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All Journal Morfologi : Jurnal Ilmu Pendidikan, Bahasa, Sastra dan Budaya Polygon: Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam
Mika Monika Fransiska Simanullang
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Arts Humanities Computer Science & IT Education Languange, Linguistic, Communication & Media

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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.
Ragam Bahasa Indonesia dalam Prompt AI: Studi Komparatif Gaya Respons ChatGPT Sirlia Sahid; Maissy Angelica Pakpahan; Mika Monika Fransiska Simanullang; Repi Meilani Putri
Morfologi : Jurnal Ilmu Pendidikan, Bahasa, Sastra dan Budaya Vol. 4 No. 3 (2026): June: Morfologi : Jurnal Ilmu Pendidikan, Bahasa, Sastra dan Budaya
Publisher : Asosiasi Periset Bahasa Sastra Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/morfologi.v4i3.2699

Abstract

This study examines the influence of standard (baku) and non-standard (informal) Indonesian language in prompt construction on the response style and structure generated by ChatGPT. The proliferation of generative AI in Indonesia presents a gap: most users interact with AI using everyday informal language, while the effect on AI response characteristics remains understudied. Using a comparative qualitative approach, two prompt variants formal standard language and informal non-standard language were tested on an identical object: an internet service package (HOME ODS, 100 Mbps, Rp150,000/month). Responses were evaluated across five dimensions: structure, completeness, analytical depth, register alignment, and practical utility. Findings show that formal-language prompts yield more hierarchically organized and elaborated responses, while informal prompts elicit concise, conversational responses marked by emoji and colloquial tone. Both prompt types produced substantively comparable information, indicating that language variety primarily shapes response style rather than content depth. These findings suggest ChatGPT adapts its register to match user input, a behavior consistent with statistical pattern prediction inherent to large language models (LLMs). Implications for Indonesian language education and AI literacy are discussed.
Co-Authors Juliana Gloria Br. Sipayung Maissy Angelica Pakpahan Maria Winarni Br Silitonga Nazwa Salsyabilla Ramadhani Repi Meilani Putri Sirlia Sahid