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All Journal International Journal of Evaluation and Research in Education (IJERE) Cakrawala Pendidikan Jurnal Buana Informatika Kubik Jurnal Sains Matematika dan Statistika Proceeding of the Electrical Engineering Computer Science and Informatics Fountain of Informatics Journal JRPM (Jurnal Review Pembelajaran Matematika) Jurnal Matematika: MANTIK Al-MARSHAD: Jurnal Astronomi Islam dan Ilmu-Ilmu Berkaitan BAREKENG: Jurnal Ilmu Matematika dan Terapan Teorema: Teori dan Riset Matematika Mathvision : Jurnal Matematika Jurnal Statistika dan Aplikasinya KUBIK: Jurnal Publikasi Ilmiah Matematika Journal of Mathematics Education and Science Leibniz: Jurnal Matematika Journal of Multidisciplinary Applied Natural Science Jurnal Matematika Integratif Engagement: Jurnal Pengabdian Kepada Masyarakat International Journal of Computing Science and Applied Mathematics-IJCSAM
Ahmad Hanif Asyhar
UIN Sunan Ampel Surabaya
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Prediksi Tinggi Gelombang dan Kecepatan Angin di Pantai Menggunakan Metode BiGRU Putri Oktavia, Nabiilah; Hakim, Lutfi; Novitasari , Dian Candra Rini; Asyhar, Ahmad Hanif; Setiawan, Fajar
Fountain of Informatics Journal Vol. 10 No. 1 (2025): Mei
Publisher : Universitas Darussalam Gontor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21111/fij.v10i1.13018

Abstract

Abstrak Indonesia terletak di antara Samudera Pasifik dan Samudera Hindia yang membuat Indonesia menjadi pusat jalur perdagangan internasional. Pada lokasi desa Karangduwur yang berlokasi di Jawa Tengah memiliki potensi ekonomi maritim yang kuat tetapi juga memiliki risiko cuaca yang besar juga. Oleh karena itu tujuan dari penelitian ini yaitu untuk memprediksi tinggi gelombang dan kecepatan angin.   Metode prediksi yang digunakan pada penelitian kali ini adalah BiGRU (Bidirectional Gated Recurrent Unit) karena BiGRU memiliki hasil prediksi yang baik dibanding metode deep learning yang lain. Penelitian ini menggunakan data time series    yang berisi data tinggi gelombang dan kecepatan angin. Data unsur cuaca diambil per 12 jam dari bulan Januari 2021 – bulan April 2024. Metode BiGRU dapat digunakan dalam memprediksi cuaca maritim dengan fungsi aktivasi paling optimal untuk prediksi tinggi gelombang dan kecepatan angin ialah Relu, serta untuk prediksi tinggi gelombang dan kecepatan angin memiliki jumlah Batch Size yang optimal terdapat pada Batch Size 16. Dengan hasil nilai MAPE untuk prediksi ketinggian gelombang sebesar 1.6434% dan untuk prediksi kecepatan angin sebesar 0.6560%. Nilai MAPE pada model BiGRU memiliki nilai yang kecil dimana kurang dari 10% maka model BiGRU dikatakan sangat baik untuk prediksi pada data cuaca maritim. Kata kunci: Cuaca, Kecepetan angin, Tinggi gelombang, BiGRU   Abstract [Prediction of Wave Height and Wind Speed ​​on the Coast Using the BiGRU Method] Indonesia is located between the Pacific Ocean and the Indian Ocean, which makes it the center of international trade routes. Karangduwur village, located in Central Java, has strong maritime economic potential but also has great weather risks. Therefore, the purpose of this research is to predict wave height and wind speed.   The prediction method used in this research is BiGRU (Bidirectional Gated Recurrent Unit) because BiGRU has good prediction results compared to other deep learning methods. This research uses time series data containing wave height and wind speed data. Weather element data is taken per 12 hours from January 2021 - April 2024. The BiGRU method can be used in predicting maritime weather with the most optimal activation function for predicting wave height and wind speed is Relu, and for predicting wave height and wind speed, the optimal number of Batch Size is Batch Size 16. With the results of the MAPE value for wave height prediction of 1.6434% and for wind speed prediction of 0.6560%. The MAPE value in the BiGRU model has a small value which is less than 10%, so the BiGRU model is said to be very good for prediction on maritime weather data. Keywords: Weather, Wind speed, Wave height, BiGRU
Analisis Pengaruh Jumlah Nilai Taksiran Dan Uang Pinjaman Terhadap Laba Bersih PT. Pegadaian Kanwil XII Surabaya Hidayati, Syahrotul; Asyhar, Ahmad Hanif; Krisnawan, Alvin
Journal of Mathematics Education and Science Vol. 8 No. 1 (2025): Journal of Mathematics Education and Science
Publisher : Universitas Nahdlatul Ulama Sunan Giri Bojonegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/james.v8i1.3462

Abstract

PT. Pegadaian is a state-owned company that focuses on secured loans. PT. Pegadaian assesses the property used as collateral before providing a loan. The purpose of this study is to see how the number of appraisals and loan money has an impact on PT. Pegadaian's net profit both partially and simultaneously. This study uses multiple linear regression analysis. The research sample and population were PT. Pegadaian Kanwil XII Surabaya, which reported the appraisal value, loan amount, and net profit during the period 2020-2023. The results showed that the net profit of PT. Pegadaian was not partially influenced by the loan and appraisal variables. This may indicate a correlation that is not strong, a significant variable that is not taken into account, or a non-linear correlation that has not been detected. However, PT. Pegadaian net profit is influenced by both variables simultaneously.
ANALYSIS OF DIABETES MELITES DISEASE USING BINARY LOGISTIC REGRESSION Anistya, Mery; Putroue Keumala Intan; Ahmad Hanif Asyhar; Wika Dianita Utami
Jurnal Statistika dan Aplikasinya Vol. 9 No. 1 (2025): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.09102

Abstract

This study aims to identify risk factors that affect the incidence of diabetes mellitus and evaluate the accuracy of the prediction model using binary logistic regression. The research method used secondary data from 140 patients at UPT Puskesmas Teja, Pamekasan, consisting of 60 diabetes negative patients and 80 diabetes positive patients. The variables analyzed included age, gender, heredity, smoking habit, body mass index (BMI), blood glucose level, cholesterol, and blood pressure. The results showed that the variables of gender and glucose levels had a significant influence on the incidence of diabetes, with significance values of 0.022 and 0.001, respectively. The gender variable has an Odds Ratio (OR) value of 0.135, indicating that female patients tend to have a lower risk of developing diabetes than men. Meanwhile, glucose levels showed a positive association with the incidence of diabetes, with each unit increase in glucose levels increasing the risk of diabetes by 1.016 times. The binary logistic regression model developed has an accuracy of 87.1% based on the Area Under Curve (AUC) value, which falls into the category of strong classification ability. This study provides important implications in supporting the development of more effective diabetes prevention and management strategies through an in-depth understanding of risk factors, so that it can be used as a basis for decision-making in public health services.
PREDICTION OF THE ELECTRIC POWER BY OSCILLATING WATER COLUMN WAVE POWER PLANTS ON BAWEAN ISLAND USING LSTM Putri, Risma Madurahma; Hakim, Lutfi; Novitasari, Dian C Rini; Asyhar, Ahmad Hanif; Setiawan, Fajar
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2287-2300

Abstract

The demand for electricity in Indonesia continues to increase in line with population growth and the expansion of economic development. This increase is not matched by the diminishing electricity resources, as fossil fuels, which are non-renewable, are being used. Therefore, there is a need for renewable energy sources that can be utilized as long-term electricity resources. The abundant marine areas in Indonesia make it a potential source of alternative energy, one form of its utilization is the Ocean Wave Power Plant using the Oscillating Water Column (OWC) method. Bawean Island in Gresik is one of the regions that has this potential, while also facing long-standing electricity supply limitations that have resulted in uneven electricity distribution among the community. The problem does not stop at power generation but also extends to the transmission system between supply and demand. This research is conducted to predict the electricity generated by the ocean wave power plant to help avoid mismatches when supplying electricity. This study uses time series data from January 1st, 2021, to May 5th, 2024, which includes wave height, length, period, and amplitude. Electricity prediction based on these parameters can be performed using deep learning-based methods that can effectively process sequential time series data, such as the Long Short Term Memory (LSTM) method, by experimenting with the number of neurons, epochs, and batch sizes. The best prediction results for the variables of height, length, period, and amplitude of the waves obtained MAPE values of 0.3657%, 0.1637%, 0.0888%, and 0.3480%, respectively. The electricity prediction results from the best parameters obtained a MAPE of 0.3549%.
Pemodelan Matematika Pada Penyebaran Penyakit Tuberculosis di Provinsi Jawa Timur Sari, Firda Yunita; Maulidya, Rahmania; Hilmi, Moh. Aditya Sirojul; Wahyudi, Sharenada Norisdita; Fransisca, Velicia; Putri, Anindya Maya; Asyhar, Ahmad Hanif; Ulinnuha, Nurissaidah
Journal of Mathematics Education and Science Vol. 7 No. 2 (2024): Journal of Mathematics Education and Science
Publisher : Universitas Nahdlatul Ulama Sunan Giri Bojonegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/james.v7i2.2733

Abstract

Tuberculosis yang banyak dikenal dengan sebutan TBC ialah suatu penyakit pernapasan yang menular, dipicu karena adanya Mycobacterium Harituberculosis. TBC menempati peringkat ke-2 setelah COVID-19 sebagai penyakit menular dengan tingkat kematian tertinggi di seluruh dunia. Pada tahun 2020 Indonesia menempati urutan ke-3 dalam kasus TBC tertinggi dibawah India dan Tiongkok. Pada tahun 2021 Provinsi Jawa Timur menjadi peringkat tertinggi ketiga dengan kasus TBC sebesar 466.297 jiwa. Penelitian ini bertujuan untuk mengetahui hasil analisis kestabilan model matematis dan simulasi dari dinamika penyebaran penyakit TBC pada tahun 2021 di Jawa Timur dengan keterbaruan yaitu perbandingan parameter uji coba menggunakan metode runge-kutta orde 4 dan model matematis SITR. Model tersebut merupakan pengembangan dari model SIR dengan menambahkan kompartemen T (treatment). Dalam penelitian didapatkan hasil dari model matematika SITR pada penyakit tuberculosis memperoleh kestabilan titik kesetimbangan endemik dan ketidakstabilan titik kesetimbangan bebas penyakit, hal ini disebabkan bilangan reproduksi dasar kedua parameter , yang menunjukkan bahwasanya Tuberculosis di Provinsi Jawa Timur berpotensi mewabah. Maka diperlukan upaya dalam mencegah dan mengendalikan penyebaran penyakit ini supaya mengurangi dampaknya terhadap kesehatan masyarakat.
Implementasi Extreme Learning Machine dengan Seleksi Fitur Particle Swarm Optimization untuk Klasifikasi Sindrom Ovarium Polikistik Mukti, Audyra Dewi Puspa; Ulinnuha, Nurissaidah; Asyhar, Ahmad Hanif
Jurnal Matematika Integratif Vol 21, No 2: Oktober 2025
Publisher : Department of Matematics, Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/jmi.v21.n2.63988.131-142

Abstract

Sindrom Ovarium Polikistik (SOPK) adalah gangguan hormonal yang sering terjadi pada wanita usia reproduktif dan menjadi salah satu penyebab utama masalah kesuburan. Sekitar 3–15% wanita di seluruh dunia mengalami kondisi ini, yang juga dapat memicu berbagai masalah kesehatan lainnya. Penelitian ini bertujuan untuk mengembangkan metode diagnosis SOPK yang lebih efisien dan akurat dengan memanfaatkan algoritma Extreme Learning Machine (ELM) yang dikombinasikan dengan seleksi fitur menggunakan Particle Swarm Optimization (PSO). ELM dipilih karena kemampuannya dalam melakukan klasifikasi secara cepat, sedangkan PSO digunakan untuk memilih fitur-fitur yang paling relevan. Hasil seleksi fitur menghasilkan 18 fitur terpilih dari total 40 fitur. Pencarian parameter terbaik dilakukan dengan pendekatan random search dan grid search. Hasil menunjukkan bahwa random search memberikan performa terbaik, dengan akurasi 95.35%, sensitivitas 96.67%, dan spesifisitas 92.65%. Tanpa seleksi fitur, ELM hanya menghasilkan akurasi 84.20%, sensitivitas 90.10%, dan spesifisitas 70.62%. Temuan ini menunjukkan bahwa seleksi fitur menggunakan PSO mampu meningkatkan performa klasifikasi ELM secara signifikan.
Simulasi Gelombang Air Laut Selat Madura Menggunakan Smoothed Particle Hydrodynamics Wiratama, Aqshal Tegar; Adikuasa, M. Biyadihie; Asyhar, Ahmad Hanif
Leibniz: Jurnal Matematika Vol. 6 No. 01 (2026): Leibniz: Jurnal Matematika
Publisher : Program Studi Matematika - Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas San Pedro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59632/leibniz.v6i01.655

Abstract

Penelitian ini bertujuan untuk mengimplementasikan metode Smoothed Particle Hydrodynamics (SPH) dalam simulasi dinamika gelombang air laut di Selat Madura. Simulasi dilakukan menggunakan perangkat lunak DualSPHysics dengan mekanisme piston-type wave-maker yang mengintegrasikan data harian tinggi dan arah gelombang dari BMKG sebagai parameter input. Karakteristik dinamika gelombang yang dihasilkan menunjukkan bahwa elevasi output simulasi memiliki pola fluktuasi yang konsisten dengan data observasi lapangan. Hasil validasi model SPH ditunjukkan dengan nilai Root Mean Square Error (RMSE) sebesar 0.0193 dan 0.0063, serta koefisien determinasi () mencapai 0.97973 dan 0.9938. Hal ini membuktikan adanya hubungan linier yang sangat kuat antara tinggi gelombang input dan output simulasi, di mana model mampu menjelaskan 99% variansi data aktual. Penelitian ini merupakan eksplorasi awal penggunaan metode berbasis partikel SPH untuk memodelkan karakteristik hidrodinamika di perairan Selat Madura.
Optimal control using pontryagin’s maximum principle: Tuberculosis spread case Muhammad Iqbal Widiaputra; Ahmad Hanif Asyhar; Wika Dianita Utami; Putroue Keumala Intan; Dian Yuliati; Muhammad Fahrur Rozi
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 10 No. 2 (2024)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.ijcsam.v10i2.4602

Abstract

Tuberculosis is one of the deadliest infectious diseases in the world. In 2020, 9.9 million people were infected and 1.5 million died. East Java province ranks third with 43,268 tuberculosis cases. This research aims to determine the results of the tuberculosis disease model and simulation without and with the use of optimal control. The mathematical model SEIR is a model that can analyze the spread of the disease tuberculosis. In this research, a variable treatment compartment to the SEIR model. It used 4 antibiotics in the intensive phase and added Isoniazid and Rifampicin in the advanced phase as the optimal control parameters. Optimal control uses Pontriagin’s maximum principle as the derivative to modify the SEIR model and is described by a Runge-Kutta order 4 scheme. It shows both the useful parameters in the optimal control with a maximum value of 1 and plots where the effect of optimal control exists further constrained the people infected with Tuberculosis.
A Mathematical Compartmental Model for Deradicalization in Indonesia: The SERTV Framework Asyhar, Ahmad Hanif; Fatmawati, Fatmawati; Windarto, Windarto; Herdicho, Faishal Farel; Abidemi, Afeez
Journal of Multidisciplinary Applied Natural Science Articles in Press
Publisher : Pandawa Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47352/jmans.2774-3047.351

Abstract

Radicalism remains a critical threat to Indonesia's national security and social cohesion, necessitating urgent efforts to understand and mitigate its spread. This study develops a mathematical model to describe the dynamics of radicalization and deradicalization in Indonesia, using a compartmental structure that divides the population into susceptible, extremist, recruiter, treated, and vaccinated groups. The model incorporates a saturated incidence rate to capture the nonlinear effects of radical interactions. Numerical simulations are carried out using the fifth-order Runge–Kutta method to illustrate the transitions between population groups. The results indicate a significant decline in extremist and recruiter populations, while vaccination against radical ideas contributes to long-term resilience. Sensitivity analysis shows that the radicalization rate and recruitment effectiveness are the most influential parameters driving the spread of radicalism. These findings provide new insights into the mechanisms of radicalization and serve as a foundation for designing evidence-based preventive strategies.
Optimalisasi Grid Search pada Extreme Gradient Boosting (XGBoost) Untuk Prediksi Pendapatan Asli Daerah (PAD) Dari Pajak Kendaraan Bermotor (PKB) Muhammad, Bintang Maulidana; Hamid, Abdulloh; Asyhar, Ahmad Hanif; Idamayanti, Retna Fetty
Teorema: Teori dan Riset Matematika Vol 11, No 1 (2026): Maret
Publisher : Universitas Galuh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25157/teorema.v11i1.22512

Abstract

PPenelitian ini mengevaluasi tiga model regresi dalam menganalisis tingkat kriminalitas di Provinsi Banten, yaitu Regresi Poisson, Regresi Geographically Weighted Regression (GWR), dan Regresi Binomial Negatif (NBR). Data mencakup delapan wilayah administratif dengan variabel penjelas seperti kepadatan penduduk, rasio penduduk terhadap polisi, tingkat pengangguran terbuka, angka kemiskinan, rata-rata lama sekolah, dan Indeks Pembangunan Manusia. Hasil menunjukkan bahwa 1) model Poisson mengalami overdispersi berat dan gagal menangkap pengaruh variabel prediktor, 2) model GWR memiliki nilai AIC terendah, namun menghasilkan parameter yang identik di semua wilayah, mengindikasikan overfitting akibat keterbatasan jumlah wilayah, 3) Model NBR memberikan deviasi rendah dan AIC yang kompetitif, menunjukkan kinerja statistik yang lebih stabil pada data terbatas. Studi ini menyoroti pentingnya mempertimbangkan ukuran sampel dalam pemilihan model spasial, serta menyarankan penggunaan NBR sebagai alternatif yang layak untuk data count yang overdispersi dengan jumlah unit kecil.
Co-Authors Abdulloh Hamid Abdulloh Hamid Abidemi, Afeez Adikuasa, M. Biyadihie Adyanti, Deasy Adyanti, Deasy Alfiah Ahmad Hidayatullah Ahmad Umar Ahmad Zaenal Arifin Ahmad Zoebad Foeady Ali Ridho Anistya, Mery Arifin, Ahmad Zaenal Deasy Adyanti Deasy Alfiah Adyanti Dian C. Rini Novitasari Dian Yuliati Dian Yuliati Fajar Darwis Dzikril Hakimi FAJAR SETIAWAN Fajar Setiawan Fanani, Aris Fatmawati, Fatmawati Firmansjah, Muhammad Fitria Febrianti Foeady, Ahmad Zoebad Fransisca, Velicia Gita Purnamasari R Hani Khaulasari Herdicho, Faishal Farel Hidayati, Syahrotul Hilmi, Moh. Aditya Sirojul ian Candra Rini Novitasari Idamayanti, Retna Fetty Krisnawan, Alvin Kusaeri Kusaeri Lia Puspita Sari Lubab, Ahmad Lutfi Hakim Lutfi Hakim M. Hasan Bisri Maulidya, Rahmania Moch. Noor Affan Anshori Moh. Hafiyusholeh Muhammad Busyro Karim Muhammad Busyro Karim, Muhammad Busyro Muhammad Fahrur Rozi Muhammad Iqbal Widiaputra Muhammad Thohir Muhammad, Bintang Maulidana Mukti, Audyra Dewi Puspa Nanang Widodo Nisa, Titin Faridatun Novitasari , Dian Candra Rini Novitasari, Dian C Rini Nur Aulia, Shofinatul Wahdah Nurissaidah Ulinnuha Putri Oktavia, Nabiilah Putri, Anindya Maya Putri, Risma Madurahma Putroue Keumala Intan Rifa Atul Hasanah Rini Novitasari, ian Candra Ririn Komaria Rozi, Muhammad Fahrur Sari, Dian Candra Rini Novita Sari, Firda Yunita Setiawan, Fajar Suwanto Suwanto Utami, Wika Dianita Vina Fitriyana Wahyudi, Sharenada Norisdita Widiaputra, Muhammad Iqbal Wika Dianita Utami Wika Dianita Utami Wika Dianita Utami Windarto, Windarto Wiratama, Aqshal Tegar Yuliati, Dian Yuliati, Dian Yuniar Farida Yusuf, Ahmad Yuyun Monita Zainullah Zuhri