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All Journal JTAM (Jurnal Teori dan Aplikasi Matematika) Basis : Jurnal Ilmiah Matematika Ecos-Preneurs: Alahyan Jurnal Pengabdian Masyarakat Multidisiplin
Dongoran, Raisha Zuhaira
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Economics, Econometrics & Finance Mathematics Nursing Public Health Social Sciences Other

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Peran Program Teaching in Society Dalam Meningkatkan Motivasi Belajar Anak-Anak Desa Pasir Permit Samin Lubis, Mara; Dongoran, Raisha Zuhaira; Salsalina Br Ginting, Iren
Alahyan Jurnal Pengabdian Masyarakat Multidisiplin Vol. 2 No. 2 (2024): (Nopember)
Publisher : PT. Alahyan Publisher Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61492/ecos-preneurs.v2i2.210

Abstract

Learning motivation is an important factor in educational success, especially in rural areas where access to educational resources is often limited. One approach to overcome this problem is through a teaching program that involves student participation, such as what is done in the Real Work Lecture (KKN). The Teaching in Society program, which is part of KKN, seeks to help increase children's motivation to learn through engaging and participatory teaching. This study examines how the 19 KKN Group is able to contribute to increasing the learning motivation of children in Pasir Permit Village through this program.
Penerapan Model Logistik Untuk Optimalisasi Portofolio Investasi Saham Syahputra, Mario; Clara, Nur Cellia; Kinanti, Tri; Dongoran, Raisha Zuhaira
Basis : Jurnal Ilmiah Matematika Vol. 4 No. 1 (2025): BASIS: Jurnal Ilmiah Matematika
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/basis.v4i1.1434

Abstract

Penelitian ini bertujuan untuk mengoptimalkan portofolio investasi saham dengan menggunakan model regresi logistik, dengan mempertimbangkan variabel volume perdagangan, harga historis, dan price to earning ratio (P/E). Data yang digunakan merupakan data sekunder dari 20 emiten yang terdaftar di Bursa Efek Indonesia, diambil pada tanggal 30 Oktober 2024. Pengolahan data dilakukan dengan menerapkan model regresi logistik untuk menganalisis hubungan antara variabel independen dan probabilitas kenaikan harga saham. Model ini dilatih dengan data historis saham untuk mengestimasi kemungkinan kenaikan harga, yang kemudian digunakan sebagai dasar dalam pemilihan saham optimal. Hasil penelitian menunjukkan bahwa dari 20 emiten yang dianalisis, terdapat tiga saham dengan probabilitas kenaikan harga di atas 50%, yaitu BREN (87,71%), BUMI (67,11%), dan EMTK (52,08%). Model ini dapat membantu investor dalam mengoptimalkan portofolio investasi jangka pendek dengan mempertimbangkan toleransi risiko masing-masing investor.
Robust Optimization Model Analysis for Online Sentiment Issues on Shopee using Support Vector Machine Dongoran, Raisha Zuhaira; Cipta, Hendra
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 3 (2025): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i3.31555

Abstract

In the digital economy era, e-commerce platforms like Shopee receive thousands of user reviews daily, which significantly influence customer perceptions and purchasing decisions. However, sentiment analysis of such reviews remains challenging due to the presence of noise, uncertainty, and dynamic data changes. This quantitative research aims to develop a more reliable sentiment classification model by integrating a Lexicon-Based labeling approach and Support Vector Machine (SVM) classification with a Robust Optimization framework. The labeling process uses a sentiment lexicon dictionary that assigns polarity values to words, classifying texts into positive, negative, or neutral categories. The classification process utilizes SVM to evaluate sentiment prediction based on key performance metrics: Accuracy, Precision, Recall, and F1-score. These performance metrics are treated as uncertain parameters in the optimization phase. The main contribution of this study is the formulation of a robust optimization model for sentiment analysis weighting problems, transforming a multi-criteria objective into a single-objective utility function. By applying polyhedral uncertainty modeling, the robust counterpart formulation accounts for worst-case scenarios in model evaluation. Numerical experiments using Python in Google Colab show that while the deterministic model achieves a higher performance score (0.865), the robust model yields a slightly lower score (0.825) but offers better stability under uncertainty. These results imply that robust optimization can enhance the reliability of sentiment classification systems in real-world e-commerce applications, providing more trustworthy insights for businesses in managing consumer feedback.
Robust Optimization Model for Green Capacitated Vehicle Routing Problem with Hamiltonian Circuit using the Nearest Neighbor Algorithm Cipta, Hendra; Widyasari, Rina; Dongoran, Raisha Zuhaira
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 2 (2026): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i2.35430

Abstract

The rapid urban expansion of Medan City has intensified the complexity of municipal waste transportation, where limited fleet capacity, congested road segments, and long travel distances to the Terjun disposal site result in high operational costs and excessive carbon monoxide (CO) emissions. In addition, daily fluctuations in waste volume introduce uncertainty that disrupts routing efficiency and increases the risk of vehicle overload. This study proposes a Robust Optimization based Green Capacitated Vehicle Routing Problem model to minimize transportation cost and CO emissions while maintaining route feasibility under demand uncertainty. The model incorporates a Hamiltonian circuit structure to ensure closed-loop routing and applies the Nearest Neighbor Algorithm (NNA) as a constructive heuristic for generating initial solutions. Compared to commonly used methods such as the Clarke–Wright Savings algorithm, NNA provides faster computational performance, simpler implementation, and more stable feasible routes when integrated with robust capacity constraints. Using real CO emission data from major arterials in Medan, the model was evaluated across multiple uncertainty levels (Γ = 0–6). The results show that the robust model reduces overload risk by up to 12%, lowers total emission cost by approximately 5% relative to the deterministic solution, and produces more environmentally efficient routing decisions even when route distance increases slightly. From an analytical perspective, the RO Green-CVRP framework enables evaluation from operational, environmental, and robustness performance dimensions. This research contributes theoretically to green robust optimization and practically supports the development of adaptive, low-emission waste transportation strategies aligned with Medan’s sustainable urban development goals.
Co-Authors Clara, Nur Cellia Hendra Cipta Kinanti, Tri Salsalina Br Ginting, Iren Samin Lubis, Mara Syahputra, Mario Widyasari, Rina