Article Per Year (5 Year)
p-Index From 2021 - 2026
1.556
P-Index
This Author published in this journals
All Journal Natural Science: Journal of Science and Technology Electronic Journal of Graph Theory and Applications (EJGTA) Jurnal Penelitian Pendidikan IPA (JPPIPA) JURNAL ILMIAH MATEMATIKA DAN TERAPAN BAREKENG: Jurnal Ilmu Matematika dan Terapan Mathvision : Jurnal Matematika Riemann : Research of Mathematics and Mathematics Education Jurnal Pengabdian Farmasi dan Sains
Selvy Musdalifah
Algebra And Combinatorics Research Group, Department Of Mathematics, Faculty Of Mathematics And Natural Sciences, Universitas Tadulako
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Immunology & microbiology Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Transportation

Published : 23 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 23 Documents
Search

 1   2   3 
COMPARATIVE ANALYSIS OF MACHINE LEARNING METHODS IN CLASSIFYING THE QUALITY OF PALU SHALLOTS Lusiyanti, Desy; Musdalifah, Selvy; Sahari, Agusman; Fajri, Iman Al
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp1853-1864

Abstract

This study conducts a comparative analysis of various machine learning methods for classifying the quality of Palu shallots based on the Indonesian National Standard (SNI). The dataset consists of 1,500 samples of Palu shallots, each characterized by 10 key features, including size, color, texture, and moisture content. Five machine learning models—Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine (SVM), and Logistic Regression—were evaluated using accuracy, precision, recall, and F1 score as performance metrics. The results indicate that Random Forest achieved the best performance with an accuracy of 95.4%, followed by Decision Tree (90.7%) and SVM (90.2%). Random Forest also excelled in precision (93.6%) and F1 Score (93.5%), making it the most reliable model for shallot quality classification. Meanwhile, SVM demonstrated a good balance between recall and precision, making it a strong alternative. Implementing machine learning models has the potential to enhance the efficiency and accuracy of agricultural product quality assurance. The findings of this study provide valuable insights for farmers, agribusiness practitioners, and researchers adopting artificial intelligence technology for more precise and efficient agricultural quality assessment.
Total Edge Irregularity Strength of Graph L_3⊙N_m Nikita; Sri Nurhayati; Trisha Magdalena A. Jauvani; Selvy Musdalifah; Iman Al Fajri; Andri
Riemann: Research of Mathematics and Mathematics Education Vol. 7 No. 2 (2025): EDISI AGUSTUS
Publisher : Program Studi Pendidikan Matematika Universitas Katolik Santo Agustinus Hippo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38114/reimann.v7i2.104

Abstract

An edge irregular total k-labeling of a simple graph  is a labeling that assigns positive integers to its vertices and edges such that the weight of every edge, defined as the sum of the labels of the edge and its two incident vertices, is distinct. The smallest integer  that allows such a labeling is called the total edge irregularity strength, denoted by  In this paper, we study the total edge irregularity strength of the corona product of a ladder graph ​ and a null graph ​, denoted by  By applying constructive labeling and analyzing the resulting edge weights, we show that all edges can be assigned distinct weights. From Theorem 1, it is obtained that . This result contributes to the development of graph labeling theory and can be extended to larger ladder graphs for further applications, including cryptography and network security.
Design and Formative Validation of a Guided Discovery Learning Model Integrated with Motivation to Reasoning and Proving Tasks Musdalifah, Selvy; Sudarsana, I Wayan; Sukayasa; Ismaimuza, Dasa; Rochaminah, Sutji; Nurhayadi
Jurnal Penelitian Pendidikan IPA Vol 12 No 2 (2026): In Progress
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v12i2.13909

Abstract

This study develops and evaluates an instructional model that integrates Guided Discovery Learning with Mathematical Reasoning and Proving (MRP) Tasks to support undergraduate students’ construction of mathematical proofs in Real Analysis. The research focuses on two foundational criteria in instructional development, namely validity and practicality. Expert review involving five specialists produced high Aiken V and ICC values, indicating strong conceptual coherence, structural alignment, and reliability of judgment across components of the model. Practicality was examined through one to one evaluation, small group testing, and a field implementation involving lecturers and students. Across these stages, the model received high ratings for clarity of instructional flow, readability of tasks, feasibility of classroom enactment, and support for structured reasoning processes. The findings demonstrate that the model is both theoretically sound and operationally feasible, providing a coherent pedagogical trajectory that guides learners from exploration toward formal proof construction. This study contributes a validated instructional model and establishes a foundation for future research on its effectiveness and broader applicability in advanced mathematics learning.
Co-Authors Afni, N Agusman Sahari Al Fajri, Iman Andi Hendra Andri Anita Ahmad Kasim Armayani Arsal Dasa Ismaimuza Desy Lusiyanti Faisal Susanto Hartayuni Sain Hervisari, Hervisari Huzaimah Huzaimah I W. Sudarsa I Wayan Sudarsana Iman Al Fajri Iman Al Fajri Ismiyanti, Ismiyanti Jeffry Kusuma Merdekawati, Dian Ayu Nasria Nacong Nikita Nikita, Nikita Noviana Noviana Nurainun Nurainun Nurhayadi Nurwijayanti Rizki Amalia S Fatimah, S Sri Nurhayati Sukayasa Sutji Rochaminah Tandi, Andri Trisha Magdalena A. Jauvani Wahyuningsi, Sri Werokati, I Yesi, Indah