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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) CAUCHY: Jurnal Matematika Murni dan Aplikasi Limits: Journal of Mathematics and Its Applications
Nurul Hidayat
Institut Teknologi Sepuluh Nopember
Computer Science & IT Engineering Mathematics

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PENGKLASTERAN DATA KATEGORIS DENGAN ALGORITMA SHARED NEAREST NEIGHBOR Alvida Mustikarukmi; M. Isa Irawan; Nurul Hidayat
Limits: Journal of Mathematics and Its Applications Vol. 6 No. 1 (2009): Limits: Journal of Mathematics and Its Applications Volume 6 Nomor 1 Edisi Mei
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

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Abstract

Pengklasteran objek data merupakan salah satu cara untuk mempermudah dalam membaca data, terutama data berdimensi tinggi. Obyek-obyek data berada dalam satu klaster jika mempunyai kesamaan yang tinggi, dan sebaliknya, berada pada klaster berbeda jika menunjukkan ketidaksamaan. Data kategoris merupakan jenis data yang sering digunakan pada database/dataset. Data teks merupakan salah satu data kategoris. Pengklasteran dengan algoritma shared nearest neighbor (SNN) didasarkan pada anggapan bahwa titik-titik akan berada dalam klaster yang sama jika jumlah shared nearest neighbor melebihi ambang batas yang ditentukan. Algoritma SNN mampu memberikan hasil pengklasteran data teks dengan baik, dimana teks dengan tingkat kesamaan yang ditentukan, akan berada pada klaster yang sama.
PEMILIHAN JENIS ASURANSI BERDASARKAN DEMOGRAFI CALON PEMEGANG POLIS DENGAN METODE NAÏVE BAYES CLASSIFIER Lailatul M. Chaira; Nurul Hidayat; Inu L. Wibowo; Imam Mukhlash
Limits: Journal of Mathematics and Its Applications Vol. 13 No. 2 (2016): Limits: Journal of Mathematics and Its Applications Volume 13 Nomor 2 Edisi No
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

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Asuransi merupakan salah satu cara untuk memproteksi diri di masa depan. Saat ini, perusahaann asuransi berlomba-lomba untuk menawarkan produk asuransi yang menjanjikan. Dalam rangka bersaing dengan kompetitor lainnya dan demi memenuhi kebutuhan nasabahnya, perusahaan asuransi memerlukan startegi bisnis yang bijak dan tepat agar produknya mendapat respon positif dari calon nasabah. Salah satu permasalahan dalam bidang asuransi adalah bagaimana menentukan jenis asuransi yang tepat untuk calon nasabah. Pada paper ini, dibahas tentang bagaimana menetukan jenis asuransi yang tepat menggunakan task dalam data mining untuk menggali informasi yang berkaitan dengan kebutuhan produk asuransi bagi calon nasabah. Metode yang digunakan untuk klasifikasi adalah Naïve Bayes Classifier. Hasil uji coba menunjukkan bahwa metode NBC mampu mengklasifikasi record dengan tingkat kinerja tertinggi sebesar 94.12% ketika proporsi pembagian data latih 90% dan data uji sebesar 10%. Karena kinerja sistem yang dihasilkan dapat dikatakan baik, sistem dianggap kredibel untuk merekomendasikan produk asuransi kepada calon nasabah
Modified of Roots Finding Algorithm of High Degree Polynomials Bandung Arry Sanjoyo; Mahmud Yunus; Nurul Hidayat
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.37278

Abstract

Although the Durand-Kerner method is widely used across various fields of computer science, especially in numerical computing, it continues to encounter challenges in locating roots of high-degree polynomials, such as issues with accuracies of roots of the polynomial zeros. Our initial tests and observations on several methods for finding polynomial roots revealed that the roots' accuracy starts to degrade noticeably for polynomials where the degree exceeds 10. Based on considerations of algebraic concepts involving polynomial vector spaces, we introduce an improvement of the Durand-Kerner algorithm aimed at improving root precision. This approach includes targeted refinements in coefficient evaluation, identification of root types, and iterative polishing techniques. We also conducted a comparative evaluation to assess its effectiveness against the original Durand Kerner method and MATLAB's roots() function. Overall, the enhanced algorithm delivers superior accuracy for complex roots—particularly in cases involving multiple zero or integer roots—outperforming both benchmarks, but its execution time increases substantially with polynomial degree.
Immuno-bioinformatics analysis of progressive alignment and logic learning machine-derived viral conserved sequences Felza Ridho; Mohammad Isa Irawan; Nurul Hidayat; Awik Puji Dyah Nurhayati
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3189-3201

Abstract

This study proposes a method to identify conserved sequence positions in viral data, with the primary goal of facilitating their translation into proteins. The approach aims to support the early detection of sub-sequences that hold promise as vaccine candidates. The method involves five key steps. First, mutation analysis using the Kimura model: analyzed mutations in the viral data using the Kimura model to provide insights into sequence variations. Second, alignment method selection to align sequences effectively, the progressive alignment approach with the neighbor-joining algorithm was chosen. Third, hybrid algorithm for identifying unchanged sequences: a hybrid algorithm that combines progressive alignment and a logic learning machine (LLM) was employed to identify unchanged sequences. Fourth, determining conserved sequences: based on the longest unchanged sequence, conserved regions within the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) data were identified. Fifth, immuno-bioinformatics analysis for vaccine candidate peptides: using immuno-bioinformatics, protein sequences were analyzed to identify potential vaccine candidates based on B cell epitopes. Experimental validation confirmed the algorithm’s ability to identify candidate proteins and generate vaccine-worthy peptides from conserved protein sequences. The streamlined approach focuses on conserved protein sequences, ensuring efficient and targeted identification of vaccine candidates. By leveraging these conserved regions, contributions can be made to the expedited development of vaccines.
Co-Authors Alvida Mustikarukmi Awik Puji Dyah Nurhayati Bandung Arry Sanjoyo Felza Ridho Imam Mukhlash Inu L. Wibowo Lailatul M. Chaira M. Isa Irawan Mohammad Isa Irawan Yunus, Mahmud