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All Journal Desimal: Jurnal Matematika Jurnal ICT : Information Communication & Technology MATHunesa: Jurnal Ilmiah Matematika Jurnal Riset Rumpun Matematika dan Ilmu Pengetahuan Alam (JURRIMIPA) MDP Student Conference Sciencestatistics: Journal of Statistics, Probability, and Its Application
Linda Rassiyanti
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Mathematics Social Sciences

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Optimizing Breast Cancer Prediction by Applying Machine Learning Vina Nurmadani; Indah Suciati; Yoga Aji Sukma; Linda Rassiyanti
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 3 No. 2 (2025): JULY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v3i2.9667

Abstract

In 2015, breast cancer ranked among the most prevalent and fatal cancers affecting women globally. Artificial intelligence is urgently needed to help medical professionals make more accurate decisions, reduce overdiagnosis, and streamline the diagnostic process. This study will implement and perform a comparative study of selected machine learning techniques algorithms, with a focus on SVM, XGBoost, and ANN, with various parameter combinations on the breast cancer dataset. Performance metrics such as accuracy, precision, recall, and F1-score were employed to evaluate and compare the algorithms. The results of this study show that the best model for predicting chronic breast cancer disease, which can help medical professionals predict chronic disease so that it can be treated quickly and accurately, is the SVM method using 8 parameters without the mitosis parameter: Clump thickness, Cell Size Uniformity, Cell Shape Uniformity, Marginal Adhesion, Single Epithelial Cell Size, Bare Nuclei, Bland Chromatin, and Normal Nuclei, with an accuracy value of 0.96 and a sensitivity value of 0.98.
Multi-objective bees algorithm for portfolio diversification Farid, Fajri; Linda Rassiyanti; Rohmi Dyah Astuti; Ade Lailani
Desimal: Jurnal Matematika Vol. 8 No. 2 (2025): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/mttkw723

Abstract

Portfolio diversification is the practice of spreading investments across different types of stocks or sectors to reduce overall risk. The basic principle is that the poor performance of one stock asset can be offset by the satisfactory performance of another stock asset. This study uses the Bees Algorithm for portfolio optimization problems, aiming to discover the combination of stock proportions in a portfolio that maximizes stock returns and minimizes risk. Then, the Sharpe ratio value is calculated and compared with conventional methods. The expected return, risk, and Sharpe ratio values for the portfolio generated using the Bees algorithm are 0.178007%, 2.353956%, and 0.0663484322, respectively. According to the results, the Bees Algorithm had better results and performance than conventional methods. As a result, the Bees Algorithm outperforms conventional approaches.
Penerapan Metode Least Significant Bit untuk Penyembunyian Pesan Rahasia dalam Gambar dengan Optimasi Ukuran File Yuliana; Rohmi Dyah Astuti; Ade Laelani; Linda Rassiyanti; Yusni Puspha Lestari; Ronal
Jurnal ICT: Information Communication & Technology Vol. 25 No. 1 (2025): JICT-IKMI, July, 2025
Publisher : LPPM STMIK IKMI Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Steganografi merupakan teknik untuk menyembunyikan pesan rahasia dalam media digital guna menjaga kerahasiaan informasi. Penelitian ini menerapkan metode penyisipan pesan menggunakan algoritma Least Significant Bit (LSB), di mana pesan rahasia disisipkan langsung ke dalam bit-bit paling tidak signifikan dari piksel citra grayscale. Setelah proses penyisipan, citra stego disimpan dalam tiga format berbeda—PNG, WebP, dan ZIP—untuk mengevaluasi dampak kompresi terhadap integritas pesan dan kualitas citra. Evaluasi dilakukan berdasarkan empat parameter: ukuran file, degradasi kualitas citra (PSNR), kesamaan struktur visual (SSIM), dan keberhasilan ekstraksi pesan. Hasil menunjukkan bahwa format PNG mampu mempertahankan kualitas citra dan integritas pesan secara optimal (PSNR 75,58 dB, SSIM 1,0000). Sebaliknya, kompresi lossy pada WebP mengganggu bit pesan sehingga menyebabkan pesan rusak. Format ZIP terbukti dapat mempertahankan file stego secara utuh. Penelitian ini menunjukan bahwa steganografi berbasis Least Significant Bit tetap efektif bila dikombinasikan dengan format gambar lossless. Format lossy seperti WebP tidak disarankan karena berisiko merusak data.
Analisis Regresi Kernel Gaussian untuk Memprediksi Indeks Pembangunan Manusia (IPM) Berdasarkan Faktor Sosial-Ekonomi Provinsi di Indonesia Rohimatul Anwar; Linda Rassiyanti; Rizka Pitri
JURNAL RISET RUMPUN MATEMATIKA DAN ILMU PENGETAHUAN ALAM Vol. 4 No. 3 (2025): Desember : JURRIMIPA: Jurnal Riset Rumpun Matematika dan Ilmu Pengetahuan Alam
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jurrimipa.v4i3.7017

Abstract

The Human Development Index (HDI) functions as a key indicator for assessing the level of welfare and overall quality of life of the population within a specific region. This study aims to examine the socio-economic factors influencing HDI at the provincial level in Indonesia using a Gaussian kernel regression approach. A nonparametric method is employed due to its flexibility in capturing nonlinear relationships between the response and predictor variables without the need to assume a specific functional form. The analysis utilizes secondary data, including education, poverty, per capita expenditure, expected years of schooling, open unemployment rate, and gross regional domestic product for each Indonesian province. The findings from this study indicate that educational factors, particularly mean years of schooling and expected years of schooling, exert the most significant impact on HDI improvement. The estimated Gaussian kernel regression model demonstrates a coefficient of determination of 0.9954 and a residual standard error of 0.3468, reflecting a very high predictive accuracy and relatively low error. These results suggest that Gaussian kernel regression is an effective nonparametric approach for analyzing human development in Indonesia.
Application of the DAG-SVM for multi-class mobile phone price classification Sihombing, Natanael Oktavianus Partahan; Christyan Tamaro Nadeak; Linda Rassiyanti; Fajri Farid
Desimal: Jurnal Matematika Vol. 8 No. 3 (2025): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v8i3.202529450

Abstract

This study investigated the application of multiclass Support Vector Machine (SVM) strategies for smartphone price range classification using the Mobile Price Classification dataset (N = 2,000). The aim was to assess whether the Directed Acyclic Graph SVM (DAG-SVM) could provide improvements in predictive performance or computational efficiency compared with the conventional One-vs-One (OvO) and One-vs-Rest (OvR) approaches. The dataset’s twenty features were standardized using Z-score normalization and split into training and testing sets with an 80:20 ratio. All models were implemented using a linear kernel and evaluated based on accuracy, macro-precision, macro-recall, macro-F1, and execution time. The results showed that both OvO and DAG-SVM achieved the highest performance, with an accuracy and macro-F1 score of 96.25%, while OvR performed substantially lower. Despite the theoretical efficiency of DAG-SVM, its Python-based sequential elimination process led to slower prediction time than OvO. This study contributed empirical evidence that execution time can diverge from theoretical expectations in practical implementations and demonstrated the importance of computational efficiency analysis when comparing multiclass SVM architectures for mobile price classification.
ANALISIS KLASIFIKASI KUALITAS HIDUP MANUSIA ANTAR KABUPATEN/KOTA DI INDONESIA MENGGUNAKAN ALGORITMA CATBOOST CLASSIFIER DAN SHAP VALUES Ayu Sofia; Linda Rassiyanti
MATHunesa: Jurnal Ilmiah Matematika Vol. 14 No. 1 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v14n1.p430-444

Abstract

Human development must go hand in hand with improving quality of life, as reflected by HDI and its influencing factors. Classifying quality of life based on HDI into developed, developing, and underdeveloped areas offers insights into the human development performance of each district/city in Indonesia. The CatBoost Classifier and SHAP values help build an accurate model while interpreting variable influences. This study analyzes human quality of life classification across districts/cities based on HDI and related factors. The CatBoost model achieved 92.23% accuracy, with the best performance in the developing class, while the underdeveloped class showed low accuracy due to data imbalance. SHAP analysis revealed that average years of schooling, per capita expenditure, and region type were key variables in the developed and developing classes, while island location and sanitation access dominated in the underdeveloped class. These findings highlight the importance of education, economic welfare, and basic infrastructure in shaping quality of life. This research also supports actuarial social risk planning, particularly in designing data- and region-based social security systems.
Pengaruh Parameter Regularisasi (λ) terhadap Stabilitas Estimasi pada Regresi Ridge Linda Rassiyanti; Rohimatul Anwar
JURNAL RISET RUMPUN MATEMATIKA DAN ILMU PENGETAHUAN ALAM Vol. 5 No. 1 (2026): April : JURRIMIPA: Jurnal Riset Rumpun Matematika dan Ilmu Pengetahuan Alam
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jurrimipa.v5i1.8726

Abstract

Multicollinearity is one of the common issues in multiple linear regression that can lead to instability in the estimation of regression coefficients. This study aims to examine the impact of multicollinearity on regression models and to evaluate the use of Ridge Regression as an alternative estimation method. The study employs simulated data consisting of 1,000 observations, including one dependent variable and four independent variables designed to exhibit high correlation. The analysis begins with model estimation using the Ordinary Least Squares (OLS) method, followed by multicollinearity testing using the Variance Inflation Factor (VIF). The OLS results indicate that most independent variables significantly influence the dependent variable, with a coefficient of determination (R²) of 0.9863. However, the high VIF values reveal the presence of strong multicollinearity in the model. To address this issue, Ridge Regression is applied, with the optimal penalty parameter determined through cross-validation, yielding a lambda value of 4.201589. The results show that the regression coefficients in the Ridge model undergo shrinkage, resulting in greater stability compared to the OLS estimates. Model evaluation indicates that the Mean Squared Error (MSE) for the OLS model is 24.77, whereas the Ridge model produces an MSE of 29.72. Although the Ridge model exhibits a slightly higher MSE, it effectively mitigates the impact of multicollinearity and provides more stable parameter estimates.
Klasifikasi Multikelas Varietas Kacang Kering Menggunakan Metode Hybrid SVM Berbasis DAG Dinda Nababan; Christyan Tamaro Nadeak; Linda Rassiyanti; Fajri Farid
MDP Student Conference Vol 5 No 2 (2026): The 5th MDP Student Conference 2026
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v5i2.14094

Abstract

This study analyzes the performance of three conventional SVM strategies, namely One-vs-One (OvO), One-vs-Rest (OvR), and Directed Acyclic Graph OvO (DAG-OvO), compared with the hybrid approach Directed Acyclic Graph Rest-vs-Rest (DAG-RvR) in the context of multiclass classification using the Dry Bean Dataset. All models are evaluated based on accuracy and macro metrics to measure the consistency of predictions between classes. The results show that both conventional and hybrid methods achieve the same high level of accuracy, namely 0.92, with Precision, Recall, and F1-score Macro values ​​that were also identical between approaches. The main difference between the approaches lies in computational efficiency. OvO and DAG-OvO show the fastest training time, while DAG-RvR is the most efficient method in the inference stage. These findings confirm that the hybrid DAG-RvR structure can accelerate the prediction process without compromising accuracy, making it worthy of consideration for applications that require fast inference.
Klasifikasi Varietas Beras Menggunakan Hybrid SVM Berbasis DAG–OVO dan RVR Marleta Cornelia Leander; Christyan Tamaro Nadeak; Linda Rassiyanti; Fajri Farid
MDP Student Conference Vol 5 No 2 (2026): The 5th MDP Student Conference 2026
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v5i2.14108

Abstract

This research proposes a hybrid Support Vector Machine (SVM) strategy for multiclass rice variety classification by combining Directed Acyclic Graph Rest-vs-Rest (DAG-RvR) with K-Means clustering. Five rice varieties were analyzed using 16 morphological and texture features extracted from the Rice Image Dataset. Three conventional SVM methods—One-vs-One (OvO), One-vs-Rest (OvR), and DAG-OvO—were evaluated as baselines. Two hybrid schemes were then developed: DAG-RvR K-Means–OvO and DAG-RvR K-Means–K-Means. Experimental results show that all methods achieve high accuracy of approximately 99%, indicating strong feature separability among rice varieties. However, the proposed DAG-RvR K-Means–OvO provides the most efficient performance, achieving the fastest training time while maintaining competitive testing speed and the highest accuracy of 0.99040. The findings demonstrate that integrating K-Means–based class partitioning with pairwise SVM classification improves computational efficiency without reducing predictive performance, making the hybrid approach suitable for fast and accurate multiclass classification tasks.
Co-Authors Ade Laelani Ade Lailani Ayu Sofia Christyan Tamaro Nadeak Dinda Nababan Fajri Farid Farid, Fajri Indah Suciati Marleta Cornelia Leander Rizka Pitri Rohimatul Anwar Rohmi Dyah Astuti Ronal Sihombing, Natanael Oktavianus Partahan Vina Nurmadani Yoga Aji Sukma Yuliana Yuliana Yusni Puspha Lestari