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All Journal Engineering, Mathematics and Computer Science Journal (EMACS)
Anderies, Anderies
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

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Combining Academia and Industry Approach for Secure Coding and Requirements Checklist in S-SDLC: Systematic Literature Review Anderies, Anderies; Rachmawati, Ika Dyah Agustia; Jingga, Kenny; Candra, Calvin Linardy
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 2 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i2.13429

Abstract

Rapid progress of digital transformation has occurred governments, organization and vendors around the world. where this rapid digital transformation is not linearly followed by the security protection of digital infrastructure and its application. For example, in Indonesia One of the largest banks was unable to operate its online and physical services for three consecutive days due to a cyber-attack. And many international organizations also experienced the same thing or even worse like bankruptcy. Because of this phenomenon the authors have performed a systematic literature review and identified there are two important phases namely requirement and coding in secure software development lifecycle (S-SDLC). In this study the authors compose 18 Secure Requirement practices (SREC) and 72 Secure Coding Checklist (SCOC) checklist based on Combining previous academia research study and international standard of open secure coding practices (OSCP) in which we target the security vulnerable most occurred to governments, organization and vendors around the world according to Open Web Application Security Project Foundation.  This checklist can be embedded in the Quality Assurance process to check in sequence whether the Requirements and Coding that are produced are safe or not from the cyber-attack. Additionally, the checklist approach is simple to understand and can be implemented to a popular public consumer automation testing tools enabling faster software development while maintaining software security.
Walk-Forward Evaluation of ROI-Based Decision Thresholds for Random Forest Loan Default Prediction Lucky, Henry; Anderies, Anderies
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 8 No. 2 (2026): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v8i2.16455

Abstract

Default prediction models for peer-to-peer lending are often tested on random data splits and judged by accuracy, which can hide temporal leakage and says little about investor returns. This study evaluates a random forest on 1,020,768 resolved 36-month Lending Club loans issued from 2007 to 2018, using 30 walk-forward windows with a 36-month gap between validation and test periods and only information available when a loan is funded. A decision threshold chosen by validation return on investment (ROI) was compared with the default threshold of 0.5, lending to every loan a grade-based rule and logistic regression, with full and downsampled training data, five random seeds, and paired tests across windows. The random forest reached a mean AUC of 63.7–64.6%, close to logistic regression (64.2–65.0%). Overall windows, the ROI-based threshold raised mean window ROI from 4.04% (lend-all) to 4.97% (difference 0.92 percentage points, 95% CI −0.29 to 2.23) while lending 51% of the capital; no difference remained significant after Holm adjustment. The share of test loans resolved by the end of the data fell from over 99% in windows 1–19 to 7% in window 30, so the negative returns of late windows reflect censoring. In the 19 matured windows, lend-all earned 9.08% on average and the random forest did not improve on it: ROI-based thresholds lowered mean ROI by 0.92–1.15 percentage points and profit by 40–54%. Return-based evaluations of default models therefore need time-ordered tests that account for unresolved loans.
Co-Authors Candra, Calvin Linardy Jingga, Kenny Lucky, Henry Rachmawati, Ika Dyah Agustia