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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Journal of ICT Research and Applications Telematika Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer JETT (Jurnal Elektro dan Telekomunikasi Terapan) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SMARTICS Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JITK (Jurnal Ilmu Pengetahuan dan Komputer) TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika Indonesian Journal of Electrical Engineering and Computer Science Journal of Dinda : Data Science, Information Technology, and Data Analytics eProceedings of Engineering Jurnal INFOTEL Journal of Information Technology and its Utilization IJoICT (International Journal on Information and Communication Technology)
Aji Gautama Putrada
Telkom University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering

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Investigating Shallow Learning Methods for Optical Character Recognition of Indonesia’s Nusantara Scripts Sulistiyo, Mahmud Dwi; Putrada, Aji Gautama; Ihsan, Aditya Firman; Yunanto, Prasti Eko; Richasdy, Donny; Sailellah, Hassan Rizky Putra; Sabrina Adinda Sari
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6648

Abstract

Indonesia has numerous regional scripts—or so-called Nusantara scripts—and recognizing them is important to preserve Indonesia's cultural heritage. The advances of AI and computer vision technologies make it possible for a machine to optically read the handwritten scripts through the Optical Character Recognition (OCR) technique. However, collecting some of the top OCR solutions and comprehensively investigating their performances on the Nusantara scripts is currently lacking. This study investigates and evaluates some shallow learning-based methods on our newly introduced datasets, consisting of more than 38,000-character images across 80 letter classes in total; here, we focus on three regional scripts: Javanese, Sundanese, and Balinese. The methods include Random Forest, SVM, Logistic Regression, and Gaussian Naïve Bayes, as well as boosting techniques such as XGBoost, Light GBM, and CatBoost. A 5-fold cross-validation approach assessed model performance based on accuracy, precision, recall, and F1-score. Based on the experimental results, the methods demonstrated their competitiveness in reaching the best models for scripts; in particular, XGBoost, Light GBM, and Random Forest-Gini were the winners for Javanese, Sundanese, and Balinese scripts, respectively. These findings demonstrate the effectiveness of ensemble learning methods for diverse handwritten scripts. Comparative analysis to prior deep learning studies is also discussed in this paper. In addition, this research also contributes to preserving Indonesian traditional scripts, as well as offers insights for future regional OCR in other countries.
Benchmarking Mobile Apps Security in Universities: An OWASP Mobile Top 10 Framework Perspective Fajar Maulana Kadir; Muhamad Irsan; Aji Gautama Putrada
IJoICT (International Journal on Information and Communication Technology) Vol. 11 No. 1 (2025): Vol. 11 No. 1 Jun 2025
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v11i1.9094

Abstract

Leading Indonesian universities such as Telkom University (Tel-U), Institut Teknologi Bandung(ITB), Universitas Indonesia (UI), and Universitas Gadjah Mada (UGM) have developed mobilebasedacademic information systems that improve the accessibility of campus services, wheresensitive information such as personal data, access credentials, and educational information arestored and managed through the mobile application. The current gap is the lack of understanding ofthe specific vulnerability profile of campus mobile applications and how these vulnerabilities canaffect the data security of educational institutions. This study conducts a comparative analysis ofvulnerabilities in campus mobile applications using the OWASP Mobile Top 10 framework as itstesting standard. In its implementation, this study uses three mobile application security testingtools: AndroBugs, Mobile Security Framework (MobSF), and QARK (Quick Android Review Kit).These three tools were chosen because of their ability to detect various types of vulnerabilitiescovered in the OWASP Mobile Top 10. By comparing vulnerability analysis results on differentcampus mobile applications, this study aims to identify common vulnerability patterns and providerecommendations for improvements following the OWASP Mobile Top 10 security standards. Thetest results show that MySIX ITB and WeAreUI have the most vulnerabilities compared to the otherthree campuses, with 24 vulnerabilities from three different tools. However, if we look at theconsensus between the three tools, MySIX ITB is the most vulnerable application, withvulnerabilities in five categories: M3, M5, M6, M8, and M9. In addition to using three differenttools to strengthen the vulnerability detection rate, we also found some new knowledge. The first isthat all three tools have the same agreement for detecting M2, M6, and M8, which shows the highreliability of the three tools for the categories mentioned. The second is the knowledge that QARKmakes the most different decisions from the other two tools. The test results show that QARK makesdifferent decisions eight times. We also learned that for the four campus mobile apps, developersshould pay more attention to two categories detected by each tool, namely M6 and M8, or InadequatePrivacy Controls and Security Misconfiguration, respectively. Finally, there is knowledge that thestrength of the four mobile apps is resistance to M2; in other words, each campus has used thirdpartylibraries well.
Co-Authors Abdillah, Hilal Nabil Abiyan Bagus Baskoro Aditya Firman Ihsan Adrian Gusti Nurcahyo Agita Rachmad Muzakhir Algi Fajardi Alieja Muhammad Putrada Andrian Rakhmatsyah Angga Anjaini Sundawa Anita Auliani Argo Surya Adi Dewantoro Aziz Nurul Iman Baginda Achmad Fadillah Bambang Setia Nugroho Bayu Kusuma Belva Rabbani Driantama Bramantio Agung Prabowo Calvin M.T Manurung Catur Wirawan W Catur Wirawan Wijiutomo Daniel Arga Amallo Dawani, Febri Dicky Prasetiyo Dita Oktaria Doan Perdana Dodi W. Sudiharto Dodi Wisaksono Sudiharto Dody Qori Utama Endro Ariyanto Erwid Musthofa Jadied Fachrial Akbar Fadhlillah Fadhlillah Fadhlurahman Irwan Fairus Zuhair Azizy Atoir Fajar Maulana Kadir Fakhri Akbar Pratama Farisah Adilia Fauzan Ramadhan Sudarmawan Fauzan, Mohamad Nurkamal Fauzan, Mohamad Nurkamal Fazmah Arif Yulianto Febrina Puspita Utari Fitra Ilham Gabe Dimas Wicaksana Gentur Cipto Tri Atmaja Hamman Aryo Bimmo Hanifa Zahra Dhiah Hilal Hudan Nuha Hirianinda Malsegianty S Ikbar Mahesa Ikke Dian Oktaviani Ikke Dian Oktaviani Ikrimah Muiz Ilham Fadli Surbakti Imas Nur Tiarani Irfan Dwi Wijaya Irfan Nugraha Januar Triandy Nur Elsan Krisna Kristiandi Hartono Kurnia Wisuda Aji Mahmud Dwi Sulistiyo Mahmud Imroba Maman Abdurohman Maman Abdurrahman Mar Ayu Fotina Mas'ud Adhi Saputra Maya Ameliasari Mohamad Nurkamal Fauzan Mohamad Nurkamal Fauzan Mohamad Nurkamal Fauzan Muhamad Irsan Muhamad Nurkamal Fauzan Muhammad Al Makky Muhammad Alkahfi Khuzaimy Abdullah Muhammad Dafa Prima Aji Muhammad Fahmi Nur Fajri Muhammad Ihsan Muhammad Kukuh Alif Lyano Muhammad Shibgah Aulia Muhhamad Affan Hasby Muhhamad Affan Hasby Muhtadu Syukur A Mulia Hanif Nando, Parlin Nando, Parlin Niken Cahyani Novian Anggis Suwastika Nuha, Hilal H Nur Alamsyah NUR ALAMSYAH Nur Alamsyah, Nur Nur Ghaniaviyanto Ramadhan Nurkamal Fauzan, Mohamad Pahlevi , Rizka Reza Pamungkas, Rizaldi Ramdlani Parman Sukarno Prasti Eko Yunanto Putrada, Alieja Muhammad Putri Azanny Raden Muhamad Yuda Pradana Kusumah Rafie Afif Andika Rahmat Suryoputro Rahmat Yasirandi Randy Agustyo Raharjo Reynaldo Lino Haposan Pakpahan Richasdy, Donny Rizki Jamilah Guci Sabrina Adinda Sari Sailellah, Hassan Rizky Putra Seli Suhesti Sena Amarta Sidik Prabowo Siti Amatullah Karimah Subkhan Ibnu Aji Sulthan Kharisma Akmal Syafrial Fachri Pane Syafwan Almadani Azra Syiarul Amrullah, Muhammad Taufik Suyanto Vera Suryani Wanda Firdaus Yahya Ermaya Yuda Prasetia Zidni Fahmi Suryandaru