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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi El-QUDWAH Gamatika Elkawnie Limits: Journal of Mathematics and Its Applications International Journal of Computing Science and Applied Mathematics Indonesian Journal of Electrical Engineering and Computer Science Mathematics and Applications (MAp) Journal Jurnal Riset Mahasiswa Matematika Jurnal Arjuna : Publikasi Ilmu Pendidikan, Bahasa dan Matematika Journal of Research on Community Engagement Limits: Journal of Mathematics and Its Applications
Mohammad Jamhuri
Universitas Islam Negeri Maulana Malik Ibrahim Malang
Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Education Engineering Health Professions Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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The Construction of Mathematical Model for the Mechanism of Protein Synthesis Involving mTORC1 from the AMPK Pathway Ari Kusumastuti; Mohammad Jamhuri; Dewi Firdaus; Nurul Anggraeni Hidayati
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol 9, No 1 (2023)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.v9i1.14351

Abstract

This study discusses the construction of mathematical models for the mechanism of protein synthesis involving the main regulator mTORC1 gene which is described in the singular pathway mTOR of the AMPK pathway. The genes in question include: AMPK, TSC2, Rheb, mTORC1 and S6K. The method used for this research is divided into two stages, namely pathway analysis for the mechanism of protein synthesis and the second is the formulation of mathematical models. Pathway analysis is performed as a reference in describing interactions in the form of kinetic reaction schemes. After the interaction scheme is created, it is then formulated into a mathematical model with the independent variable being time. Mathematical models for the mechanism of protein synthesis involving mTORC1 of the AMPK pathway in the form of ordinary time-dependent differential equations involving independent variables [TSC2], [pAMPK], [pTSC2], [Rheb^{GTP}], [Rheb^{GDP}], [mTORC1], [Raptor], [aRaptor], [Deptor], [PRAS40], [mTOR], [amTORC1], [mLST8], [S6K1] and [pS6K1].
Penyelesaian Numerik Model Pemangsa-Mangsa dengan Metode Jaringan Fungsi Radial Basis Menggunakan Trigonometric Shape Parameter Muhammad Thahiruddin; Mohammad Jamhuri
Jurnal Arjuna : Publikasi Ilmu Pendidikan, Bahasa dan Matematika Vol. 1 No. 4 (2023): Agustus : Jurnal Arjuna : Publikasi Ilmu Pendidikan, Bahasa dan Matematika
Publisher : Asosiasi Riset Ilmu Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/arjuna.v1i4.135

Abstract

One mathematical model in the form of a system of nonlinear ordinary differential equations is the predator-prey model. The predator-prey model explains population changes of one prey population and one predator population due to changes in time. The radial basis function network method is used to find a numerical solution to the predator-prey model. The radial basis function network method can directly approximate the function and derivative of the prey-prey model using a basis function. The basis function used is a multiquadric basis function. Numerical solutions using the radial basis function network method obtained from this research show high accuracy and low error. The absolute error obtained from the two simulations with Δt = 0.01 each is 0.0066 in the first simulation and 0.022 in the second simulation. The errors obtained are relatively small because each only represents 0.66% of the initial value of the first type and 0.5% of the initial value of the second type. This shows that the radial basis function network method is efficient in calculating the predator-prey model solution.
Hydrophobicity signal analysis for robust SARS-CoV-2 classification Jamhuri, Mohammad; Irawan, Mohammad Isa; Mukhlash, Imam; Tri Puspaningsih, Ni Nyoman
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1294-1305

Abstract

Rapid and accurate classification of viral pathogens is critical for effective public health interventions. This study introduces a novel approach using convolutional neural networks (CNN) to classify SARS-CoV-2 and non-SARS-CoV-2 viruses via hydrophobicity signal derived from DNA sequences. Conventional machine learning methods grapple with the variability of viral genetic material, requiring fixed-length sequences and extensive preprocessing. The proposed method transforms genetic sequences into image-based representations, enabling CNNs to handle complexity and variability without these constraints. The dataset includes 8,143 DNA sequences from seven coronaviruses, translated into amino acid sequences and evaluated for hydrophobicity. Experimental results demonstrate that the CNN model achieves superior performance, with an accuracy of over 99.84% in the classification task. The model also performs well with extended sequence lengths, showcasing robustness and adaptability. Compared to previous studies, this method offers higher accuracy and computational efficiency, providing a reliable solution for rapid virus detection with potential applications in bioinformatics and clinical settings.
PENGGUNAAN PARTICLE SWARM OPTIMIZATION PADA JARINGAN SYARAF TIRUAN UNTUK KLASIFIKASI SINYAL RADAR Jamhuri, Mohammad; Utomo, Tri
MAp (Mathematics and Applications) Journal Vol 6, No 2 (2024)
Publisher : Universitas Islam Negeri Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/map.v6i2.8961

Abstract

Klasifikasi sinyal radar merupakan salah satu tugas penting yang memiliki aplikasi luas, termasuk dalam domain militer, navigasi, dan pengawasan cuaca. Jaringan Syaraf Tiruan (JST) telah terbukti efektif dalam menyelesaikan tugas klasifikasi kompleks berkat kemampuannya dalam memodelkan pola dan hubungan non-linear dalam data. Salah satu tantangan mendasar dalam implementasi JST adalah penentuan jumlah node optimal pada hidden layer, yang secara signifikan memengaruhi performa model. Penelitian ini mengusulkan pendekatan berbasis Particle Swarm Optimization (PSO) untuk mengoptimalkan konfigurasi JST dalam klasifikasi sinyal radar. PSO, sebagai algoritma optimasi berbasis populasi yang terinspirasi dari perilaku sosial kawanan, memungkinkan eksplorasi ruang solusi secara lebih efisien dan efektif dibandingkan metode tradisional. Hasil penelitian menunjukkan bahwa penerapan PSO pada JST secara signifikan meningkatkan metrik performa model, termasuk accuracy, precision, recall, dan F1-score, dibandingkan dengan metode baseline. Namun demikian, penggunaan PSO tidak memberikan peningkatan efisiensi dalam hal waktu komputasi. Temuan ini memberikan kontribusi penting dalam pengembangan model pembelajaran mesin yang lebih akurat untuk aplikasi praktis seperti pengawasan cuaca dan sistem pertahanan, sekaligus memperkaya kajian teoretis di bidang optimasi dan jaringan syaraf tiruan.
Perencanaan Pengadaan Sanitasi Lingkungan di Taman Nasional Bromo Tengger Semeru dengan Pendekatan Sanitasi Total Berbasis Masyarakat Jamhuri, Mohammad; Sujarwo, Imam; Alisah, Evawati
JRCE (Journal of Research on Community Engagement) Vol 1, No 2 (2020): Journal of Research on Community Engagement
Publisher : Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrce.v1i2.31899

Abstract

Bromo Tengger Semeru National Park (TNBTS) is one of Indonesia’s premier tourist destinations, experiencing surging visitor numbers each year. During peak hours—especially early morning for sunrise—tourists flock to popular spots such as Penanjakan, Seruni, Mentingen, Kingkong, and Bukit Cinta, often causing long queues at toilet facilities. This community service program aimed to (1) identify sanitation needs in critical TNBTS locations, (2) propose additional sanitation facilities where required, and (3) conduct community mentoring to ensure sustainable involvement in facility planning and management. The methods included field surveys, queueing analysis, visitor forecasting, water requirement and cost calculations, and a Community-Based Total Sanitation (STBM) approach for local empowerment.Results indicate a need for more toilet units at several sunrise viewpoints and in the jeep and motorcycle parking areas. Queueing analysis suggests that to maintain waiting times under one minute per person at high-traffic spots, at least nine toilets for men and thirteen for women are necessary. Economic feasibility calculations reveal potential for self-financing through toilet fees, since visitor numbers are projected to rise above one million annually within five years. Community mentoring is crucial for collaborative efforts and shared ownership among local managers, residents, and relevant government agencies, thereby ensuring the facilities’ long-term sustainability.
Pelatihan Penyusunan Bahan Ajar Berbasis Multimedia Bagi Guru-Guru Yayasan Ali Imron Pakamban Sumenep Jauhari, Mohammad Nafie; Juhari, Juhari; Jamhuri, Mohammad
JRCE (Journal of Research on Community Engagement) Vol 2, No 1 (2020): Journal of Research on Community Engagement
Publisher : Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrce.v2i1.10333

Abstract

Learning media is a useful tool and intermediary for facilitating the teaching and learning process to streamline communication between teachers and students. This community service activity is divided into three main activities, namely the delivery of concepts or theories of multimedia, types of multimedia, the use of multimedia, and making presentations using practical and interactive PowerPoints tools; second, training on how to use PowerPoint effectively; and third, evaluation on the ability of training participants in making multimedia-based teaching materials.
Mall Customer Segmentation Using K-Means Clustering Optimized by the Elbow Method Eva Yuliana, Rossima; Mariatul Ulya, Diah; Jamhuri, Mohammad
Jurnal Riset Mahasiswa Matematika Vol 4, No 5 (2025): Jurnal Riset Mahasiswa Matematika
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrmm.v4i5.33389

Abstract

This study explores the effectiveness of K-Means clustering for segmenting mall customers based on demographic and behavioral features, using the Mall Customers dataset. The segmentation process focuses on three numerical attributes—age, annual income, and spending score—with an additional engineered feature: the spending-to-income ratio. After applying min-max normalization and log transformation, the Elbow Method was employed to determine the optimal number of clusters ($K=5$). The resulting clusters were evaluated using internal validation metrics, including Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index. K-Means clustering achieved the best overall performance compared to Gaussian Mixture Models (GMM), DBSCAN, and Agglomerative Hierarchical Clustering. Five interpretable customer profiles emerged, ranging from high-spending young professionals to low-engagement senior customers. These clusters were visualized using PCA for dimensionality reduction and further interpreted through descriptive statistics and domain-based labeling. Business implications were derived by aligning each cluster with strategic marketing recommendations. Overall, the findings reaffirm the utility of classical clustering frameworks such as K-Means—when rigorously validated and thoughtfully interpreted—for deriving actionable insights in customer analytics.
Penurunan Model Traffic Flow Berdasarkan Hukum-Hukum Kesetimbangan Fitria, Binti Tsamrotul; Jamhuri, Mohammad
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 3, No 3 (2014): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v3i3.2945

Abstract

Penelitian ini membahas tentang penurunan model makroskopis masalah traffic flow berdasarkan hukum-hukum kesetimbangan, yaitu hukum kesetimbanganmassa dan hukum kesetimbangan momentum.Asumsi yang digunakan adalah bahwa sepanjang interval jalan tidak ditemukan persimpangan yang menyebabkan perubahan jumlah kendaraan. Langkah-langkah dalam penurunan model persamaan tersebut adalah: (1)menurunkan persamaan kontinuitas dan persamaan momentum sebagai persamaan pengatur, (2) menentukan variabel-variabel yang mempengaruhi traffic flow yaitu kepadatan, kecepatan dan fluks kendaraan, (3) menurunkan model berdasarkan hukum-hukum kesetimbangan tersebut. Model yang dihasilkan dalam skripsi ini dikenal sebagai persamaan Transport, dimana persamaan tersebut menyatakan kepadatan kendaraan per satuan luas jalan yang dipengaruhi oleh kecepatan. Untuk kecepatan kendaraan yang konstan, maka model tersebut menjadi model linier. Sedangkan bila kecepatan kendaraan bergantung pada kepadatan kendaraan maka persamaan tersebut menjadi non linier. Bentuk non linier dari persamaan traffic flow ini dikenal sebagai persamaan Burger.Solusi dari model yang dihasilkan didapat dengan menggunakan metode finite differenceskema FTBS untuk bentuk yang linier dan menggunakan metode Lax Wendroffskema FTCS untuk bentuk yang non linier.
Reliable and Efficient Sentiment Analysis on IMDb with Logistic Regression Ulya, Diah Mariatul; Juhari, Juhari; Yuliana, Rossima Eva; Jamhuri, Mohammad
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.33809

Abstract

Understanding public opinion at scale is essential for modern media analytics. We present a reproducible, leakage-safe evaluation of logistic regression (LR) for binary sentiment classification on the IMDb Large Movie Review dataset and compare it with five widely used baselines: multinomial Naive Bayes, linear support vector machine (SVM), decision tree, k-nearest neighbors, and random forest. Using a standardized text pipeline (HTML stripping, stopword removal, WordNet lemmatization) with TF–IDF unigrams–bigrams and nested, stratified cross-validation, we assess threshold-dependent and threshold-independent performance, probability calibration, and computational efficiency. LR attains the best overall balance of quality and speed, achieving 88.98% accuracy and 89.13% F1, with strong ranking performance (OOF ROC–AUC ≈ 0.9568; PR–AUC ≈ 0.9554) and well-behaved calibration (Brier ≈ 0.0858). Training completes in seconds per fold and CPU inference reaches about 2.46×10^6 samples per second. While a calibrated linear SVM yields slightly higher precision, LR delivers higher F1 at markedly lower compute. These results establish LR as a robust, transparent baseline that remains competitive with more complex neural and ensemble approaches, offering a favorable performance–efficiency trade-off for practical deployment and reproducible research on IMDb sentiment classification.
Cross-Dataset Evaluation of Support Vector Machines: A Reproducible, Calibration-Aware Baseline for Tabular Classification Syafi'ah, Nurus; Jamhuri, Mohammad; Pranata, Farahnas Imaniyah; Kusumastuti, Ari; Juhari, Juhari; Pagalay, Usman; Khudzaifah, Muhammad
Jurnal Riset Mahasiswa Matematika Vol 4, No 6 (2025): Jurnal Riset Mahasiswa Matematika
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrmm.v4i6.33438

Abstract

Support Vector Machines (SVMs) remain competitive for small and medium-sized tabular classification problems, yet reported results on benchmark datasets vary widely due to inconsistent preprocessing, validation, and probability calibration. This paper presents a calibration-aware, cross-dataset benchmark that evaluates SVMs against classical baselines—Logistic Regression, Decision Tree, and Random Forest—under leakage-safe pipelines and statistically principled protocols. Using three representative binary datasets (Titanic survival, Pima Indians Diabetes, and UCI Heart Disease), we standardize imputation, encoding, scaling, and nested cross-validation to ensure comparability. Performance is assessed not only on discrimination metrics (accuracy, precision, recall, F1, PR--AUC) but also on probability reliability (Brier score, Expected Calibration Error) and threshold optimization. Results show that tuned RBF--SVMs consistently outperform Logistic Regression and Decision Trees, and perform comparably to Random Forests. Calibration (Platt scaling, isotonic regression) substantially reduces error and improves decision quality, while domain-specific features enhance Titanic prediction. By embedding all steps in a transparent, reproducible protocol and validating across multiple datasets, this study establishes a rigorous methodological baseline for SVMs in tabular binary classification, providing a reference point for future machine learning research.
Co-Authors Ari Kusumastuti Binti Tsamrotul Fitria, Binti Tsamrotul Dewi Firdaus Eva Yuliana, Rossima Evawati Alisah Fatma Mufidah, Fatma Imam Mukhlash Imam Mukhlash Imam Sujarwo Juhari Juhari, Juhari Mariatul Ulya, Diah Mohammad Isa Irawan Mohammad Nafie Jauhari Mohammad Syaiful Arif, Mohammad Syaiful Muhammad Khudzaifah Muhammad Thahiruddin Ni Nyoman Tri Puspaningsih Nurul Anggraeni Hidayati Pranata, Farahnas Imaniyah Subiono Subiono - Syafi'ah, Nurus Ulya, Diah Mariatul Usman Pagalay Utomo, Tri Yuliana, Rossima Eva