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All Journal Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi)
Adam M Tanniewa
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Computer Science & IT

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Sistem Pendukung Keputusan Menentukan Kelayakan Calon Pengantin Dengan Metode SAW di Kabupaten Pasangkayu Ardiansyah; Adam M Tanniewa
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

The rate of early marriage in Pasangkayu Regency has increased to 14.68%, making it occupy first position in West Sulawesi. This resulted in the DP3AP2KB of Pasangkayu Regency working hard in granting marriage permits, even though the selection process carried out by the DP3AP2KB of Pasangkayu Regency was still manual and not properly computerized. To overcome this, it is necessary to build a system to streamline the selection process for prospective brides and grooms who are well computerized. This system will utilize the Decision Support System (SPK) process using the Simple Additive Weighting (SAW) method which focuses on six criteria, namely age, employment, education, parental permission letter, SKBS, and tetanus injection. The output produced is in the form of a ranking table which will make it easier to determine the suitability of prospective brides and grooms. The results of this research show that 3 prospective brides (6%) were declared "Very Eligible" for marriage with a reference value between 0.850 to 1.000; Furthermore, 29 prospective brides (58%) were declared "Suitable" for marriage with a reference value between 0.650 to 0.849 and 18 prospective brides (36%) were declared "Considering" for marriage with a reference value between 0.000 to 0.649.
Implementasi Algoritma Support Vector Learning Terhadap Analisis Sentimen Penggunaan Aplikasi Tiktok Shop Seller Center Sarina, Sarina; Adam M Tanniewa
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

E-commerce is experiencing rapid growth in Indonesia, followed by the increasing popularity of the Tiktok Shop application. This research aims to conduct sentiment analysis on user reviews of the Tiktok Shop Seller Center application on the Google Play Store using the Support Vector Learning (SVM) method and Text Mining techniques. This research collects review data in Indonesian from May to July 2023. This data includes ratings, comment content and review dates. The sentiment analysis results allow grouping reviews into positive or negative, and SVM with various kernels (Linear, RBF, Polynomial, and Sigmoid) is used to classify the sentiment. This research has the potential to provide important insights into users' views of the Tiktok Shop Seller Center and contribute to the development of sentiment analysis in the context of e-commerce in Indonesia.
Co-Authors Ardiansyah Sarina Sarina