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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) GUYUB: Journal of Community Engagement 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 Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering Social Sciences Other

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Penentuan Bantuan Siswa Miskin Menggunakan Fuzzy Tsukamoto Dengan Perbandingan Rule Pakar dan Decision Tree (Studi Kasus : SDN 37 Bengkulu Selatan) Akbar, Riolandi; 'Uyun, Shofwatul
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 4: Agustus 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.0813191

Abstract

Penelitian penentuan calon bantuan siswa miskin ini di Sekolah Dasar Negeri 37 Bengkulu Selatan. Masalah yang terjadi ada ketidaksesuaian dari hasil output dalam pemberian bantuan siswa miskin, belum digunakannya metode keputusan untuk setiap kriteria dan masih menggunakan penilaian prediksi atau perkiraan untuk calon penerima bantuan. Metode penelitian yang dilakukan menggunakan Fuzzy Tsukamoto dengan perbandingan dua metode yaitu rule pakar dan Decision Tree SimpleCart. Tahapan penelitian ini dimulai dengan menganalisis output dengan melakukan seleksi dari sejumlah alternatif hasil, kemudian melakukan pencarian nilai bobot setiap atribut dari Fuzzy Tsukamoto dengan metode perbandingan rule pakar dan Decision Tree SimpleCart. Selanjutnya menentukan parameter batasan fungsi keanggotaan fuzzy meliputi kartu perlindungan sosial, nilai rata-rata raport, tanggungan, penghasilan orang tua, prestasi dan kepemilikan rumah. Analisis hasil yang diperoleh dari pengujian terhadap 75 data siswa dan telah dilakukan klasifikasi menggunakan Fuzzy Tsukamoto didapatkan hasil akurasi dengan metode rule pakar sebesar 72% dan metode Decision Tree SimpleCart sebesar 76%. Hasil akurasi tersebut di simpulkan bahwa metode Decision Tree SimpleCart mempunyai tingkat akurasi yang lebih tinggi dari metode rule pakar sehingga lebih mampu dalam menyeleksi serta mencari nilai bobot penentuan bantuan siswa miskin.  AbstractResearch on the determination of candidates for assistance from poor students in South Bengkulu 37 Primary School. The problem that occurs is there is a mismatch of the output results in the provision of assistance to poor students, the decision method has not been used for each criterion and is still using predictive or estimated assessments for prospective beneficiaries. The research method used was Fuzzy Tsukamoto with a comparison of two methods, namely expert rule, and SimpleCart Decision Tree. The stages of this research began by analyzing the output by selecting many alternative results, then searching for the weight value of each attribute from Fuzzy Tsukamoto with the method of expert rule comparison and the SimpleCart Decision Tree. Next determine the parameters of the fuzzy membership function limit includes social protection cards, the average value of report cards, dependents, parents' income, achievements, and homeownership. Analysis of the results obtained from testing of 75 student data and classification using Fuzzy Tsukamoto has obtained accuracy with the expert rule method by 72% and the SimpleCart Decision Tree method by 76%. The accuracy results are concluded that the SimpleCart Decision Tree method has a higher level of accuracy than the expert rule method so that it is better able to select and search for the weighting value of determining the assistance of poor students. 
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.
Evaluation of the Maturity Level of Information Technology Security Systems Using KAMI Index Version 4.2 (Case Study: Islamic Boarding Schools in Yogyakarta Special Region Province) Arromdoni, Bad’ul Hilmi; Nuruzzaman, Muhammad Taufiq; 'Uyun, Shofwatul; Sugiantoro, Bambang
IJID (International Journal on Informatics for Development) Vol. 12 No. 1 (2023): IJID June
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.2023.3987

Abstract

The development of information technology worldwide has changed very rapidly. There has been a data theft on the information system belonging to one of the most prominent Islamic Boarding Schools in the Yogyakarta area. Thus, special attention is needed to evaluate information technology security using the Information Security Index version 4.2. The research methods include extracting information, literature study, data collection, data validation, data analysis, and recommendations. The evaluation results are at the basic framework fulfilment level with a value of 343; the electronic system category has a low status with a value of 15 and 5 improvements; the governance category,  the risk management category,  the framework category,  the asset management category, and the information security technology category, have a maturity level II status with 12, five, eight, four, and eight recommendations respectively, while the supplement category for third party security areas with a value of 60%, securing cloud infrastructure services 56% and protecting personal data 61% with 14 recommendations.
Analysis of Factors Affecting the Students’ Acceptance Level of E-Commerce Applications in Yogyakarta Using Modified UTAUT 2 Candra, Dori Gusti Alex; Nuruzzaman, Muhammad Taufiq; 'Uyun, Shofwatul; Sugiantoro, Bambang; Pratiwi, Millati
IJID (International Journal on Informatics for Development) Vol. 12 No. 1 (2023): IJID June
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.2023.3990

Abstract

Yogyakarta is listed as the region with the highest number of residents engaging in e-commerce transactions. A total of 10.2% of the population are active e-commerce sellers, while 16.7% belong to the buyer category. Research by IDN Times showed that e-commerce application users have been dominated by students, with a percentage of 44.2%.  The purpose of this study is to analyze the factors that influence students’ level of acceptance of e-commerce applications in Yogyakarta using the modified UTAUT 2. This is quantitative research with multiple linear regression models using SPSS software version 25 with a sample size of 303 people. Data analysis in this study was conducted in a few steps, including descriptive analysis, validity test, reliability test, classical assumption test and hypothesis testing. The results of this study indicate that the student’s level of acceptance of e-commerce applications is within good criteria. The variables that have a positive effect on the behaviour intention (BI) are performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), hedonic motivation (HM), habit (HB), price value (PV), perceived risk (PR), perceived security (PS), and trust (TR) are variables that negatively affect the variable behaviour intention (BI). All independent variables affect the dependent variable or behaviour intention (BI) with a total of 63.3% and the difference with a total of 36.7% is caused by other factors not examined by the researcher.
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.
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 F. 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 Fauzi, Muhammad Dzulfikar Hardandrito, Awan Gumilang Heni Hapsari Iin Intan Uljanah Imam Riadi Istianto, Yudi Iza Fajarendra, Yusril 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 Edi Iswanto Muhammad Fadzlur Rahman Muhammad Rifqi Ma'arif Muhammad Taufiq Nuruzzaman Murdifin, Murdifin Mustafid, Ahmad 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 Siti Helmiyah Sri Hartati subanar subanar Sucinta, Hanny Handayani Toni Efendi Tundo, Tundo Ulfah, Aniq Noviciate Yulis Rijal Fauzan Yuni Madikhatun Yuni Madikhatun