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All Journal Panrita Abdi - Jurnal Pengabdian pada Masyarakat Sinkron : Jurnal dan Penelitian Teknik Informatika Tech-E JRST (Jurnal Riset Sains dan Teknologi) Jurnal Media Elektro : Journal of Electrical Power, Informatics, Telecommunication, Electronics, Computer and Control System Journal of Informatics and Communication Technology (JICT) Journal of Informatics and Communication Technology (JICT)
Arif Rahman Hakim
Universitas Medika Suherman
Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Mathematics Other

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Journal : Tech-E

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Implementation of Random Forest Algorithm on Palm Oil Price Data Arif Rahman Hakim; Dewi Marini Umi Atmaja; Amat Basri; Muhamad Syafii
Tech-E Vol. 6 No. 2 (2023): The Tech-E Journal Vol. 6 No. 2 publishes research papers in such informatics:
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v6i2.1757

Abstract

One of the potential commodities that are widely cultivated in Indonesia is palm oil, and palm oil or commonly referred to as palm oil is one of the processed products of palm oil which generates the most important foreign exchange for Indonesia. Data mining is a process that utilizes mathematical techniques, statistics, artificial intelligence, and machine learning techniques to extract and identify useful information and related knowledge from large databases [3], including palm oil price data. Random Forest is one of the methods in the decision tree. A decision tree is a flowchart shaped like a tree with a root node that is used to collect data that is used to solve problems and make decisions. In this study, a random forest algorithm was used to classify palm oil price data from 2014 to 2019. The classification method used the random forest algorithm on palm oil data using the Mtry parameter of 1 and the Ntree parameter of 500 resulting in an accuracy percentage of 100%. The most influential variable (importance variable) in the classification model using the resulting random forest algorithm is the palm oil variable.
Identifikasi Kebangkrutan Perusahaan Menggunakan Algoritma Regresi Linear Berganda Deny Haryadi; Arif Rahman Hakim; Dewi Marini Umi Atmaja; Amat Basri; Risma Adisty Nilasari
Tech-E Vol. 6 No. 2 (2023): The Tech-E Journal Vol. 6 No. 2 publishes research papers in such informatics:
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Corporate bankruptcy can hurt the company and affect the state of the economy. Therefore, many interested parties want to know the business situation related to the company. These parties include creditors, auditors, shareholders, and management itself who have an interest in knowing the state of the company in the context of bankruptcy. The past financial statements of a company can be used to predict future financial conditions using report analysis techniques. In the risk assessment process, expert knowledge is still seen as an important task, because expert predictions are subjective. This study aims to predict the bankruptcy of the company using influencing factors such as the level of research and development costs, the growth rate of total assets, and the current asset turnover rate. The method used in this research is the prediction method using the Linear Regression Algorithm. Based on the test results show that the variables or attributes used in this study have a significant effect, as evidenced by using a linear regression algorithm to be able to produce a Root Mean Squared Error value: 0.162 +/- 0.000.
Co-Authors Amat Basri Amat Basri Amat Basri Andri Ariyanto Deny Haryadi Dewi Marini Umi Atmaja Dewi Marini Umi Atmaja Dewi Marini Umi Atmaja Muhamad Syafii Risma Adisty Nilasari Tugiman Tugiman