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All Journal JATI (Jurnal Mahasiswa Teknik Informatika) ALEXANDRIA: Journal of Economics, Business, and Entrepreneurship Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Aulia, Windy
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Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Social Sciences

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ESTIMASI RUTE TERDEKAT DARI UNIVERSITAS NEGERI MEDAN KE SPBU TERDEKAT MENGGUNAKAN ALGORITMA GREEDY Dobry Sianipar, Freyro; Hidayatul Arifin, Muhammad; Aulia, Windy; Harliana, Putri
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 6 (2024): JATI Vol. 8 No. 6
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i6.11803

Abstract

Penentuan rute terdekat merupakan masalah optimasi yang penting dalam berbagai bidang, terutama dalam transportasi dan navigasi. Penelitian ini bertujuan untuk mengestimasi rute terdekat dari Universitas Negeri Medan (Unimed) menuju beberapa Stasiun Pengisian Bahan Bakar Umum (SPBU) di sekitarnya dengan menggunakan algoritma Greedy. Algoritma Greedy dipilih karena kesederhanaannya dan kemampuannya untuk memberikan solusi yang cepat dengan memilih jarak terpendek pada setiap langkah. Meskipun demikian, algoritma ini hanya mempertimbangkan solusi lokal terbaik dan tidak menjamin solusi optimal secara global. Penelitian ini dilakukan secara perkiraan tanpa data geografis aktual, namun tetap memberikan pemahaman mendalam tentang cara kerja algoritma Greedy dalam kasus rute jarak dekat. Hasil penelitian menunjukkan bahwa algoritma Greedy dapat menghasilkan estimasi rute yang efisien dan cepat, meskipun solusi yang dihasilkan mungkin tidak selalu optimal secara keseluruhan. Peta visualisasi yang dihasilkan dari simulasi algoritma ini membantu memperjelas pola pergerakan yang dipilih. Penelitian ini memberikan wawasan tentang potensi penerapan algoritma Greedy dalam sistem navigasi sederhana dan bagaimana pendekatan ini dapat diadaptasi untuk skenario yang lebih kompleks di masa depan.
ANALISIS SENTIMEN MASYARAKAT TERHADAP LARANGAN PENGECER MENJUAL LPG 3 KG BERSUBSIDI MENGGUNAKAN ALGORITMA NAÏVE BAYES Hidayatul Arifin, Muhammad; Amelia Vega S. Meliala, Ruth; Amanah, Fadilla; Aulia, Windy; Arnita, Arnita; Ramadhani, Fanny
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 4 (2025): JATI Vol. 9 No. 4
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i4.13883

Abstract

Kebijakan pemerintah yang melarang pengecer untuk menjual gas LPG 3 kg bersubsidi menghasilkan berbagai komentar di media sosial, terutama YouTube, di mana banyak komentar mengungkapkan kekhawatiran tentang kesulitan akses dan dampak ekonomi terhadap masyarakat kecil. Dengan menggunakan algoritma Naive Bayes, penelitian ini mengkaji sentimen publik terhadap kebijakan tersebut. Data dikumpulkan melalui proses scraping komentar YouTube; setelah itu, IndoBERT digunakan untuk melakukan pelabelan otomatis dan pembersihan data. Teknik SMOTE digunakan untuk mengatasi ketidakseimbangan kelas dalam dataset, sedangkan metode TF-IDF digunakan untuk mengekstraksi fitur teks. Hasil analisis menunjukkan bahwa 70% komentar bersentimen negatif, dan model multinomial Naive Bayes mencapai akurasi 85,67%. Penggunaan oversampling juga terbukti meningkatkan recall untuk kelas sentimen positif dan netral. Hasilnya menunjukkan bahwa untuk membuat kebijakan yang diterapkan lebih mudah dipahami dan diterima oleh masyarakat, pemerintah harus merevisi strategi komunikasi dan distribusi LPG bersubsidi.
Implementation of IoT and Machine Learning for Monitoring and Prediction of Tank Water Levels Wahyudi, Rizky; Kiswanto, Dedy; Aulia, Windy; Audy Priscilia, Selfi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1936

Abstract

The availability and quality of clean water in household storage tanks are essential yet often overlooked until problems such as depletion or contamination occur. Manual monitoring methods that rely on physical inspection tend to be inefficient, prone to delay, and unable to support predictive decision-making. This study proposes an automated monitoring solution by integrating Internet of Things (IoT) technology with Machine Learning-based analysis. The system is developed using an ESP32 microcontroller that continuously collects real-time data from an ultrasonic sensor to measure water level and a turbidity sensor to assess water clarity. The time-series data obtained is then analyzed using two algorithmic approaches. Linear Regression is employed to model the water depletion rate and generate predictions regarding the estimated remaining duration before the tank reaches an empty state. In parallel, Random Forest is applied as a comparative model to validate prediction accuracy under non-linear consumption patterns. Experimental results demonstrate that the combined IoT–Machine Learning framework provides accurate, timely, and informative insights for users. The proposed system improves water usage efficiency and strengthens early warning capabilities, making it a practical solution for supporting effective household water management.
The Influence of Tiktok Live Streaming and Influencer Engagement on Consumers Purchase Intention: A Systematic Literature Review Damania, Alfina; Quro, Umul; Nurlisyana, Wina; Aulia, Windy
ALEXANDRIA (Journal of Economics, Business, & Entrepreneurship) Vol. 7 No. 1 (2026): April (PROCESS)
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/alexandria.v7i1.1344

Abstract

Digital developments have brought significant changes in product marketing and marketing. TikTok, one of the entertainment platforms, has now become an effective marketing channel. This study aims to analyze the influence of TikTok live shopping and influencers on purchase intention through a systematic and comprehensive approach. This research method is a synthetic literature review (Systematic Literature Review/SLR) supported by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) framework, analyzing 10 selected journas from 2021 to 2025 obtained from various reputable academic databases such as Scopus, Web of Science, ScienceDirect, and Google Scholar. The results of the study indicate that TikTok live streaming or live shopping influences consumer purchase intention because interactivity and the quality of the real-time experience have important roles as determinants of purchase intention. In addition, influencers influence TikTok consumer purchase intention by being influenced by several factors, such as credibility, authenticity, and emotional connection. It is recommended that further research develop a more holistic conceptual model of how digital social influence and interactive experiences shape modern consumption behavior, while also providing a solid theoretical foundation for subsequent SLR research and scholarly publications.
Co-Authors Amanah, Fadilla Amelia Vega S. Meliala, Ruth Arnita Arnita Audy Priscilia, Selfi Damania, Alfina Dedy Kiswanto Dobry Sianipar, Freyro Hidayatul Arifin, Muhammad Nurlisyana, Wina Quro, Umul Ramadhani, Fanny Wahyudi, Rizky