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Rudianto Artiono
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MATHunesa: Jurnal Ilmiah Matematika
Published by Universitas Negeri Surabaya
ISSN : 23019115     EISSN : 2716506X     DOI : https://doi.org/10.26740/mathunesa
Core Subject : Education,
Mathematics
MATHunesa is a mathematical scientific journal published by the Department of Mathematics, Faculty of Mathematics and Natural Sciences, The State University of Surabaya with e-ISSN 2716-506X and p-ISSN 2301-9115. This journal is published every four months in April, August, and December. One volume consists of three publication numbers. MATHunesa aims at providing a platform and encourages emerging scholars and academicians globally to share their professional and academic experiences to explore, but not limited to the following topics: 1. Analysis Mathematics, 2. Algebra, 3. Applied Mathematics, 4. Statistics, 5. Computation, 6. Combinatorics, and 7. Also giving an opportunity to show the power of innovation and finding new things in the field of mathematics. This journal was published online for the first time in 2013 as part of the graduation for students majoring in Mathematics at the State University of Surabaya.
Arjuna Subject : Matematika - Matematika (Lain-Lain)
Articles 767 Documents
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Pemodelan Sistem Autoparametrik Dua Derajat Kebebasan yang Tereksitasi Eksternal Tiara Mey Putri; Abadi
MATHunesa: Jurnal Ilmiah Matematika Vol. 14 No. 02 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v14n02.p226 - 234

Abstract

Getaran yang terjadi pada bangunan tinggi dapat dimodelkan dalam sistem pegas-massa dengan peredam linier yang berfungsi melepas energi atau meredam getaran. Meskipun peredam linier dapat digunakan untuk meredam getaran sistem, peredam ini memiliki keterbatasan saat sistem bekerja di dekat kondisi resonansi. Oleh karena itu, dapat digunakan sistem autoparametrik sebagai alternatif dalam meredam getaran. Salah satu model autoparametrik diperkenalkan oleh Tondl dan Nabergoj (1990), yaitu sistem autoparametrik dua derajat kebebasan yang tereksitasi eksternal yang dapat dijadikan sebagai model idealisasi dari bangunan yang bergetar dalam penelitian ini. Penelitian ini bertujuan untuk menurunkan model dari sistem tersebut melalui mekanika Lagrange. Selanjutnya, sebagai ilustrasi perilaku sistem, digunakan metode multiple scales untuk memperoleh solusi semitrivial dengan mempertimbangkan kondisi resonansi primer dan kondisi resonansi internal 1:2. Hasil penelitian menunjukkan bahwa dengan menggunakan mekanika Lagrange model matematika sistem autoparametrik dua derajat kebebasan tereksitasi eksternal berhasil diturunkan. Selain itu, solusi semitrivial yang diperoleh menunjukkan bahwa saat sistem sekunder diam, sistem utama berosilasi dengan amplitudo yang dipengaruhi oleh amplitudo eksitasi eksternal (α̅ ), parameter redaman (κ̅), serta parameter detuning (σ) yang menyatakan besarnya penyimpangan dari kondisi resonansi tepat. Dengan mengambil parameter ε = 0,1, α̅ = 0,08, κ̅ = 0,1, σ₁ = 0,8, dan σ₂ = 0,5, grafik visualisasi menunjukkan bahwa sistem utama berosilasi secara periodik dengan amplitudo sebesar 0,00998. Kata Kunci: getaran, sistem autoparametrik, mekanika Lagrange, metode multiple scales, solusi semitrivial.
PEMODELAN PENYEBARAN POLUSI UDARA DI KOTA MEDAN MENGGUNAKAN PERSAMAAN ADVEKSI-DIFUSI Bless Trini Zalukhu; Riza Prastya; Alvi Sahrin Nasution; Indah Putri Sitompul; Nafisa Naila Lubis; Tio Arini Pasaribu
MATHunesa: Jurnal Ilmiah Matematika Vol. 14 No. 02 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v14n02.p235 - 243

Abstract

Polusi udara di Kota Medan semakin berdampak akibat emisi industri dan kendaraan bermotor yang menghasilkan partikula PM2.5. Penelitian ini bertujuan memodelkan penyebaran PM2.5 menggunakan persamaan adveksi-difusi dua dimensi. Data meteorologi dan Stasiun Belawan Maret 2026 menunjukkan kecepatan angin rata-rata 2,3 m/s dari arah Timur. Data konsentrasi PM2.5 selama 31 hari berkisar 14,4-63,9 . Sembilan titik sumber emisi yang ditetapkan meliputi kawasan industri, pusat kota, perdagangan, organisasi, perkantoran, dan wisata. Persamaan diselesaikan secara numerik dengan metode Crank-Nicolson pada domain 50 50 km selama 72 jam. Hasil simulasi menunjukkan konsentrasi PM2.5 tertinggi di Kawasan Industri Medan Timur dan Pusat Kota (63,9 ). Pola sebaran asimetris memanjang ke Barat hingga 22 km akibat arah angin dari Timur. Tiga sumber emisi membantu lebih dari 70% total polusi . Validasi model menghasilkan RMSE 4,2 (akurasi 92,8%). Jarak pemukiman aman lebih direkomendasikan dari 10 km ke arah Barat dari sumber utama. Kata Kunci: Adveksi-Difusi, PM2.5, Kota Medan, Crank-Nicolson, Efek Spasial
COMPARATIVE ANALYSIS OF FUZZY TIME SERIES MARKOV CHAIN AND FUZZY TIME SERIES CHENG MODELS IN INFLATION PREDICTION KUDUS REGENCY Mursidah; Findasari; Ade Ima Afifa Himayati
MATHunesa: Jurnal Ilmiah Matematika Vol. 14 No. 02 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v14n02.p244-252

Abstract

Inflation is one of the important economic indicators that reflects price stability and people's purchasing power. Unpredictable inflation fluctuations require accurate forecasting methods to support planning and policy-making, especially at the regional level. This study aims to compare the performance of Markov Chain Fuzzy Time Series (FTS) and Cheng FTS in predicting inflation in Kudus Regency and to determine the most effective and efficient model. The data used is monthly inflation data for Kudus Regency, which is analyzed through the stages of determining the universe of discourse, interval formation, fuzzification, fuzzy logic relationship formation, and defuzzification. The accuracy level of the model is evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results showed that the Markov Chain FTS model performed better than the Cheng FTS model. The Markov Chain FTS produced an MAE value of 0.1811 and an RMSE of 0.2371, which were smaller than those of the Cheng FTS, which produced an MAE of 0.2659 and an RMSE of 0.3656. This advantage is due to the Markov Chain FTS's ability to utilize transition adjustments between states, making it more adaptive to data dynamics. Thus, it can be concluded that the Markov Chain FTS is the most effective and efficient model for predicting inflation in Kudus Regency. Keywords: Fluctuations, Fuzzy Time Series, Markov chain, MAE, RMSE.
PREDIKSI NILAI INFLASI DAERAH DI JAWA TIMUR MENGGUNAKAN METODE ORDINARY KRIGING febrika ade eliza; affiati oktaviarina
MATHunesa: Jurnal Ilmiah Matematika Vol. 14 No. 02 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v14n02.p263-270

Abstract

Inflasi merupakan indikator penting dalam perekonomian, namun tidak semua daerah memiliki data inflasi yang lengkap. Penelitian ini bertujuan untuk memprediksi nilai inflasi daerah di Jawa Timur menggunakan metode Ordinary Kriging. Data yang digunakan terdiri dari 11 daerah tersampel untuk memprediksi nilai inflasi pada 4 daerah tidak tersampel di kawasan Gerbangkertosusilo. Penentuan model semivariogram teoritis dilakukan dengan membandingkan model spherical, exponential, dan gaussian berdasarkan nilai Mean Absolute Percentage Error (MAPE). Hasil perhitungan menunjukkan bahwa model spherical menghasilkan MAPE sebesar 20,66%, exponential sebesar 17,74%, dan gaussian sebesar 30,12%. Model exponential memiliki nilai MAPE terkecil sehingga dipilih sebagai model terbaik. Predksi menggunakan Ordinary Kriging dengan model exponential menghasilkan estimasi inflasi Kabupaten Mojokerto sebesar 1,38%, Kabupaten Bangkalan sebesar 1,63%, Kabupaten Sidoarjo sebesar 1,42%, dan Kabupaten Lamongan sebesar 1,51%. Hasil penelitian menunjukkan bahwa metode Ordinary Kriging mampu memberikan prediksi inflasi yang representatif pada daerah yang belum memiliki data pengamatan Kata Kunci: Inflasi, Gerbangkertosusilo, Semivariogram Eksperimental, Semivariogram Teoritis, Ordinary Kriging
KLASIFIKASI LAJU PERTUMBUHAN PENDUDUK ANTAR PROVINSI DI INDONESIA MENGGUNAKAN METODE K-NEAREST NEIGHBOR Agung Atra Perkasa; Emon Sejahtera Zendrato; Ilham Pratama; Hizkia Simamora
MATHunesa: Jurnal Ilmiah Matematika Vol. 14 No. 02 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v14n02.p271-278

Abstract

Population data plays a crucial role in development planning, yet its utilization has not yet been optimized to generate truly informative insights. This study aims to classify population growth rates across provinces in Indonesia into low, medium, and high categories using the K-Nearest Neighbor (KNN) algorithm. The research process includes data collection and preprocessing, category labeling using the quantile method, data normalization via Min-Max Scaling, and splitting the data into training and test sets with a 70:30 ratio. The KNN model was built using parameter values of k, namely 3, 5, and 7, and the best value of k was selected based on the model evaluation results. Evaluation was performed using a confusion matrix by calculating the accuracy value. The test results showed that the best model was obtained at k = 5 with an accuracy of 75%. These findings indicate that KNN can identify similarities in demographic characteristics across provinces quite well, although there are still classification errors in classes with closely related characteristics. Therefore, the KNN method can serve as a simple and effective approach for population data analysis and has the potential to support data-driven decision-making.
ANALISIS KOMPARATIF PERNIKAHAN DINI PEREMPUAN ANTARA DUA PROVINSI DENGAN HOTELLING’S T² laudya_meitaneia sianturi; Steffany Claussia Fernanda; Alysha Khanza Dwi Avianti; Shindi Shella May Wara; Muhammad Nasrudin
MATHunesa: Jurnal Ilmiah Matematika Vol. 14 No. 02 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v14n02.p279-287

Abstract

Early marriage of women under 17 years old is a social problem that affects education, health, and community welfare. This study aims to analyze differences in socioeconomic characteristics related to early marriage between East Java and Central Java Provinces using a multivariate approach. Secondary data from 2022 obtained from the Central Bureau of Statistics covers 73 districts/cities with variables including the percentage of early marriage (X1), mean years of schooling (X2), and poverty percentage (X3). Analysis was conducted using Hotelling's T² test, preceded by Mardia's multivariate normality test and Box's M covariance matrix homogeneity test. Assumption testing results confirmed that the data met multivariate normality and covariance matrix homogeneity requirements. The Hotelling's T² test revealed no significant multivariate difference between the two provinces (T² = 7.1221; p-value = 0.084). Follow-up t-tests on each variable also showed no significant differences. These findings indicate that the socioeconomic characteristics related to early marriage in East Java and Central Java exhibit relatively similar patterns. Therefore, early marriage prevention strategies in both provinces can be designed using comparable approaches, while still considering local variations at the district/city level. Keywords: early marriage, Hotelling's T², multivariate, socioeconomic, Java.
ANALISIS TERHADAP TREN KEMISKINAN DI JAWA TENGAH PERIODE 2022-2024 DENGAN RM-MANOVA Amelia Rizqyna Putri; Raveena Ayu Desember Suryoputri; Yuniar Rachmawati; Shindi Shella May Wara; Muhammad Nasrudin
MATHunesa: Jurnal Ilmiah Matematika Vol. 14 No. 02 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v14n02.p288-295

Abstract

Kemiskinan di Jawa Tengah masih menjadi persoalan pembangunan yang penting karena berkaitan dengan kualitas hidup masyarakat dan menunjukkan variasi kondisi antarkabupaten/kota. Penelitian ini bertujuan untuk menganalisis tren kemiskinan di Jawa Tengah periode 2022–2024 menggunakan pendekatan Repeated Measures Multivariate Analysis of Variance (RM-MANOVA). Data yang digunakan merupakan data sekunder yang bersumber dari Badan Pusat Statistik Jawa Tengah, mencakup 35 kabupaten/kota selama tahun 2022, 2023, dan 2024 dengan total 105 observasi. Indikator yang dianalisis meliputi persentase penduduk miskin, tingkat pengangguran terbuka, gini ratio, dan rata-rata lama sekolah. Hasil analisis deskriptif menunjukkan bahwa persentase penduduk miskin dan tingkat pengangguran terbuka cenderung menurun, sedangkan rata-rata lama sekolah cenderung meningkat selama periode pengamatan, sementara gini ratio relatif stabil. Hasil uji asumsi menunjukkan bahwa data memenuhi prasyarat RM-MANOVA. Namun, hasil uji RM-MANOVA menunjukkan bahwa tidak terdapat perbedaan signifikan pada profil kemiskinan Jawa Tengah secara multivariat antar tahun 2022–2024. Uji lanjut paired t-test dengan koreksi Bonferroni menunjukkan bahwa persentase penduduk miskin, tingkat pengangguran terbuka, dan rata-rata lama sekolah berbeda signifikan pada seluruh pasangan tahun, sedangkan gini ratio tidak menunjukkan perbedaan signifikan. Hasil penelitian ini diharapkan dapat memberikan gambaran yang lebih komprehensif mengenai dinamika kemiskinan di Jawa Tengah dan menjadi bahan pertimbangan dalam evaluasi kebijakan penanggulangan kemiskinan. Kata Kunci: kemiskinan, Jawa Tengah, RM-MANOVA, indikator sosial-ekonomi, tren
Pengembangan Chatbot inapinep.id Berbasis Natural Language Processing sebagai Sistem Rekomendasi Hotel di Pulau Jawa Siti Naia Hesti Rachmawati; Fanny Widya Cahyani; Sonya Audina Akbar; Shindi Shella May Wara; Amri Muhaimin
MATHunesa: Jurnal Ilmiah Matematika Vol. 14 No. 02 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v14n02.p296-306

Abstract

Pesatnya pertumbuhan sektor pariwisata di Pulau Jawa menyebabkan terjadinya fenomena information overload bagi wisatawan dalam menentukan akomodasi yang sesuai. Penelitian ini bertujuan untuk mengembangkan asisten virtual bernama inapinep.id, sebuah chatbot berbasis Natural Language Processing (NLP) yang berfungsi sebagai sistem rekomendasi hotel. Metode yang digunakan adalah Term Frequency-Inverse Document Frequency (TF-IDF) untuk pembobotan fitur dan Cosine Similarity untuk menghitung tingkat kemiripan antara kueri deskriptif pengguna dengan dataset hotel. Tahapan preprocessing difokuskan pada pembersihan teks, normalisasi sinonim, dan deduplikasi kata tanpa menggunakan proses stemming guna menjaga integritas istilah teknis fasilitas hotel. Hasil penelitian menunjukkan bahwa sistem mampu memberikan rekomendasi yang presisi dengan skor kemiripan tertinggi mencapai 0,6687 pada pengujian kueri spesifik. Uji statistik Chi-Square mengonfirmasi bahwa fitur "lift" merupakan parameter pembeda paling signifikan (p = 0,0071) dalam menentukan kecenderungan rating hotel tinggi dalam dataset. Sistem juga dilengkapi dengan mekanisme guardrails untuk menangani kueri di luar topik guna menjaga kualitas interaksi. Penelitian ini membuktikan bahwa integrasi model pembobotan kata dan analisis statistik mampu menghasilkan sistem rekomendasi yang cerdas dan intuitif dalam membantu navigasi wisatawan di Pulau Jawa. Kata Kunci: Chatbot, Natural Language Processing, TF-IDF, Cosine Similarity, Rekomendasi Hotel.
Generalized Poisson Regression Model on factors influencing the number of poor people in South Central Timor Regency 2024 Sisilia Ome; Robertus Dole Guntur; Maria Agustina Kleden; Keristina Br. Ginting
MATHunesa: Jurnal Ilmiah Matematika Vol. 14 No. 02 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v14n02.p307-316

Abstract

Poverty remains a major issue in Timor Tengah Selatan Regency (TTS), which has a relatively high poverty rate in East Nusa Tenggara Province. This condition is not only caused by low household income but also reflects limitations in meeting basic needs. The number of poor people is categorized as count data, which in statistical analysis often exhibits overdispersion, making the standard Poisson regression model less appropriate. Therefore, this study applies the Generalized Poisson Regression (GPR) approach to examine the effects of various factors on the number of poor people, using secondary data from 2024 covering 32 sub-districts in Timor Tengah Selatan Regency. The independent variables in this study include the open unemployment rate, total population, labor force, population density, and population growth rate. The results show that all independent variables have a significant effect on the number of poor people. Specifically, the open unemployment rate has a coefficient of -0.07289, total population is 1.11417, labor force is 1.44963, and population density is 0.00094, while population growth rate has a coefficient of -0.03301, indicating that an increase in this variable can reduce the number of poor people by approximately 3.25 percent.
SEGMENTASI DAN ANALISIS KETIDAKPATUHAN PEMBAYARAN PAJAK REKLAME KOTA BANDAR LAMPUNG MENGGUNAKAN ALGORITMA K-PROTOTYPES CLUSTERING Rizky Ahmad Rifai; Werry Febrianti
MATHunesa: Jurnal Ilmiah Matematika Vol. 14 No. 02 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v14n02.p317-323

Abstract

Advertising tax constitutes a significant component contributing to the Local Own-Source Revenue of Bandar Lampung. A persistent problem identified is the high volume of advertising tax arrears, reflecting non-compliance in payment among tax objects. This research aims to perform segmentation and analysis of advertising tax non-compliance by employing the K-Prototypes Clustering algorithm. The data for this study were sourced from the archives of the Regional Revenue Agency of Bandar Lampung (Badan Pendapatan Daerah) for the billing period of April 2025, encompassing a total of 949 tax objects. The research methodology includes data pre-processing (categorical data transformation and numerical variable normalization using the z-score method), descriptive analysis, application of the K-Prototypes Clustering algorithm, and result evaluation utilizing the Average Silhouette Width (ASW). The clustering results yield three primary clusters, which represent taxpayer categories with varying degrees of non-compliance, analyzed based on the variables: principal arrears, payment delay, interest, tax year, tax month, and Technical Implementation Unit (UPTD). The evaluation using ASW produced a value of 0.422, indicating that the K-Prototypes Clustering algorithm is sufficiently effective in grouping the advertising tax objects.

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