Article Per Year (5 Year)
p-Index From 2021 - 2026
0.888
P-Index
This Author published in this journals
All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan MATHEMA: JURNAL PENDIDIKAN MATEMATIKA Journal of Fundamental Mathematics and Applications (JFMA)
Vantika, Sandy
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Transportation

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
FLOWER POLLINATION ALGORITHM (FPA): COMPARING SWITCH PROBABILITY BETWEEN CONSTANT 0.8 AND DOUBLE EXPONENTGUNAKAN DOUBLE EXPONENT Afrianti, Yuli Sri; Sulaiman, Fadhil Hanif; Vantika, Sandy
Journal of Fundamental Mathematics and Applications (JFMA) Vol 6, No 2 (2023)
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jfma.v6i2.18996

Abstract

Flower Pollination Algorithm (FPA) is an optimization method that adopts the way flower pollination works by selecting switch probabilities to determine the global or local optimization process. The choice of switch probability value will influence the number of iterations required to reach the optimum value. In several previous literatures, the switch probability value was always chosen as 0.8 because naturally the global probability is greater than local. In this article, comparison is studied to determine the switch probability by using the Double Exponent rule. The results are analyzed using Hypothesis Testing to test whether there is a significant difference between the optimization results. The study involved ten testing functions, and results showed that the 0.8 treatment is significantly different from the Double Exponent. However, in general no treatment is better than the other.
Penerapan Pembelajaran Berbasis Proyek untuk Meningkatkan Prestasi Belajar Siswa pada Materi Limit Fungsi Trigonometri Vantika, Sandy; Sukardi, Sukardi; Afifi, Fakhrunnisa Cahya; Sudarmono, Sudarmono; Dewi, Vini Komala
MATHEMA: JURNAL PENDIDIKAN MATEMATIKA Vol 6, No 1 (2024): MATHEMA
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jm.v6i1.3009

Abstract

Limit fungsi trigonometri merupakan salah satu materi matematika yang sulit dipahami oleh siswa. Sementara itu, pembelajaran berbasis proyek menjadi model pembelajaran yang direkomendasikan menjelang pelaksanaan Kurikulum Merdeka secara holistik. Berkaitan dengan hal tersebut, tujuan penelitian ini adalah untuk mengetahui pengaruh pelaksanaan pembelajaran berbasis proyek terhadap prestasi belajar siswa, khususnya pada materi limit fungsi trigonometri sesuai dengan silabus Kurikulum 2013. Subjek penelitian ini adalah 34 orang siswa kelas XII tahun ajaran 2021/2022, yaitu siswa yang menerima pembelajaran konvensional (sebagai anggota kelompok kontrol), dan 33 orang siswa kelas XII tahun ajaran 2022/2023, yaitu siswa yang menerima pembelajaran berbasis proyek (sebagai anggota kelompok perlakuan) yang dipilih dengan menggunakan teknik penyampelan acak sederhana, di SMA Santo Paulus Pontianak. Penelitian ini merupakan penelitian deskriptif kuantitatif. Pengaruh pelaksanaan berbasis proyek terhadap prestasi belajar siswa dapat diketahui dengan membandingkan nilai rata-rata penilaian tengah semester ganjil yang diperoleh siswa pada dua angkatan tersebut. Hasil penelitian ini menunjukkan bahwa terdapat pengaruh pelaksanaan pembelajaran berbasis proyek terhadap prestasi belajar siswa yang terukur dari diperolehnya nilai statistik-t sebesar -2,0541 (nilai-p sebesar 0,04421). Secara spesifik, nilai rata-rata penilaian tengah semester siswa yang menerima pembelajaran berbasis proyek lebih tinggi dibandingkan dengan siswa yang menerima pembelajaran konvensional. Penelitian ini menyimpulkan bahwa pembelajaran berbasis proyek dapat meningkatkan prestasi belajar siswa, khususnya pada materi limit fungsi trigonometri, sehingga dapat menjadi model pembelajaran yang direkomendasikan dalam rangka menyongsong Kurikulum Merdeka.
POISSON REGRESSION MODELLING OF AUTOMOBILE INSURANCE USING R Vantika, Sandy; Yudhanegara, Mokhammad Ridwan; Lestari, Karunia Eka
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (598.566 KB) | DOI: 10.30598/barekengvol16iss4pp1399-1410

Abstract

Automobile insurance benefits are protecting the vehicle and minimizing customer losses. Insurance companies must provide funds to pay customer claims if a claim occurs. Insurance claims can be modelled by Poisson regression. Poisson regression is used to analyze the count data with Poisson distributed data responses. this paper, the data model of sample is automobile insurance claims from the companies in one year (in 2021) of observation which contains three types of insurance products, i.e., Total Loss Only (TLO), All Risk, and Comprehensive. The results of data analysis show that the highest number of claims comes from Comprehensive insurance products, especially if the premium value gets more extensive. In contrast, the least comes from TLO insurance products.
A NOVEL APPROACH TO SYMBOLIC DATA CLUSTERING USING ENHANCED K-MEANS ALGORITHM Serviana Husain, Husty; Wahyu Indratno, Sapto; Vantika, Sandy
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1263-1282

Abstract

Clustering is a crucial technique in image analysis, yet traditional methods such as K-Means often struggle when dealing with complex, high-dimensional, or uncertain data. This limitation reduces their effectiveness in accurately grouping images, particularly when variability and overlapping features exist across categories. To address this problem, this paper introduces a novel approach that integrates symbolic data with the K-Means algorithm to cluster image data more effectively. By symbolically representing both color intensity and spatial features, we enhance the algorithm’s ability to handle variability and uncertainty. We test our method on the CIFAR-10 dataset, where it achieves a clustering accuracy of 94.0% with an Adjusted Rand Index of 0.7, outperforming traditional methods such as K-Means (82.5%), DBSCAN (78.1%), and Hierarchical clustering (81.3%). Our results demonstrate that symbolic data analysis offers a more flexible and accurate solution for image clustering, with potential applications in fields such as medical image processing and environmental monitoring. Limitations and directions for future research are also discussed.
Co-Authors Afifi, Fakhrunnisa Cahya Dewi, Vini Komala Karunia Eka Lestari Mokhammad Ridwan Yudhanegara Serviana Husain, Husty SUDARMONO SUDARMONO Sukardi Sukardi Sulaiman, Fadhil Hanif Wahyu Indratno, Sapto Yuli Sri Afrianti