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Journal of Mathematics and Scientific Computing With Applications
Published by Yayasan Pena Cendekia Insani
ISSN : 27985512     EISSN : 27985776     DOI : -
Core Subject : Education,
Mathematics
Journal of Mathematics and Scientific Computing With Applications is a broad-based journal covering all branches of computational or applied mathematics with special encouragement to researchers in theoretical computer science and mathematical computing. It covers all major areas, such as numerical analysis, discrete optimization, linear and nonlinear programming, theory of computation, control theory, theory of algorithms, computational logic, applied combinatorics, coding theory, cryptographic, fuzzy theory with applications, differential equations with applications. Journal features research papers in all branches of mathematics that have some bearing on the application to scientific problems, including areas of actuarial science, mathematical biology, mathematical economics, and finance.
Arjuna Subject : Matematika - Matematika (Lain-Lain)
Articles 4 Documents
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Search results for , issue "vol. 7 no. 1 (2026)" : 4 Documents clear
HYBRID RF–XGBOOST STACKING ENSEMBLE FOR LOW-RESOURCE INDONESIAN TOXIC COMMENT CLASSIFICATION ON ROBLOX Kartika Imam Santoso; Gatot Susilo; Saefurrohman; Eko Supriyadi; Edi Widodo
Journal of Mathematics and Scientific Computing With Applications Vol. 7 No. 1 (2026)
Publisher : Pena Cendekia Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53806/jmscowa.v7i1.1428

Abstract

This study developed a hybrid stacking ensemble combining Random Forest and XGBoost for multi-class toxic comment detection in Indonesian gaming chat on Roblox. The dataset comprised 10,702 labeled comments across four toxicity categories: Violence, Harassment, Racist, and Neutral. Text preprocessing included comprehensive techniques such as case folding, tokenization, slang normalization, and stemming, while features were extracted using TF-IDF representation. The stacking ensemble achieved 91.4% accuracy and 91.3% macro F1-Score, significantly surpassing standalone baselines (Random Forest 87.2%, XGBoost 89.5%). McNemar’s test confirmed statistical significance of the improvement over XGBoost (p<0.001). Analysis revealed distinct linguistic patterns across toxicity categories. Importantly, the model operates at 2,500 predictions per second on CPU without GPU infrastructure, enabling practical deployment on resource-constrained gaming platforms. These findings demonstrate the effectiveness of hybrid ensemble learning for low-resource multilingual toxic content moderation.
PARAMETRIC SURVIVAL ANALYSIS OF TESTICULAR CANCER USING AN EXPONENTIAL MODEL WITH TYPE I CENSORED DATA Idrus Syahzaqi; Ardi Kurniawan; Valerina Marischa Usmarasima; Aurora Gie Nur Wahyudi; Muhamad Zaky Ramadhani; Nuzzulia Calvina Izumy
Journal of Mathematics and Scientific Computing With Applications Vol. 7 No. 1 (2026)
Publisher : Pena Cendekia Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53806/jmscowa.v7i1.1509

Abstract

Quantitative prognostic evaluation is essential for testicular cancer, yet standard analyses often overlook model assumptions. This study evaluates patient survival times using a parametric exponential model under a type I censoring structure (n = 134, from cBioPortal). Parameter estimation via Maximum Likelihood Estimation yielded 82 uncensored and 52 censored observations. The Anderson-Darling test (p = 0.067) indicated statistical adequacy for the exponential distribution while the Weibull distribution was rejected (p < 0,010), confirming the assumption of a constant hazard, estimating a Mean Time to Failure of 64,54 months with a constant hazard rate of 0.0155 per month. However, these model-based estimates face critical clinical limitations. The small sample size and the rigid assumption of a constant hazard fail to capture the dynamic, time-varying biological progression of cancer. While this parametric approach provides a simplified baseline, clinical conclusions must be drawn cautiously; future research requires larger cohorts and flexible, non-constant hazard models to ensure actual clinical generalizability.
MARKET SEGMENTATION OF STUDENT E-WALLET USERS USING K-MEANS CLUSTERING AND RANDOM FOREST Diah Putri Kartikasari; Ilka Zufria
Journal of Mathematics and Scientific Computing With Applications Vol. 7 No. 1 (2026)
Publisher : Pena Cendekia Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53806/jmscowa.v7i1.1582

Abstract

The rapid growth of e-wallet use among students creates a need for computational workflows that explore variation in survey-based behavior and perception. This study aimed to segment student e-wallet users and identify features associated with the resulting clusters. Data were collected through an online questionnaire, and 569 valid responses were analyzed using 13 behavioral and perception features. K-Means produced two exploratory clusters: High-Perception Promo-Responsive Users (86.3%) and Lower-Perception Selective Users (13.7%). Because internal indices disagreed and the Silhouette score of 0.568 indicated only moderate separation, k = 2 was retained as an interpretability-oriented judgment; Silhouette and Davies-Bouldin supported k = 2, whereas Calinski-Harabasz and the elbow pattern supported k = 3. Random Forest was applied as a post-clustering interpretability tool to examine label reproducibility and identify influential features. Ease of use, trust, promotion and incentives, security, and risk perception were dominant, but the self-reported data require cautious interpretation.
TYPE II CENSORED EXPONENTIAL DISTRIBUTION FOR ESTIMATING SURVIVAL AND LIFE EXPECTANCY IN BLADDER CANCER PATIENTS Idrus Syahzaqi; Ardi Kurniawan; Nazwa Salsabila; Dinar Rahma Fauzia; Diah Ayu Nofitasari
Journal of Mathematics and Scientific Computing With Applications Vol. 7 No. 1 (2026)
Publisher : Pena Cendekia Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53806/jmscowa.v7i1.1600

Abstract

Bladder cancer incidence and mortality continue to rise, yet global studies on bladder cancer survival prediction remain dominated by nomogram and Cox regression-based approaches overlooking Type II censoring. Using secondary data from 80 bladder cancer patients (48 uncensored, 32 Type II censored) from a public cBioPortal repository, this study applied the Type II censored exponential distribution, selected for its memoryless property and constant hazard rate, to estimate the survival function, hazard function, confidence interval, and Mean Time to Failure (MTTF). Descriptive statistics, a goodness-of-fit test, and maximum likelihood estimation were applied. The data followed an exponential distribution with a mean survival time of 33.95 months, an estimated parameter of 38.01 months, a hazard rate of 0.026, and an MTTF of 38.02 months, with a 95% CI of 29.35-51.56 months. These findings offer preliminary insight relevant to clinical decision-making and SDG Target 3.4 on reducing premature mortality from non-communicable diseases.

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