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All Journal IJID (International Journal on Informatics for Development) Infoman's: Jurnal Ilmu-Ilmu Manajeman dan Informatika JOINT (Journal of Information Technology Jurnal Teknik Informatika (JUTIF) Jurnal Interkom : Jurnal Publikasi Ilmiah Bidang Teknologi Informasi dan Komunikasi TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Infomans: Jurnal Ilmu-ilmu Informatika dan Manajemen Router : Jurnal Teknik Informatika dan Terapan JIMT: Jurnal Informatika, Multimedia dan Teknik Router : Jurnal Teknik Informatika dan Terapan Jurnal Sistem Informasi dan Ilmu Komputer Jasmine : Journal of Intelligent Systems and Machine Learning
Dody Herdiana
Universitas Sebelas April
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Social Sciences Other

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The Impact of Algorithms on Decision-Making in Daily Life: A Polling Study of Technology Users Dwi Yuniarto; Yopi Hidayatul Akbar; Aedah Abd. Rahman; Dody Herdiana
IJID (International Journal on Informatics for Development) Vol. 14 No. 1 (2025): IJID June
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2025.4973

Abstract

Algorithms have become an integral part of everyday life, particularly in entertainment, shopping, and navigation. This study examines how algorithms influence individual decision-making. Data were collected through an online poll involving 200 respondents, selected using a statistical sampling method. The results indicate that 55% of respondents perceive algorithms as having a significant influence on their decisions, while 28% report a moderate impact. A confidence interval analysis (95%) has been included to ensure statistical accuracy. The study highlights the importance of digital literacy in mitigating algorithmic bias and suggests future research on how socio-cultural factors shape algorithmic perceptions. This research contributes to understanding the extent of algorithmic influence on daily decision-making and raises user awareness of technology’s impact. The implications include the importance of digital literacy to mitigate dependency and bias in algorithm usage and the potential to develop more transparent and ethical algorithmic systems. Future research could explore the relationship between users' awareness of algorithms and their behaviors in various contexts and evaluate ways to enhance public understanding of how algorithms function in the evolving digital ecosystem.
Public Sentiment Toward Rupiah Redenomination on Social Media X Agung Febrian; Dody Herdiana; M. Agreindra Helmiawan
JASMINE: Journal of Intelligent Systems and Machine Learning Vol. 1 No. 1 (2026)
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jasmine.v1i1.10121

Abstract

This study examines public sentiment toward the proposed Indonesian Rupiah redenomination policy using data collected from Social Media X. The research applies a structured computational sentiment analysis pipeline, beginning with automatic sentiment labeling using a transformer-based language model, followed by classification using a Support Vector Machine with Term Frequency–Inverse Document Frequency feature representation. The dataset was collected over a one-day period from 7 November 2025 to 8 November 2025 to capture immediate public reactions to the policy discourse. Experimental results show that the classification model achieved an accuracy of 0.68 with balanced classification performance. Rather than aiming to optimize predictive accuracy, this study focuses on identifying general sentiment tendencies and patterns of public opinion regarding currency redenomination. The findings indicate that negative sentiment dominates the discourse, reflecting public concern and hesitation toward the policy, while a substantial proportion of neutral sentiment suggests ongoing evaluation and uncertainty among users. These results highlight the complexity of public responses to monetary policy communication and demonstrate the potential of social media analysis to provide an indicative overview of public sentiment in the digital public sphere. The study also acknowledges limitations related to automatic labeling and the inherent ambiguity of social media language, emphasizing that the findings should be interpreted as exploratory insights rather than definitive conclusions.
Evaluation of Machine Learning Algorithms for Predicting Phishing Attacks in Higher Education Environments: An Experimental Framework for Enhancing Cybersecurity in Academic Institutions Akmal Muhammad Poetra; Dody Herdiana; Muhammad Agreindra Helmiawan
JASMINE: Journal of Intelligent Systems and Machine Learning 2026: Articles in Press
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jasmine.vi.10178

Abstract

This study evaluates the performance of several machine learning algorithms Logistic Regression, Support Vector Machine, Random Forest, and XGBoost in predicting phishing attacks within higher education environments. Due to the limited availability of anonymized institutional datasets, the research employs a conceptual experiment design and simulation-based approach that mirrors the characteristics of phishing incidents commonly encountered by academic users. The simulated dataset includes URL-based indicators, HTML features, email text elements, and behavioral metadata. The experimental protocol covers synthetic data generation, domain-specific feature engineering, stratified k-fold cross-validation, hyperparameter tuning via grid search, and performance evaluation using accuracy, precision, recall, F1-score, and ROC/AUC. The simulation results indicate that ensemble-based models (Random Forest and XGBoost) outperform linear and kernel-based models, especially in scenarios with class imbalance typical of campus environments. The discussion highlights implications for real-world campus cybersecurity operations, limitations of conceptual simulations, and future research needs such as real-world validation and the integration of user behavior features. The main contribution is a complete experimental framework that can be executed with real institutional datasets, providing guidance for model selection and deployment in higher education cybersecurity systems.
Analisis Sentimen Ulasan Google Play pada Aplikasi Tahu Sumedang Menggunakan Lexicon-Based Khairil Sidik; Dody Herdiana; M. Agreindra Helmiawan; Asep Saeppani
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 5 No 2 (2025): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol5No2.pp356-361

Abstract

This study examines user perceptions of the Tahu Sumedang application based on reviews submitted on the Google Play Store. The purpose of this research is to identify user sentiment and understand the aspects that influence positive and negative evaluations of the application. The lexicon-based sentiment analysis method is used to classify each review into sentiment categories through a dictionary of words with positive and negative polarity. The results show variations in user sentiment that reflect satisfaction with certain features and concerns about technical performance, navigation, and application stability. The findings highlight several aspects that require improvement to optimize the quality of digital public services. The conclusion of this study emphasizes the importance of user feedback as an evaluation basis for developing more responsive and efficient public service applications.
Quantitative Analysis of the Key Factors Driving Cybersecurity Awareness Among Information Systems Users Helmiawan, Muhammad Agreindra; Firmansyah, Esa; Herdiana, Dody; Akbar, Yopi Hidayatul; Subiyakto, A’ang; Rahman, Titik Khawa Abdul
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4861

Abstract

Cybersecurity threats are increasingly complex and widespread, posing significant risks to individuals and organizations. However, many studies tend to address the technological or behavioral aspects separately. The study uses a survey-based quantitative approach using PLS-SEM to analyze key factors that influence cybersecurity awareness, including demographics, training, psychological bias, and organizational culture. The findings suggest that several constructs-such as threat awareness, perceived risk, and education-significantly predict cybersecurity awareness and behaviour. Notably, the model yields an R² value of up to 0.703 with a strong path significance (p < 0.05), which underscores the robustness of the relationship. This study offers an integrated perspective on cybersecurity by bridging the psychological, educational, and organizational dimensions. It highlights cybersecurity awareness as a mediating construct that links upstream factors to secure user behavior-a relational structure that has not been explored in previous research.
Implementasi Algoritma Random Forest dalam Pengukuran Kesiapan Transformasi Digital Desa Kaduwulung Menuju Desa Cerdas Berbasis SNI ISO 37122:2019 Esa Firmansyah; Muhammad Agreindra Helmiawan; Dody Herdiana; Dwi Yuniarto
Infoman's : Jurnal Ilmu-ilmu Informatika dan Manajemen Vol. 19 No. 2 (2025): Infoman's
Publisher : LPPM & Fakultas Teknologi Informasi UNSAP

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The readiness of healthcare workers and village officials in adopting digital technology is a decisive factor for the success of transforming into a smart village. This study aims to measure the digital transformation readiness level of Kaduwulung Village using the SNI ISO 37122:2019 standard through village data mapping and the implementation of the Random Forest algorithm for digital maturity classification. The research methodology employs a quantitative approach using the Design Thinking model combined with the Technology Acceptance Model (TAM) evaluation. The design results demonstrate that the integration of automated scoring features can assist in faster decision-making for public services. Based on the testing, the system obtained a System Usability Scale (SUS) score of 74 and a User Experience Questionnaire (UEQ) score of 1.98, proving that technology adoption readiness is significantly influenced by ease of navigation and system infrastructure support.
Investigating the Mediating Role of Cybersecurity Awareness in Bridging Cognitive Factors and Secure Behavioural Intentions: A Quantitative Approach Using PLS-SEM Muhammad Agreindra Helmiawan; Yanyan Sofiyan; Esa Firmansyah; Dody Herdiana; Irfan Fadil; Titik Khawa Abdul Rahman
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.4.5716

Abstract

Cybersecurity threats have become increasingly sophisticated, rendering the human element a critical vulnerability despite advanced technical safeguards. This study investigates the cognitive drivers of cybersecurity awareness and secure behaviour through the lens of Protection Motivation Theory (PMT), specifically examining the mediating role of Cybersecurity Awareness (CA). A quantitative approach using Partial Least Squares Structural Equation Modelling (PLS-SEM) was employed to analyse data from students and faculty members in a higher education setting. The findings substantiate that Self-Efficacy (β=0.412,p<0.05) and Response Efficacy (β=0.385,p<0.05) are significant predictors of CA, with the model achieving a robust R2 value of 0.703. Crucially, the mediation analysis identifies CA as a vital cognitive bridge that translates internal confidence into Secure Behavioural Intentions. These results offer an integrated framework for developing targeted intervention strategies in academic institutions. For the field of Informatics, this research underscores the urgency of designing human-centric security systems that prioritize psychological empowerment to foster sustainable digital resilience against an evolving threat landscape.
Evaluasi Algoritma Machine Learning untuk Klasifikasi URL Berbahaya Menggunakan Fitur Leksikal Muhammad Rizal Daffa Khoirudin; Muhammad Agreindra Helmiawan; Dody Herdiana
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 4 No. 3 (2026): Agustus: Jurnal Sistem Informasi dan Ilmu Komputer
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59581/jusiik-widyakarya.v4i3.6617

Abstract

The growth of digital services has increased the risk of malicious URLs, such as those used for phishing, malware, and website defacement, making rapid and accurate detection mechanisms essential. This study aims to evaluate the performance of five machine learning algorithms Logistic Regression, Decision Tree, Random Forest, HistGradient Boosting, and Gaussian Naive Bayes, in classifying URLs into four classes using lexical features extracted directly from the URL structure. The dataset used is the Malicious URLs Dataset from Kaggle, which, after preprocessing, yielded 641,113 URLs. Each URL was transformed into 25 lexical features, and the data was then split using a stratified split with an 80:20 ratio. Evaluation was conducted using the metrics accuracy, precision, recall, macro F1-score, and confusion matrix, along with an experiment involving the removal of protocol features to analyze its impact on model performance. The results showed that Random Forest delivered the best performance with an accuracy of 94.98% and a macro F1-score of 93.03%, outperforming the other algorithms tested. Protocol feature removal reduced the performance of all models; however, Random Forest maintained a high level of accuracy, indicating that the combination of lexical features effectively represents the characteristics of URLs. These findings indicate that an approach based on lexical features and Random Forest has the potential to be a lightweight, efficient, and reliable solution for supporting the multi-class detection of malicious URLs in cybersecurity systems.
Co-Authors Abd. Rahman, Aedah Aedah Abd. Rahman Agung Febrian Akmal Muhammad Poetra Asep Saeppani Dwi Yuniarto Dwi Yuniarto Esa Firmansyah Esa Firmansyah Firmansyah, Esa Hidayat, Faishal Akbar Irfan Fadil Khairil Sidik Muhammad Agreindra Helmiawan Muhammad Arifin Ilham Muhammad Rizal Daffa Khoirudin Rahman, Titik Khawa Abdul Ramdhani, Ravi Rizki Subiyakto, A’ang Suryadi, Mulya Titik Khawa Abdul Rahman Yan Yan Sofiyan Yanyan Sofiyan Yayan Cahyan Yopi Hidayatul Akbar Yopi Hidayatul Akbar