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Repeater: Publikasi Teknik Informatika dan Jaringan
Published by Asosiasi Profesi Telekomunikasi Dan Informatika Indonesia
ISSN : 30467284     EISSN : 30467276     DOI : 10.62951
Core Subject : Science,
Computer Science & IT
Repeater : Publikasi Teknik Informatika dan Jaringan berisikan naskah hasil penelitian di bidang Teknik Informatika dan Jaringan
Arjuna Subject : Ilmu Komputer (semua kategori) - Jaringan Komputer dan Komunikasi
Articles 107 Documents
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Analisis Etika Profesional terhadap Risiko Keamanan pada Sistem OpenClaw Muhammad Faiqul Umam Dzunnuroeni; Muhammad Zaky Ramadhan; Evy Nurmiati
Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 4 No. 3 (2026): Juli : Repeater : Publikasi Teknik Informatika dan Jaringan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/repeater.v4i3.929

Abstract

This study aims to evaluate the implementation of the built-in security model in a local installation of the OpenClaw autonomous agent and to analyze its implications for information technology (IT) professional ethics. The study employed a qualitative method using a single-case study approach. Primary data were collected through direct simulation experiments comparing secure and insecure configurations within an isolated environment. The results indicate that OpenClaw incorporates native security controls based on a single trust boundary and provides adequate security auditing capabilities. The system also includes data protection mechanisms that minimize the risk of misuse when operated according to the recommended configuration. However, configuring the network to be publicly accessible (0.0.0.0) without authentication violates the principle of least privilege and significantly increases the risk of unauthorized access. These findings demonstrate that vulnerabilities in self-hosted autonomous agents are not solely attributable to technical weaknesses in the system but also reflect failures in adhering to IT professional ethics. Therefore, responsibility is clearly shared between developers and IT practitioners. Developers are responsible for providing adequate security mechanisms, while IT professionals have an ethical obligation to maintain system security, protect user privacy, and ensure that security configurations are implemented in accordance with established best practices.
Optimasi Hiperparameter XGBoost Regression untuk Prediksi Harga Saham BBNI Berbasis Transaksi Historis Muhammad Irfan
Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 4 No. 3 (2026): Juli : Repeater : Publikasi Teknik Informatika dan Jaringan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/repeater.v4i3.950

Abstract

Stock investment in the banking sector, such as PT Bank Negara Indonesia (Persero) Tbk (BBNI), carries high risks due to dynamic market volatility, necessitating accurate prediction methods to support investment decision-making. This study aims to optimize the performance of the XGBoost Regression algorithm in predicting BBNI stock prices based on historical transaction data from the last five years. The methodology applied includes data preprocessing for price format validation, feature engineering using technical indicators (SMA, EMA, MACD, RSI), and hyperparameter optimization using the Grid Search Cross-Validation technique. The experimental results demonstrate that hyperparameter optimization effectively refines the model's predictive stability. While maintaining a highly precise Mean Absolute Percentage Error (MAPE) of 2.00%, the Grid Search technique successfully reduced the nominal error (RMSE) and improved the model's goodness-of-fit (R-Squared). These findings confirm that the optimized model offers superior generalization capabilities in capturing price volatility compared to the baseline model. Consequently, this optimized predictive model provides a robust analytical tool for investors and financial analysts to mitigate risks and formulate effective trading strategies in the highly fluctuating banking stock market.
Prediksi dan Deteksi Bug pada Visual Studio Code menggunakan Algoritma Naive Bayes Siti Hardianti; Roberto Kaban
Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 4 No. 3 (2026): Juli : Repeater : Publikasi Teknik Informatika dan Jaringan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/repeater.v4i3.953

Abstract

Software quality is a critical aspect of modern software engineering. One of the primary challenges developers face is the early detection of bugs before software is released into the production environment. This study develops a bug prediction model using the Naive Bayes algorithm applied to the JM1 dataset from NASA's Metrics Data Program, sourced from Kaggle. The JM1 dataset consists of source code metrics from a NASA project, comprising 10,885 modules with 21 numerical features that include Halstead and McCabe metrics. All experimental stages were conducted using Python with the Pandas, Scikit-learn, NumPy, and Matplotlib libraries. Experimental results show that the Naive Bayes model achieved an accuracy of 79.93%, an ROC-AUC value of 0.6761, precision of 46.26%, recall of 23.52%, and an F1-Score of 31.18%. These findings indicate that while Naive Bayes effectively identifies non-defective modules, it faces challenges in detecting defective modules due to significant class imbalance (80.65% vs. 19.35%). The contributions of this study include an in-depth analysis of the impact of class imbalance on bug prediction performance, as well as recommendations for handling techniques such as SMOTE and ensemble learning to improve future performance.
Systematic Literature Review: Pemanfaatan Teknologi Internet of Things dan Kecerdasan Buatan untuk Optimalisasi Efisiensi Energi pada Smart Home Rizki Pangestu
Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 4 No. 3 (2026): Juli : Repeater : Publikasi Teknik Informatika dan Jaringan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/repeater.v4i3.960

Abstract

The residential sector accounts for a significant portion of global energy consumption. The integration of the Internet of Things (IoT) and Artificial Intelligence (AI) presents a promising solution to mitigate energy waste through Smart Home Energy Management Systems (HEMS). This Systematic Literature Review (SLR) adheres to the PRISMA protocol to analyze 20 primary experimental studies published between 2021 and 2025. This study aims to evaluate the effective hardware architecture, the most accurate machine learning/optimization algorithms, and the measurable economic impact of these systems. The findings reveal that hybrid deep learning models, such as LSTM+GRU and Wavelet-LSTM-SVR, achieve high prediction accuracies exceeding 95%. For demand response scheduling, hybrid metaheuristic algorithms like HGPO and MMGO can reduce electricity costs by up to 57.8% and peak-to-average ratio (PAR) by 74.68% in cluster dwellings. Additionally, the integration of Large Language Models (LLMs) significantly enhances user compliance and sustained energy optimization. Overall, IoT and AI implementation in smart buildings is capable of reducing daily operational costs significantly. Future research should address data privacy and the scalability of peer-to-peer energy trading.
Evaluasi Keamanan Website Unm.ac.id Menggunakan Metodologi Penetration Testing Berdasarkan Framework OWASP Top 10 Muhammad Fadlu Rahman; Satria Gunawan Zain; Abdul Rahman Patta
Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 4 No. 3 (2026): Juli : Repeater : Publikasi Teknik Informatika dan Jaringan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/repeater.v4i3.962

Abstract

The high incidence of cyberattacks across various sectors reported by the National Cyber and Crypto Agency (BSSN) poses a serious threat to data and service security in application systems, including at Makassar State University, which relies on web-based information technology for many of its activities, thereby creating potential risks to its website security. This study aims to evaluate the security of the domain unm.ac.id and its subdomains using penetration testing methods, identify security vulnerabilities based on the OWASP Top 10, and develop relevant mitigation strategies. The method used is penetration testing through several stages: information gathering, enumeration, vulnerability scanning, exploitation, OWASP Top 10–based security classification, risk assessment using CVSS 3.1, and formulation of mitigation strategies. The results identified 9 validated vulnerability types, with the majority of OWASP Top 10 classifications falling under security misconfiguration, cryptographic failures, and the use of vulnerable or outdated components. Most vulnerabilities were at medium, low, and informational levels. It can therefore be concluded that security mechanisms on the website have been implemented, but periodic evaluation and reinforcement of security are still required to minimize potential risks and prevent future attacks.
Rancang Bangun Aplikasi Bank Sampah Berbasis Mobile dengan Pendekatan Gamifikasi: Studi Kasus: Kelurahan Cipameungpeuk, Kabupaten Sumedang Deni Andayani; Agun Guntara; Yanyan Sofiyan
Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 4 No. 3 (2026): Juli : Repeater : Publikasi Teknik Informatika dan Jaringan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/repeater.v4i3.963

Abstract

Waste management at the community level in Indonesia faces a persistent challenge: low household participation in waste sorting, compounded by manual administrative systems in waste bank operations. This study aimed to design and develop a gamification-based mobile application, SampahKu, to support waste bank operations at Kelurahan Cipameungpeuk, Sumedang. The application was built using the Flutter framework with Firebase as backend infrastructure and integrated three gamification elements points, badges, and leaderboard grounded in the Octalysis Framework and Self-Determination Theory (SDT). Development followed an Agile/Scrum methodology across iterative sprints. Functional evaluation was conducted through Black Box Testing (BBT) on 15 test scenarios, achieving a 100% pass rate. User Acceptance Testing (UAT) was carried out with five purposively selected participants (three residents and two waste bank officers) through a Likert-scale questionnaire on five dimensions: point system, badges, leaderboard, motivation, and ease of use. Overall UAT score reached 94% (very acceptable category). While these results indicate that users positively perceived the gamification elements after a brief trial session, the study acknowledges that this does not constitute proof of sustained behavioral change. Longitudinal evaluation with a larger sample is needed to validate long-term motivational impact. This study contributes a theoretically grounded gamification design model for community-level environmental applications in Indonesia.
Beyond AI Assistance: Developing a Human-AI Collaborative Learning Model to Enhance Data Science Competencies , Netti Herawati
Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 4 No. 3 (2026): Juli : Repeater : Publikasi Teknik Informatika dan Jaringan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/repeater.v4i3.966

Abstract

The rapid adoption of generative artificial intelligence (AI) has significantly influenced higher education, particularly in Data Science programs where students increasingly utilize AI tools for programming, data analysis, and problem solving. However, unstructured AI utilization may lead to superficial learning and excessive reliance on automated outputs, potentially reducing the development of higher-order thinking skills. This study aims to develop and evaluate the Human-AI Collaborative Learning (HACL) model as a pedagogical framework that positions generative AI as a cognitive partner in learning processes. A mixed-methods sequential explanatory design was applied involving 120 undergraduate Data Science students, with 60 students participating in the experimental group through the HACL model and 60 students in the control group using conventional AI-assisted learning. Quantitative data were collected through questionnaires and project-based competency assessments, while qualitative data were obtained from observations, interviews, reflective journals, and learning artifacts. The results indicate that students implementing the HACL model achieved greater improvements in AI Literacy, Computational Thinking, Critical Thinking, and Data Science Competency compared with the control group. Structural equation modeling confirmed that HACL positively influenced learning outcomes through AI Literacy and higher-order thinking skills as mediating factors. Qualitative findings showed that structured human-AI collaboration promoted iterative reasoning, critical evaluation of AI outputs, reflective learning, and responsible AI practices. This study introduces the HACL model as a practical framework consisting of six sequential phases to support meaningful, ethical, and human-centered AI integration in Data Science education.

Page 11 of 11 | Total Record : 107

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