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All Journal Sistemasi: Jurnal Sistem Informasi Jurnal Abdi: Media Pengabdian Kepada Masyarakat
Salamun Rohman Nudin
State University of Surabaya
Humanities Computer Science & IT Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Mobile-based Big Five Personality Score Prediction from Handwriting using VGG19 Maharani Sekar Hapsari; Salamun Rohman Nudin
SISTEMASI Vol 15, No 6 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i6.6380

Abstract

Handwriting represents a non-verbal behavioral cue that can be used to identify individual personality characteristics. However, conventional manual assessment of personality scores is inherently subjective and requires specialized expertise, highlighting the need for a more objective, efficient, and consistent automated approach. This study aims to develop a system for predicting personality trait scores from handwriting images. The personality assessment is based on the Big Five Personality model, comprising Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. The research methodology adopts an adapted Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, including the stages of business understanding, data understanding, data preparation, model development, evaluation, and system implementation. The proposed system employs the VGG19 deep learning architecture and compares the performance of three optimization algorithms: Adam, Stochastic Gradient Descent (SGD), and RMSProp. The evaluation results demonstrate that RMSProp achieved the best performance on the validation dataset, with a Mean Squared Error (MSE) of 0.0128, Mean Absolute Error (MAE) of 0.0930, Root Mean Squared Error (RMSE) of 0.1133, Pearson Correlation Coefficient (PCC) of 0.4648, and an accuracy of 90.70%. The VGG19 model optimized with RMSProp was subsequently deployed in a mobile application capable of receiving handwriting image inputs and generating predicted Big Five personality trait scores. These findings demonstrate that integrating deep learning with mobile applications offers a practical and effective solution for predicting Big Five personality trait scores from handwriting images.
Pendampingan Pembelajaran Digital Berbasis Deep Learning untuk Penguatan Kompetensi Pedagogik Guru CLC Tawau Ali Imron; Hendri Irawan; M. Jullyo Bagus Firdaus; Andhega Wijaya; R.R. Nanik Setyowati; Sarmini; Rahman Abidin; Iqbalullah Azam Ramadhan; Nuh Krama Hadianto; Salamun Rohman Nudin
Jurnal ABDI: Media Pengabdian Kepada Masyarakat Vol. 12 No. 1 (2026): JURNAL ABDI : Media Pengabdian Kepada masyarakat
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/abdi.v12i1.58032

Abstract

Limited access due to the distribution of Community Learning Centers (CLCs) in Tawau, combined with low teacher competency, has hindered the optimal implementation of deep learning-based instruction. Yet, CLCs play a strategic role as the educational vanguard for Indonesian diaspora children—shaping character, instilling national values, and strengthening global citizenship literacy. To enhance the competency of CLC teachers in Tawau, this community service initiative was undertaken, featuring a hybrid pedagogical workshop focused on developing teaching modules and digital learning media grounded in deep learning principles. The program began with an online orientation and pedagogical education session—conducted via synchronous discussion—to establish a foundational understanding among participants regarding the program's objectives and implementation mechanisms. The subsequent phase involved an in-person workshop dedicated to developing teaching modules and digital learning media through intensive guidance, resulting in ready-to-use instructional products. The final stage comprised monitoring, evaluation, and a Focus Group Discussion (FGD) aimed at establishing a CLC teacher learning community; this community serves as a platform for collaboration and the sharing of best practices, while ensuring the sustainability of deep learning-based digital instruction. The workshop results demonstrated that the CLC teachers in Tawau possess strong pedagogical competence in planning and executing instruction.
Co-Authors Ali Imron Andhega Wijaya Hendri Irawan Iqbalullah Azam Ramadhan Maharani Sekar Hapsari Muhammad Jullyo Bagus Firdaus Nuh Krama Hadianto R.R. Nanik Setyowati Rahman Abidin Sarmini