Article Per Year (5 Year)
p-Index From 2020 - 2025
1.084
P-Index
This Author published in this journals
All Journal Desimal: Jurnal Matematika Jurnal ICT : Information Communication & Technology MATHunesa: Jurnal Ilmiah Matematika Jurnal Riset Rumpun Matematika dan Ilmu Pengetahuan Alam (JURRIMIPA) Sciencestatistics: Journal of Statistics, Probability, and Its Application
Linda Rassiyanti
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Education Mathematics Social Sciences

Published : 7 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 7 Documents
Search

 1 
Growth Model Study Using a Comparison of Gompertz, Logistic, and Weibull Models Suciati, Indah; Vina Nurmadani; 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.v3i2.9423

Abstract

Coronavirus Disease or COVID-19 has been a concern for the world, including Indonesia. The very rapid transmission of COVID-19 has had a wide impact on all communities around the world, especially Indonesia. To see the transmission of COVID-19 cases, which continues to increase rapidly, we can use a growth model. The growth model is a non-linear regression model that is used to describe growth behavior. These models can be exponential, sigmoidal, or S-shaped curves. The purpose of this study was to determine the growth curve model of positive COVID-19 cases in Indonesia using the Gompertz, Logistic, and Weibull models. After that, the model evaluation will be carried out using the coefficient of determination as a parameter, so that the best model will be obtained that can predict more accurately the growth of positive COVID-19 cases in Indonesia. The best model that can predict the growth of positive COVID-19 cases in Indonesia is the Gompertz model, with a coefficient of determination is 0.99064.
Perbandingan Estimator Robust Huber dan Tukey’s Biweight terhadap Berbagai Skema Pencilan dalam Regresi Linier Linda Rassiyanti; Indah Suciati; Vina Nurmadani; Yoga Aji Sukma
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.v3i2.9630

Abstract

Regresi linier secara umum menggunakan pendekatan Ordinary Least Squares (OLS) namun sering kali mengalami gangguan ketika data mengandung pencilan (outlier), yang dapat menyebabkan estimasi parameter menjadi bias dan tidak akurat. Regresi robust dikembangkan untuk mengatasi kelemahan OLS dengan menurunkan sensitivitas terhadap pencilan. Terdapat dua fungsi kerugian yang sering digunakan dalam regresi robust, yaitu Huber Loss dan Tukey’s Biweight Loss. Penelitian ini bertujuan untuk membandingkan performa dua metode regresi robust, yaitu Huber Loss dan Tukey’s Biweight, dalam menghadapi berbagai skema pencilan. Data simulasi dibangkitkan dengan parameter intersep dan slope masing-masing sebesar 3 dan 2, kemudian ditambahkan pencilan secara sistematis pada variabel X, Y, maupun keduanya, dengan proporsi 10%, 20%, dan 30%. Hasil analisis menunjukkan bahwa Tukey’s Biweight memberikan estimasi parameter yang lebih stabil pada kondisi pencilan ekstrem, terutama saat pencilan terjadi pada variabel Y atau kombinasi X dan Y. Sedangkan, Huber Loss cenderung menghasilkan Mean Squared Error (MSE) yang lebih rendah dalam beberapa kondisi, mencerminkan adanya trade-off antara bias dan variansi. Dengan demikian, Tukey’s Biweight lebih cocok untuk pencilan ekstrem, sedangkan Huber Loss lebih efisien dalam kondisi pencilan ringan hingga sedang. Linear regression, commonly estimated using the Ordinary Least Squares (OLS) method, is known for its sensitivity to outliers, which can lead to biased and inefficient parameter estimates. Robust regression was developed to overcome the weaknesses of OLS by reducing sensitivity to outliers. Two commonly used loss functions in robust regression are Huber Loss and Tukey’s Biweight Loss. This study aims to compare the performance of these two robust regression methods—Huber Loss and Tukey’s Biweight—in handling various outlier scenarios. Simulated data were generated with intercept and slope parameters set at 3 and 2, respectively, and outliers were systematically introduced to the X variable, the Y variable, or both, in proportions of 10%, 20%, and 30%. The analysis results indicate that Tukey’s Biweight provides more stable parameter estimates under extreme outlier conditions, especially when outliers occur in the Y variable or in both X and Y. Meanwhile, Huber Loss tends to yield lower Mean Squared Error (MSE) in certain conditions, reflecting a classic trade-off between bias and variance. Therefore, Tukey’s Biweight is more suitable for extreme outliers, whereas Huber Loss is more efficient under mild to moderate outlier conditions.
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 HUBUNGAN ANTARA ANGKA TIDAK SEKOLAH (ATS), TINGKAT KEMISKINAN, DAN ANGKA MELEK HURUF (AMH) DENGAN BIPLOT Linda Rassiyanti; Ayu Sofia; Rizka Pitri
MATHunesa: Jurnal Ilmiah Matematika Vol. 13 No. 2 (2025)
Publisher : Universitas Negeri Surabaya

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

Abstract

Tujuan dari penelitian ini adalah untuk memperdalam analisis tentang hubungan antara pendidikan, literasi, dan kemiskinan. Metode penelitian yang digunakan pada penelitian ini adalah metode analisis biplot yang merupakan teknik statistik deskriptif yang memungkinkan penyajian visual simultan dari objek pengamatan dan variabel dalam ruang dua dimensi. Data yang digunakan dalam penelitian ini adalah Rata-Rata Lama Sekolah (RLS), Angka Melek Huruf (AMH), dan Persentase Orang Miskin. Tiap variabel dilakukan analisis desktiptif untuk mengetahui sebaran datanya, lalu selanjutnya dilakukan analisis biplot, dan menarik kesimpulan. Hasil penelitian menunjukkan bahwa RLS dan AMH memiliki hubungan yang kuat dengan tingkat kemiskinan (persentase orang miskin). Tingkat pendidikan yang lebih tinggi secara umum berkorelasi dengan tingkat kemiskinan yang lebih rendah. Provinsi DKI Jakarta dan Kep. Riau memiliki keberhasilan dalam taraf pendidikan yang tinggi dan kemiskinan rendah, sedangkan Provinsi Papua Pegunungan dan Nusa Tenggara Timur masih memerlukan perhatian untuk mengurangi kemiskinan dan meningkatkan taraf pendidikan. Provinsi Papua Pegunungan dan Papua Tengah terlihat jauh dari kelompok lainnya, menunjukkan karakteristik yang unik, seperti kemiskinan sangat tinggi dan pendidikan sangat rendah.
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 : Pusat riset dan Inovasi Nasional

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.
Co-Authors Ade Laelani Ade Lailani Ayu Sofia Farid, Fajri Indah Suciati Rizka Pitri Rohimatul Anwar Rohmi Dyah Astuti Ronal Suciati, Indah Vina Nurmadani Yoga Aji Sukma Yuliana Yuliana Yusni Puspha Lestari