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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) TEKNIK INFORMATIKA snimed Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jupiter Semantik Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Systems Engineering and Business Intelligence JOIN (Jurnal Online Informatika) Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI IJID (International Journal on Informatics for Development) Aplikasia: Jurnal Aplikasi Ilmu-Ilmu Agama KOMPUTA : Jurnal Ilmiah Komputer dan Informatika JISKa (Jurnal Informatika Sunan Kalijaga) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi Engagement: Jurnal Pengabdian Kepada Masyarakat
Shofwatul ‘Uyun
Department Of Informatics, Faculty Of Science And Technology, Universitas Islam Negeri Sunan Kalijaga Yogyakarta, Indonesia
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Social Sciences Other

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Skew Correction and Image Cleaning Handwriting Recognition Using a Convolutional Neural Network Uyun, Shofwatul; Rahardyan, Seto; Anshari, Muhammad
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1712

Abstract

Handwriting recognition is a study of Optical Character Recognition (OCR) which has a high level of complexity. In addition, everyone has a unique and inconsistent handwriting style in writing characters upright, affecting recognition success. However, proper pre-processing and classification algorithms affect the success of pattern recognition systems. This paper proposes a pre-processing method for handwriting image recognition using a convolutional neural network (CNN). This study uses public datasets for training and private datasets for testing. This pre-processing consists of three processes: image cleaning, skew correction, and segmentation. These three processes aim to clean the image from unnecessary ink streaks. In addition, to make angle corrections to characters in italics in their writing. The model testing process uses image test data of handwriting that are not straight. There are three images based on the inclination angle: less than 45 degrees, equal to 45 degrees, and more than 45 degrees. Picture cleaning removes unnecessary strokes (noise) from the image using a layer mask, whereas skew correction changes the handwriting to an upright posture based on the detected angle. The pre-processing model we propose worked optimally on handwriting with a skew angle of fewer than 45 degrees and 45 degrees. Our proposed model generally works well for handwriting with fewer than 45 degrees skew with an accuracy of 88,96%. Research with a similar scope can continue to improve optimization with a focus on algorithms related to analysis layout studies. Besides that, it can focus more on automation in the segmentation process of each character.
A Systematic Review of Convolutional Neural Network Models for Tomato Leaf Disease Detection Sanora, Fiki; Mufafaq, Naufal Hafizh; Uyun, Shofwatul
Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi Vol. 5 No. 1 (2026)
Publisher : Department of Informatics Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/snati.v5.i1.45303

Abstract

Tomato leaf disease can cause a decline in productivity and crop failure, making early detection very important in precision farming practices. Manual detection methods, which are still commonly used in the field, have limitations in terms of speed and accuracy, requiring an automated image-based approach. Convolutional Neural Networks (CNNs) have become a leading technique in plant disease classification, but the diversity of architecture used requires systematic study to identify the most effective model. This study summarizes, compares, and evaluates CNN models for tomato leaf disease detection through a Systematic Literature Review (SLR) that adopts the PRISMA guidelines, covering the stages of identification, screening, feasibility assessment, and inclusion. A search in Scopus (2022–2025) using the query: (“Convolutional Neural Network” OR ‘CNN’) AND (‘tomato’ AND “leaf disease detection”) yielded 21 relevant articles. Analysis shows common preprocessing such as image resizing, data augmentation, and denoising. The best CNN architecture is InceptionV3 (most frequently used and high performing), followed by DenseNet201, MobileNetV2, and ResNet152V2. Architectures with optimal depth and high computational efficiency are preferred. This study provides a comprehensive map of CNN models to support architecture selection in tomato leaf disease detection. Future research directions include improving image quality, integrating attention mechanisms, semantic segmentation, and developing concise and efficient models for field applications.
Penerapan Metode Ensemble Learning dalam Klasifikasi Risiko Abrasi Menggunakan Citra Satelit Google Earth Engine Fajarendra, Yusril Iza; 'Uyun, Shofwatul
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 1: Februari 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026131

Abstract

Abrasi menjadi masalah utama yang mempengaruhi ekosistem dan pemukiman di wilayah pesisir dengan dampak kemunduran garis pantai yang mengancam bangunan dan ekosistem yang ada didalamnya. Permasalahan utama terletak pada pemantauan, analisis dan klasifikasi risiko abrasi secara akurat menggunakan citra satelit. Data citra dengan resolusi tinggi membutuhkan komputasi yang efisien. Keterbatasan akan jumlah data adalah faktor utama yang menyebabkan model overfitting sehingga dilakukan penerapan teknik augmentasi data untuk menghasilkan sampel data sintetis dan meningkatkan kemampuan generalisasi model. Penelitian ini menggunakan data citra satelit Sentinel-2 yang diambil dari Google Earth Engine dan Google Colab untuk pemotongan dan serta dilakukan pelabelan data, dengan tiga kelas tingkatan abrasi: rendah, sedang, dan tinggi yang memiliki karakteristik citra yang berbeda. Langkah awal adalah evaluasi lima arsitektur CNN (Xception, InceptionV3, MobileNet, DenseNet, dan VGG16) melalui Transfer Learning dan K-Fold Cross-Validation. Hasilnya menunjukkan kinerja yang bervariasi, mengindikasikan tidak ada model tunggal yang optimal untuk dataset abrasi yang kompleks. Menanggapi keterbatasan ini, pendekatan (Boosting) Ensemble Learning diterapkan untuk membangun model yang lebih stabil dan general, dengan tujuan menggabungkan kekuatan prediksi berbagai arsitektur. Meskipun DenseNet menjadi model tunggal terbaik dengan akurasi 95,13%, penerapan Boosting Ensemble berhasil meningkatkan performa signifikan hingga 96,45%. Hasil ini membuktikan sinergi model memberikan solusi yang lebih unggul dan andal dibandingkan model tunggal.   Abstract Abrasion is a major problem affecting ecosystems and settlements in coastal areas, with the impact of shoreline retreat threatening buildings and the ecosystem within them. The main problem lies in the accurate monitoring, analysis, and classification of abrasion risks using satellite imagery. High-resolution imagery data requires efficient computing. Limitations in the amount of data are the main factor causing model overfitting, so data augmentation techniques are applied to generate synthetic data samples and improve model generalization capabilities. This study uses Sentinel-2 satellite imagery data taken from Google Earth Engine and Google Colab for data slicing and labeling, with three classes of abrasion levels: low, medium, and high, which have different image characteristics. The initial step was the evaluation of five CNN architectures (Xception, InceptionV3, MobileNet, DenseNet, and VGG16) through Transfer Learning and K-Fold Cross-Validation. The results showed varying performance, indicating that there is no single optimal model for complex abrasion datasets. In response to this limitation, an Ensemble Learning (Boosting) approach was applied to build a more stable and general model, with the aim of combining the predictive power of various architectures. Although DenseNet was the best single model with 95.13% accuracy, applying Ensemble Boosting significantly improved performance to 96.45%. This result demonstrates that model synergy provides a superior and more reliable solution than a single model.
Data Search Process Optimization using Brute Force and Genetic Algorithm Hybrid Method Yudha Riwanto; Muhammad Taufiq Nuruzzaman; Shofwatul Uyun; Bambang Sugiantoro
IJID (International Journal on Informatics for Development) Vol. 11 No. 2 (2022): IJID December
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2022.3743

Abstract

High accuracy and speed in data search, which are aims at finding the best solution to a problem, are essential. This study examines the brute force method, genetic algorithm, and two proposed algorithms which are the development of the brute force algorithm and genetic algorithm, namely Multiple Crossover Genetic, and Genetics with increments values. Brute force is a method with a direct approach to solving a problem based on the formulation of the problem and the definition of the concepts involved. A genetic algorithm is a search algorithm that uses genetic evolution that occurs in living things as its basis. This research selected the case of determining the pin series by looking for a match between the target and the search result. To test the suitability of the method, 100-time tests were conducted for each algorithm. The results of this study indicated that brute force has the highest average generation rate of 737146.3469 and an average time of 1960.4296, and the latter algorithm gets the best score with an average generation rate of 36.78 and an average time of 0.0642.
A Systematic Review of Transfer Learning and Data Augmentation in Neural Machine Translation of Low-Resource Languages Khuluq, Nur Fikri; Muzhaffar, Muh Naufal; 'Uyun, Shofwatul
Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi Vol. 5 No. 2 (2026)
Publisher : Department of Informatics Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/snati.v5.i2.47528

Abstract

Modern Neural Machine Translation (NMT) systems have achieved state-of-the-art, performance, largely due to the availability of large-scale parallel corpora. However, the translation quality of NMT for Low-Resource Languages ​​(LRL) remains limited due to data sparsity. Numerous studies have proposed different strategies to address this challenge. Among the most widely adopted strategies are Transfer Learning (TL) and Data Augmentation (DA) strategies. This research aims to present a systematic review of how these techniques, including Back-Translation (BT), Hybrid Transfer Learning (HTL), and the utilization of self-supervised objectives such as Masked Language Modeling (MLM), Causal Language Modeling (CLM), and Denoising Autoencoder (DAE), affect the quality improvement of NMT for LRL. The findings show that a hybrid combination of TL and DA with a self-supervised objective is the most effective solution for extremely low-resource scenarios, capable of producing the highest translation quality (highest BLEU score) and outperforming baseline models and traditional methods such as Statistical Machine Translation (SMT).
Co-Authors Abdullah, Mohd. Fikri Azli bin Agung Nur Hidayat Ahmad Mustafid Ahmad Subhan Yazid Akbar, Riolandi Akhmad Imam Fahrizal Alfarizi, Naufal Faiz Arif Riyandi Arromdoni, Bad’ul Hilmi Awaliyah, Dien Fitri Bambang Sugiantoro Danang Aji Bimantoro Daru Prasetyawan Diniati Ruaika Dony Fahrudy Dori Gusti Alex Candra Eka Sulistiyowati Eka Sulistyowati Eka Sulistyowati, Eka Elvanisa Ayu Muhsina Elvanisa Ayu Muhsina, Elvanisa Ayu Endra Yuliawan Fahrudy, Dony Fajarendra, Yusril Iza Hardandrito, Awan Gumilang Helmiyah, Siti Heni Hapsari Iin Intan Uljanah Imam Riadi Istianto, Yudi Iza Fajarendra, Yusril Khuluq, Nur Fikri Lina Choridah Madikhatun, Yuni Mardlian, M. Sa’id Abdurrohman Kunta Maria Ulfah Siregar Mufafaq, Naufal Hafizh MUHAMMAD ABDUL GHOFUR Muhammad Akid Musyafa Muhammad Anshari, Muhammad Muhammad Dzulfikar Fauzi Muhammad Edi Iswanto Muhammad Fadzlur Rahman Muhammad Rifqi Ma'arif Muhammad Taufiq Nuruzzaman Murdifin, Murdifin Mustafid, Ahmad Muzhaffar, Muh Naufal Nuri Guntur Perdana Nurochman Nurochman Nuruzzaman, Muhammad Taufiq Pratiwi, Millati Qonitat, Ihda Imroatun Qorry Aina Fitroh Rahardyan, Seto Rebeccah Ndungi Ridha , M Noor Tasiur Rijal Fauzan, Yulis Riwanto, Yudha Rizal Fauzan, Yulis Rosalia Susilowati Rosalin, Rizqi Praimadi Saiful Machbub Mutaqin Sanora, Fiki Sari, Luky Vianika Sherly Andini Sri Hartati subanar subanar Sucinta, Hanny Handayani Toni Efendi Tundo, Tundo Ulfah, Aniq Noviciate Yudha Riwanto Yulis Rijal Fauzan Yuni Madikhatun Yuni Madikhatun