Article Per Year (5 Year)
p-Index From 2021 - 2026
0.947
P-Index
This Author published in this journals
All Journal Jurnal Buana Informatika Telematika Seminar Nasional Informatika (SEMNASIF) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan VOLT : Jurnal Ilmiah Pendidikan Teknik Elektro Jurnal Sisfokom (Sistem Informasi dan Komputer) Indonesian Journal of Electrical Engineering and Computer Science JUDIMAS (Jurnal Inovasi Pengabdian Kepada Masyarakat) Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Jurnal Pengabdian Mitra Masyarakat (JPMM) Jurnal Informatika dan Multimedia Jurnal Abdimastek (Pengabdian Masyarakat Berbasis Teknologi) Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi Uranus: Jurnal Ilmiah Teknik Elektro, Sains dan Informatika
Dhanar Intan Surya Saputra
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Library & Information Science Medicine & Pharmacology Social Sciences Other

Published : 14 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 14 Documents
Search

 1   2 
Evaluasi Keamanan Sistem Pada Aplikasi Catatmak Dengan Metode Kualitatif Berbasis Pengkodean Tematik Fariz Nur Fikri Zaki; Putri Awaliatuz Zahra; Vidia Alma Cyrilla; Wahyu Latifatun; Ranggi Praharaningtyas Aji; Dhanar Intan Surya Saputra
Jurnal IT UHB Vol 6 No 2 (2025): Jurnal Ilmu Komputer dan Teknologi
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/ikomti.v6i2.1871

Abstract

This study evaluates the implementation of data security and privacy mechanisms in the Catatmak mobile application, a local personal finance tool. It addresses the increasing risks associated with the handling of sensitive user data, particularly in digital financial platforms used by the general public. A qualitative method was employed, using semi-structured interviews with the main developer of the app, who also oversees the system’s technical infrastructure. The interview explored data collection policies, encryption and authentication mechanisms, as well as role-based access control. In parallel, static and dynamic security assessments were conducted using Mobile Security Framework (MobSF) and the OWASP Application Security Verification Standard (ASVS). Results indicate that Catatmak enforces key security practices including HTTPS encryption, OTP-based login, encrypted cloud storage, and RBAC-based access segmentation. Despite these efforts, user-related vulnerabilities remain dominant, particularly weak password habits and careless sharing of OTP codes. The developer emphasized that “most threats don’t come from hackers, but from users giving away their own credentials.” As a result, the study recommends the integration of two-factor authentication (2FA), user security education, and the adoption of Secure Software Development Lifecycle (SDLC) principles. These insights are expected to inform the development of more secure financial apps within the Indonesian digital ecosystem.
Perancangan Website Pelayanan dan Edukasi Satres PPA PPO Polresta Banyumas Berbasis Laravel Adi Maulana Putra Hidayat; Dhanar Intan Surya Saputra
Bridge : Jurnal Publikasi Sistem Informasi dan Telekomunikasi Vol. 4 No. 2 (2026): Mei : Bridge : Jurnal Publikasi Sistem Informasi dan Telekomunikasi
Publisher : Asosiasi Profesi Telekomunikasi Dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/bridge.v4i2.876

Abstract

Public service institutions currently utilize digital technology to improve information accessibility for society. Satres PPA and PPO Polresta Banyumas has an important role in providing services, protection, and education related to violence cases against women and children. However, information regarding services, reporting procedures, and educational materials is still not fully accessible to the public. This study aims to design and develop a service and educational website for Satres PPA and PPO Polresta Banyumas using the Laravel framework. The research method used in this study is the Waterfall method, which consists of requirement analysis, system design, implementation, testing, and maintenance stages. The website was developed using Laravel, MySQL, and Bootstrap to support responsive interface design. The results show that the developed website is able to provide service information, educational articles, reporting procedures, and contact information effectively. System testing results indicate that all website features function properly according to user requirements. The website is expected to improve public access to information and increase awareness regarding the protection of women and children.
Analisis Komparatif Pemanfaatan Generative AI Gemini dan Grok dalam Pembuatan Konten Edukasi Visual Satreskrim Polresta Banyumas R. Vitto Mahendra; Risko Nur Rizqi; Oktaviano Rifky Ramadhani; Dhanar Intan Surya Saputra
Uranus: Jurnal Ilmiah Teknik Elektro, Sains dan Informatika Vol. 4 No. 2 (2026): Juni: Uranus: Jurnal Ilmiah Teknik Elektro, Sains dan Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/uranus.v4i2.1623

Abstract

The rise in cybercrime in Indonesia has prompted law enforcement agencies to optimize their preventive communication strategies based on visual content. This study conducts a comparative analysis of the use of two generative artificial intelligence platforms—Gemini (Google DeepMind) and Grok (xAI)—in the production of visual educational content by the Criminal Investigation Unit of the Banyumas City Police. The methodology employed is a comparative experimental research approach using identical prompt instruments across two main scenarios: the prevention of motor vehicle theft and the prevention of online fraud. The evaluation was conducted based on three assessment dimensions: contextual relevance, production speed (response time), and content filtering mechanisms. The study’s findings indicate that Grok outperforms Gemini in terms of production speed, the depth of local identity representation, and visual quality tailored to social media audiences, while Gemini demonstrates superiority in the dimensions of formality and consistency of output for the context of official institutional communication. The implications of this research point toward a recommendation for a complementary approach in the synergistic use of both platforms in accordance with the specific communication needs of the Criminal Investigation Unit.
Evaluating the Impact of SMOTE-Based Data Balancing on Decision Bias and Algorithmic Fairness in XGBoost-Based Student Dropout Prediction Les Endahti; Taqwa Hariguna; Dhanar Intan Surya Saputra
Telematika Vol 19, No 2: August (2026)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v19i2.3425

Abstract

Student dropout prediction is a key application of Educational Data Mining for supporting early intervention in higher education. However, previous studies have primarily focused on improving predictive accuracy, while the effects of data balancing on decision bias and algorithmic fairness remain underexplored. This study proposes a comprehensive evaluation framework that integrates predictive performance, decision bias, and algorithmic fairness to assess the impact of the Synthetic Minority Over-sampling Technique (SMOTE) on Extreme Gradient Boosting (XGBoost) for student dropout prediction. Experiments were conducted using the publicly available Predict Students Dropout and Academic Success dataset containing 4,424 student records. After excluding the Enrolled class, the dataset was transformed into a binary classification problem consisting of 2,209 Graduate (60.9%) and 1,421 Dropout (39.1%) instances. Two models were compared: a baseline XGBoost classifier and an XGBoost classifier trained with SMOTE. Predictive performance was evaluated using Accuracy, Precision, Recall, F1-score, and ROC-AUC, while decision bias and algorithmic fairness were assessed using the False Negative Rate (FNR), Statistical Parity Difference (SPD), Disparate Impact (DI), Equal Opportunity Difference (EOD), and Average Odds Difference (AOD). The baseline model achieved higher Accuracy (93.11% vs. 92.29%), Precision (91.49% vs. 89.86%), F1-score (91.17% vs. 90.18%), and a lower FNR (0.0915 vs. 0.0951), whereas both models produced comparable ROC-AUC values (0.972). McNemar's test indicated that the difference in predictive performance was not statistically significant (p = 0.264). Although SMOTE did not improve predictive performance, it produced modest reductions in Statistical Parity Difference (0.2310–0.2218), Equal Opportunity Difference (0.0298–0.0233), and Average Odds Difference (0.0295–0.0235), indicating a slight improvement in fairness metrics while maintaining comparable discrimination capability. These findings highlight the trade-off between predictive performance and algorithmic fairness and demonstrate that evaluating predictive performance together with decision bias and fairness provides a more comprehensive assessment of educational machine learning models, supporting the development of responsible AI-based educational decision-support systems.
Co-Authors Adi Maulana Putra Hidayat Andi Sunyoto Anisa Putri Amalia Awal Rafi Hardika Dwi Fatma Diningrum Eka Nuraini Ema Utami Fariz Nur Fikri Zaki Haviluddin Haviluddin Hellik Hermawan Imam Tahyudin Kabul Agus Purwanto Kholifatun Anisa Les Endahti Luky Rafi Anuggilarso Mahardika, Fajar Oktaviano Rifky Ramadhani Putri Awaliatuz Zahra R. Vitto Mahendra Ranggi Praharaningtyas Aji Risko Nur Rizqi Rizky Arif Fauzi Rujianto Eko Saputro Shelviana Aprilliani Nurjanah Sitaresmi Wahyu Handani Taqwa Hariguna Ully Purbandani Vidia Alma Cyrilla Wahyu Latifatun