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Recursive Journal of Informatics
Published by Universitas Negeri Semarang
ISSN : -     EISSN : 29866588     DOI : https://doi.org/10.15294/rji
Core Subject : Science,
Computer Science & IT
Recursive Journal of Informatics published by the Department of Computer Science, Universitas Negeri Semarang, a journal of Information Systems and Information Technology which includes scholarly writings on pure research and applied research in the field of information systems and information technology as well as a review-general review of the development of the theory, methods, and related applied sciences. We hereby invite friends to post articles and citation articles in our journals. We appreciate it if you would like to submit your paper for publication in RJI. The RJI publication period is carried out 2 periods in a year, namely in March and September.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 57 Documents
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SQL Injection Detection Using Character-Level Embedding with Convolutional Neural Network and Gated Recurrent Unit Ahmad Andri Alfian; Subhan Subhan
Recursive Journal of Informatics Vol. 4 No. 2 (2026): September 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v4i2.62096

Abstract

Abstract. SQL Injection is one of the most common cyberattacks targeting web applications by exploiting malicious SQL queries to gain unauthorized access to database systems. Conventional SQL Injection detection methods generally rely on manually engineered features or word-level representations, which may overlook fine-grained syntactic characteristics contained in SQL queries. Purpose: This study aims to develop and evaluate a SQL Injection detection model based on Character-level Embedding, Convolutional Neural Network (CNN), and Gated Recurrent Unit (GRU). The proposed model is expected to preserve character-level information while improving the capability to distinguish legitimate SQL queries from SQL Injection attacks. Methods/Study design/approach: The proposed model combines Character-Level Embedding, CNN, and GRU for binary classification of SQL queries. The dataset used is the SQL Injection Dataset obtained from the Kaggle website, consisting of 30,907 SQL queries with 19,529 legitimate queries and 11,378 SQL Injection queries. Before model training, the dataset was preprocessed using character-level tokenization, sequence padding, and dataset splitting with a ratio of 80:10:10 for training, validation, and testing. The model was implemented using TensorFlow and Keras, trained with the Adam optimizer and Binary Cross-entropy loss function, and evaluated using Accuracy, Precision, Recall, F1-score, and Confusion Matrix. Result/Findings: The proposed CNN-GRU model achieved an Accuracy of 99.64%, Precision of 100%, Recall of 99.03%, and F1-score of 99.51% indicating that the model effectively classifies legitimate SQL queries and SQL Injection attacks with high performance. Novelty/Originality/Value: It can be concluded that integrating Character-Level Embedding with hybrid CNN-GRU architecture enables the proposed model to learn both local character patterns and long-term sequential dependencies without requiring manual feature engineering. Therefore, the proposed approach provides an effective alternative for SQL Injection detection and has the potential to improve web application security.
Analysis Of The Successful Implementation of A Disaster Management Information System In Supporting Effective Disaster Response Decision Making Using The Delone And Mclean Is Success Model Rahman Pradipta; Rahmad Bahri; Khairul Muttaqin
Recursive Journal of Informatics Vol. 4 No. 2 (2026): September 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v4i2.62630

Abstract

Abstract. Disaster management requires accurate, reliable, and timely information to support effective decision making, particularly during emergency response situations. This study evaluates selected dimensions of DMIS implementation success - System Quality, Information Quality, User Satisfaction, and Decision-Making Effectiveness at the Regional Disaster Management Agency (Badan Penanggulangan Bencana Daerah - BPBD) of Langsa City. Purpose: This study evaluates whether System Quality and Information Quality are associated with Decision-Making Effectiveness directly and through User Satisfaction in the implementation of a Disaster Management Information System. Methods/Study design/approach: A quantitative approach was employed involving 35 employees of BPBD Langsa City who use disaster-management information systems. A saturated sampling technique was applied because the entire population was included as respondents. Data were collected using a five-point Likert-scale questionnaire and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS. The analysis consisted of measurement model evaluation and structural model evaluation. Result/Findings: The measurement model demonstrated satisfactory validity and reliability, with all outer loading values exceeding 0.70, composite reliability values ranging from 0.908 to 0.937, and Average Variance Extracted values ranging from 0.665 to 0.749. System Quality had a positive and significant effect on Decision-Making Effectiveness (β = 0.362; t = 2.779; p = 0.009). Information Quality had a positive but non-significant effect (β = 0.245; t = 1.710; p = 0.096), while User Satisfaction also showed a positive but statistically non-significant effect at the 5% significance level (β = 0.317; t = 2.021; p = 0.051). The model explained 54.6% of the variance in Decision-Making Effectiveness. Novelty/Originality/Value: This study extends the application of the DeLone and McLean IS Success Model to the public-sector disaster-management context by positioning Decision-Making Effectiveness as the organizational outcome of Disaster Management Information System implementation and User Satisfaction as a mediating construct.
Robustness of Bayesian-Optimized Random Forest Against Feature-Space Evasion Attacks for Spam Detection Moh. Aminudin; Riza Arifudin
Recursive Journal of Informatics Vol. 4 No. 2 (2026): September 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v4i2.62732

Abstract

Abstract. Email spam filters may perform well on unmodified messages yet remain vulnerable to deliberate feature manipulation. Although hyperparameter optimization can improve clean-data performance, such gains do not establish robustness under test-time perturbations. A paired, leakage-controlled evaluation is therefore needed to assess both properties under identical conditions. Purpose: This study compared the clean classification performance and feature-space robustness of a library-default Random Forest (RF-default) and a Bayesian-optimized Random Forest (BO-RF) on Spambase. Methods/Study design/approach: Exact duplicates were removed, and predictor-identical records were grouped to prevent cross-fold leakage. Evaluation used two repetitions of five-fold nested stratified group-aware cross-validation with shared outer folds. Bayesian Optimization maximized inner-validation spam-class F1. Frozen outer-test folds were assessed on clean inputs, class-center-directed perturbations at α = 0.05, 0.10, 0.20, and 0.40, and 30 magnitude-matched random directions. Retained inference assessed within-model clean-versus-directed and directed-versus-random contrasts in F1, Recall, and model-specific attack success rate (ASR). Result/Findings: BO-RF yielded slightly higher means across all four clean metrics; clean F1 was 93.65 ± 0.88% versus 93.53 ± 0.98% for RF-default. Bayesian Optimization selected the effective default configuration in nine of ten outer folds. At directed α = 0.40, F1 fell to 85.20% for RF-default and 85.31% for BO-RF, while Recall fell to 77.94% and 78.03%, respectively; directed ASR was approximately 15.5% for both models. All 48 retained within-model contrasts met Holm-adjusted thresholds, indicating degradation relative to clean inputs and a stronger effect of directed than magnitude-matched random perturbations. No direct between-model inferential family was retained, so robustness comparisons remain descriptive. Novelty/Originality/Value: Under the evaluated Spambase feature-space setting, clean-F1 Bayesian Optimization yielded only marginal descriptive gains and did not demonstrate a clear robustness advantage over the effective RF-default configuration. The findings show that clean-metric optimization should not be treated as a proxy for evasion robustness.
Development of an Academic Chatbot Based on Natural Language Processing for Student Information Services at Universitas Bina Darma Muhammad Arif Rahmad Syahputra; Widyanto; Muhammad Nasir
Recursive Journal of Informatics Vol. 4 No. 2 (2026): September 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v4i2.62866

Abstract

Purpose: Repetitive academic inquiries and substantial linguistic variation in informal Indonesian student messages can reduce the consistency and timeliness of university information services. This study develops and evaluates an academic chatbot for student information services at Universitas Bina Darma through WhatsApp. Methods/Study design/approach: An applied engineering-research approach was employed. An exploratory questionnaire involving 100 active students was used to identify dominant information needs and representative query expressions, while verified institutional documents were used separately to construct controlled service responses. The implemented architecture integrates Baileys as the WhatsApp messaging gateway, Node.js/Express as the orchestration layer, Dialog flow ES for intent and entity recognition, and MySQL for conversation logging and monitoring. The final agent contains 129 custom intents, 22 entities, and 2,572 training-phrase entries. An operational confidence threshold of 0.70 was applied to route uncertain inputs to fallback handling. Evaluation was conducted at four layers: 13 black-box functional scenarios, 24 intent cases representing major service families and out-of-scope input, 15 response-conformity cases, and six end-to-end integration scenarios. Result/Findings: All black-box scenarios produced the expected outputs. Intent agreement reached 95.83%, response conformity reached 86.67%, and all integration scenarios were successful. The difference between intent agreement and response conformity indicates that correct intent recognition does not necessarily guarantee an equally specific or institutionally appropriate answer when response coverage is incomplete. Novelty/Originality/Value: The contribution of this study is therefore positioned as applied system engineering rather than a new NLP algorithm. It demonstrates a deployable, confidence-controlled architecture that separates informal-query recognition from verified institutional delivery and evaluates recognition, answer conformity, and technical integration as distinct quality dimensions. Broader intent coverage, human-user evaluation, threshold calibration, load testing, and dynamic integration with institutional information systems remain necessary for wider deployment.
ITEBIS PGRI Dewantara Academic Service Chatbot Based on IndoBERT and Retrieval-Augmented Generation (RAG) Syaiful Imron; Arbiati Faizah; Sugianto
Recursive Journal of Informatics Vol. 4 No. 2 (2026): September 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v4i2.62938

Abstract

Abstract. Academic administrative services must stay responsive beyond regular hours, yet repetitive student inquiries add to staff workload. Retrieval-Augmented Generation (RAG) with a fine-tuned language model offers a way to automate accurate, round the clock responses in Indonesian. Purpose: This study develops a prototype academic-service chatbot for ITEBIS PGRI Dewantara Jombang, integrating a fine-tuned IndoBERT model with RAG architecture to address limited-service hours and repetitive handling of routine student inquiries. Methods/Study design/approach: The system used 2,517 SQuAD-format question-answer pairs, split into 72% training (1,812), 8% validation (201), and 20% test (504). IndoBERT (indobenchmark/indobert-base-p1) was fine-tuned as an extractive reader for five epochs (batch size 8, learning rate 3 × 10⁻⁵) with early stopping, then converted to ONNX format and INT8-quantized for CPU-based local deployment. The prototype separates a Laravel web interface from a FastAPI backend running the RAG pipeline, retrieving passages from a MySQL knowledge base via FAISS search (top-k = 5, min score 0.30) and catching frequent query-answer pairs in Redis to cut redundant database access and inference latency. Evaluation used black-box testing across five scenarios. Result/Findings: On the 504-pair test set, the model achieved an Exact Match of 71.32% and an F1-Score of 86.63%. All five black-box scenarios passed, including the system’s ability to give database-grounded responses within the tested scenarios rather than fabricated answers, though retrieval for categories with fewer passages (e.g., finance) was more sensitive to question phrasing Novelty/Originality/Value: The novelty lies not in any single element but in combining four components: a fine-tuned IndoBERT reader for Indonesian, an academic-domain RAG architecture, a fully functional end-to-end web prototype rather than model evaluation alone, and an ONNX INT8-quantized reader paired with a Redis caching layer that jointly reduce inference latency and database load a combination not jointly addressed in prior IndoBERT or RAG-based chatbot studies.
Human-Centered AI in Practice: Peer Facilitators as Technostress Reducers in a Mixed-Methods LMS Study Ilham Agung Pambudi; Nuur Wachid Abdul Majid; Muhamad Akda Fathul Barri; Ayu Permata Sari
Recursive Journal of Informatics Vol. 4 No. 2 (2026): September 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v4i2.64092

Abstract

Purpose: While the rapid integration of generative artificial intelligence (AI) offers significant pedagogical potential, it often outpaces the psychological scaffolding that anxious learners require, creating a severe affective barrier known as technostress. To address this critical gap, this study investigates whether structured peer facilitation, embedded within a Learning Management System (LMS) based AI course, can effectively reduce undergraduate students' AI anxiety and strengthen their conceptual understanding. Methods/Study design/approach: An explanatory sequential mixed-methods design was conducted involving 33 undergraduate students encountering generative AI instruction for the first time. Quantitative data encompassing AI anxiety, conceptual understanding, and technical self-efficacy were collected pre- and post-intervention, followed by analysis using Wilcoxon signed-rank tests and Spearman's rank correlation. Subsequently, qualitative data from open-ended evaluations were examined through reflexive thematic analysis to contextualize and deeply explain the quantitative shifts. Result/Findings: The intervention yielded a statistically significant reduction in AI anxiety (Z = -2.151, p = .032) alongside a significant improvement in conceptual understanding (Z = -1.990, p = .047). Although technical self-efficacy and career projection improved, the changes did not reach statistical significance. Furthermore, facilitator evaluation exhibited a significant negative correlation with anxiety reduction (rs = -.389). Thematic analysis confirmed that peer facilitators effectively acted as technical translators and technostress reducers, establishing the psychological safety necessary for learning. Novelty/Originality/Value: This research uniquely repositions peer mediation as the vital affective "more capable other" within Vygotsky's Zone of Proximal Development, prioritizing the resolution of emotional barriers before cognitive engagement with AI tools. The study extends Human-Centered AI (HCAI) theory by delivering robust empirical evidence that pairing generative AI modules with human peer facilitation creates a synergistic learning ecosystem. This provides educators with a scalable, practical solution for mitigating technostress in modern digital education.
A Comparative Analysis of the Effectiveness and Efficiency of Manual and Automated Testing in Software Quality Assurance Amara Seviany Agustin; Nuur Wachid Abdul Majid; Md Baharuddin Bin Abdul Rahman
Recursive Journal of Informatics Vol. 4 No. 2 (2026): September 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v4i2.64137

Abstract

Purpose: This study aims to analyze the effectiveness, bottlenecks, and reliability of automated testing and manual testing, as well as to compare the two approaches in detecting bugs and supporting software product quality. Accelerating release cycles can be counterproductive if functional stability is ignored; thus, evaluating these two quality assurance methods is crucial to finding an optimal balance in software development. Methods/Study design/approach: The study employed a quantitative approach through a survey of 105 respondents, comprising QA Engineers, software developers, and academics, using a five-level Likert scale questionnaire. The collected data were rigorously analyzed using descriptive statistics, the Wilcoxon Signed-Rank Test, and the Friedman Test to objectively evaluate the performance, reliability, and operational bottlenecks of both testing methods. Result/Findings: The results demonstrate that automated testing is highly effective for regression testing and repetitive execution, while manual testing remains significantly effective for UI/UX evaluation and complex dynamic cases. Both methods face distinct challenges. Automated testing is bottlenecked by script maintenance efforts and flaky tests, whereas manual testing is hindered by lengthy execution times and human error. Automated testing was found to be significantly more accurate in detecting recurring functional bugs. Furthermore, an integrated combination of both automated and manual testing was proven to substantially reduce escaped defects. Novelty/Originality/Value: This research provides empirical evidence confirming that despite the strong push for automation in modern Agile and DevOps environments, neither method can completely replace the other. Both testing approaches are most effective when utilized complementarily according to their intrinsic characteristics. This synergy is essential to prevent bottlenecks in the software release cycle and ensure superior product quality.

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