Article Per Year (5 Year)
p-Index From 2021 - 2026
5.443
P-Index
This Author published in this journals
All Journal JIKO (Jurnal Informatika dan Komputer) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Petir Computer Science (CO-SCIENCE) Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication Klik - Jurnal Ilmu Komputer Arcitech: Journal of Computer Science and Artificial Intelligence Prioritas : Jurnal Pengabdian Kepada Masyarakat Mestaka: Jurnal Pengabdian Kepada Masyarakat Journal of Computer Engineering: Progress, Application and Technology MDP Student Conference Journal of Embedded Systems, Security and Intelligent Systems Jurnal Sistem Informasi Komputer dan Teknologi Informasi Jurnal Teknik Indonesia (JU-TI) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Daniel Udjulawa
Universitas Multi Data Palembang
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Social Sciences Other

Published : 35 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 35 Documents
Search

 1   2   3   4 
PELATIHAN BRANDING, DESAIN KEMASAN DAN PEMASARAN DIGITAL GUNA MENINGKATKAN DAYA SAING UMKM SANTAN KELAPA Dorie P. Kesuma; Lisa Amelia Fransen; Daniel Udjulawa; Ery Hartati
FORDICATE Vol 5 No 2 (2026): April 2026
Publisher : Universitas Multi Data Palembang, Fakultas Ilmu Komputer dan Rekayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/fordicate.v5i2.15872

Abstract

The Erni Susilo coconut milk processing Micro, Small, and Medium Enterprise, located in Kemuning District, faces challenges regarding low competitiveness compared to similar products. This issue stems from ineffective brand identity management, simple packaging design, and suboptimal digital marketing. This community service project aims to improve the business quality of the owner through training in brand identity, packaging design, and digital marketing. The methodology employs a participatory approach, including focus group discussions, counseling, practical training, and mentoring. Results indicate an increase in the participant's understanding of the importance of brand identity, the ability to design more attractive packaging, and the skills to utilize social media for promotion. In conclusion, the training approach successfully improved the entrepreneur's knowledge and skills in utilizing technology to develop the business, thereby encouraging potential sales growth in the future.
Optimasi Hyperparameter CNN dengan Arsitektur VGG16 Menggunakan Grid Search Untuk Klasifikasi Penyakit Buah Delima Muhammad Fawzan; Daniel Udjulawa
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 5 No. 2 (2025): December 2025
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v5i2.15175

Abstract

Early detection of pomegranate fruit diseases is crucial to reduce yield losses and improve harvest quality; however, visual identification in the field is often subjective and difficult due to the similarity of symptoms among different diseases. This study aims to develop a pomegranate fruit disease classification model using a Convolutional Neural Network (CNN) based on the VGG16 architecture, optimized through the Grid Search method. The dataset consists of five classes: 886 Alternaria samples, 116 Anthracnose samples, 966 Bacterial Blight samples, 631 Cercospora samples, and 1,450 Healthy samples, resulting in a total of 5,099 images. The dataset underwent preprocessing and data augmentation to increase variability and prevent overfitting. After balancing the dataset, it was split into 70% training data, 20% validation data, and 10% testing data. Hyperparameters such as epoch, batch size, learning rate, and optimizer were evaluated using Grid Search to determine the optimal configuration. The results indicate that the best performance was achieved using 100 epochs, a batch size of 32, a learning rate of 0.0001, and the Adam optimizer. The proposed model achieved a testing accuracy of 99.59%, with precision, recall, and F1-score values of 0.996. These findings demonstrate that the optimized VGG16-based CNN model is highly effective in accurately classifying pomegranate fruit diseases.
Pelatihan Pembuatan Website HTML Menggunakan VS Code Di SMP Xaverius Maria Palembang Fellycia Caroline; Serenity Devina Suryanto; Raphael Lee; Jonathan Jason Constantine; Daniel Udjulawa; Muhammad Rizky Pribadi
Mestaka: Jurnal Pengabdian Kepada Masyarakat Vol. 5 No. 3 (2026): JUNI 2026
Publisher : Pakis Journal Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58184/mestaka.v5i3.1001

Abstract

The development of digital technology requires students to possess basic skills in utilizing technology creatively and productively, including creating simple websites. However, most students still do not understand the basic process of website development. This community service activity aimed to provide training on creating profile websites using HTML with the assistance of Visual Studio Code for class 9 students at SMP Xaverius Maria Palembang. The methods used in this activity were community education and hands-on training through material presentation, demonstrations, independent practice, discussions, and evaluations. The results showed that participants were able to understand the basic structure of HTML and follow the process of creating simple websites properly. In addition, participants showed high enthusiasm during the activity and began to understand the fundamentals of simple website development. This activity provided new experiences for participants and helped improve students’ knowledge and interest in the field of information technology.
Deteksi dan Klasifikasi Penyakit Pada Buah Kakao Menggunakan Yolov11 Rizky Ridho Ramadhan; Daniel Udjulawa
JURNAL INFORMATIKA DAN KOMPUTER Vol 10, No 1 (2026): February 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v10i1.2399

Abstract

AbstrakPenelitian ini mengembangkan sistem deteksi dan klasifikasi penyakit pada buah kakao menggunakan model YOLOv11m berbasis teknologi computer vision dan deep learning. Produksi kakao Indonesia mengalami penurunan signifikan dalam satu dekade terakhir, dengan rata-rata penurunan produksi tahunan mencapai 2,38%. Salah satu faktor utama penyebab penurunan tersebut adalah serangan penyakit pada buah kakao, seperti Black Pod Rot, Frosty Pod Rot, dan serangga Mirid, yang menghambat produktivitas perkebunan. Minimnya pengetahuan petani dalam mengidentifikasi jenis penyakit menyebabkan penanganan yang tidak tepat, sehingga diperlukan sistem deteksi otomatis berbasis teknologi untuk membantu petani mendeteksi penyakit dengan cepat dan akurat. Model deteksi yang dikembangkan dalam penelitian ini bertujuan untuk mengatasi masalah tersebut. Dataset yang digunakan terdiri dari tiga skenario: (1) dataset asli, (2) dataset yang diaugmentasi dengan Roboflow, dan (3) dataset dengan augmentasi Roboflow dan input-space oversampling. Evaluasi model menunjukkan bahwa YOLOv11m yang dilatih dengan dataset augmentasi dan oversampling menghasilkan precision 80,8%, recall 62,9%, dan mAP50 73,1% pada data uji, menunjukkan peningkatan signifikan dibandingkan dataset asli. Selain itu, pada data test, model ini mencapai precision 80,8%, recall 62,9%, dan mAP50 73,1%, yang menunjukkan kemampuan deteksi yang lebih baik. Dengan teknik augmentasi ini, penurunan recall pada beberapa kelas minoritas dapat diminimalisir.
Day–Ahead Sulfuric Acid Production Forecasting Using Optuna–Tuned Machine Learning Models: An Industrial Case Study Fransiska Prihatini Sihotang; Daniel Udjulawa; Intan Cahya Sucita
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 3 (2026): September 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i3.13845

Abstract

Purpose – This study develops and evaluates a day-ahead forecasting framework for sulfuric acid production in a continuous chemical manufacturing process and examines whether machine-learning models provide meaningful predictive value beyond simple persistence forecasting. Methods – Five years of operational production data were processed through a validated ETL pipeline and transformed using calendar, lag, and rolling-window features. Six regression algorithms were screened using chronological time-series validation, followed by equal-budget Optuna hyperparameter optimization of selected candidates. The final model was evaluated on an untouched holdout period and through repeated walk-forward validation against persistence, seasonal-naive, and rolling-mean baselines. Permutation importance was used to examine predictor contributions. Findings – Random Forest achieved the strongest development-stage performance and produced accurate day-ahead forecasts. However, its advantage over persistence was marginal on the final holdout and inconsistent across repeated temporal evaluations. Current-day production overwhelmingly dominated predictor importance, indicating strong persistence in the underlying industrial process. Performance also deteriorated during maintenance-related shutdown conditions. Research Implications – Industrial forecasting systems should prioritize robust temporal validation and comparison with simple operational baselines before adopting more complex machine-learning models. Originality – The study provides a leakage-aware forecasting and evaluation pipeline that combines baseline benchmarking, model interpretation, and deployment within a prototype decision-support system for continuous chemical production.
Co-Authors Abdul Rahman Andreyas Andreyas Anggoro Aryo P. Dafid Dafid Dafid Dafid Dandy, Dandy Darmawan, Dean Dorie P. Kesuma Famerdi, Farhan Agung Fanny Krisvyanti Fatchurrachman, Ahmad Febbiola Febbiola Febbiola, Febbiola Fellycia Caroline Fransiska Prihatini Sihotang Hartati, Ery Intan Cahya Sucita Ja'far, Ja'far Jonathan Jason Constantine Kadafi, Aldo Kasanova, Sinyo Kurniawan, Marthin Hokita Lisa Amelia Fransen M Fajri Septian Dwi Cahyo Martin, Nicolas Marzali, Azizurrahman Michael Roda Robert Muhammad Fawzan Muhammad Haviz Irfani Muhammad Rizky Pribadi Muhammad Totti Alfarabi Muhyi Haadi Sewithto Mulyati Mulyati Mulyati Mulyati Novan Wijaya Nyimas Artina Oktarina, Serly Pramudiya, Relin Ramadhan, Ahmad Wildan Rizky Raphael Lee Regiza Giovanno Rizky Ridho Ramadhan Serenity Devina Suryanto Setiawan, Jerry Shela Shela Simanjuntak, Andreas Jeremy Obet Sutanto, Steven Yesua Timoteus Ivan Sariyo Victoria Valensita Robert Vincent Vincent Vincentius Hansel Irsansaputra William William Wiwik Handayani Yoannita, Yoannita Yohannes, Yohannes Zulnardi, Andre