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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) eProceedings of Engineering Journal of Fuzzy Systems and Control (JFSC) Control Systems and Optimization Letters
Pima Hani Safitri
Telkom University
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

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Verifikasi Tanda Tangan Online Menggunakan Algoritma Genetika Dan Support Vector Machine Pima Hani Safitri; Anditya Arifianto; Kurniawan Nur RAMADHANI
eProceedings of Engineering Vol 5, No 2 (2018): Agustus 2018
Publisher : eProceedings of Engineering

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Abstract

Abstrak Tanda tangan merupakan salah satu alat autentifikasi yang sering digunakan. Banyak hal didunia ini yang diresmikan menggunakan tanda tangan. Setiap orang memiliki karakteristik tanda tangan yang cukup beragam. Pengenalan tanda tangan secara offline masih mungkin memiliki banyak kesalahan karena itu dikembangkan pengenalan tanda tangan secara online dengan menggunakan fitur-fitur dinamis dari tanda tangan. Pada penelitian ini, dibangun dua skema yaitu tanpa pemilihan fitur menggunakan Algoritma Genetika dan tanpa pemilihan fitur. Sistem verifikasi ini menggunakan algoritma Support Vector Machine(SVM) untuk memverifikasi tanda tangan karena SVM sudah terbukti di penelitian sebelumnya dapat menghasilkan akurasi yang baik. Penelitian ini juga ditujukan untuk menemukan fitur-fitur yang penting dalam sebuah tanda tangan dari enam kelompok fitur yang diuji. Dataset yang digunakan adalah dataset SVC2004 yang berisi tanda tangan 5 orang yang masing masing memiliki 20 tanda tangan asli dan 20 tanda tangan palsu yang ditiru oleh professional. Hasil penelitian menunjukkan Algoritma Genetika dapat menghasilkan akurasi 94.40% dan lebih baik 4.21% dibandingkan tanpa melalui pemilihan fitur. Kelompok fitur yang berpengaruh adalah kelompok fitur Geometry dan Miscellaneous karena menghasilkan akurasi yang lebih baik daripada kelompok fitur lainnya. Kata kunci : verifikasi tanda tangan, algoritma genetika, Support Vector Machine(SVM), kelompok fitur Abstract Signatures are one of the most commonly used authentication tools. Many things in this world are inaugurated using signatures. Everyone has signature characteristics that are quite diverse. The verification of offline signatures may still have many errors because it developed the verification of signatures online by using the dynamic features of the signature. In this research, two schemes are built without the feature selection using Genetic Algorithm and without feature selection. This verification system uses the Support Vector Machine (SVM) algorithm to verify the signature because SVM has been proven in previous research to produce good accuracy. The study is also intended to find important features in a signature of the six groups of features tested. The dataset used is a SVC2004 dataset containing 20 authentic signatures and 20 fake signatures imitated by professionals of 5 users. The results showed Genetic Algorithm can produce 94.40% and 4.21% better than without the selection of features. Influential feature groups are Geometry and Miscellaneous feature groups because they produce better accuracy than other feature groups. Keywords: signature verification, Genetic Algorithm, Support Vector Machine(SVM), feature group
ADAPTIVE AL-QUR’AN MEMORIZATION RECOMMENDATION SYSTEM BASED ON FUZZY LOGIC COGNITIVE MEMORY AND PROFILE MATCHING Afifah Fikriyah Dhiya'ulhaq; Muhammad Dzulfikar Fauzi; Pima Hani Safitri
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.8048

Abstract

Memorizing mutasyabihat verses in the Qur’an is particularly challenging due to similarities in structure, linguistic patterns, and semantic density that place a heavy load on short-term memory. Conventional memorization approaches do not account for individual cognitive differences when dealing with verse complexity. This study proposes an adaptive recommender system based on cognitive modeling to align verse group selection with the user’s memory profile.The system models memory capacity as a multidimensional profile using fuzzy inference derived from three quantitative indicators: continuous memory score, total correct recall, and average response time. This profile is matched with verse group feature vectors through a profile matching approach and a weighted Euclidean distance similarity measure within a Multi-Attribute Decision Making (MADM) framework. Four verse characteristics are considered: thematic (35%), semantic (25%), linguistic (25%), and pattern (15%).An adaptive calibration phase combines 20% of the initial cognitive profile with 80% of actual memorization performance, reflecting the dominance of behavioral evidence over initial assessment. System evaluation employs the Top-N Accuracy method commonly used in recommender systems.Testing with 29 participants resulted in a Top-3 success rate of 66% and an overall Top-N accuracy of 62.07%. These results indicate that cognitive profile–based multidimensional similarity can adaptively match verse complexity to individual memory capacity. This study demonstrates that fuzzy cognitive modeling and profile matching can be effectively implemented in adaptive personalized learning systems to optimize memorization of mutasyabihat verses
Hybrid GA-GWO with Dual-Vector Encoding for Indonesian School Timetabling Akbar Muhammad Sadat; Alqis Rausanfita; Pima Hani Safitri
Journal of Fuzzy Systems and Control Vol. 4 No. 2 (2026): Vol. 4 No. 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i2.418

Abstract

School timetabling is a complex combinatorial optimization problem that involves assigning subjects, teachers, and classes to predefined time slots while satisfying numerous institutional constraints. In many Indonesian junior high schools, scheduling is still performed using manual approaches, which are often time-consuming and prone to conflicts. Compared with university timetabling, school timetabling presents additional challenges due to fixed class groups, rigid subject allocations, teacher availability constraints, and institutional regulations. To address these challenges, this study proposes a hybrid optimization framework that combines a Guided Genetic Algorithm (GA) and Grey Wolf Optimizer (GWO) for the school timetable. The proposed framework incorporates dual-vector solution encoding to provide a structured representation of scheduling components and support efficient constraint handling during the optimization process. In addition, a majority-voting and guided mutation strategy is employed to enhance the balance between exploration and exploitation. The proposed method was evaluated using real-world scheduling data from an Indonesian junior high school consisting of 27 classes, 54 teachers, 13 subjects, and 36 time slots. Experimental results show that the proposed hybrid GA-GWO achieved a fitness improvement of 95.84%, reducing the fitness value from 16,120 to 670, compared with improvements of 89.83% and 94.31% obtained by Traditional GA and Guided GA, respectively. Although the proposed method required approximately 28 minutes of execution time, it produced the highest overall timetable quality among the evaluated approaches. These findings demonstrate that the integration of dual-vector encoding, majority voting, and guided mutation within a hybrid GA-GWO framework can effectively improve timetable optimization for real-world Indonesian school scheduling environments.
Broad Learning System: A Derivation-Based Mathematical Formulation Dimas Chaerul Ekty Saputra; Dyah Putri Rahmawati; Affifah Mutiara Pertiwi; Muhammad Ijaz Shafarin; Kharisma Monika Dian Pertiwi; Thinzar Aung Win; Irianna Futri; Pima Hani Safitri
Control Systems and Optimization Letters Vol 4, No 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i1.294

Abstract

Broad Learning System is a wide learning framework that constructs nonlinear feature representations while enabling efficient model training through analytical solutions. This paper presents a derivation-based formulation of Broad Learning System that explains the mathematical structure underlying the learning process. The model constructs an expanded feature representation through feature mapping nodes followed by enhancement nodes that further enrich the learned representation. The learning problem is then expressed as a linear model in the constructed feature space, and the output weights are obtained using ridge regularized least squares optimization. This formulation allows the training process to be solved directly using matrix operations without iterative gradient based procedures. In addition, an incremental learning mechanism is introduced to enable efficient parameter updates when new samples or additional nodes are incorporated into the model. The presented formulation highlights how Broad Learning System combines nonlinear feature construction with computationally efficient closed form learning, providing a clear theoretical interpretation of the learning process.
Co-Authors Affifah Mutiara Pertiwi Afifah Fikriyah Dhiya'ulhaq Akbar Muhammad Sadat Alqis Rausanfita Anditya Arifianto Arip Ramadan Dimas Chaerul Ekty Saputra Dyah Putri Rahmawati Irianna Futri Kharisma Monika Dian Pertiwi Kurniawan Nur Ramadhani Muhammad Dzulfikar Fauzi Muhammad Ijaz Shafarin Muhammad Ilham Alhari Thinzar Aung Win