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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Riset Mahasiswa Matematika
Juhari Juhari
UIN Maulana Malik Ibrahim Malang
Mathematics

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Solusi Numerik Model Gerak Osilasi Vertikal dan Torsional Pada Jembatan Gantung Hendrik Widya Permata; Ari Kusumastuti; Juhari Juhari
Jurnal Riset Mahasiswa Matematika Vol 1, No 1 (2021): 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 | Full PDF (1586.086 KB) | DOI: 10.18860/jrmm.v1i1.13409

Abstract

Model gerak osilasi vertikal dan torsional merupakan model yang menggambarkan gerak osilasi vertikal dan gerak torsional pada batang yang digantung. Gerak osilasi vertikal merupakan gerak naik turun suatu benda yang terjadi terus berulang, dan kemudian pada waktu tertentu akan berhenti atau mengalami redaman. Gerak torsional merupakan getaran sudut dari suatu objek yang mengalami rotasi. Model gerak osilasi dan torsional pada dasarnya merupakan sistem persamaan diferensial orde dua. Tujuan dari penelitian ini adalah untuk mengetahui solusi numerik model gerak osilasi vertikal dan torsional menggunakan metode Adams-Bashforth-Moulton orde empat, lima, dan enam. Model gerak osilasi vertikal dan torsional terlebih dahulu diselesaikan menggunakaan metode Runge-Kutta-Fehlberg orde lima untuk mendapatkan solusi awal kemudian model tersebut diselesaikan menggunakan metode Adams-Bashforth-Moulton orde empat, lima dan enam. Hasil solusi numerik setiap metode Adam-Bashforth-Moulton selanjutnya diuji dengan galat relatif. Hasil simulasi numerik model gerak osilasi vertikal dan torsi diperoleh bahwa gerak osilasi vertikal dan gerak torsional merupakan gerak harmonik teredam dan semakin tinggi orde pada metode Adams-Bashforth-Moulton maka akan lebih cepat galat relatif menuju nilai nol dan sebaliknya
Estimasi Parameter Capital Assets Pricing Model Dengan Metode Generalized Method of Moments Dalam Perhitungan Value At Risk Diah Maghfiroh Wahyuni; Abdul Aziz; Juhari Juhari
Jurnal Riset Mahasiswa Matematika Vol 1, No 1 (2021): 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 | Full PDF (1062.298 KB) | DOI: 10.18860/jrmm.v1i1.13413

Abstract

Capital Assets Pricing Model merupakan persamaan regresi antara premi risiko aset terhadap premi risiko pasar. Risiko ada jika pembuat keputusan tidak memiliki data untuk menyusun suatu dugaan. Pendugaan tersebut dapat dilakukan dengan generalized method of moments.Penelitian ini bertujuan untuk mengetahui hasil estimasi parameter pada Capital Assets Pricing Model menggunakan Generalized Method of Moments pada data saham PT. Indofood Tbk., serta mendapatkan nilai Value at Risk pada data saham PT. Indofood Tbk..Hasil yang diperoleh  yaitu :  , m=1,2,…. Dengan nilai  maka model regresi pada saham PT. Indofood Tbk.. yaitu . Dengan tingkat signifikansi 5%, investasi awal Rp10.000.000,00 , kerugian yang akan ditanggung oleh investor adalah Rp1.265.800,00 .
An Enhanced Vogel Approximation Method for Solving the Transportation Problem Shofiyatul Malik Mumtaza; Juhari Juhari
Jurnal Riset Mahasiswa Matematika Vol 5, No 4 (2026): Jurnal Riset Mahasiswa Matematika
Publisher : Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrmm.v5i4.39562

Abstract

The transportation problem is a classical linear programming model for allocating shipments from several supply points to several demand points at minimum total cost. A high-quality initial basic feasible solution is important because it can reduce the number of improvement iterations required by subsequent optimality tests. This study proposes an Enhanced Vogel Approximation Method (EVAM), a penalty-based modification of Vogel's Approximation Method that uses the three smallest active costs in each row or column. The method was tested on a balanced $5\times5$ transportation instance and compared with the Improved Vogel's Approximation Method (IVAM), followed by Stepping Stone improvement. The results show that IVAM produced an initial solution equal to the optimal cost of 59,356, whereas EVAM produced an initial cost of 60,727. After three Stepping Stone iterations, EVAM reached the same optimal cost of 59,356, with an initial optimality gap of 2.31\%. These findings indicate that EVAM is simple and transparent, although it does not dominate IVAM on the tested instance.
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.
Co-Authors Abdul Aziz Ari Kusumastuti Diah Maghfiroh Wahyuni Diah Mariatul Ulya Hendrik Widya Permata Mohammad Jamhuri Rossima Eva Yuliana Shofiyatul Malik Mumtaza