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All Journal bit-Tech Jurnal Sistem Informasi Bisnis (JUNSIBI)
Heni Lusiana Dewi
Universitas Pembangunan Nasional Veteran Jawa Timur
Computer Science & IT

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ANALISIS DAN DESAIN SISTEM INFORMASI RESERVASI STUDIO RSB BERBASIS WEBSITE MENGGUNAKAN ICONIX PROCESS Izra Noor Zahara Aliya; Heni Lusiana Dewi; Cendana Putri Aulia; Seftin Fitri Ana Wati; Anindo Saka Fitri
Jurnal Sistem Informasi Bisnis (JUNSIBI) Vol 4 No 1 (2023): Jurnal Sistem Informasi Bisnis (JUNSIBI)
Publisher : Program Studi Sistem Informasi Institut Bisnis dan Informatika (IBI) Kosgoro 1957

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55122/junsibi.v4i1.614

Abstract

Studio RSB merupakan salah satu studio musik yang biasanya digunakan sebagai tempat latihan paduan suara di daerah Kota Surabaya. Sebagai upaya untuk memanfaatkan teknologi internet yang saat ini sedang berkembang pesat dan juga untuk mempermudah pelayanan dalam reservasi studio, dibuatlah sebuah sistem yang dapat melakukan reservasi Studio RSB secara online. Sebelumnya, reservasi Studio RSB dilakukan secara manual dengan menghubungi pemiliknya langsung. Proses ini dirasa kurang efektif dalam hal pemesanan dan manajemennya. Setelah dilakukan observasi, dibangunlah sistem informasi reservasi Studio RSB berbasis Web. Dengan menggunakan website, pengelolaan akan lebih mudah karena terhubung melalui internet. Dalam pembangunan sistem informasi reservasi Studio RSB ini, peneliti menggunakan metode ICONIX Process untuk tool perancangannya. Hasil yang didapatkan berupa desain antarmuka website Reservasi Studio RSB yang nantinya akan diimplementasikan. Dengan dibuatnya sistem informasi reservasi ini, diharapkan dapat mempermudah proses reservasi Studio RSB serta meningkatkan kualitas pelayanannya.
Comparison of Adam, RMSprop, and SGD on DenseNet121 for Tomato Leaf Disease Classification Heni Lusiana Dewi; Amalia Anjani Arifiyanti; Abdul Rezha Efrat Najaf
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2684

Abstract

Diseases affecting tomato leaves can severely impact agricultural productivity, as they can reduce crop yields and quality significantly. A swift and dependable identification of these diseases is vital for ensuring prompt interventions and the successful implementation of disease control strategies. This study focus on evaluating and comparing the efficiency of three separate optimizers, such as Adam, RMSProp, and SGD on the pretrained Convolutional Neural Network (CNN) architecture DenseNet121. There has been no previous research that directly compares the performance of Adam, RMSProp, and SGD optimizers on the DenseNet121 model for classifying tomato leaf diseases using the Plant Village dataset. These optimizers are crucial in the training process by influencing the model’s ability to converge and generalize well on new, unseen data. Experimental procedures were performed using a labeled dataset of tomato leaf images, which included healthy leaves and various disease classes. Out of the three optimization techniques tested, the DenseNet121 model trained with the Adam optimizer consistently outperformed the others. It achieved the highest evaluation metrics, with an accuracy of 0.9800, precision of 0.9807, recall of 0.9800, and F1-score of 0.9800 on the test set. These outcomes suggest that the model has a strong and balanced classification performance, capable of correctly identifying disease conditions with minimal errors. Based on these findings, the DenseNet121 architecture combined with the Adam optimizer is considered the optimal model used to recognize various tomato leaf diseases in this study.
Co-Authors Abdul Rezha Efrat Najaf Amalia Anjani Arifiyanti Anindo Saka Fitri Cendana Putri Aulia Izra Noor Zahara Aliya Seftin Fitri Ana Wati