasizing how ride-hailing services complicate traditional matching paradigms. Existing approaches often fail to account for real-world unpredictability, leading to suboptimal assignments. To bridge this gap, we formulate a data-driven robust optimization framework that bounds waiting time fluctuations within a box uncertainty set, ensuring reliable performance under worst-case scenarios. Using simulation data from Manhattan taxi trips, we compare our robust model against deterministic benchmarks, demonstrating its superiority in reducing average waiting times and enhancing system reliability, even under high uncertainty. Our results highlight the practical viability of robust optimization for ride-hailing platforms operating in dynamic environments.