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Purba, Winda Nia
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Computer Science & IT

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IMPLEMENTASI METODE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM UNTUK MENENTUKAN TINGKAT KEPUASAN TERHADAP PELAYANAN AKADEMIK UNIVERSITAS PRIMA INDONESIA Purba, Winda Nia; Mendyeta, Cristine; Simanullang, Reno; Kienan, Olwen; Raj, Pravin
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 1 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i1.1212

Abstract

This study highlights the importance of understanding student satisfaction with academic services at Universitas Prima Indonesia. Using the Adaptive Neuro-Fuzzy Inference System (ANFIS) method, the research identifies factors such as teaching quality, access to academic resources, and class schedules that influence student satisfaction. Data was collected through questionnaires and observations, then processed with ANFIS to clean, train, and test the data. The findings indicate that the variables of tangibles, responsiveness, reliability, empathy, and assurance significantly affect student satisfaction. The ANFIS model used demonstrates that student satisfaction can be predicted with relatively high accuracy, with an average root mean square error of 0.083297. This study provides guidance for improving the quality of academic services at Universitas Prima Indonesia, emphasizing the importance of professional and high-quality services to meet student expectations.
PERBANDINGAN PENERAPAN ALGORITMA K-MEANS DAN FUZZY C-MEANS DALAM ANALISIS CLUSTERING TERHADAP PERGERAKAN HARGA HISTORIS SAHAM BANK RAKYAT INDONESIA Purba, Winda Nia; Hartanto, Ricky
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 2 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i2.1214

Abstract

This study aims to analyze and compare the application of K-Means and Fuzzy C-Means algorithms for clustering historical stock price movements of Bank Rakyat Indonesia (BRI). Clustering is a method that groups data based on similarities, crucial in stock data analysis to aid more precise investment decision-making. The K-Means algorithm deterministically assigns each data point to a single cluster, while Fuzzy C-Means allows partial membership across multiple clusters, offering greater flexibility. The research findings indicate that the K-Means algorithm forms three primary clusters with a Silhouette Score of 0.4667, which defines clusters more clearly than Fuzzy C-Means, which has a score of 0.4199. The clusters produced by K-Means provide better-defined separations among stocks with medium, high, and low prices, based on price movements and transaction volume. In contrast, Fuzzy C-Means, despite its ability to handle overlapping data, results in less clearly defined clusters compared to K-Means. Based on these results, the K-Means algorithm is deemed more effective for clustering analysis in the context of BRI stocks. This research is expected to contribute to the development of more comprehensive stock movement analysis models and support investors in making better-informed investment decisions.
Co-Authors Kienan, Olwen Mendyeta, Cristine Raj, Pravin Ricky Hartanto, Ricky Simanullang, Reno