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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi The Asian Journal of Technology Management (AJTM) JURNAL NASIONAL TEKNIK ELEKTRO Jurnal Sains Matematika dan Statistika NUMERICAL (Jurnal Matematika dan Pendidikan Matematika) Sistemasi: Jurnal Sistem Informasi Math Didactic: Jurnal Pendidikan Matematika JOIV : International Journal on Informatics Visualization ZERO : Jurnal Sains, Matematika dan Terapan INTEGER: Journal of Information Technology Jurnal Matematika: MANTIK JMPM: Jurnal Matematika dan Pendidikan Matematika MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Zero : Jurnal Sains, Matematika, dan Terapan JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Frontier Energy System and Power Engineering JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Pengabdian Masyarakat Asia Mujtama': Jurnal Pengabdian Masyarakat Jurnal Riset Mahasiswa Matematika Jurnal Inovasi Teknologi dan Edukasi Teknik Jurnal Informatika: Jurnal Pengembangan IT JMT (Jurnal Matematika dan Terapan) Jurnal ilmiah teknologi informasi Asia
Vivi Aida Fitria
Institut Teknologi Dan Bisnis Asia Malang, Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Physics Social Sciences Transportation Other

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Analisis Dinamik Skema Euler Untuk Model Predator-Prey Dengan Efek Allee Kuadratik Fitria, Vivi Aida; Afiyah, S. Nurul
JMPM: Jurnal Matematika dan Pendidikan Matematika Vol 2 No 1: Maret
Publisher : Prodi Pendidikan Matematika Universitas Pesantren Tinggi Darul Ulum Jombang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/jmpm.v2i1.774

Abstract

Pada penelitian ini dilakukan pendekatan numerik menggunakan skema Euler pada model predator-prey dengan efek alelopati. Perilaku dinamik dari model diskrit yang diperoleh kemudian dianalisis, yaitu eksistensi dan kestabilan titik kesetimbangan model tersebut. Analisis kestabilan titik kesetimbangan menunjukkan bahwa titik kepunahan predator dan predator-prey bersifat tidak stabil tetapi titik kepunahan prey dan titik keberhasilan hidup predator-prey bersifat stabil dengan syarat tertentu. Dari simulasi numerik menunjukkan bahwa hasil yang diperoleh sesuai dengan hasil analisis.
Klasifikasi Penyakit Ginjal Kronis Menggunakan K-Nearest Neighbors dengan Feature selection Pearson Correlation Coefficient Khadiki, Mohammad Rizan; Fitria, Vivi Aida
Journal of Information System Research (JOSH) Vol 6 No 3 (2025): April 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i3.7131

Abstract

Chronic kidney disease (CKD) is a global health issue that impacts quality of life and mortality rates. CKD often shows no symptoms in its early stages, earning it the nickname "silent disease," which complicates early detection efforts. This study aims to develop a classification model for CKD using the K-Nearest Neighbors (KNN) algorithm combined with the Pearson Correlation Coefficient feature selection method to enhance model performance. Feature selection is employed to reduce data dimensionality and prevent overfitting. The Kaggle "Chronic Kidney Disease" dataset is used in this study. Evaluation results show that the model with feature selection achieved an accuracy of 93.37%, precision of 91.9%, recall of 93.37%, and F1-score of 91.48%, while the model without feature selection achieved an accuracy of 91.27%, precision of 87.24%, recall of 91.27%, and F1-score of 88.99%. The contribution of this research is to improve the classification performance of chronic kidney disease by utilizing feature selection methods to achieve a better balance between precision and recall while reducing classification errors.
Utilizing AI to Optimize Product Sales at UD Bima Baru Widayanti, Lilis; Vivi Aida Fitria; Adriani Kala’lembang; Widya Adhariyanty Rahayu; Suastika Yulia Riska
Jurnal Pengabdian Masyarakat Vol. 6 No. 1 (2025): Jurnal Pengabdian Masyarakat
Publisher : Institut Teknologi dan Bisnis Asia Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jpm.v6i1.2454

Abstract

Purpose: The study aims to evaluate the effectiveness of activities in reaching participants, achieving training goals, improving proficiency, and enhancing sales through AI technologies. Method: This study teaches and evaluates the use of AI in sales optimization through lectures, demonstrations, tasks, and question-and-answer meetings. How well the activity worked is judged by how well the players met the goals and understood the material. Practical Application: The participants from UD. Bima Baru showed high levels of enthusiasm and engagement during each session of the activity. This indicates the possibility for enhancing their skills, operational efficiency, and revenue, while also fostering collaboration and fostering creativity in the future. Conclusion: Artificial intelligence (AI) has considerable potential to augment sales for MSMEs, like UD Bima Baru, through data-driven decision-making. Effective AI adoption requires practical experience, underscoring the significance of collaboration between academia and MSMEs in providing education, training, and mentorship. This collaboration fosters technological adoption and enhances local economic growth by generating practical, concrete ideas. Future training must include sequential courses for MSMEs to leverage AI.
Boundless Creativity: Vlogging with a Smartphone in the Digital Era Kala'lembang, Adriani; Riska, Suastika Yulia; Widayanti, Lilis; Rahayu, Widya Adhariyanty; Fitria, Vivi Aida
Jurnal Pengabdian Masyarakat Vol. 6 No. 1 (2025): Jurnal Pengabdian Masyarakat
Publisher : Institut Teknologi dan Bisnis Asia Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jpm.v6i1.2475

Abstract

Purpose: This community service aims to enhance the technical skills of students at SMK Negeri 12 Malang in digital vlog creation. Method: The program involves training sessions using lectures and hands-on practice to improve lighting techniques. Practical Application: This initiative has a significant impact on vlog production by following essential steps, including framing techniques, lighting, and video editing. Conclusion: This program enhances students' creativity and skills in vlog creation.
Orca Predation Algorithm as an Innovative Solution for IEEE 30 Bus Vivi Aida Fitria; Zahratul Laily Binti Edaris; Azwar Riza Habibi; Lilis Widayanti
JURNAL NASIONAL TEKNIK ELEKTRO Vol 14, No 3: November 2025
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v14n3.1296.2025

Abstract

The effective operation of the IEEE 30 Bus power system requires economic dispatch optimization to minimize production costs, align energy supply with demand, and ensure system stability. This economic dispatch problem is complex due to its non-linear characteristics, interdependence between generators, and the need to combine cost minimization with power loss reduction. Conventional optimization techniques often struggle to find global solutions, easily get stuck in local optima, and require significant computational time. This study introduces the Orca Predation Algorithm (OPA) as a new approach to address these challenges. Inspired by the hunting behavior of orcas, OPA balances exploration and exploitation through two distinct phases: pursuit and attack. Evaluated on the IEEE 30-Bus system using power loss computation with coefficient B, the algorithm ensures that generator output power allocation meets demand at the lowest cost. OPA's performance is comprehensively compared with Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), and Bat Algorithm. The results consistently show that OPA achieves the lowest total cost of $772,754 while maintaining superior system stability and effectively minimizing power losses among the evaluated algorithms. These findings highlight the significant potential of OPA to enhance energy management and advance power system optimization.
Inexact Generalized Gauss--Newton--CG for Binary Cross-Entropy Minimization Jamhuri, Mohammad; Sari, Silvi Puspita; Amiroch, Siti; Juhari, Juhari; Fitria, Vivi Aida
Jurnal Riset Mahasiswa Matematika Vol 5, No 2 (2025): 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.v5i2.34739

Abstract

Binary cross-entropy (BCE) minimization is a standard objective in probabilistic binary classification, yet practical training pipelines often rely on first-order methods whose performance can be sensitive to step-size choices and may require many iterations to reach low-loss solutions. This paper studies an inexact curvature-based solver that combines a (generalized) Gauss–Newton approximation with conjugate gradient (CG) inner iterations for minimizing the regularized BCE objective in full-batch logistic regression. At each outer iteration, the method computes a descent direction by approximately solving a damped Gauss–Newton system in a matrix-free manner via repeated products with X and X⊤, and terminates CG according to a relative-residual inexactness rule. Numerical experiments on three benchmark datasets show that the proposed Inexact GGN–CG can substantially reduce the number of outer iterations on smaller numerical data, while remaining competitive in predictive performance, and can improve both validation and test mean BCE on larger mixed-type data after one-hot encoding. In particular, on Adult Census Income the method achieves lower test mean BCE (0.3176 ± 0.0044) and higher F1-score (0.6623 ± 0.0066) than Adam and gradient descent under the same regularization-selection protocol, at the cost of additional CG work. These results highlight how damping and inexactness jointly govern the trade-off between curvature-solve effort, wall-clock time, and achieved BCE values in deterministic logistic-regression training.
Enhancing Accuracy in Stock Price Prediction: The Power of Optimization Algorithms Vivi Aida Fitria; Lilis Widayanti
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3785

Abstract

The purpose of this research was to improve the accuracy of stock price prediction by implementing optimization algorithms on forecasting methods, in this case, the exponential smoothing method. This research implemented the Particle Swarm Optimization (PSO) and Bat Algorithm metaheuristic optimization algorithms to determine the single-exponential smoothing method’s smoothing parameters. Before implementing the optimization algorithm, the way to determine the smoothing parameters was by trial-and-error method, which is considered less effective. Therefore, the novelty of this research is tuning the parameters of the exponential smoothing method using a comparison of two metaheuristic algorithms, namely the particle swarm optimization algorithm compared to the bat algorithm. The Single Exponential Smoothing method with PSO and Bat algorithms was proven to improve accuracy. The alpha parameter found by the PSO algorithm is 0.9346, and the bat algorithm is 0.936465. With a MAPE of 1.0311%, it was better than the MAPE generated in the Single Exponential smoothing method by trial and error of 1.0316%. This research contributes to providing insight that in a highly sensitive stock prediction situation, metaheuristic algorithms can be used to create more accurate and efficient prediction results.
Co-Authors A.N. Afandi Adriani Kala'lembang Adriani Kala’lembang Afiyah, S. Nurul Afiyah, S. Nurul Arif Setiawan Aripriharta - Azwar Riza Habibi Azwar Riza Habibi, Azwar Riza Budi Santoso Budi Santoso Danang Sulistyo Danang Sulistyo Fadhli Almu'iini Fadhli Almu’iini Ahda Farokhah, Lia Heru Wahyu Herwanto Ibrohim Juhari Juhari, Juhari Kala'lembang, Adriani Khadiki, Mohammad Rizan Lilis Widayanti Lukman Hakim Lukman Hakim Mohammad Jamhuri Mohammad Slamet Mufidatul Islamiyah Mufidatul Islamiyah Muhammad Saikhul Masykur Nira Rusanti Nira Rusanti Putri Ade Wasna Rina Dewi Indahsari Rudi Hartono S. Nurul Afiyah Santoso, Risa Sari, Silvi Puspita Siti Amiroch, Siti Siti Nurul Afiyah Siti Nurul Afiyah Suastika Yulia Riska Sulistyo, Danang Arbian Titania Dwi Andini Widya Adhariyanty Rahayu Yudistira Arya Sapoetra Yudistira Arya Sapoetra Yudistira Arya Sapoetra Zahratul Laily Binti Edaris