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EDUMATIC: Jurnal Pendidikan Informatika
Published by Universitas Hamzanwadi
ISSN : -     EISSN : 25497472     DOI : 10.29408
Core Subject : Science, Education,
Computer Science & IT Education
EDUMATIC: Jurnal Pendidikan Informatika (e-ISSN: 2549-7472) adalah jurnal ilmiah bidang pendidikan informatika yang diterbitkan oleh Universitas Hamzanwadi dua kali setahun yaitu pada bulan Juni dan Desember. Adapun fokus dan skup jurnal ini adalah (1) Komputer dan Informatika dalam Pendidikan; (2) Model Pembelajaran dan Model TIK; (3) Pengembangan Media Pembelajaran Berbasis Teknologi Informatika; (4) Interaksi Manusia dan Komputer; (5) Sistem Informasi dan Teknologi Informasi.
Arjuna Subject : -
Articles 464 Documents
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Hybrid Human AI SDLC for Rapid SaaS Development: Evidence from a 60 Days Case Study Fawwazie, Muhammad Hilmy Haidar; Daud, Nathan; Muhammad Iqbal Rabani; Supriyanto, Budi Fajar
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.34361

Abstract

There is a vacuum in the risk of architectural changes in critical systems because macro-architectural governance in AI-based software development is frequently ignored in current scholarly debate. The purpose of this study is to assess how well the Visualize, Integrate, Build, Execute (VIBE) architecture addresses the stability-speed contradiction in SaaS development. This study triangulated data from 465 automated CI/CD pipeline logs, 124 AI instruction tactic documentation records, and 42 test cases using comparative performance analysis and process tracking using an explanatory mixed-methods case study methodology on a stock market analytics platform. The study's key conclusions show a 50% boost in development efficiency, reducing a 60-day cycle to 30 days while preserving system reliability with an average latency of 1.2 seconds and a 99.9 percent availability rate. Specialist synergy was identified where humans became the primary cognitive players in architectural design at 90 percent, and AI as the executor of basic syntax at 85 percent. The research concludes that the architectural anchoring mechanism by humans is crucial for mitigating the risks of non-deterministic AI outputs. Theoretically, this study introduces the concept of human-AI cognitive alignment, while practically providing a validated roadmap for modernization of sensitive infrastructure such as Electronic Medical Records.
Forward Chaining Expert System for Optimizing Marketing Strategies in Social Commerce Platforms Rudiansyah, Andhika; Rukhviyanti, Novi
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.34382

Abstract

The complexity of digital performance indicators in social commerce environments poses significant challenges for small and medium enterprises (SMEs) in formulating coherent and actionable marketing strategies. This study develops and evaluates a forward chaining based expert system to support structured, data driven, and interpretable marketing decision-making. A design science research methodology was employed, encompassing problem identification, artifact development, and evaluation. Knowledge was elicited through literature synthesis, expert consultation, and empirical observation, and subsequently formalized into IF–THEN production rules within a structured knowledge base. The system applies a forward chaining inference mechanism to process key indicators, including followers, engagement rate, promotion frequency, and conversion rate, in order to generate prioritized strategic recommendations. Evaluation was conducted using scenario-based testing and expert validation to assess accuracy, consistency, and contextual appropriateness. The results demonstrate complete alignment between system outputs and expert judgment across all evaluation scenarios, indicating high reliability and logical consistency of the rule-based reasoning process. The system also produces context-sensitive and interpretable recommendations aligned with varying levels of business performance. This study contributes by advancing rule-based decision support systems in social commerce and providing an explainable and practically applicable tool to enhance marketing decision quality among SMEs.
Deep Learning for PM2.5 Prediction under Zero-Inflated Tropical Rainfall: RNN vs GRU Yuliyono, Ikhsan; Supriyanto, Aji
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.34418

Abstract

Air quality forecasting in maritime tropical regions is challenged by zero-inflated rainfall regimes, where prolonged dry periods are intermittently disrupted by extreme precipitation, generating highly non-linear PM₂.₅ dynamics and limiting the effectiveness of conventional predictive models. This study evaluate the predictive performance of Recurrent Neural Networks (RNN) and Gated Recurrent Units (GRU) under such distributional conditions. A quantitative experimental design with a comparative approach is employed using 1,461 daily observations from the Central Java Climatology Station, incorporating rainfall, temperature, and relative humidity as predictors; a chronological data split preserves temporal dependencies, and performance is assessed using MAE, RMSE, MAPE, and R² metrics. The results indicate that GRU achieves only a marginal 4.3% reduction in MAE relative to RNN, while both models exhibit substantial predictive failure, as evidenced by negative R² values and MAPE exceeding 300%, with predictions collapsing toward the mean and failing to capture extreme pollution events. These findings demonstrate that standard recurrent architectures with conventional loss functions are intrinsically limited in modeling zero-inflated environmental data, contributing empirical evidence on the boundary conditions of deep learning in tropical air quality forecasting and underscoring the necessity for specialized modeling approaches to support reliable early warning systems.
Demand-Based Product Classification Using K-Means with Intermittency Metrics Anggraini, Ariska Nur; Amali, Amali; Anwar, Muhammad Syaibani
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.34435

Abstract

Inventory management at multi-SKU distribution companies becomes complex when most products have unstable and intermittent demand patterns. At PT JJA, procurement is still reactive without the use of historical patterns, while the previous approach generally relied on aggregate indicators such as average sales so that it has not been able to comprehensively capture temporal dynamics. This study aims to group products based on temporal demand patterns using K-Means Clustering in 11,988 transactions for the 2020–2025 period which are processed into 261 products through monthly aggregation, with features of average sales, coefficient of variation (CV), zero_month_ratio, Average Demand Interval (ADI), and trends. The results showed four optimal clusters (k = 4) with a Silhouette Score of 0.62 and an unbalanced distribution, where one cluster dominated 240 products. The values of zero_month_ratio (>0.80), ADI up to >12 months, and CV up to >3.5 show intermittent demand patterns and long-tail structures. The study confirms that the integration of temporal features (ADI, zero_month_ratio, CV, and trend) transforms the representation of demand from static aggregates to dynamic structures, while linking segmentation results with more adaptive procurement strategies to reduce the risk of overstock and understock.

Page 47 of 47 | Total Record : 464

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