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

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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.
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.
Similarity Analysis of User Trajectories Based on Haversine Distance and Needleman Wunsch Algorithm Mohammad Jamhuri; Mohammad Isa Irawan; Imam Mukhlash
Elkawnie Vol. 7 No. 2 (2021)
Publisher : Faculty of Science and Technology Universitas Islam Negeri Ar-Raniry

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22373/ekw.v7i2.9232

Abstract

Abstract: In this paper, we discuss the similarity between two trajectories using the Needleman Wunsch algorithm. The calculation steps are interpolating the trajectory, calculating the distance between the trajectory coordinates, identifying the equivalent length, transforming trajectories into a sequence of alphabetic letters, aligning the sequences, and measuring the magnitude of the similarity based on the alignment results. The similarity obtained is compared directly to the length of the trajectories shared by the two lines. The calculation results show that the accuracy of the alignment method reaches more than 90%. Abstrak: Dalam tulisan ini dibahas cara perhitungan persentase kesamaan dari dua buah lintasan menggunakan algoritma Needleman Wunsch dan perhitungan secara manual berdasarkan irisan dari lintasan-lintasan tersebut. Pada perhitungan menggunakan algoritma Needleman Wunsch, tahapan-tahapan yang dilakukan adalah menginterpolasi lintasan, menghitung jarak antara titik-titik koordinat dari kedua lintasan, mengidentifikasi jarak yang ekivalen, mengubah lintasan menjadi sekuens huruf alfabet, menyejajarkan sekuens, dan menentukan besarnya kesamaan berdasarkan hasil penyejajaran. Kesamaan yang diperoleh dari metode penyejajaran dibandingkan secara langsung dengan panjang jalur yang dilalui bersama oleh kedua lintasan, hasil perhitungan menunjukkan bahwa akurasi metode penyejajaran mencapai lebih dari 90%.
Reliable and Efficient Sentiment Analysis on IMDb with Logistic Regression Diah Mariatul Ulya; Juhari Juhari; Rossima Eva Yuliana; Mohammad Jamhuri
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.v10i2.33809

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.
Newton Divided Difference Optimization for Fingerprint-Based Neural Virtual Screening against Avian Influenza A/H9N2 Siti Amiroch; Mohammad Jamhuri; Awawin Mustana Rohmah; Mohammad Hamim Zajuli Al Faroby; Chairul Anwar Nidom; Reviany Vibrianita Nidom
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1416

Abstract

Avian influenza A/H9N2 poses persistent zoonotic and veterinary threats, yet efficient computational tools for antiviral compound prioritization remain underdeveloped, particularly with respect to optimizer behavior in high-dimensional neural screening. This study proposes Newton Divided Difference (NDD) optimization, a lightweight positive diagonal curvature-aware training strategy, as a novel optimizer for fingerprint-based neural virtual screening against avian influenza A/H9N2, with the objective of evaluating its performance across aligned molecular fingerprint representations and chemically structured validation protocols. An aligned benchmark of 1,459 molecules consisting of 615 candidate active compounds and 844 decoys was represented by EState (79 features), PubChem (881 features), and Klekota–Roth (4,860 features) fingerprints, sharing identical molecule identities, labels, and split assignments. A fixed multilayer perceptron (MLP) classifier was trained with NDD and seven baseline optimizers under stratified, scaffold-key, and similarity-cluster split protocols across five repeated seeds. NDD achieved the highest descriptive ROC-AUC (Receiver Operating Characteriztic – Area Under the Curve) and PR-AUC (Precision-Recall Area Under the Curve) on Klekota–Roth fingerprints under scaffold-key and similarity-cluster protocols, and remained competitive under the stratified split with ROC-AUC of 0.9872. Architecture-sensitivity tests confirmed stable NDD performance across multiple network configurations, with ROC-AUC values ranging from 0.9876 to 0.9890. Compared with Hessian-free optimization, NDD reduced per-run runtime from approximately 50–54 seconds to approximately 8 seconds on Klekota–Roth under the same CPU-only configuration while achieving comparable ranking performance. The novelty of this work lies in the first systematic assessment of NDD for H9N2 neural virtual screening, demonstrating that positive diagonal curvature-aware scaling provides a practical, stable, and computationally efficient optimization alternative in sparse high-dimensional ligand-based screening settings, although external validation and prospective experimental confirmation remain necessary before practical antiviral prioritization.
Co-Authors Ari Kusumastuti Awawin Mustana Rohmah Binti Tsamrotul Fitria, Binti Tsamrotul Chairul Anwar Nidom Dewi Firdaus Diah Mariatul Ulya Evawati Alisah Fatma Mufidah, Fatma Imam Mukhlash Imam Mukhlash Imam Sujarwo Juhari Juhari Juhari Juhari, Juhari Mohammad Hamim Zajuli Al Faroby Mohammad Isa Irawan Mohammad Nafie Jauhari Mohammad Syaiful Arif, Mohammad Syaiful Muhammad Thahiruddin Ni Nyoman Tri Puspaningsih Nurul Anggraeni Hidayati Reviany Vibrianita Nidom Rossima Eva Yuliana Siti Amiroch Subiono - Utomo, Tri