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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Tech-E JRST (Jurnal Riset Sains dan Teknologi) JITK (Jurnal Ilmu Pengetahuan dan Komputer) JMM (Jurnal Masyarakat Mandiri) Jurnal Media Elektro : Journal of Electrical Power, Informatics, Telecommunication, Electronics, Computer and Control System Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) PROFICIO: Jurnal Pengabdian Masyarakat Bulletin of Information Technology (BIT) International Journal Software Engineering and Computer Science (IJSECS) Journal of Informatics and Communication Technology (JICT) IT For Society : Journal of Information Technology eProceedings of Engineering Innovative: Journal Of Social Science Research Journal of Informatics and Communication Technology (JICT) Smatika Jurnal : STIKI Informatika Jurnal IJCOSIN : Indonesian Journal of Community Service and Innovation
Dewi Marini Umi Atmaja
Telkom University
Humanities 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 Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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Journal : tech-e

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Identifikasi Kebangkrutan Perusahaan Menggunakan Algoritma Regresi Linear Berganda Haryadi, Deny; Rahman Hakim, Arif; Marini Umi Atmaja, Dewi; Basri, Amat; Adisty Nilasari, Risma
Tech-E Vol. 6 No. 2 (2023): TECH-E (Technology Electronic)
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.
Type 2 Diabetes Mellitus Diagnosis Model Using the C4.5 Algorithm Ruaida Susanti; Dewi Marini Umi Atmaja; Arif Rahman Hakim; Amat Basri
Tech-E Vol. 7 No. 2 (2024): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v7i2.2676

Abstract

Type 2 Diabetes Mellitus (DM) is a metabolic disorder characterized by elevated blood sugar resulting from decreased insulin secretion by pancreatic beta cells and/or impaired insulin function (insulin resistance). Over the last 50 years, there has been a rapid increase in the prevalence of diabetes, paralleling the rise in obesity rates. This study aims to develop a diagnostic model for type 2 DM using C4.5, incorporating feature selection and analyzing age and gender parameters of Type II DM patients. The research employs the Cross-Industry Standard Process for Data Mining (CRISP-DM). Based on the dataset used, the C4.5 model demonstrated superior performance compared to SVM and Random Forest, achieving an AUC value of 72.5%, indicating a reasonably good classification level. The predominant gender among Type II DM patients is female, comprising 210 patients or 54.8% in the age range of 18-94 years, while 173 male patients or 45.2% fall within the age range of 23-80 years.
Implementation of Random Forest Algorithm on Palm Oil Price Data Rahman Hakim, Arif; Atmaja, Dewi Marini Umi; Basri, Amat; Syafii, Muhamad
Tech-E Vol. 6 No. 2 (2023): TECH-E (Technology Electronic)
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.
Performance Analysis of Classification and Regression Tree (CART) Algorithm in Classifying Male Fertility Levels with Mobile-Based Arif Rahman Hakim; Dewi Marini Umi Atmaja; Amat Basri; Andri Ariyanto
Tech-E Vol. 7 No. 1 (2023): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v7i1.2110

Abstract

Fertility is the ability to produce offspring in a man or the ability of the reproductive organs to work optimally in fertilization. Fertility rates have declined drastically in the last fifty years. Machine Learning is a field devoted to understanding and building learning methods. This study will use machine learning algorithms to classify male fertility levels, namely the Classification and Regression Tree (CART) algorithm and the K-Fold Cross Validation validation method. The fertility dataset used in this study was obtained from the UCI Machine Learning website, with a total of 100 data and the variables used are Age, Childish diseases, Accident or serious trauma, Surgical intervention, High fevers in the last year, Frequency of alcohol consumption, Smoking habit, Number of hours spent sitting per day and Diagnosis. K-Fold Cross Validation can be used together with CART to measure the performance of the CART model on different data, so as to avoid overfitting or underfitting the CART model. Based on the calculation of the CART algorithm and the K-Fold Cross Validation validation method (K = 1 to K = 9), the average accuracy value for training data is 98.70% and the average accuracy value for testing data is 81.16%. The results of this study have proven that the CART algorithm can be used to classify the level of fertility in men well. In addition, the classification model formed can be implemented into a mobile application (android) so that it is easy to use and understand.
Co-Authors Adelia Chitra Sazkia Adi Kuncoro Adisty Nilasari, Risma Al Zena Ismail Alan Abyan Maftuh Amat Basri Amat Basri Amat Basri Andri Ariyanto Andri Ariyanto Antika Zahrotul Kamalia Arif Rahman Arif Rahman Hakim Arif Rahman Hakim Arif Rahman Hakim Arif Rahman Hakim Arif Rahman Hakim Arif Rahman Hakim Aufa Hafiy Andhika Basri, Amat Daniswara Rafi Pandora Deny Haryadi Dio Kurnia Sandi Fadhil Rozi Hendrawan Faiz Rochmatullah Widhaputra Hadiana, Asep Id Haikal Apriandi Ilham Roni Yansyah Jonathan Gunawan Komang Diah Devi Pramesty Muttaqin, Widang Naufal Fadhilah Fitrah Netanel Danur Wendra Putri Rizkiyah Rila Mandala Rimbawan Oprasto, Raditya Ruaida Susanti Sanjaya, Andreas Rino Suwaryo, Niko Syafii, Muhamad Syifa Nurgaida Yutia Tugiman Tugiman Tugiman Tugiman Wina Witanti Yakub Zaenur Rozikin Zaidan Mufaddhal Zhawa Kamela