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Polygon: Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam
Published by Asosiasi Riset Ilmu Matematika dan Sains Indonesia
ISSN : 30326249     EISSN : 30465419     DOI : 10.62383
Core Subject : Science,
Computer Science & IT
Jurnal ini adalah jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam yang bersifat peer-review dan terbuka. Bidang kajian dalam jurnal ini termasuk sub rumpun Ilmu Komputer, dan Ilmu Pengertahuan Alam.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 105 Documents
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Evaluasi Efektivitas Model Klasifikasi Sentimen untuk Analisis Opini Publik terhadap Kebijakan Lingkungan Berdasarkan Data Media Sosial Berbahasa Indonesia Dada Suhaida; Adisti Primi Wulan; Rosanti Rosanti; Dianna Dianna
Polygon : Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam Vol. 2 No. 2 (2024): Maret: Polygon : Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/polygon.v2i2.951

Abstract

Background: Public opinion analysis has become increasingly important in the digital era, where social media platforms generate large-scale textual data reflecting public perceptions toward environmental policies. Advances in Natural language processing (NLP) and machine learning enable systematic sentiment classification to support data-driven decision-making. Objective: This study aims to evaluate the effectiveness of several sentiment classification models in analyzing Indonesian-language social media data related to environmental policies. Method: The research employed a text mining pipeline including data crawling, preprocessing (case folding, tokenization, stopword removal, and stemming), and vectorization using TF-IDF. Three classification models Logistic Regression, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) were trained and evaluated using accuracy and F1-score metrics. Results: Experimental findings indicate that LSTM achieved the highest performance with 91.7% accuracy and 91.2% F1-score, outperforming SVM (88.5%) and Logistic Regression (84.2%). Sentiment distribution analysis shows that public opinion is dominated by positive sentiment (47.5%), followed by neutral (32.0%) and negative (20.5%). Overall: The results demonstrate that deep learning-based models provide more robust contextual understanding and more reliable sentiment mapping for environmental policy analysis.
Sistem Pendukung Keputusan Seleksi Penerimaan Karyawan Baru di Winmart Menggunakan Metode Seleksi Berbasis Web Herlina Baro Lolu; Andreas Ariyanto Rangga; Paulus Mikku Ate
Polygon : Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam Vol. 4 No. 2 (2026): Maret: Polygon : Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/polygon.v4i2.955

Abstract

The selection process for accepting new employees is one of the important stages in a company to ensure that the candidates accepted have qualifications that suit the company's needs. At WINMART, the selection process is still carried out manually, so it is less efficient and prone to errors. Therefore, a system is needed that can assist in more objective and efficient decision making. This Decision Support System (DSS) is designed to assist the selection process for recruiting new employees using the Simple Additive Weighting (SAW) method, which can assess several relevant criteria, such as work experience, education, skills and competency tests. This system was built on a web basis, so it can be easily accessed by parties involved in the selection process, such as HRD and managers. The SAW method was chosen because of its ability to convert various subjective criteria into more objective numerical scores, so that selection results can be more transparent and accountable. By using this system, it is hoped that it can increase efficiency, accuracy and transparency in the new employee selection process at WINMART, as well as facilitate decision making in selecting candidates who best suit the desired criteria.
Penerapan Algoritma Dijkstra dalam Optimasi Rute Terpendek dari Stasiun Kereta Api Medan ke Universitas Negeri Medan Nazwa Salsyabilla Ramadhani; Juliana Gloria Br. Sipayung; Maria Winarni Br Silitonga; Mika Monika Fransiska Simanullang
Polygon : Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam Vol. 4 No. 3 (2026): Mei : Polygon : Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/polygon.v4i3.961

Abstract

The increasing complexity of urban transportation systems demands intelligent and measurable navigation methods. Medan City, the capital of North Sumatra Province, has a dense road network with multiple route options that often confuse road users. Dijkstra's Algorithm, developed by Edsger Wybe Dijkstra in 1959, is a greedy-based computational approach proven effective for solving the shortest path problem on non-negative weighted graphs. This study applies Dijkstra's Algorithm to determine the shortest route from Medan Railway Station to Universitas Negeri Medan (UNIMED). The road network was modeled as an undirected weighted graph with 15 nodes and 16 edges, where edge weights represent actual road distances measured via Google Maps. The graph has a density of 0.152, confirming its sparse graph characteristic. Three alternative routes were identified and analyzed. The algorithm was implemented in Python 3 using the heapq module as a priority queue. Results show that the optimal route is A → B → C → E → F → M → N → O via Jl. M.T. Haryono, Jl. Aipda KS Tubun, Jl. Madong Lubis, and Jl. Prof. H.M. Yamin, with a total distance of 6.64 km. This achieves 99.1% accuracy compared to Google Maps, with a deviation of only 0.06 km. The optimal route is 6.25% more efficient than Alternative Route 1 (7.30 km) and 11.9% more efficient than Alternative Route 2 (7.54 km). The algorithm executes in under 1 millisecond with time complexity O((V+E) log V). These findings confirm Dijkstra's Algorithm as highly effective for medium-scale urban road network optimization.
Reconceptualizing Student Agency in AI-Supported STEM Learning: A Qualitative Study of Autonomy, Regulation, and Dependency Nofamataro Zebua
Polygon : Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam Vol. 4 No. 2 (2026): Maret: Polygon : Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/polygon.v4i2.962

Abstract

This study explores student agency in Artificial Intelligence (AI)-supported STEM learning environments, addressing a critical gap in existing literature that predominantly focuses on learning outcomes rather than learner-centered processes. Drawing on an interpretive qualitative approach, this research investigates how students experience autonomy, self-regulation, and decision-making when interacting with AI technologies in STEM education. Data were collected from 15 participants engaged in AI-supported learning through in-depth semi-structured interviews, supported by observations and document analysis. The data were analyzed using thematic analysis to identify recurring patterns and meanings related to student agency. The findings reveal that student agency is a dynamic and multidimensional construct shaped by the interplay between technological affordances and learner engagement. Four major themes emerged: enhanced autonomy, development of self-regulated learning, negotiated decision-making, and ambivalent dependency on AI. While AI technologies provide adaptive support that empowers students to take control of their learning, they also introduce the risk of over-reliance, which may reduce cognitive engagement. This study contributes to the theoretical advancement of student agency by conceptualizing it as a spectrum rather than a fixed attribute, highlighting the dual role of AI as both an enabler and a constraint. The findings offer important pedagogical implications for designing AI-supported STEM learning environments that promote active, reflective, and responsible learning. Future research is recommended to explore this phenomenon across diverse contexts and through longitudinal designs.
Navigasi Privasi di Era Digital: Peran Kesadaran Pengguna dalam Mitigasi Kebocoran Informasi di Media Sosial Zahra Azkiya; Evy Nurmiati
Polygon : Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam Vol. 4 No. 3 (2026): Mei : Polygon : Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/polygon.v4i3.968

Abstract

The rapid digitalization in Indonesia, reaching 180 million active social media users, has not been accompanied by adequate security system resilience, thereby triggering massive data breach risks. This study aims to analyze the privacy navigation mechanisms of the digital society as an instrument for mitigating information leaks. The method used is descriptive qualitative with a literature study (library research) approach, which examines primary and secondary literature related to regulations, digital behavior, and user psychological factors. The research findings indicate that privacy navigation in the digital era has not operated optimally due to the dominance of social existence needs, which triggers the privacy paradox phenomenon. Although users possess knowledge regarding cyber risks, the desire for social validation through self-disclosure often overrides technical protection logic. The practice of using secondary accounts (second accounts) was found to be a form of manual navigation, yet its effectiveness remains dependent on individual digital literacy. The implications of this research emphasize that mitigating information leaks requires the integration of critical user awareness, platform governance transparency, and consistent law enforcement through the PDP Law. Digital awareness must transform into reflexive protective behavior to maintain informational sovereignty in cyberspace.  

Page 11 of 11 | Total Record : 105

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