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All Journal Jurnal Informatika dan Teknik Elektro Terapan Journal Of Artificial Intelligence And Software Engineering
Alqhifari, Azka
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Computer Science & IT

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ANALISIS PENGARUH TINGKAT AKURASI KLASIFIKASI CITRA MEBEL MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK Alqhifari, Azka; Nurdin, Nurdin; Taufiq, Taufiq
Jurnal Informatika dan Teknik Elektro Terapan Vol 13, No 2 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i2.6199

Abstract

Furniture is a household necessity that serves to complement a room. The design models of furniture are varied, ranging from general or mass-market designs to those made for specific needs. Sofas, tables, chairs, swivel chairs, TV cabinets, wardrobes, beds, and bookshelves are the items analyzed in this study. The fact that there are always more and better furniture models available is one of the reasons the researcher chose these furniture items. The goal of furniture object classification is to help categorize objects automatically, making it easier for users to quickly search for the furniture products they want, and the system can recommend items based on the classification results. This study uses 3 architectures with 3 testing scenarios. The architectures used are MobileNetV1, ResNet-50, and VGGNet-19. Scenario S1 uses image dimensions of 128x128 with 50 epochs, scenario S2 uses image dimensions of 128x128 with 100 epochs, and scenario S3 uses image dimensions of 128x128 + grayscale with 50 epochs. The accuracy results are differentiated according to the scenarios used, namely S1, S2, and S3. The detailed accuracy results are as follows: Scenario S1 using the MobileNetV1 architecture achieved an accuracy of 94.31%. The highest accuracy in scenario S2 was also achieved with the MobileNetV1 architecture at 94.31%. For the highest accuracy in scenario S3, MobileNetV1 achieved an accuracy of 72.61%. The fastest computation time for S1 was 871.97 seconds, for S2 it was 1763.04 seconds, and for S3 it was 436.32 seconds. Among the three architectures used, MobileNetV1 stands out as the best architecture in terms of accuracy and classification speed in this study
Analysis Of Customer Understanding Level Of The E-Policy System In The Sinar Mas Online Insurance Application In The Lhokseumawe Branch Work Area Muliana, Syarifah; Nurdin, Nurdin; Alqhifari, Azka; Khaidar, Al; Jessika, Jessika
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7824

Abstract

Digital transformation in the insurance industry is driving companies to adopt electronic systems, including the implementation of e-policies as a replacement for physical policy documents. This study aims to analyze the level of customer understanding of the e-policy system on the Sinar Mas Online Insurance application in Lhokseumawe branch. The research method used is a quantitative approach with data collection techniques through distributing questionnaires to 100 active customers. The results show that most customers are aware of the existence of e-policies, but still face obstacles in understanding their functions, legality, and how to access documents through the Sinar Mas Online application. Factors such as age, education level, and experience using digital services have been shown to influence the level of customer understanding. This study recommends the need for continuous education and the development of a more intuitive application interface to improve digital literacy and user convenience in accessing e-policies. These findings are expected to provide evaluation material for companies in improving their information systems and digital communication strategies for customers.
ANALISIS PENGARUH TINGKAT AKURASI KLASIFIKASI CITRA MEBEL MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK Alqhifari, Azka; Nurdin, Nurdin; Taufiq, Taufiq
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 2 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i2.6199

Abstract

Furniture is a household necessity that serves to complement a room. The design models of furniture are varied, ranging from general or mass-market designs to those made for specific needs. Sofas, tables, chairs, swivel chairs, TV cabinets, wardrobes, beds, and bookshelves are the items analyzed in this study. The fact that there are always more and better furniture models available is one of the reasons the researcher chose these furniture items. The goal of furniture object classification is to help categorize objects automatically, making it easier for users to quickly search for the furniture products they want, and the system can recommend items based on the classification results. This study uses 3 architectures with 3 testing scenarios. The architectures used are MobileNetV1, ResNet-50, and VGGNet-19. Scenario S1 uses image dimensions of 128x128 with 50 epochs, scenario S2 uses image dimensions of 128x128 with 100 epochs, and scenario S3 uses image dimensions of 128x128 + grayscale with 50 epochs. The accuracy results are differentiated according to the scenarios used, namely S1, S2, and S3. The detailed accuracy results are as follows: Scenario S1 using the MobileNetV1 architecture achieved an accuracy of 94.31%. The highest accuracy in scenario S2 was also achieved with the MobileNetV1 architecture at 94.31%. For the highest accuracy in scenario S3, MobileNetV1 achieved an accuracy of 72.61%. The fastest computation time for S1 was 871.97 seconds, for S2 it was 1763.04 seconds, and for S3 it was 436.32 seconds. Among the three architectures used, MobileNetV1 stands out as the best architecture in terms of accuracy and classification speed in this study
Co-Authors Jessika, Jessika Khaidar, Al Muliana, Syarifah Nurdin Nurdin Nurdin Nurdin Taufiq Taufiq