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All Journal Jurnal Penelitian Teknologi Informasi dan Sains Saber: Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi
Fungki Wahyu
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Computer Science & IT

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Perbandingan Algoritma Time Series Dan Fuzzy Inference System Dalam Analisis Data Deret Waktu Fungki Wahyu; Billy Hendrik
Jurnal Penelitian Teknologi Informasi dan Sains Vol. 1 No. 3 (2023): SEPTEMBER : JURNAL PENELITIAN TEKNOLOGI INFORMASI DAN SAINS
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jptis.v1i3.711

Abstract

Time series data analysis is a crucial process for understanding patterns and trends within temporal data. In this endeavor, two primary approaches have emerged: Time Series algorithms, which focus on statistical modeling, and the Fuzzy Inference System (FIS), which adopts fuzzy logic. This article delineates a comparison between these two approaches within the context of time series data analysis.Firstly, Time Series algorithms, such as ARIMA and ETS, offer a robust approach to modeling statistical patterns within temporal data. By considering autoregressive (AR) and moving average (MA) components, along with differencing effects within the time series, these algorithms can identify trends, seasonality, and other fluctuations. However, these algorithms tend to be more complex and rely on a profound understanding of statistics.Secondly, the Fuzzy Inference System (FIS) employs fuzzy logic principles to address uncertainty in time series analysis. Utilizing fuzzy membership functions and rule-based logic, FIS can extract information from data imbued with uncertainty, making it more suitable for situations where data is not entirely clear or structured. Nevertheless, FIS requires expert knowledge to determine appropriate fuzzy rules.The comparison between these two approaches considers several factors, including analysis complexity, data type, and dependence on expert knowledge. Time Series algorithms are better suited for in-depth statistical analysis and mathematical modeling, while FIS is more adept at handling fuzzy data and uncertainty. In some cases, combining both approaches could yield superior results, with FIS assisting in mitigating uncertainty within Time Series models.This article enhances the understanding of these approaches in time series data analysis and provides guidance for practitioners in selecting an approach aligned with their needs. Furthermore, the article underscores the potential for further development in combining the positive aspects of both approaches to tackle more intricate challenges in time series data analysis.  
Perancangan Sistem Informasi Peramalan Produksi Teh Menggunakan Metode Fuzzy Tsukamoto Berbasis Web Fungki Wahyu; Billy Hendrik
SABER : Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi Vol. 1 No. 4 (2023): Oktober : Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi
Publisher : STIKes Ibnu Sina Ajibarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59841/saber.v1i4.449

Abstract

Todays, technology is also widely used in various fields for various needs. Based on data from. Various sectors, such as education, business, tourism, to the agricultural sector, widely use websites. There are various implementations or applications of technological developments in agriculture, one of which is the application of calculating an agricultural product in Kerinci experienced ups and downs due to less stable weather conditions. Calculations in determining an agricultural product can use several calculation methods, one of which is the Tsukamoto fuzzy method. Tsukamoto's fuzzy method is an extension of monotone reasoning. In Tsukamoto's method, every consequence has a rule in the form of IF-THEN that must be represented in a fuzzy set with a monotone arrangement function. So this requires a system that can produce predictions in tea production in Kerinci tea plantations to meet market needs. The solution offered is a production prediction system in Kerinci plantations using the Tsukamoto fuzzy method. The prediction results obtained in this study were a 36% increase in tea sales production.
Co-Authors Billy Hendrik