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All Journal Brilliance: Research of Artificial Intelligence Informatics and Enginering Dedication MDP Student Conference
Maximilliano, Wesley
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Mathematics Other

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Pelatihan Java Fundamental Kepada Siswa/I SMA Xaverius 3 Palembang ., Vincent; Sinaga, Dep'niel; Hendriko, Viky; Irsansaputra, Vincentius Hansel; Maximilliano, Wesley
FORDICATE Vol 3 No 1 (2023): November 2023
Publisher : Universitas Multi Data Palembang, Fakultas Ilmu Komputer dan Rekayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/fordicate.v3i1.5062

Abstract

Perkembangan teknologi di Dunia semakin berkembang dan maju, mulai dari teknologi yang terkecil hingga terbesar telah mengalami perubahan yang signifikan, Perkembangan teknologi juga didukung oleh Algoritma dari bahasa pemrograman yang dibangun dalam struktur teknologi yang digunakan, misalnya pada perangkat lunak (Software) maupun perangkat keras (Hardware). Bahasa pemrograman menjadi juru kunci utama dalam pembuatan algoritma pada suatu teknologi seperti Cloud Computing, Artificial Intelligence, dan Internet Of Things. Dengan adanya program pelatihan Java Fundamentals ini diharapkan menjadi bekal bagi siswa/i tentang dasar bahasa pemrograman Java, program pelatihan ini ditujukan kepada siswa/i SMA Xaverius 3 Palembang secara langsung/luring dengan mengunjungi sekolah SMA Xaverius 3. Setelah program pelatihan ini dilaksanakan berdasarkan hasil dari Exercise 1 diperoleh nilai dengan rata-rata 90% dari 27 murid. Program pelatihan ini memberikan peluang tinggi bagi siswa/i yang ingin mempelajari serta memahami bahasa pemrograman Java untuk masa depan mereka.
Implementasi Sistem Informasi Kehadiran Karyawan di PT Anugerah Alam Konstruksi Maximilliano, Wesley; Krisna Putra, Jelvin; Yoannita, Yoannita
MDP Student Conference Vol 4 No 1 (2025): The 4th MDP Student Conference 2025
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v4i1.10956

Abstract

PT Anugerah Alam Konstruksi faces challenges in employee attendance recording, which is still done manually, where field workers submit photos via WhatsApp, while office staff attendance is recorded directly by the admin. This method results in disorganized attendance data and requires more time for processing. To address this issue, an Android and web-based Employee Attendance Information System was designed and developed. This study employs an iterative method, allowing system development to be carried out in stages through multiple cycles. The stages include requirements analysis, system design using an Entity Relationship Diagram (ERD), implementation using Dart for the Android application and PHP for the web administration, as well as system testing and evaluation. The Android application enables employees to clock in based on predefined time and location, while the web-based system facilitates admins in managing, verifying, and compiling attendance reports. The implementation of this system is expected to enhance operational efficiency and company productivity through more accurate and organized attendance recording.
Comparative Analysis of MobileNetV3-Large and Small for Corn Leaf Disease Classification Maximilliano, Wesley; Rachmat, Nur
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.6259

Abstract

Corn leaf disease represents a significant threat to agricultural productivity, capable of causing substantial economic losses in Indonesia. Conventional identification methods, which rely on visual observation by farmers, are frequently subjective, time-consuming, and inaccurate. This study conducts a systematic comparative analysis of two efficient Convolutional Neural Network (CNN) architecture variants, MobileNetV3-Large and MobileNetV3-Small, for the classification of four corn leaf conditions: Gray Leaf Spot, Common Rust, Northern Leaf Blight, and Healthy. The research further evaluates the influence of two prevalent optimizers, Adam and Stochastic Gradient Descent (SGD), to ascertain the most optimal model configuration through hyperparameter tuning. The models were trained and evaluated using a local image dataset from Sampang, Indonesia, comprising 4000 images. The methodology included image preprocessing, data augmentation, and hyperparameter tuning of the learning rate and batch size. The results demonstrate that both architectures achieved exceptionally high accuracy. The principal finding reveals that MobileNetV3-Small unexpectedly outperformed its larger variant, attaining a peak accuracy of 99.5% with the SGD optimizer, a learning rate of 0.01, and a batch size of 32. In comparison, MobileNetV3-Large reached a maximum accuracy of 99.0% under a similar configuration. These findings underscore the considerable potential of lightweight architectures for the development of rapid, accurate, and field-deployable plant disease diagnostic applications on mobile devices using deep learning.
Co-Authors ., Vincent Hendriko, Viky Irsansaputra, Vincentius Hansel Krisna Putra, Jelvin Nur Rachmat Sinaga, Dep'niel Yoannita, Yoannita