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All Journal Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika Uranus: Jurnal Ilmiah Teknik Elektro, Sains dan Informatika
Silvia Amara
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Computer Science & IT

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Penerapan Metode MAUT dalam Menentukan Lokasi Cabang Baru Tokepangsit Medan di Kabupaten Langkat Silvia Amara; Cinta Apriliza; Sherly Rohana; Amirullah Wahid; Safrizal Safrizal
Uranus : Jurnal Ilmiah Teknik Elektro, Sains dan Informatika Vol. 3 No. 1 (2025): Uranus : Jurnal Ilmiah Teknik Elektro, Sains dan Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/uranus.v3i1.658

Abstract

Every business definitely wants to expand its business by opening new branches in different regions to increase income and become a successful business. To open a new business branch, of course, a lot of consideration is needed before making a decision. So that the decision is correct and in accordance with needs, Research is used in its implementation. Therefore, this research applies a decision support system using the Multi Attribute Utility Theory (MAUT) method in selecting a new branch location for the Tokepangsit Medan business which will open a new branch in Langkat Regency. With this method, the decision result obtained is alternative 4, namely Selesai District, which gets the highest score of 0.640.
Analisis Sentimen Masyarakat terhadap Program Makan Siang Gratis di Indonesia Tahun 2024 Menggunakan Long Short-Term Memory (LSTM) Silvia Amara; Novriyenni, Novriyenni; Muammar Khadapi
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 3 No. 4 (2025): Juli : Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v3i4.930

Abstract

The free lunch program is a goverment initiative aimed at addressing the issue of stunting in Indonesia. This program focuses on toddlers, school-age children and pregnant women. Various opinions have emerged from the public regarding this initiative, especially through sosial media platform X (Twitter) and news portals. In this research, sentiment analysis was conducted to understand public responses to the program, whether they are positive, neutral or negative. To evaluate the accuracy of the sentiment analysis perfomed, a deep learning approach was applied using the Long Short-Term Memory (LSTM) algorithm. The results show that public sentiment varies responses, on social media X tend to be negative, while those on news portals tend to be positive toward the free lunch program in Indonesia. Through LSTM-based testing, sentiment analysis on tweet data achieved an accuracy of 88.6%, with a precision of 84.6%, recall of 88.6% and an F1-Score of 86.3%. Meanwhile, sentiment analysis on news portal data reached an accuracy of 89%, with a precision of 81.7%, recall of 89% and an F1-Score of 85.1%.
Co-Authors Amirullah Wahid Cinta Apriliza Muammar Khadapi Novriyenni, Novriyenni Safrizal Safrizal Sherly Rohana