Article Per Year (5 Year)
p-Index From 2021 - 2026
1.037
P-Index
This Author published in this journals
All Journal KUMARA CENDEKIA Information System Abdimas Mimbar Sekolah Dasar IJIE (Indonesian Journal of Informatics Education) JURNAL INDRIA (Jurnal Ilmiah Pendidikan Prasekolah dan Sekolah Awal) Jurnal ULTIMATICS Journal of Marine-Earth Science and Technology Media of Computer Science
Yudianto Sujana
Universitas Sebelas Maret
Arts Humanities Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 13 Documents
Search

 1   2 
A Comparative Study of Machine Learning Models for Sentiment Analysis of Dana App Reviews Sujana, Yudianto
IJIE (Indonesian Journal of Informatics Education) Vol 7, No 2 (2023): IJIE (Indonesian Journal of Informatics Education) - December
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijie.v7i2.93132

Abstract

Sentiment analysis of user reviews has become increasingly important for mobile app developers, as it can provide valuable insights into customer satisfaction and guide the improvement of app features. In this study, we compared the performance of three machine learning models - Support Vector Machine, Neural Network, and Bidirectional Long Short-Term Memory - in classifying the sentiment of user reviews for the Dana mobile application. Our results showed that the Bi-LSTM model outperformed the other models, achieving the highest accuracy, precision, recall, and F1-score. The superior performance of the Bi-LSTM model can be attributed to its ability to capture long-term dependencies and contextual information within the review text, which is crucial for accurate sentiment analysis. These findings highlight the effectiveness of deep learning techniques in handling the complexities of language and sentiment analysis, particularly in the context of user-generated content. The insights from this study can inform the development of more accurate and efficient sentiment analysis tools for mobile app reviews, ultimately benefiting both app developers and users.
Evaluating the Capability of VGG16 Trained on Kaggle Dataset for Detecting Tomato Diseases in Indonesian Tegar Satriya Wiguna; Febri Liantoni; Yudianto Sujana
ULTIMATICS Vol 18 No 1 (2026): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v18i1.4252

Abstract

Tomato leaf diseases pose a significant threat to agricultural productivity in Indonesia, often leading to severe yield losses. This study evaluates the effectiveness of the VGG16 convolutional neural network in detecting tomato diseases, particularly when trained on the standardized PlantVillage dataset and applied to local agricultural conditions in Sragen, Central Java. The research involved data preprocessing using background removal and resizing techniques, model training via transfer learning, and deployment through a FastAPI backend and React Native frontend. The VGG16 model achieved high accuracy 82% on the PlantVillage test set but exhibited a sharp decline 25% accuracy when tested on locally sourced images, highlighting limited generalization capabilities. These findings emphasize the necessity of incorporating local datasets and domain adaptation strategies to develop AI-based plant disease detection tools that are effective in real-world settings. The study underscores the importance of contextualizing AI solutions for local agricultural environments to ensure their practical applicability and reliability.
Preprocessing Image for License Plate Detection: A Systematic Literature Review Riyan Bagas Dwi Prasetyo; Vugar Abdullayev; Nurcahya Pradana Taufik Prakisya; Yudianto Sujana; Rahmat Siswanto
Media of Computer Science Vol. 2 No. 2 (2025): December 2025
Publisher : CV. Digital Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69616/mcs.v2i2.241

Abstract

Rapid population growth contributes to an increase in the volume of vehicles, creating major challenges in their management. One potential solution is the application of deep learning-based artificial intelligence technology for automatic detection of vehicle license plates. This research uses a Systematic Literature Review (SLR) approach to evaluate the performance of various deep learning architectures in the detection process. Out of 125 articles identified, 20 articles were selected based on specific selection criteria. The analysis revealed that preprocessing techniques, such as HE, AHE, ECHE, CLAHE, and ECLACHE, have significant contributions in the processing of vehicle license plate datasets. These techniques were able to improve the visual quality of the images, thus supporting the detection process with an accuracy rate of more than 95%. This research also identified challenges, such as high computational requirements and large-scale data processing. Further research is recommended to apply preprocessing on standardized datasets to develop a reliable, efficient and sustainable detection system.
Co-Authors Adriani Rahma Pudyaningtyas Agus Efendi Alifah, Nurul Lailani Alifia Nisa Nursanti Amalia Desy Puspitasari Amir Amir Aris Budianto, Aris Asih Mukharoh Basori Basori Denaro, Lino Garda Djaelani Djaelani Dwi Maryono Dyah Yuni Kurniawati Dyah Yuni Kurniawati, Dyah Yuni Elia Supriani Endang Sri Markamah Fatihul Ilmy, Hafsah Febri Liantoni Forry Stella Nasanjaya Hanung Tegar Pambudi Hartono Hartono Hertiana Yuni Kharismawati Hesti Wulandari Indah Rukmana Sari Indah Saptarini Iud Puspita Wijianingsih Joko Daryanto Karsono Karsono Karsono Karsono Kuswadi Kuswadi Lies Lestari Mahendra Lalita Mulyatno Maria Edel Frudis Aga Maryam Mar'atus Sholikah Mida Septi Karunia Muhammad Hassan Massaty, Muhammad Hassan Muhammad Munif Syamsuddin Muhammad Munif Syamsudin Murni Ramli Murni Ramli Nanda Muna Ayuni Ngadino Yustinus Ngadino Yustinus Novita Ambaryani Nur Hidayattur Rohmah, Nur Hidayattur Nurcahya Pradana Taufik Prakisya Nurul Kusuma Dewi Peduk Rintayati Rahmat Siswanto Rengganis Nahar Kharisma Reni Dew Nur Isnaini Rika Novellidianingrum Riyan Bagas Dwi Prasetyo Roihanah Hardiyani Rosihan Ari Yuana, Rosihan Ari Ruli Hafidah Sahni Damerianta Purba. Skom. MM Samidi Samidi Saparida Rahmi Saputro, Andito Haryo Siti Hitthotunnahdliyyah Al Badawi Siti Wahyuningsih Suciati *, Suciati Tegar Satriya Wiguna Tiara Ratnasari Tri Ulya Wardati Qariah Usada Usada Vugar Abdullayev Warananingtyas Palupi Warananingtyas Palupi Wihidayat, Endar Suprih