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All Journal INFORMAL: Informatics Journal IJIES (International Journal of Innovation in Enterprise System) International Journal of Accounting, Management, Economics and Social Sciences (IJAMESC)
Muhamad Arief Hidayat
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Law, Crime, Criminology & Criminal Justice Social Sciences

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Klasifikasi Penyakit pada Citra Buah Jeruk Menggunakan Convolutional Neural Networks (CNN) dengan Arsitektur Alexnet Dwiretno Istiyadi Swasono; Mohammad Abuemas Rizq Wijaya; Muhamad Arief Hidayat
INFORMAL: Informatics Journal Vol 8 No 1 (2023): Informatics Journal (INFORMAL)
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v8i1.38563

Abstract

Citrus fruit is a plant that is very susceptible to disease. Diseases that often attack citrus fruits are usually in the form of spots on the fruit. Diagnostics of citrus fruit diseases are usually carried out by experts manually which can cause the results to be subjective. Not all farmers are experts in diagnosing citrus fruit diseases. Therefore, this study proposes a system for diagnosing citrus fruit diseases using computer vision based on deep learning. So that the model can be used on computers with limited resources, this study proposes the Alexnet model, which is relatively light but has proven excellent accuracy in classifying several datasets. The dataset used is citrus fruit disease images of 1790 images which are divided into 4 classes, namely blackspot, canker, grenning, and healthy fruit. The best results achieved with a scenario of 90% training data and 10% validation data are with an accuracy of 94,34%, a precision of 93,0%, a recall of 94,0%, and an F1-score of 95,0%. The best results are obtained with a combination of dropout, batch normalization, and fully-connected layer scenarios in the classifier layers section.
THE INFLUENCE OF EARNING MANAGEMENT, OPERATIONAL COSTS AND TAX PLANNING ON INCOME TAX PAYABLE Hamida Hunein; Muhamad Arief Hidayat; Listya Sugiyarti
International Journal of Accounting, Management, Economics and Social Sciences (IJAMESC) Vol. 3 No. 3 (2025): June
Publisher : ZILLZELL MEDIA PRIMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61990/ijamesc.v3i3.517

Abstract

This study aims to analyze how the variables of Earning Management, Operating Costs, and Tax Planning affect Income Tax Payable in Energy sector companies listed on the Indonesia Stock Exchange. The research method carried out is a quantitative research method with a panel data regression technique and the type of data used in this study is secondary data. In this study, to obtain a sample, special criteria are needed, so the purposive sampling method is used. This study has a population of 87 companies and obtained 19 company samples and the results of observations include 95 research data for five years in the 2019-2023 period. The analysis used in this study was using panel data regression with EViews 13 software. The results of the model selection test in this study show that the best model to use is the Fixed Effect Model (FEM). This study obtained results, namely simultaneously, Earning Management, Operational Costs, and Tax Planning have an effect on Income Tax of Accounts Receivable and partially, Earning Management has no effect on Income Tax of Accounts Receivable, Operational Costs affect Income Tax of Accounts Receivable, Tax Planning has no effect on Income Tax of Accounts Receivable.
Integration of Colbp and Viola Jones Feature Extraction Methods in Gender Classification Based on Facial Image Whinar Kukuh Rizky Ardana; Tio Dharmawan; Muhamad Arief Hidayat
International Journal of Innovation in Enterprise System Vol. 8 No. 1 (2024): International Journal of Innovation in Enterprise System
Publisher : School of Industrial and System Engineering, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/ijies.v8i01.216

Abstract

Nowadays face recognition still being a hot topics to be discussed especially it’s utility for genderclassification. Gender classification is an easy task for human but it’s a challenging task for computersbecause it doesn’t have capability for recognizing human gender without feature extraction. There arestill many researches about facial image feature extraction for gender classification. Geometryfeatures and Texture Features are well perform features for gender classification. This paper willpresents fusion of those feature in order to find an improvement for gender classifications task. Eachfeatures will be extracted using Viola Jones Algorithm and Compass Local Binary Pattern method.Both features will be combined using concatenated method in dataframe format. Viola Jonesalgorithm has an issues when detecting each facial regions so it causes outliers in each geometryfeatures. The proposed method will be evaluated on color FERET dataset that contains facial images.Classification task will be done using Random Forest and Backpropagation. The result is fusionfeatures present an improvement in gender classification using Backpropagation with 87% accuracy.It prove that proposed method perform better than single feature extraction method.
Co-Authors Dwiretno Istiyadi Swasono Hamida Hunein Listya Sugiyarti Mohammad Abuemas Rizq Wijaya Tio Dharmawan Whinar Kukuh Rizky Ardana