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All Journal GRADIEN Forum Geografi Panrita Abdi - Jurnal Pengabdian pada Masyarakat Journal of Geoscience, Engineering, Environment, and Technology JURNAL ILMIAH GEOMATIKA BAREKENG: Jurnal Ilmu Matematika dan Terapan CARADDE: Jurnal Pengabdian Kepada Masyarakat Teorema: Teori dan Riset Matematika Jurnal Matematika UNAND ARSY : Jurnal Aplikasi Riset kepada Masyarakat Griya Journal of Mathematics Education and Application TRIBUTE: JOURNAL OF COMMUNITY SERVICES Jurnal Sains dan Teknologi Jurnal Ilmiah Ilmu Terapan Universitas Jambi Diophantine Journal of Mathematics and Its Applications
Zulfia Memi Mayasari
Departemen Matematika, FMIPA, Universitas Bengkulu
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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Penerapan Metode Logika Fuzzy Sugeno dalam Pengambilan Keputusan Penentuan Jumlah Produksi Sembiring, Destaria Br; Mayasari, Zulfia Memi
Diophantine Journal of Mathematics and Its Applications Vol. 4 No. 2 (2025): Vol. 4 No. 2 (2025)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/diophantine.v4i2.39150

Abstract

The rapid development of the industry has led to increasingly fierce competition among companies, driving the need for operational efficiency and maximum profit. One of the main challenges faced by companies is determining the optimal production quantity to meet market demand and manage inventory efficiently. Inaccuracies in production planning, such as excess or insufficient stock, can reduce cost efficiency and customer satisfaction. The production decision-making process is often faced with uncertainty caused by limited information and incomplete data, making traditional approaches such as statistical calculations not always effective. As a solution, the Fuzzy logic method, particularly the Sugeno method, offers a flexible approach to managing uncertainty. This method uses human logic-based rules to model the relationship between demand, inventory, and production quantity adaptively. This research aims to explore the application of the Fuzzy Sugeno method in determining the optimal production quantity based on demand and supply data. Based on the analysis of tests conducted on the production quantity calculation application using the Fuzzy Sugeno method, a truth value of 81.63% was obtained. This high truth level indicates that the implementation of the Fuzzy Sugeno method is effective in determining the production quantity.
Penanganan Multikolinieritas dalam Regresi Saham GOTO Menggunakan PCA, Ridge, LASSO, dan PLS Fachri Faisal; Ratna Widayati; Zulfia Memi Mayasari; Siska Dwi Kumala; Aisyah Nooravieta Setiawan; Nur El Hasanah; Revika Putri Asharia
Griya Journal of Mathematics Education and Application Vol. 6 No. 1 (2026): Maret 2026
Publisher : Pendidikan Matematika FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/griya.v6i1.1047

Abstract

This study aims to address the problem of multicollinearity in a multiple regression model of the daily closing stock price of PT GoTo Gojek Tokopedia Tbk (GOTO) during the period from 2022 to early 2025. Multicollinearity occurs when independent variables are highly correlated, which can lead to inefficient and unreliable parameter estimates. GOTO’s stock price experienced high volatility following its Initial Public Offering (IPO) in April 2022, making it necessary to apply appropriate analytical approaches to identify factors influencing its price movements. The study uses the closing price as the dependent variable, with opening price, high price, low price, and trading volume as independent variables. The methods employed include multiple regression and several approaches to handle multicollinearity, namely variable elimination, Principal Component Analysis (PCA), Ridge Regression, LASSO Regression, and Partial Least Squares (PLS) Regression. The initial multiple regression model achieved an R² of 0.9990 and an RMSE of 2.88, but Variance Inflation Factor (VIF) analysis indicated severe multicollinearity. After applying the alternative methods, PLS Regression demonstrated the best performance, with an R² of 0.9990 and an RMSE of 0.0318. Therefore, it can be concluded that PLS Regression is a more stable and accurate method for addressing multicollinearity and improving the prediction of GOTO’s stock prices. Abstrak Penelitian ini bertujuan menangani masalah multikolinearitas dalam model regresi berganda terhadap harga saham penutupan harian PT GoTo Gojek Tokopedia Tbk (GOTO) selama periode 2022 hingga awal 2025. Multikolinearitas terjadi ketika variabel bebas saling berkorelasi kuat sehingga menyebabkan estimasi parameter menjadi tidak efisien dan kurang akurat. Harga saham GOTO mengalami volatilitas tinggi sejak IPO April 2022, sehingga diperlukan pendekatan analisis yang tepat untuk mengidentifikasi faktor-faktor yang memengaruhi pergerakan harga. Data penelitian menggunakan variabel Terakhir sebagai variabel dependen, serta Pembukaan, Tertinggi, Terendah, dan Volume sebagai variabel independen. Metode yang digunakan meliputi regresi berganda dan beberapa pendekatan penanganan multikolinearitas, yaitu penghapusan variabel, Principal Component Analysis (PCA), Ridge Regression, LASSO Regression, dan Partial Least Squares (PLS) Regression. Model awal menghasilkan R² sebesar 0,9990 dan RMSE 2,88, namun terindikasi multikolinearitas tinggi berdasarkan nilai VIF. Setelah penerapan metode alternatif, PLS Regression memberikan performa terbaik dengan R² = 0,9990 dan RMSE = 0,0318. Dengan demikian, PLS Regression dinilai paling stabil dan akurat dalam mengatasi multikolinearitas serta meningkatkan ketepatan prediksi harga saham GOTO.
ANN-ENHANCED ARIMA MODELS FOR SST-BASED SEASONAL FISH-CATCH FORECASTING AND DECISION-SUPPORT IN BENGKULU WATERS, INDONESIA Rizal, Jose; Afandi, Nur; Rahman, Refpo; Astuti, Mulia; Mayasari, Zulfia Memi; Faisal, Fachri; Yosmar, Siska
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 10 No. 4 (2026): Volume 10, Nomor 4, August 2026
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v10i4.55311

Abstract

This study presents a comparative evaluation of hybrid ARIMA-family forecasting models combined with Artificial Neural Networks (ANNs) for seasonal fish-catch prediction in Bengkulu waters, Indonesia, while assessing the role of sea surface temperature (SST) as an environmental predictor. Monthly SST data from NASA’s Giovanni portal and pelagic fish-catch records collected between January 2017 and June 2025 were used to develop ARIMA, ARIMAX, SARIMA, and SARIMAX models, whose residuals were subsequently modeled using Feedforward Neural Networks (FFNN) and Long Short-Term Memory (LSTM) networks to capture nonlinear temporal dependencies. Among the evaluated models, the hybrid ARIMA–LSTM achieved the highest forecasting accuracy on the available dataset, with an RMSE of 76.779 and a MAPE of 19.223%, whereas hybrid models that explicitly incorporate SST as a linear exogenous predictor showed lower predictive performance. These findings suggest that although SST remains an ecologically important environmental driver of pelagic fisheries, its predictive contribution may be better captured by nonlinear, lag-dependent relationships embedded in historical fish-catch observations rather than by contemporaneous linear exogenous modeling. Overall, this study provides empirical evidence for selecting appropriate hybrid forecasting models under practical fisheries data conditions and highlights their potential application as analytical components within fisheries Decision Support Systems (DSS) for adaptive fisheries management.
Co-Authors A.A. Ketut Agung Cahyawan W Angel Syarifatunnisa Arif Ismul Hadi Boko Susilo Boko Susilo Dyah Setyo Rini Fachri Faisal Fachri Faisal Fadilah Fadilah Fadilah Fadilah, Fadilah Fajri Ramadan Hana Taqqiyah Fachri Jose Rizal Lidya Savitri Meca Nerdika Muhammad Farid Mulia Astuti Mustopa Ramdhon MUSTOPA RAMDHON Nova Asri Ramdhani Nur Afandi Nur Afandi Nur El Hasanah Putri Jelita, Cici Ratna Raden Mohamad Herdian Bhakti Ratna Widayati Reflis Reflis Reflis, Reflis Refpo Rahman Revika Putri Asharia Satria Putra Utama Sembiring, Destaria Br Setiawan, Aisyah Nooravieta Siska Dwi Kumala Siska Yosmar Suwarsono Suwarsono Suwarsono Suwarsono Ulfasari Rafflesia Winalia Agwil Yudhi Harini Bertham, Yudhi Harini Yulian Fauzi