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All Journal Journal of Community Service (Abdisci) Journal Of Engineering Sciences (Improsci) Journal of Society and Development
Maturidi, Ade Johar
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Humanities Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences Other

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Journal : Journal Of Engineering Sciences (Improsci)

 1 
Evaluation of the Multiple Regression Analysis Algorithm on Stock Market Prediction Setiawan, Mohamat; Maturidi, Ade Johar; Novianti, Dian
Jurnal Improsci Vol 2 No 2 (2024): Vol 2 No 2 October 2024
Publisher : Ann Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62885/improsci.v2i2.494

Abstract

Financial time series is one of the most challenging applications of modern time series forecasting. The financial time series is closely related to noise, non-stationary, and deterministic chaos. The characteristics suggest that no complete information can be obtained from the past behavior of financial markets to fully capture the dependency between future prices and that of the past. The data collection method was collected from the Stock Market Online Application "MetaTrader version 4" type "Daily" with a time range from "03/09/2001 to 25/07/2012", as many as 2052 data", with the attributes "Date, Open, High, Low, Close, Volume" with the main attribute "Close" using the Support vector machine algorithm, artificial neural network, and multiple linear regression. The conclusion of the value that is close to the series value is the value by testing on the support vector machine algorithm, with the parameter for the RMSE value that is close to the "0" value obtained from the measurement results on the SVM algorithm on the RBF kernel (radial base function) with a value of "gamma" γ = 100 with the value of RMSE = 0.000, and SE = 0.000. with prediction accuracy error = 0.976
Implementation Of Speech Recognition For Voice Command Use Maturidi, Ade Johar; Osman, Didan; Sulaeman, Muhamad
Jurnal Improsci Vol 3 No 1 (2025): Vol 3 No 1 August 2025
Publisher : Ann Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62885/improsci.v3i1.1008

Abstract

Background. Physical limitations of a person sometimes make it impossible to operate a computer with only a keyboard and mouse, Aims. One tool that can be used is a voice command, which is part of speech recognition technology. Methods. The voice signal will be normalized first, and then the coefficient values will be calculated using the Linear Predictive Coding (LPC) and Fast Fourier Transform (FFT) methods. After the coefficient value is obtained, recognition is performed using the backpropagation method of the Artificial Neural Network. Conclusion. The artificial neural network backpropagation method is used because it can adjust its own weights and produce error values that we can determine, thereby improving accuracy. Implementation. This study implements a voice command system using MARF as its speech engine and Java as its programming language.
Herbal Treatment Expert System For Ent Disease Diagnosis Using The Certainty Factor Method Based On Android Maturidi, Ade Johar; Fajri, Muhammad Ali; Nurgaha, Deni
Jurnal Improsci Vol 3 No 2 (2025): Vol 3 No 2 October 2025
Publisher : Ann Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62885/improsci.v3i2.1048

Abstract

Background. Public understanding of ENT (ear, nose, and throat) diseases remains low. Many people still rely on experts manually, resulting in high costs for the general public. Aims. This research was conducted to develop a tool to assist users in diagnosing ENT (ear, nose, and throat) diseases in humans. This tool, an expert system, is designed to diagnose and provide herbal treatment recommendations. Result. This expert system application produces an application program or tool that can be used to diagnose possible ENT diseases in humans based on user-entered symptoms. Conclusion. System testing demonstrated that the system can diagnose ENT diseases based on patient symptoms, despite the uncertainty of those symptoms. Implementation. The diagnostic results are accompanied by a Certainty Factor value, which indicates the level of accuracy and predictability of the possible ENT disease in humans, along with the corresponding herbal treatment.
Co-Authors Fajri, Muhammad Ali Irfan , M. Taufik Irfan, Moch Taufik Muangsal, Muangsal Novianti, Dian Nurgaha, Deni Osman, Didan Salsabilauthor, Syauqiyyah Setiawan, Moh Setiawan, Mohamat Suharti, Lilis Sulaeman, Muhamad