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All Journal JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Dhanny Rukmana Manday
Universitas Prima Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Perancangan Sistem Informasi Inventaris Barang Berbasis Web Secara Online Pada Universitas Prima Indonesia Dhanny Rukmana Manday; Steven Wijaya; Jefrin Waruwu
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 6 No. 2 (2023): Jutikomp Volume 6 Nomor 2 Oktober 2023
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v6i2.4039

Abstract

The importance of inventory management for business success and how technology can help improve inventory management. The design of an online web-based inventory information system at Prima Indonesia University is discussed in this article. The research aims to improve inventory management business profitability at Prima Indonesia University. One of the approaches used to collect data for this project is the waterfall system development process, which also includes interviews, observations, documentation, and literature studies. Blackbox testing is a technique used to test the system in publication. Functional testing verifies that the system can operate according to the functional requirements established during the analysis and design stages. Testing the system's ability to meet non-functional criteria, such as security, speed, and performance, is known as non-functional testing. After testing, the author makes improvements and fixes to the system found during testing. The support and maintenance stages are carried out regularly to ensure the system runs well and meets user needs. The results showed that the web-based online inventory system developed for Prima Indonesia University using the waterfall model can help improve inventory management and business profitability. The system can reduce errors in inventory management and speed up the process of searching and retrieving inventory data. System testing shows that the system can function properly and meet the requirements at the analysis and design stages.
Identifikasi Tingkat Kematangan Buah pada Tanaman Kelapa Sawit Menggunakan Algoritma Convolutional Neural Network dan Pendekatan Deep Learning William Owen Wijaya; Dhanny Rukmana Manday; Agrifa Insani Napitupulu; Mardi Turnip; Saroha Manurung
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 4 No 2(SEMNASTIK) (2024): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akunt
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol4No2(SEMNASTIK).pp232-240

Abstract

Palm oil quality is largely determined by the free fatty acid (FFA) content, which is influenced by the ripeness of the fruit. Traditionally, determining the ripeness level of palm oil fruit relies on visual inspection by experts, which is time-consuming and dependent on individual skill. To address this, a system has been developed using the Convolutional Neural Network (CNN) method to automate the ripeness classification process. This study focuses on classifying palm oil fruit into three categories: ripe, unripe, and overripe, using a dataset of 1,380 images with 460 images per class. The dataset was split into 80% training data and 20% validation data. The CNN architecture employed was MobileNetV2, known for its simplicity and low computational complexity. Images were resized to 224 x 224 pixels, and two optimizers—Adam and RMSProp—were compared with learning rates of 0.001 and 0.0001 over 30 epochs. The best results were achieved using the Adam optimizer with a learning rate of 0.001, yielding a training accuracy of 91% and a test accuracy of 87%. This shows promising potential for automated palm oil fruit ripeness detection.
Co-Authors Agrifa Insani Napitupulu Jefrin Waruwu Mardi Turnip, Mardi Saroha Manurung Steven Wijaya William Owen Wijaya