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All Journal Scientific Journal of Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Technology and Informatics (JoTI) Jurnal Algoritma Journal of Information System Exploration and Research Scientific Journal of Informatics Jurnal Masyarakat Mengabdi (JUMADI)
Tanzilal Mustaqim
Universitas Telkom
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Mechanical Engineering Social Sciences Other

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Journal : Scientific Journal of Informatics

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A Deep Learning Model Comparation for Diabetic Retinopathy Image Classification Mustaqim, Tanzilal; Safitri, Pima Hani; Muhajir, Daud
Scientific Journal of Informatics Vol. 12 No. 1: February 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i1.20939

Abstract

Purpose: This study compares the performance of various deep learning models for diabetic retinopathy (DR) classification, emphasizing the impact of different optimization functions. Early detection of DR is vital for preventing blindness, and the research investigates how optimization functions influence the classification accuracy and efficiency of several convolutional neural networks (CNNs). This study fills a gap in the existing literature by examining how optimization functions affect model performance in conjunction with architectural considerations. Methods: This paper uses the APTOS 2019 dataset, which comprises 3,663 retinal fundus images classified into five classes of diabetic retinopathy severity. Four CNN-based models, including CNN, ResNet50, DenseNet121, and EfficientNet B0, were trained using five optimization techniques: Adam, SGD, RMSProp, AdamW, and NAdam. The performance of the experimental scenarios was evaluated through accuracy, precision, recall, F1-score, training duration, and model size. Result: EfficientNet B0 demonstrated superior computational efficiency with a minimal model size of 16.16 MB. Subsequently, DenseNet121 with the SGD optimizer achieved the highest test accuracy of 96.86%. The experimental results indicate that the optimizer significantly influences model performance. AdamW and NAdam yield superior outcomes for deeper architectures such as ResNet50 and DenseNet121. Novelty: This paper offers an analytical examination of deep learning models and optimization techniques for DR classification, helping to clarify the trade-offs between computational efficiency and classification performance. The findings contribute to the development of more accurate and efficient DR detection systems, which could be utilized in real-world, resource-limited settings.
Comparative Analysis of High School Student and AI-Generated Essays Using IndoBERT and Linguistic Features Adani, Muhammad Harits Shofwan; Rausanfita, Alqis; Mustaqim, Tanzilal
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.27732

Abstract

Purpose: The purpose of this study is to address the growing challenge of distinguishing between essays written by humans and essays generated by AI, particularly in the context of high school education in Indonesia. This study aims to analyze the semantic and linguistic differences between student-written and ChatGPT-generated in Indonesian language. Methods: The study employs an IndoBERT-based semantic model trained with triplet loss to generate paragraph-level embeddings, allowing the measurement of semantic similarity within and between essay classes. Additionally, linguistic features such as lexical diversity, word count, modal usage, and stopword ratio were extracted to capture stylistic and structural differences. These three key features are combined and used as input to a neural network classifier. Result: The IndoBERT-based semantic model successfully grouped student-written and ChatGPT-generated essays into distinct clusters. The similarity scores within student essays ranged from 0.7 to 0.9, while the similarity between classes was mostly negative with a few outliers, reflecting the cosine similarity metric used in this study, which has a range of -1 to 1. The classification model showed a 90.55% accuracy and an AUC of 0.9999 when evaluated on the independent test set defined in the Data Preparation stage. These results suggest that student-written and ChatGPT-generated essays form distinct semantic clusters. Students’ essays show more linguistic diversity, while ChatGPT essays show consistency in the coherence and formality aspects of the essays. Novelty: This study provides empirical insights of semantic similarities and linguistic features to differentiate between human and AI-generated essays in the Indonesian language. It contributes to supporting academic integrity efforts and highlighting the need for further research across different writing models and contexts.
Co-Authors Adani, Muhammad Harits Shofwan Alqis Rausanfita Arif Sanjani, Lukman Aris Kusumawati Asmawati, Diah Chastine Fatichah Diana Purwitasari Dimas Renggana, Christiant Dwi Hariyanto, Muhammad Fandisya Rahman Helisyah Nur Fadhilah Hilya Tsaniya Jumanto Jumanto, Jumanto Mohammad Sholik Much Aziz Muslim Muhajir, Daud Mustafa Kamal Nanik Suciati Rifdatun Ni’mah Riza Akhsani Setyo Prayoga Rizkika, Rizal Rahman Safitri, Pima Hani Titus Kristanto Vessa Rizky Oktavia Wachda Yuniar Rochmah Yosza Dasril