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All Journal Journal of the Indonesian Mathematical Society Journal of Mathematical and Fundamental Sciences Journal of Engineering and Technological Sciences Indonesia Symposium on Computing KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan Limits: Journal of Mathematics and Its Applications Jurnal Matematika UNAND Journal of Research in Mathematics Trends and Technology Jurnal Teknik Informatika (JUTIF) Jurnal Pengabdian Kepada Masyarakat (JPKM) Tabikpun MATHunesa: Jurnal Ilmiah Matematika Jurnal Siger Matematika Jurnal Matematika Integratif BACA: Jurnal Dokumentasi dan Informasi Integra: Journal of Integrated Mathematics and Computer Science Limits: Journal of Mathematics and Its Applications
Aang Nuryaman
Universitas Lampung
Astronomy Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Library & Information Science Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Penerapan Algoritma K-Nearest Neighbor Pada Data Kategorik Untuk Klasifikasi Harga Laptop Citra Puspa Tria; Aang Nuryaman; Ahmad Faisol; Eri Setiawan
Jurnal Siger Matematika Vol 4, No 2 (2023): Jurnal Siger Matematika
Publisher : FMIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jsm.v4i2.9234

Abstract

Salah satu algoritma klasifikasi yang banyak digunakan adalah k-nearest neighbor (KNN) di mana algoritma ini mengklasifikasikan objek berdasarkan kelas mayoritas dari k objek terdekat di sekitarnya. Pada umumnya, ukuran kedekatan antar objek dapat dihitung dengan menggunakan jarak Euclidean. Akan tetapi, apabila data yang digunakan adalah data kategorik, maka jarak tersebut dirasa kurang tepat untuk digunakan. Salah satu alternatif ukuran kedekatan antar objek yang dapat digunakan untuk data kategorik adalah jarak weighted simple matching coefficient (WSMC). Penelitian ini dilakukan untuk membangun model klasifikasi untuk penentuan kelas rentang harga laptop berdasarkan 4 fitur yang ditawarkan, yaitu merek laptop, prosesor, kapasitas RAM, dan kapasitas penyimpanan. Terdapat dua proporsi pembagian data latih dan data uji untuk membangun model klasifikasi, yaitu 80:20 dan 90:10. Berdasarkan hasil analisis, model klasifikasi terbaik diperoleh ketika proporsi data yang digunakan adalah 90:10 dan jumlah k sebanyak 17, dengan nilai akurasi yang diperoleh sebesar 86.96%, recall sebesar 50%, presisi sebesar 66.67%, dan f1-score sebesar 57.14%.
Penerapan Model Vector Error Correction Model (VECM) pada Peramalan Data Nilai Ekspor dan Nilai Impor Seluruh Komoditas di Provinsi Lampung Tahun 2022 Mega Putri; Widiarti Widiarti; Aang Nuryaman; Warsono Warsono
Jurnal Siger Matematika Vol 4, No 2 (2023): Jurnal Siger Matematika
Publisher : FMIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jsm.v4i2.12583

Abstract

Model Vector Error Correction Model (VECM) merupakan turunan dari model Vector Autoregressive (VAR) untuk data tidak stasioner dan terdapat hubungan kointegrasi. Model VAR merupakan model peramalan data deret waktu multivariat yang menghubungkan nilai peramalan dengan nilai-nilai data pada periode sebelumnya. Data deret waktu multivariat yang digunakan pada penelitian ini adalah data Nilai Ekspor dan Nilai Impor seluruh komoditas di Provinsi Lampung tahun 2015-2021. Penelitian ini bertujuan untuk melakukan peramalan pada periode Januari sampai Desember 2022 dengan model VECM. Berdasarkan data diperoleh model VECM yaitu model VECM (1) dengan nilai MAPE sebesar 17,01%.
Penyelesaian Masalah Nilai Awal PDB Linier Orde Tiga Dengan Koefisien Konstan Menggunakan Metode Dekomposisi Adomian Fathudin Fathudin; Aang Nuryaman; Ahmad Faisol
Jurnal Matematika Integratif Vol 19, No 2: Oktober 2023
Publisher : Department of Matematics, Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/jmi.v19.n2.41896.213-222

Abstract

Metode Dekomposisi Adomian (MDA) telah banyak digunakan dalam menyelesaikan model matematika dalam bentuk persamaan diferensial, baik Persamaan Diferensial Biasa (PDB) maupun Persamaan Diferensial Parsial (PDP). Metode ini dibagi menjadi tiga langkah inti. Langkah pertama adalah menguraikan bagian F dari persamaan operator Fy(x)=g(x) menjadi L dan R, di mana L adalah operator linier yang memiliki invers dan R adalah operator linier lainnya. Langkah kedua adalah mengoperasikan invers dari operator L pada persamaan ini untuk mendapatkan y(x) dan langkah ketiga mengasumsikan solusi yang diperoleh pada langkah kedua berbentuk deret yang memberikan relasi rekursif dan kemudian menyelesaikannya. Penelitian ini akan menerapkan Metode Dekomposisi Adomian pada masalah nilai awal persamaan diferensial biasa linier orde ketiga dengan koefisien konstan baik homogen maupun tak homogen. Berdasarkan perbandingan solusi eksak dengan solusi menggunakan Metode Dekomposisi Adomian hingga suku keempat, hasilnya menunjukkan bahwa solusi hampiran sesuai dengan solusi eksak.
MODEL MATEMATIKA OPTIMALISASI KEUNTUNGAN PENJUALAN: REGRESI LINEAR BERGANDA Ardiyanti, Lily; Ahidiyah, Siti; Mardani, Maya; Danuari Fitrio, Beni; Nuryaman, Aang
MATHunesa: Jurnal Ilmiah Matematika Vol. 12 No. 3 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v12n3.p662-670

Abstract

SOLUTION OF FULLY FUZZY NONLINEAR EQUATION SYSTEMS USING GENETIC ALGORITHM Fatimatuzzahra, Fatimatuzzahra; Nuryaman, Aang; Zakaria, La; Sutrisno, Agus
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1169-1178

Abstract

A system of nonlinear equations is a collection of several interrelated non-linear equations. Currently, systems of nonlinear equations are used not only on crisp but also on fuzzy numbers. A fuzzy number is an ordered pair function that has a degree of membership [0,1]. Meanwhile, a fully fuzzy system of equations is a system of equations that applies fuzzy number arithmetic operations. The solution of non-linear equation systems is usually complicated to solve analytically, so numerical methods are used as an alternative to solve these problems. In this research, the steps to find the solution of nonlinear fully fuzzy equation systems using genetic algorithms are studied, which in the solution process is based on the theory of evolution and natural selection. The solution steps taken are first converting the fully fuzzy system of equations into a system of crisp equations, next constructing the system of strict equations as a multi-objective optimization problem, and lastly solving the optimization problem using a genetic algorithm which includes initialization, evaluation, selection, crossover, and mutation. As illustrations, several cases of nonlinear fully fuzzy and dual fully fuzzy systems of equations on triangular fuzzy numbers and trapezoidal fuzzy numbers are given. The approximate solutions obtained using genetic algorithms produce solutions that are close to their analytic solutions.
MAPPING EARTHQUAKE MAGNITUDES IN BENGKULU PROVINCE AND SURROUNDING AREAS USING ROBUST ORDINARY KRIGING Swita, Baki; Astuti, Mulia; Faisal, Fachri; Nuryaman, Aang
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/barekengvol19iss3pp1537-1552

Abstract

Bengkulu Province, situated in a subduction zone between the Indo-Australian and Eurasian plates, is highly susceptible to significant seismic activity, including major earthquakes in 2000 and 2007 with magnitudes exceeding 7. This research investigates the geographical distribution of earthquake magnitudes in Bengkulu Province and surrounding areas from 2000 to 2023. Understanding these spatial patterns is crucial for enhancing disaster preparedness and risk mitigation strategies in this high-risk region. Previous studies on earthquake distribution in Indonesia have provided valuable insights but often struggle with outliers and data variability, limiting their accuracy. Conventional Ordinary Kriging methods, though widely used, are sensitive to outliers, leading to potential inaccuracies. This study addresses these limitations by applying a robust Ordinary Kriging approach, which effectively mitigates the influence of outliers, thereby improving prediction reliability. The research utilizes earthquake data, including geographical coordinates and recorded magnitudes. It applies both classical and robust experimental semivariograms (Cressie-Hawkins) to model the spatial structure using theoretical variogram models—spherical, exponential, and Gaussian. The best-fit model is determined based on the lowest root mean square error (RMSE), ensuring accurate representation of spatial patterns. The results demonstrate that robust Ordinary Kriging accurately maps the spatial distribution of earthquake magnitudes, revealing clusters of higher magnitude events in specific regions of Bengkulu Province. These findings identify high-risk areas, providing essential data for disaster mitigation and risk management planning. This study significantly contributes to the field of seismology and geostatistics by enhancing the accuracy of magnitude distribution mapping. The resulting maps support local governments, urban planners, and disaster response organizations in developing more effective mitigation strategies, improving infrastructure resilience, and strengthening early warning systems. Ultimately, this research aims to foster safer, more prepared communities in Bengkulu Province and beyond.
Variational homotopy perturbation method for solving systems of homogeneous linear and nonlinear partial differential equations Faradilla, Atika; Nuryaman, Aang; Asmiati, Asmiati; Nur, Dewi Rakhmatia
Desimal: Jurnal Matematika Vol. 4 No. 2 (2021): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v4i2.7825

Abstract

The variational homotopy perturbation method is developed by combining variational iteration method and homotopy perturbation method. Variational iteration method has an efficient process in solving a wide variety of equations and systems of equations. Meanwhile, homotopy perturbation method yields a very rapid convergence of the solution series in most cases. The developed method, variational homotopy perturbation method, took full advantage of both methods. In this study, we described an application of the variational homotopy perturbation method to solve systems of homogeneous partial differential equations. Here we consider some initial value problems of homogeneous partial differential equation systems with two and three variables. The results show that the obtained solution using this method was in agreement with the solution using the homotopy analysis method and variational iteration method, which prove the validity of the variational homotopy perturbation method when applied to systems of partial differential equations.
Application of singular spectrum analysis (SSA) method on forecasting train passengers data in sumatera Fitriani, Debi Nur; Widarti, Widiarti; Nuryaman, Aang; Setiawan, Eri
Desimal: Jurnal Matematika Vol. 6 No. 3 (2023): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v6i3.19040

Abstract

A time series is a series of observations of a variable that is collected, recorded, or observed over a period of time in sequence. Singular Spectrum Analysis is a powerful method to analyze time series data by decomposing the original time series data into several small components that can be identified, such as trend, periodic, and noise components. One of the datasets that can be used is data on the number of train passengers in Sumatera in 2013–2022. In this study, the Singular Spectrum Analysis method is used to forecast the number of train passengers in Sumatera in 2013–2022. The best Singular Spectrum Analysis model in this study was obtained at a window length of 22 and a number of groups of 8, with a MAPE value of 19.55%.
Optimizing Multi-Layer Perceptron Performance in Sentiment Classification through Neural Network Feature Extraction Alam, Muhammad Fikri; Nuryaman, Aang; Khotimah, Purnomo Husnul; Parlina, Anne; Sihombing, Andre
BACA: Jurnal Dokumentasi dan Informasi Vol. 46 No. 1 (2025): BACA: Jurnal Dokumentasi dan Informasi (Juni)
Publisher : Direktorat Repositori, Multimedia, dan Penerbitan Ilmiah - Badan Riset dan Inovasi Nasional (BRIN Publishing)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/baca.2025.8240

Abstract

There are some problems with using the Multi-Layer Perceptron (MLP) model for complex tasks because it can be hard to understand hierarchical relationships and tends to overfit data with a lot of dimensions. This research proposes an enhanced MLP model for sentiment classification by integrating feature extraction layers from advanced neural networks, specifically the Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (Bi-LSTM). These layers aim to improve the model's representation capabilities by capturing more nuanced features. To evaluate the performance improvements of this augmented MLP model, metrics such as accuracy, precision, recall, F1-score, and the Area Under the Curve for Receiver Operating Characteristics (ROC-AUC) were employed. A key metric focus is the delta value, representing changes in the ROC-AUC, to assess the significance of these enhancements. The integration of CNN as a feature extraction layer yielded optimal ROC-AUC results, achieving values of 93.30% and 93.00%, which reflect an improvement of 0.51% and 4.46% over the baseline model. These findings indicate that adding feature extraction layers significantly enhances MLP performance in sentiment classification tasks. Future research may explore the potential of using alternative neural networks as feature extractors to continue advancing MLP capabilities in complex NLP applications.
IMPLEMENTATION OF FUZZY C-MEANS AND FUZZY POSSIBILISTIC C-MEANS ALGORITHMS ON POVERTY DATA IN INDONESIA Kurniasari, Dian; Kurniawati, Virda; Nuryaman, Aang; Usman, Mustofa; Nisa, Rizki Khoirun
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1919-1930

Abstract

Cluster analysis involves the methodical categorization of data based on the degree of similarity within each group to group data with similar characteristics. This study focuses on classifying poverty data across Indonesian provinces. The methodologies employed include the Fuzzy C-Means (FCM) and Fuzzy Probabilistic C-Means (FPCM) algorithms. The FCM algorithm is a clustering approach where membership values determine the presence of each data point in a cluster. On the other hand, the FPCM algorithm builds upon FCM and Possibilistic C (PCM) algorithms by incorporating probabilistic considerations. This research compares the FCM and FPCM algorithms using local poverty data from Indonesia, specifically examining the Partition Entropy (PE) index value. It aims to identify the optimal number of clusters for provincial-level poverty data in Indonesia. The findings indicate that the FPCM algorithm outperforms the FCM algorithm in categorizing poverty in Indonesia, as evidenced by the PE validity index. Furthermore, the study identifies that the ideal number of clusters for the data is 2.
Co-Authors . Wamiliana Agus Yodi Gunawan Ahidiyah, Siti AHMAD FAISOL Ahmad Faisol Akmal Akmal Alam, Muhammad Fikri Anne Parlina Ardiyanti, Lily Asmiati Asmiati Asmiati Asmiati Asmiati Asmiati Atika Faradilla Bagus Hayatul Jihad Bagus Hayatul Jihad Citra Puspa Tria Dalfa Habibah Nurul Aini Danuari Fitrio, Beni Dewi Rakhmatia Nur Dian Kurniasari Dian Kurniasari Dina Eka Nurvazly Eri Setiawan Eri Setiawan Fachri Faisal Faradilla, Atika Fathudin Fathudin Fatimatuzzahra Fatimatuzzahra Fitriani Fitriani Fitriani, Debi Nur Hestina P., Shelvira Ismawati Ismawati Khotimah, Purnomo Husnul Kuntjoro Adji Sidarto Kurniawati, Virda La Zakaria La Zakaria Mardani, Maya Mega Putri Muhammad Ihsan Abyan Muhammad Ihsan Abyan Mulia Astuti Mustofa Usman Mustofa Usman Nabilla Yolanda Paramitha Nisa, Rizki Khoirun Notiragayu Notiragayu Nur Hamzah Nur, Dewi Rakhmatia Ranti Vidia Mahyunis Raratia ulsa Alfian Rudi Ruswandi Rudi Ruswandi Saidi, Subian Saiful Rohman Sihombing, Andre Soleh Fajar Junjunan Soleh Fajar Junjunan Sutrisno, Agus Swita, Baki Utami, Bernadhita Herindri Samodera Warsono Warsono Warsono Warsono Widarti, Widiarti Widiarti Widiarti Widiarti Wiranto, Ahmad Rizki Yogi Wibisono Budhi Zakaria, La