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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 Solo International Collaboration and Publication of Social Sciences and Humanities 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 Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice 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. 
Research Trends of Recommendation Systems in Digital Libraries: Bibliometric Analysis and Literature Review Amarudin, Hanif; Afiyah, Ishmah; Shofwatul 'Uyun
Solo International Collaboration and Publication of Social Sciences and Humanities Vol. 3 No. 03 (2025): Main Thema: Integration of Universal Values in the Dynamics of Social Sciences
Publisher : Walidem Institute and Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61455/sicopus.v3i03.456

Abstract

Objective: This study maps trends, approaches, challenges, and future research directions in digital library recommendation systems. Theoretical framework: The study focuses on recommendation systems in digital libraries, exploring Collaborative Filtering (CF), Content-Based Filtering (CBF), and hybrid approaches. It emphasizes algorithm optimization to address data sparsity and cold-start issues, and the integration of deep learning for improved accuracy and personalization. Literature review: The literature review tracks the evolution of recommendation systems from CF and CBF to hybrid and deep learning models, focusing on accuracy and cold-start issues. It highlights the growing use of advanced models and the challenges of algorithm optimization and data scarcity. Methods: A Systematic Literature Review (SLR) was conducted following the PRISMA framework. Literature was searched on Scopus using keywords related to recommendation systems. Data was analyzed using RStudio with Bibliometrix and VOSviewer for keyword network visualization. Results: This study shows a significant trend in the development of digital library recommendation systems, with publications increasing rapidly since 2014 and peaking in 2024. Collaborative Filtering (CF) remains the dominant approach, but hybrid approaches and deep learning techniques are increasingly being applied to improve accuracy and relevance. The main challenges faced include algorithm optimization, data scarcity, and cold starts, as well as the use of hybrid and deep learning techniques that require more resources. Further research is needed to develop more efficient and personalized algorithms in the digital library recommendation system. Implications: The research offers insights to improve recommendation system efficiency and relevance in digital libraries, addressing key algorithmic challenges. Novelty: This research provides a deeper understanding of recommendation system applications in digital libraries, identifying challenges, future directions, and solutions that combine various algorithms to enhance user experience.
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.
Co-Authors Abdullah, Mohd. Fikri Azli bin Afiyah, Ishmah Agung Nur Hidayat Ahmad Mustafid Ahmad Subhan Yazid Akbar, Riolandi Akhmad Imam Fahrizal Alfarizi, Naufal Faiz Amarudin, Hanif 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 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 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