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All Journal EDUMATIC: Jurnal Pendidikan Informatika Journal of Software Engineering, Information and Communication Technology J-Intech (Journal of Information and Technology)
Fernando, Daud
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Library & Information Science

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Comparison of Machine Learning Algorithms in the Role of Hepatitis Patient Disease Classification Fernando, Daud; Huwaidi, Faris; Ananto, Muhammad Hafidz; Pramadya, Sahrial
Journal of Software Engineering, Information and Communication Technology (SEICT) Vol 4, No 2: Desember 2023
Publisher : Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/seict.v4i2.64393

Abstract

Hepatitis is one of the diseases with the highest patient percentage. about a third of the world population are afflicted with hepatitis. In several cases, patients show symptoms while in the other cases, patients show no symptoms. hepatitis is commonly caused by hepatitis A, B, C, D or E virus and yellow fever virus (YFV). hepatitis can be detected through blood test. From the blood sample, we could extract information like Alanine Transferase (ALT), bilirubin, creatine, Alkaline Phosphatase (ALP), Aspartate Aminotransferase (AST) and Gamma Glutamyl Transferase (GGT) levels, the levels of these compound will be able to determine whether the patient is afflicted or not. To raise the information processing effectiveness, machine learning can be applied to help processing the information. Several algorithms like support vector machine (SVM), decision tree, K-Nearest Neighbor (KNN), Random Forest and X-Gradient Boost (XGBoost) can be used to process hepatitis data. This research is aimed to determine which algorithm has the highest accuracy in diagnosing hepatitis.
Algoritma SARIMA sebagai Pendukung Strategi Peramalan HPS dalam Persaingan Tender di LPSE Indonesia Fernando, Daud; Syawanodya, Indira; Muhammad, Raditya
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.28009

Abstract

Tender in Indonesia's Electronic Procurement Service (LPSE) is the procurement of goods/services in the form of public facilities and managed by the provider with the lowest estimated price (HPS) value during the reverse auction process. The fluctuating value of HPS and the tight competition of competitors make winning for providers increasingly difficult and competitive. The purpose of this research is to create a forecasting model of the HPS value of tenders in LPSE Indonesia using the Seasonal Autoregressive Integrated Moving Average (SARIMA) algorithm. This type of research is experimental research to determine the order of the best SARIMA model. The research variables used are tender publication date and HPS value as much as 747,098 tender data from historical data from web scraping of the LPSE website with a withdrawal date range of January 7, 2013 to November 30, 2022. The data analysis technique uses data exploration analysis to determine the characteristics of the data distribution and then the implementation of the SARIMA forecasting algorithm. The results of this study show that the SARIMA((5,1,1),(4,1,1,7)) model is the optimal model with an evaluation value of mean absolute percentage error (MAPE) percentage error value of 33.56% which in relation to LPSE can provide a reasonable forecasting value. The results of forecasting for the next 30 days show that the distribution of HPS values is in the range of 680 million - 700 million rupiah in the period December 2022.
Model Klasifikasi Penyebab Turnover Karyawan Menggunakan Kerangka Kerja CRISP-DM Fernando, Daud; Guntara, Rangga Gelar
J-INTECH (Journal of Information and Technology) Vol 12 No 02 (2024): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v12i02.1502

Abstract

The problem of high employee turnover in a company has several negative impacts in terms of cost, energy, and time and one of them is felt by the fictitious Company “XYZ”. The purpose of this research is to classify the causes of employee turnover in the industry using a classification machine learning model on two different algorithms namely Random Forest and Decision Tree. In addition, this study addresses the implications of previous classification research, employee classification in the education industry, which suggests comparing the evaluation of two machine learning model performances. There are 10 variables and 9,540 historical employee data used in the research. The research technique or method used is Cross-industry Standard Process for Data Mining (CRISP-DM). The results of this study show that the Random Forest classification model is the optimal machine learning model with an AUC - ROC value reaching 0.9988. RapidMiner was used to revalidate the performance of the machine learning model using the same parameters and resulted in the highest accuracy value of 85.04% for the Random Forest model compared to the Decion Tree model.
Co-Authors Ananto, Muhammad Hafidz Guntara, Rangga Gelar Huwaidi, Faris INDIRA SYAWANODYA Muhammad, Raditya Pramadya, Sahrial