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All Journal Jurnal Ilmiah Informatika Komputer Jurnal Ilmiah Teknologi dan Rekayasa Jurnal Ilmiah KOMPUTASI Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Sains dan Informatika Jurnal Teknologi Sistem Informasi dan Aplikasi JISICOM (Journal of Information System, Infomatics and Computing) Journal of Comprehensive Science Eduvest - Journal of Universal Studies
Lulu Chaerani Munggaran
Gunadarma University
Aerospace Engineering Automotive Engineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Neuroscience Social Sciences

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Penerapan Metode ADDIE dalam Membangun Sistem Informasi Manajemen Aset Berbasis Web pada Unit Kerja Khusus Pusat Pengembangan Kedokteran Indonesia Akrom, Mohamad Akromudin; Munggaran, Lulu Chaerani
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 3 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v7i3.41849

Abstract

Special Work Unit of the Indonesian Medical Development Centre (UKK PUSBANGKI) has a problem in managing its assets, where 30% of data processing uses a semi-computerised system and the remaining 70% manually, so it has the potential to suffer losses for the loss of its assets. The purpose of this research is to design a website-based asset management information system so that asset management can be integrated properly. The method used in this research uses the ADDIE method (Analysis, Design, Development, Implementation, Evaluation) with a systematic and structured approach in designing the programme. The web-based asset management application is built using Bootstrap 3 on the front-end (HTML, CSS, Javascript) which has advantages in terms of website appearance with responsive design and can be customised according to end-user needs. In addition, the use of XAMPP (Apache, MySQL, PHP) and Unified Modeling Language (UML) simultaneously can increase efficiency in the process of developing and producing reliable and secure applications. Based on the results of implementation and evaluation, it can be concluded that the web-based asset management information system has succeeded in increasing 90% time and cost efficiency in tracking assets, as well as 95% effectiveness in reducing incidents of asset loss, so that the work of the asset management unit at UKK PUSBANGKI can be carried out properly, quickly and integrated.
Chili Leaf Health Classification using Xception Pretrained Model Wulandari, Yestika Dian; Munggaran, Lulu Chaerani; Setiawan, Foni Agus; Satya, Ika Atman
Sistemasi: Jurnal Sistem Informasi Vol 13, No 3 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i3.3943

Abstract

As one of the high-demand horticultural crops, chili peppers have a significant impact on the economy of Indonesia. However, despite the growing demand and interest in chili peppers, their production often faces disruptions due to crop failures. One of the leading causes of such failures is pests and diseases. Among all parts of the chili plant, chili leaves are the most susceptible to damage. Distinguishing between healthy and unhealthy chili leaves can serve as an early detection step for chili diseases and preventive measures to contain their spread. Convolutional Neural Network (CNN) are effective algorithms for image classification. The development of CNN has led to the use of models previously trained on large datasets to accurately classify relatively small datasets. One such pretrained model known for its exceptional classification capabilities is Xception. By utilizing the pretrained Xception model trained on the ImageNet dataset for the classification of healthy and unhealthy chili leaf images, our model achieved an accuracy of 91% on a dataset containing 2136 images. Furthermore, the model achieved a 100% success rate by correctly predicting all 10 out of 10 given images.
Co-Authors Akrom, Mohamad Akromudin Anindito Yoga Pratama Dela Harmilasari Eko Rudi Suteja Esty Purnamasari Hasana, Uswatun Herawan, Hery Ika Atman Satya Muhammad Khalif Fadhilah Raufani, Hasnadhia Rayhan Gusty Alif Sari Pranasti, Adira Setiawan, Foni Agus Sri Astuti Winarti Winarti Wulandari, Yestika Dian