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All Journal Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Ekonomi KIAT Indonesian Journal of Jamu
Ary Prabowo
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Health Professions Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology

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Enterprise Risk Managemenut (ERM) and Corporate Value in Indonesia Poppy Camenia Jamil; Sinta Yulyanti; Ary Prabowo
Jurnal Ekonomi KIAT Vol. 35 No. 1 (2024): Juni 2024
Publisher : UIR Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/kiat.2024.17807

Abstract

Enterprise Risk Management (ERM) create value added for the company by facilitate management in controling various risk which caused uncertainties condition by integrating all types of risk using integrated tools and tachniques, and then cordinate thr activites of risk management to all operating unit within an organization so that all types of risk can be minimized. The implementation of Enterprise Risk Management (ERM) of the firm. The objectives of this research is to identify the effect of Enterprise Risk Management (ERM) and firm size, ROA, and manajerial ownership as control variables on firm value that is proxied by Tobin’s Q. population of this research was manufacturing companies listed in indonesia stock exchange (IDX) in 2019-2020. The used method in this researc is multiple linier Regression-Ordinary Last Square ( OLS) and hypotheses testing using t-test to test the regresion coefficients with level of signification of 5%. Results showed that Enterprise Risk Management (ERM) hassignificant positive effect on the firm value. Size of the company has significant positive effect on the firm value. ROA has significant positive effect on the firm value. While the managerial owner shipe has significant negative effect on the firm value.
Pengaruh Penerapan Algoritma Pemrograman Dalam Dunia Pekerjaan (Studi Kasus: Metode Deep Learning) Niken Rahma Diasri; Arini Winur Baeti; Ary Prabowo
Computer Science and Information Technology Vol 6 No 1 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i1.8531

Abstract

Algoritma, khususnya yang berbasis pada pendekatan deep learning, telah memberikan dampak signifikan dalam berbagai bidang pekerjaan. Studi ini mengeksplorasi pengaruh penerapan algoritma deep learning dalam dunia kerja dengan menyoroti penerapannya di beberapa industri utama seperti manufaktur, kesehatan, keuangan, dan teknologi informasi. Penelitian ini menggunakan pendekatan studi kasus untuk mengidentifikasi bagaimana algoritma tersebut meningkatkan efisiensi, akurasi, dan automasi dalam proses bisnis. Hasil penelitian menunjukkan bahwa penerapan deep learning memberikan keuntungan signifikan dalam prediksi data, analisis keputusan, dan pemrosesan informasi, namun juga menimbulkan tantangan terkait dengan keterampilan tenaga kerja dan etika dalam penggunaannya. Studi ini memberikan wawasan mengenai peran penting algoritma dalam transformasi digital dunia kerja, serta tantangan yang harus dihadapi untuk optimalisasi keuntungannya di masa depan.
Identification of Java Tea Adulteration by Babadotan and Tekelan using Machine Learning Ary Prabowo; Wisnu Ananta Kusuma; Annisa; Mohamad Rafi
Jurnal Jamu Indonesia Vol. 7 No. 3 (2022): Jurnal Jamu Indonesia
Publisher : Tropical Biopharmaca Research Center, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jji.v7i3.273

Abstract

Java Tea (Orthosiphon aristatus) is a common herbal medicinal plant that functions as a health treatment and treats various diseases. The high demand for Java Tea causes high prices and a decrease in the amount of medicinal plant raw materials, causing various quality control problems such as the content of various bioactive components and adulteration from babadotan and tekelan. So far, the detection of adulteration has been carried out by various analyzes, including chemical analysis and statistical methods to process data. The data used is of high dimension with a very high-density level, thus causing difficulties in classification. The mixed data of Orthosiphon aristatus consists of 1201 features with a total sample of 216. This study uses a Random Forest (RF) method with a total of 100 trees, and the RF method is combined with the Recursive Feature Elimination (RFE) method. In the RF and RFE that can be produced, the optimum value for the number of features is 244. The experimental evaluation results revealed that the proposed method could achieve a high accuracy of 81.82% in identifying Orthosiphon aristatus.
Co-Authors Annisa Arini Winur Baeti M. Rafi Niken Rahma Diasri Poppy Camenia Jamil Sinta Yulyanti Wisnu Ananta Kusuma