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Scientific Journal of Informatics
Published by Universitas Negeri Semarang
ISSN : 24077658     EISSN : 24600040     DOI : https://doi.org/10.15294/sji.vxxix.xxxx
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering
Scientific Journal of Informatics (p-ISSN 2407-7658 | e-ISSN 2460-0040) published by the Department of Computer Science, Universitas Negeri Semarang, a scientific 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. The SJI publishes 4 issues in a calendar year (February, May, August, November).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 200 Documents
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Leveraging Internet of Things and Artificial Intelligence in Smart Agriculture to Enhance Food Security and Sustainable Farming: A Systematic Review Belinda Ndlovu; Kudakwashe Maguraushe
Scientific Journal of Informatics Vol. 13 No. 3: August 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i3.49540

Abstract

Purpose: Achieving global food security while maintaining environmentally sustainable agricultural systems remains a critical challenge amid population growth, climate variability, and resource constraints. Artificial Intelligence (AI) and the Internet of Things (IoT) have emerged as transformative technologies that support data-driven agricultural practices. This study systematically examines the applications, opportunities, challenges, and adoption factors of AI-IoT integration in smart agriculture, with particular emphasis on its potential contributions to food security and sustainable farming. Methods: A systematic literature review (SLR) was conducted following the PRISMA 2020 guidelines. Publications were retrieved from Scopus, IEEE Xplore, and ScienceDirect covering the period 2020-2024. From an initial 431 records, 17 empirical studies met the inclusion criteria and were analysed using narrative and thematic synthesis. Result: The review shows that AI-IoT technologies are primarily applied in crop disease detection, precision agriculture, environmental monitoring, yield prediction, and livestock health monitoring. These technologies enable real-time decision support, early disease detection, productivity improvement, and resource optimisation. However, challenges remain, including data limitations, infrastructure constraints, integration complexity, and high deployment costs. Novelty: The study proposes a layered smart agriculture framework linking technological infrastructure, application domains, adoption conditions, operational outcomes, and sustainability impacts. The findings highlight key factors necessary for successful implementation, including infrastructure readiness, affordability, technological reliability, and capacity development for farmers. Crucially, the review demonstrates that the field of AI-IoT smart agriculture is technically advanced but socio-technically incomplete: the evidence base is dominated by proof-of-concept studies from Asia, with no empirical representation from Africa or the Americas, creating what this study terms an AI-IoT agricultural equity gap that fundamentally limits the technology’s contribution to global food security. The five-layered framework introduced here provides the first inductively derived organising structure that explicitly connects AI-IoT infrastructure to SDG-aligned food security outcomes, offering a replicable analytical scaffold for future empirical and policy research in this domain.
Adversarial Vulnerabilities in Cooperative Multi-Agent Reinforcement Learning for Distributed 5G Security Belinda Mutunhu Ndlovu; Kudzaishe Lawal Chizengwe
Scientific Journal of Informatics Vol. 13 No. 3: August 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i3.50261

Abstract

Purpose: This paper focuses on examining the robustness of a cooperative Multi-Agent Reinforcement Learning (MARL)-based Intrusion Detection System (IDS) for intrusion detection in decentralised 5G security settings. Even though MARL techniques have proven effective against dynamic threats in decentralized 5G networks, current research has not considered any adversarial scenarios at all. Methods: A cooperative MARL-based Intrusion Detection System was developed through the CRISP-DM approach. Radio Access Network (RAN), MEC, and Core agents were trained using Centralised Training with Decentralised Execution (CTDE) and Deep Q-Network (DQN) methods. The algorithm was tested on the NSL-KDD and UNSW-NB15 datasets against Fast Gradient Sign Method (FGSM) evasion attacks (ε = 0.05-0.30) and Byzantine poisoning attacks with 5%, 10%, and 20% compromised agents. Result: The model achieved 96.94% accuracy on NSL-KDD and 85.15% on UNSW-NB15 in clean scenarios. The FGSM attack at ε = 0.20 resulted in substantial performance deterioration, leading to accuracy drops of 50.14 and 45.26 percentage points, respectively, and a simultaneous increase in false positives. Byzantine poisoning produced smaller but persistent decreases in accuracy of 12.03 and 2.62 percentage points, respectively. Novelty: This study provides among the first empirical evaluations of adversarial fragility in cooperative MARL-based intrusion detection within distributed 5G-oriented security abstractions, demonstrating that cooperative intelligence alone does not guarantee adversarial robustness.
A Hybrid Quantum-Classical Workflow for Early-Stage Molecular Discovery: A Workflow-Level Proof-of-Concept Charnelle Razo; Belinda Ndlovu
Scientific Journal of Informatics Vol. 13 No. 3: August 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i3.50262

Abstract

Purpose: Classical computational approaches struggle to model molecular quantum interactions, making drug discovery costly and time-consuming. Quantum computing shows promise in molecular modelling, but much of the existing work focuses on isolated proof-of-concept tasks. These studies usually do not examine the full molecular discovery process. This study demonstrates the feasibility of a unified hybrid quantum–classical workflow for early-stage molecular discovery and examines how quantum-based molecular representations influence later optimisation and prediction. Methods/Study design/approach: The study developed and evaluated a five-stage hybrid quantum-classical workflow integrating query interpretation, molecular screening with quantum re-ranking, VQE-based energy estimation,  property prediction and report generation. The system was implemented using Qiskit, Qiskit Nature, RDKit, and spaCy on the QM9 dataset using noiseless state-vector simulation. Result/Findings: The NLP component was evaluated across four tiers: 180 template-based queries, 160 independently written queries, 1,000 independently written queries, and 1,000 externally sourced BioASQ Task B questions. F1-scores were 89.9%, 82.1%, 73.0%, and 71.3%. Screening achieved constraint satisfaction rates of 90%-100%. Quantum-informed ranking differed substantially from cosine similarity and evaluated the utility of this through top-k enrichment and multi-property hit rates. MIFM achieved a mean VQE absolute error of 0.0156 Ha compared with 0.0218 Ha for ZZFeatureMap and 0.0248 Ha for cosine similarity. VQE and property-prediction evaluations, scalability experiments, and simulated NISQ tests provided broader evidence of workflow behaviour. Novelty/Originality/Value: This study presents a reproducible hybrid quantum–classical workflow for early-stage molecular discovery, extended with a chemistry-aware MolecularInteractionFeatureMap (MIFM). The study does not claim universal quantum advantage or physical quantum-hardware validation.
A Comparative Study of Deep Learning Architectures for Content-Based Image Retrieval on a Stone Texture Dataset Maulana Taufiqurrohman; Suhendro Irianto
Scientific Journal of Informatics Vol. 13 No. 3: August 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i3.51297

Abstract

Purpose: This study addresses a persistent gap in geological content-based image retrieval (CBIR): the reliance of existing sequence-based and hybrid deep learning models on static, two-dimensional rock texture representations that they were not originally designed to handle. Methods: Four architectures were evaluated on a unified 9,853-image, ten-category Stone-2D dataset, partitioned into training and validation subsets at an 8:2 ratio (7,882/1,971 images), with FAISS employed for similarity search over 128-dimensional embeddings. Findings: On an identical held-out test set, all four architectures achieved comparable performance (82.4%–85.2% accuracy; macro F1 0.826–0.860), with a standard 2D CNN achieving the highest accuracy (85.2%) and the custom pseudo-3D MineralNet architecture achieving the highest macro F1 (0.860). No architecture dominated across both metrics. Novelty: These results indicate that, once preprocessing is aligned with the native dimensionality of the image data, architectural complexity provides limited additional benefit for static geological texture classification — challenging the common assumption that specialized or sequential architectures are necessary for this task. A working CBIR retrieval pipeline was validated with representative query examples, demonstrating semantically consistent nearest-neighbour retrieval across distinct rock textures.
An Ablation Study on Stacked LSTM and SMOTE for Government Policy Sentiment Classification Sima Maulina; Catur Edi Widodo; Budi Warsito
Scientific Journal of Informatics Vol. 13 No. 3: August 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i3.53853

Abstract

Purpose: We compare four deep learning configurations of Standard LSTM without SMOTE, Standard LSTM with SMOTE, Stacked LSTM without SMOTE, and Stacked LSTM with SMOTE used for multi-class sentiment classification of Indonesian public comments on the government’s Free Nutritious Meals Program (MBG) policy posted on Instagram. Most existing research on this topic relies on single-layer LSTM models that fail to address class imbalance and the layered, often sarcastic semantic structure commonly found in informal Indonesian social media writing at the same time. We test whether a Stacked LSTM architecture combined with SMOTE can simultaneously model sequential word dependencies and high-level semantic patterns, while correcting the bias that class imbalance tends to introduce. Methods: We collected 1,000 Instagram comments from the account @ferryirwandi using the Instaloader library and labeled them into three sentiment classes with TextBlob. Preprocessing was carried out through six steps: cleaning, case folding, slang normalization with a custom dictionary, stopword removal, tokenization, and stemming via Sastrawi after which TF-IDF was used for feature extraction. The data was split 80:20 into training and testing sets before applying any oversampling; SMOTE was applied only to the training data, so the test set remained intact and free of data leakage. Performance was measured using Accuracy, Macro-Precision, Macro-Recall, and Macro-F1 Score, obtained from a multiclass confusion matrix based on a One-vs-Rest scheme. Result: Scenario 3 (Stacked LSTM without SMOTE) produced the top result, reaching 95.00% Accuracy, Macro-Precision of 0.9276, Macro-Recall of 0.8752, and Macro-F1 Score of 0.8973 the best outcome among the four configurations. In contrast, Scenario 4 (Stacked LSTM with SMOTE) performed worse, with accuracy dropping to 92.50% and the F1-Score falling to 0.8178. We attribute this decline to feature space overlap: the synthetic minority samples generated by SMOTE landed too close to the dominant neutral class within the high-dimensional, sparse TF-IDF feature space. Novelty: Our findings provide empirical support for the idea that hierarchical representations built by stacked recurrent layers are well suited to capturing the complex context, sarcasm, and long-range semantic relationships found in Indonesian political discourse. We also propose the four-scenario ablation design as a reusable method for disentangling how much architectural depth and data balancing each contribute, both independently and jointly, when working with imbalanced Indonesian social media sentiment data. Practical Implications: For practitioners working with TF-IDF features on narrow-topic datasets, our findings suggest that Stacked LSTM without oversampling is the more reliable configuration for multi-class sentiment analysis of Indonesian government-policy discourse on social media.
A Quantitative Comparative Analysis of NASA-TLX Workload between Staff and Non-Staff Employees in a Pharmaceutical Manufacturing Organization: Implications for Knowledge Management Rian Bimo Ankhal; Muharman Lubis; Hanif Fakhrurroja; Muhammad Fakhrul Safitra
Scientific Journal of Informatics Vol. 13 No. 3: August 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i3.54141

Abstract

Purpose: In pharmaceutical production, personnel may be exposed to different workload situations, since their job functions are affected by strict operational and regulatory standards. Non-personnel workers and personnel workers have different duties which can affect their perception of workload and their participation in knowledge sharing activities. However, empirical evaluations of the workload characteristics of these employee cohorts by means of the NASA Task Load Index (NASA-TLX) are still scarce. This study explores the perceived workload attributes of staff and non-staff workers on six modified NASA-TLX scales. The implications of the identified trends for knowledge management strategies are discussed. Methods: The method used for this research was quantitative, comparative and cross-sectional. The study’s data were collected using a structured questionnaire. The sample selected for the study was 30 staff and non-staff members of a pharmaceutical manufacturing company using purposive sampling. The tool assessed six modified NASA-TLX workload factors; Mental Demand, Physical Demand, Temporal Demand, Performance, Effort and Frustration Level using a five-point Likert scale. Dimension total Pearson correlation analysis and Cronbach’s alpha reliability test were performed to assess the instrument with the same data set. Descriptive analyses of the workload characteristics of staff and non-staff personnel were conducted using group mean scores and mean differences. No assertions regarding statistical significance were provided. Result: Non-staff personnel had higher mean scores than staff personnel in Mental Demand, Physical Demand, Temporal Demand, Effort and Frustration Level. For workload, the largest mean differences were found in Temporal Demand and Effort. Non-staff personnel also reported a higher Performance score. The Performance items, however, are positively worded and imply that higher scores reflect a more positive view of work accomplishment and quality, not a greater workload. The patterns shown here indicate that role-specific knowledge management practices can support continued improvement, learning and knowledge sharing activities. Novelty: This study presents the analysis of staff and non-staff employees according to their role and a complete workload analysis in the context of a pharmaceutical manufacturing environment with the modified NASA-TLX framework. The results provide a pragmatic basis for the development of knowledge management strategies relevant to the operational tasks and workload characteristics of each category of employees.
Business Intelligence Dashboard for Outdoor Equipment Rental Bundling Recommendations Using FP-Growth Ahmad Zaki Azizi; Febrian Murti Dewanto; Aris Tri Jaka Harjanta
Scientific Journal of Informatics Vol. 13 No. 3: August 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i3.57444

Abstract

Purpose: This study aims to address the underutilization of transaction data in the outdoor equipment rental industry by developing an integrated Business Intelligence (BI) dashboard using the FP-Growth algorithm to generate product bundling recommendations and support data-driven decision-making. Methods: A quantitative approach based on the Knowledge Discovery in Databases (KDD) framework was employed using 596 rental transactions from Batas Outdoor Rental recorded between January and May 2026. The data were preprocessed and transformed into a binary matrix using TransactionEncoder. FP-Growth was applied with a minimum support of 2% (0.02), while association rules were generated using a minimum confidence of 30% (0.30) and validated with a lift threshold of 1.20. Results: A quantitative approach based on the Knowledge Discovery in Databases (KDD) framework was employed using 596 rental transactions from Batas Outdoor Rental recorded between January and May 2026. The data were preprocessed and transformed into a binary matrix using TransactionEncoder. FP-Growth was applied with a minimum support of 2% (0.02), while association rules were generated using a minimum confidence of 30% (0.30) and validated with a lift threshold of 1.20. Novelty: This study integrates FP-Growth-based transaction analysis with an interactive BI dashboard specifically for outdoor equipment rentals. Unlike previous studies focusing primarily on product combinations or promotional packages, the proposed approach provides an end-to-end decision-support framework connecting transaction analysis, automated bundling recommendations, and interactive visualization.
Improving Scrum Implementation Quality in Software Development through an Integrated Capability Maturity Assessment and Decision-Making Framework Farand Farhansyah; Eko Kuswardono Budiardjo; Alex Ferdinansyah
Scientific Journal of Informatics Vol. 13 No. 3: August 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i3.58224

Abstract

Purpose: This study evaluates the quality of Scrum implementation in the New Corporate Website project at XYZ Bank and proposes process improvement recommendations using an integrated framework based on the Capability Maturity Model Integration (CMMI) 2.0. CMMI 2.0 was selected because it provides comprehensive Practice Areas for assessing organizational capability beyond Scrum-specific maturity models. Methods: A convergent parallel mixed-method case study was employed involving 14 AHP experts, 17 Scrum appraisal respondents, and 3 interview participants. Quantitative and qualitative data were collected through questionnaires, semi-structured interviews, and document observations. The Analytical Hierarchy Process (AHP) was used to prioritize organizational problems, the Weighted Scoring Model (WSM) mapped the prioritized problems to the most relevant CMMI 2.0 Practice Areas, and Scrum capability was evaluated using the Key Process Area (KPA) Rating and SCAMPI C appraisal approach. Results: Five Practice Areas were selected for appraisal. Verification and Validation (VV) was the only Practice Area that achieved Capability Level 1 with a KPA score of 96.16%, whereas Project Planning (PLAN), Process and Quality Assurance (PQA), Peer Review (PR), and Requirements Development and Management (RDM) remained at Capability Level 0. Based on the identified weaknesses, 11 improvement recommendations were formulated and organized into short-, medium-, and long-term implementation phases to support continuous software process improvement. Novelty: This study proposes an integrated framework that combines AHP, WSM, and CMMI 2.0 to systematically prioritize Practice Areas before capability assessment. The proposed framework provides a structured decision-making mechanism that links organizational problems with capability appraisal and offers practical guidance for improving Scrum implementation and supporting continuous software process improvement.
Automation of Sprint Cost Estimation in Agile Projects Using a Random Forest-Based Decision Support System Faidatul Hikmah; Sinung Suakanto
Scientific Journal of Informatics Vol. 13 No. 3: August 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i3.58315

Abstract

Purpose: The manual construction of a Work Breakdown Structure (WBS) is inherently susceptible to human cognitive biases; hence, the objective of this research shifts toward Intelligent Automation, leveraging a Decision Support System (DSS) to replicate expert financial logic. Methods: The dataset comprises 30 validated sprint records from 23 internal projects; other WBS documents were excluded due to incomplete sprint details. Pre-processing involved completeness checks and One-Hot Encoding of team role compositions, without feature scaling. A Decision Tree Regressor, Random Forest Regressor, and Hybrid Model (Voting Regressor) were evaluated using 3-fold cross-validation based on MAPE, RMSE, and R² metrics. Result: The Random Forest Regressor demonstrated the best relative performance among the three models, achieving a MAPE of 23.55%, an RMSE of IDR 35.27 million, and an R² of -0.535. Across the 30 sprint predictions, the Random Forest model showed a mean deviation of 23.55% ± 31.69%, a median of 9.98%, and a range of 0.78%–125.33%, highlighting performance variation across sprints and supporting the model's application within a human-in-the-loop framework. Feature importance analysis identified `sprint_duration_days` as the dominant feature, followed by `role_programmer_jr` and `role_qa_sr`. Novelty: These findings demonstrate the system's practical contribution, particularly in streamlining budgeting bureaucracy by accelerating the process from days of manual preparation to just a few minutes through automated execution.
Virtual vs Human Influencers: A Comparative Analysis Of Campaign Effectiveness Using Deepfake Detection Technology From The Perspective Of Ethics And State Responsibility Under The Rule Of Law Safira Hasna Setiyani; Nabila Aliffia; Fanny Agustina Sri Rahayu
Scientific Journal of Informatics Vol. 13 No. 3: August 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i3.59356

Abstract

Purpose: This study aims to examine the effectiveness of AI-driven influencer marketing, develop an Xception-based Convolutional Neural Network (CNN) for deepfake detection, and analyze ethical and legal responsibility for artificial intelligence use from a rule-of-law perspective. Methods: A mixed-methods approach was employed by integrating Computer Science, Management, and Law perspectives. Quantitative analysis used Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the relationships among AI Personalization, AI Interaction, User Experience, and Trust. The Xception-based CNN was evaluated using standard classification metrics. Qualitative analysis involved expert interviews and examination of legal principles concerning AI governance, transparency, accountability, and consumer protection. Result: The Xception-based CNN achieved 97.12% accuracy and an AUC of 0.9920 in distinguishing authentic from AI-generated content. PLS-SEM results indicate that AI Interaction significantly influences User Experience and Trust, while User Experience has the strongest effect on Trust (β = 0.520; p < 0.001). The model explains 53.3% of the variance in Trust, and User Experience significantly mediates the relationship between AI Interaction and Trust. The legal analysis highlights the need for stronger AI governance addressing transparency, disclosure of AI-generated content, consent, accountability, and consumer protection. Novelty: The novelty of this study lies in integrating deepfake detection, AI-driven influencer marketing, consumer trust analysis, and rule-of-law perspectives into a unified framework for responsible digital marketing. This integrated approach provides a multidisciplinary perspective for balancing technological innovation with ethical responsibility, legal accountability, and consumer protection.

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