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All Journal Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal INFOTEL
Nur Wakhidah
Universitas Semarang, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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The Comparative Analysis Of Multi-Criteria Decision-Making Methods (MCDM) In Priorities Of Industrial Location Development Agusta Praba Ristadi Pinem; Aria Hendrawan; Nur Wakhidah
JURNAL INFOTEL Vol 16 No 4 (2024): November 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i4.1099

Abstract

The process of prioritizing the development of an industrial area's site is a matter that necessitates a mature approach. The establishment of an industrial region has significant social implications for the surrounding locality. However, it is also necessary to take into account the availability of variables that facilitate the functioning of such an industrial zone. The goal of the study "A Comparative Analysis of Multi-Criteria Decision Making Methods (MCDM) for Determining the Priority of Industrial Area Location Development" is to compare and contrast different MCDM methods in the context of deciding which industrial area locations should be developed first. A case study was undertaken, examining various possible industrial sites for future development. Multiple approaches, namely MOORA, WASPAS, ARAS, COPRAS, and AHP, are employed to ascertain the prioritization of industrial area development locations. This study presents a comparative analysis of each approach by using the Spearman Rank correlation and utilizing the factual data obtained from the Department of Capital Plantation and Integrated One Door Services (DPMPTSP). The external research is anticipated to involve a comprehensive review of the literature on the efficacy of Multiple Criteria Decision Making (MCDM) methods. This research has the potential to assist both governmental bodies and private entities in establishing priorities for the development of industrial areas, taking into account prevailing circumstances and conditions while also considering various significant factors and criteria.
Komparasi Metode SVM Dan Random Forest Pada Analisis Sentimen Ulasan Aplikasi Open AI Zuli Chofifah; Nur Wakhidah
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 10, No 2 (2025): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v10i2.925

Abstract

In this study, the sentiment analysis of ChatGPT application reviews gathered from the Google Play Store is compared using the Support Vector Machine (SVM) and Random Forest techniques. The google-play-scraper package was used to scrape for the dataset. The data was subjected to a number of preparation procedures before categorization, such as text normalization, stopword removal, character removal, and stemming using the Sastrawi package. After classifying each review using both algorithms, the sentiment of each review was labeled according to its rating score. According to the experimental results, Random Forest attained an accuracy of 94.00%, whereas SVM achieved 95.00%. According to these results, SVM performs marginally better than Random Forest at identifying the sentiment of user reviews of OpenAI applications.
Co-Authors Agusta Praba Ristadi Pinem Aria Hendrawan, Aria Zuli Chofifah