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Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI)
Published by Universitas Pendidikan Ganesha
ISSN : 20898673     EISSN : 25484265     DOI : -
Core Subject : Science, Education, Engineering,
Computer Science & IT Education Engineering
Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) is a collection of scientific articles in the field of Informatics / ICT Education widely and the field of Information Technology, published and managed by Jurusan Pendidikan Teknik Informatika, Fakultas Teknik dan Kejuruan, Universitas Pendidikan Ganesha. JANAPATI first published in 2012 and will be published three times a year in March, July, and December. This journal is expected to bridge the gap between understanding the latest research Informatika. In addition, this journal can be a place to communicate and enhance cooperation among researchers and practitioners.
Arjuna Subject : -
Articles 666 Documents
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Forecasting of BBNI Stock Prices under Middle East Geopolitical Uncertainty Using Multivariate Time Series: A Comparative Study Eka Sari; Titik Rahmawati; Agung Priyanto; Julia Kurniasih; Fuadhillah Kirana Putri
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 15 No. 2 (2026)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v15i2.115773

Abstract

Global geopolitical uncertainties, such as the Middle East crisis, inherently trigger high volatility and systemic risks in capital markets, potentially threatening the stability of banking sector stocks. This study aims to analyze the structural shifts in trends and stock price volatility of PT Bank Negara Indonesia (Persero) Tbk (BBNI), while concurrently conducting a comparative evaluation of various multivariate-based short-term forecasting models amidst the crisis. The methodology compares five computational model architectures integrating exogenous macroeconomic variables (BI Rate, Inflation, and USD/IDR Exchange Rate), comprising the statistical approach of Autoregressive Integrated Moving Average with Exogenous Inputs (ARIMAX), machine learning techniques (Random Forest and XGBoost), and deep learning frameworks (Long Short-Term Memory [LSTM] and Gated Recurrent Unit [GRU]). Daily data are divided chronologically into a pre-geopolitical period (January 2024–May 2025) and a geopolitical crisis period (June 2025–May 2026).Trend analysis results reveal a unique market adaptation phenomenon, wherein the geopolitical crisis period paradoxically exhibits a positive trend recovery characterized by lower and more stable market risk within a volatility range of 0.010 to 0.026, compared to the pre-geopolitical period which peaked at 0.041. Predictive performance evaluation demonstrates that the tree-based ensemble model, specifically XGBoost, achieves the highest accuracy and efficiency, registering the minimum error metrics (MAE: 22.19; RMSE: 30.99; MAPE: 0.56%). The ARIMAX(2,0,1) model ranks second (MAPE: 0.65%) and successfully confirms a strong, negative linear influence of the BI Rate variable on stock prices. Conversely, the LSTM and GRU architectures underperform due to data scarcity constraints inherent to parameter-dense models, as well as a smoothing effect that hinders the capture of extreme price fluctuations. This study confirms that ensemble learning approaches, such as XGBoost, are superior, adaptive, and robust for modeling moderate-scale financial data amidst dynamic macroeconomic volatility.
Energy-Efficient APTEEN LoRa Wireless Sensor Network for Soil Moisture Monitoring in Precision Agriculture Hanugra Aulia Sidharta; Ilham Thoriq Nizar Abadi; Diah Risqiwati; Denar Regata
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 15 No. 2 (2026)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v15i2.115860

Abstract

Precision agriculture requires energy efficient wireless sensor networks (WSNs) that operate reliably over long distances in harsh outdoor environments. This paper presents a field implementation and experimental evaluation of the APTEEN routing protocol on a LoRa RFM95W WSN for soil moisture monitoring in rice fields. The proposed system consists of ESP32 sensor nodes and a sink node, organized into APTEEN clusters and equipped with soil moisture and power monitoring sensors, communicating over LoRa links up to 260 m under non line of sight (NLOS) conditions. The network is evaluated in two configurations, with and without APTEEN, using quality of service (QoS) metrics including latency, throughput, received signal strength indication (RSSI), signal to noise ratio (SNR), energy consumption, and estimated node lifetime. Experimental results show that the APTEEN configuration reduces average current consumption by approximately 2.9% and increases the estimated node lifetime by about 0.7 - 1.1 h for a 3000 mAh battery compared with the non-protocol baseline, while maintaining end to end latency in the range of 1 - 2 s and reliable communication at distances up to 260 m. These findings demonstrate that APTEEN provides modest but measurable improvements in energy efficiency and communication stability in LoRa WSN deployments for real-time soil moisture monitoring in precision agriculture, while also offering preliminary but meaningful evidence from a pilot-scale field validation under constrained deployment conditions.
Mental Workload Evaluation in Adaptive Boss Battle Gamification for PHP Programming Education: A Two-Phase NASA-TLX Analysis Dimas Wahyu Wibowo; Aryo Wahyu Nugroho; Eka Larasati Amalia; Venny Meida Hersianty
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 15 No. 2 (2026)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v15i2.115989

Abstract

Programming education, particularly server-side PHP, imposes high cognitive demands through complex syntax and abstract logic, while conventional instruction often fails to sustain engagement, motivating gamification as an alternative. This study evaluates student mental workload in CodeBossArena, an adaptive Boss Battle gamification system developed with the ADDIE model. An adaptive pre-test classifies students into Easy, Medium, or Hard Boss Battles, supported by five gamification elements (XP, Badges, Leaderboard, Progress Bar, and a Countdown Timer). The core contribution is a two-phase workload comparison, diagnostic pre-test versus adaptive gamified intervention, rather than gamification or NASA-TLX individually. Workload was measured with the Raw NASA-TLX across two phases. Thirty-six students from Politeknik Negeri Malang participated: an Adaptive Group (n=26) at system-recommended difficulty and an Independent Group (n=10) who self-selected an easier level. Shapiro-Wilk tests guided paired t-test or Wilcoxon selection. The Adaptive Group showed a significant TLX Total increase from Phase 1 (M=62.63) to Phase 2 (M=68.27); t(25)=2.746, p=0.011, d=0.539. Temporal Demand changed most (W=8.0, p=0.0003, r=0.715), identifying the Countdown Timer as the dominant element, whereas the Independent Group showed no change (p=0.790), consistent with the adaptive mechanism. Badge acquisition was associated with lower frustration, and in-system logs indicated competency attainment per level. Because NASA-TLX measures perceived workload rather than engagement or learning, the latter remain to be confirmed in controlled studies.
Machine Learning-Based Mental Health Classification Using Physiological and Physical Activity Data from Wearable Sensors Didi Supriyadi; Annisa Aprili Monti
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 15 No. 2 (2026)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v15i2.116184

Abstract

Mental health profoundly impacts individuals’ quality of life, productivity, and holistic well-being. The early identification of mental health disorders remains problematic, owing largely to the dependence on subjective evaluation methods. This study addresses these limitations by establishing a machine-learning-based mental health classification framework that leverages physiological and physical activity metrics from wearable sensors. Key physiological features, including heart rate, heart rate variability (HRV), sleep quality, and stress levels, along with physical activity data, were systematically collected and preprocessed via cleaning, normalization, and encoding. The performance of three distinct machine learning algorithms, namely Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), was evaluated using accuracy, precision, recall, and F1 score. Empirical results revealed that the Random Forest model attained the highest classification performance, with accuracy, precision, recall, and F1-score values of 96.34%, 96.37%, 96.34%, and 96.34%, respectively, outperforming both SVM and KNN models. The findings underscore the utility of multimodal physiological and behavioral data from wearable devices as objective markers for mental health status. By integrating physiological indicators, activity patterns, and psychological assessments into a cohesive machine learning architecture, this research advance’s objective, continuous mental health monitoring.
Analysis of the Effectiveness and Efficiency of Cruise Ship Crew Visa Processing Using Machine Learning and Process Mining Nyoman Alvia Wirayani; Made Sudarma; Ngurah Indra ER
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 15 No. 2 (2026)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v15i2.116946

Abstract

The visa processing workflow for cruise ship crews frequently encounters delays caused by the high volume of applications and the limited availability of embassy appointment quotas, where estimation processes are still primarily conducted manually. This study aims to analyze the visa processing workflow, identify process bottlenecks, and predict processing duration as well as future surges in visa application volumes. A hybrid approach integrating Process Mining and Machine Learning is proposed in this study using historical cruise ship crew visa application data from 2022 to 2025. Process Mining was utilized to map the actual workflow and identify the primary bottleneck occurring during the visa appointment waiting stage. Furthermore, K-Means Clustering was applied to identify visa application patterns based on submission timing characteristics and process complexity, resulting in three clusters: urgent applications, early applications, and complex visa types. The final stage involved the implementation of a Linear Regression model to predict visa processing duration and application volume surges. The evaluation results demonstrate satisfactory model performance, where the visa processing duration prediction model achieved an MAE of 1.96, RMSE of 2.58, and an R-squared value of 0.65. Meanwhile, the application volume surge prediction model achieved an MAE of 5.65, RMSE of 6.74, and an R-squared value of 0.80. The findings indicate that the integration of Process Mining and Machine Learning has the potential to improve visa management efficiency through data-driven recommendations, such as scheduling document collection at least 60 days prior to departure.
YOLOv8-Based Defect Inspection of Can Packaging Using Multi-View Frame Fusion in Manufacturing Systems Nova Eka Budiyanta; Florentia Angelina; Riana Magdalena Silitonga; Stefani Prima Dias Kristiana
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 15 No. 2 (2026)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v15i2.117366

Abstract

Packaging quality is an important factor in ensuring product quality and safety, particularly for can packaging that is widely used in food and beverage industry. During the production process, defects such as dents, scratches, and label damage may occur on both the inner and outer surfaces of cans, potentially posing health risks to consumers. Therefore, an automated inspection system capable of performing comprehensive, accurate, and rapid inspection is required. This study proposes a can packaging defect inspection system based on the single-stage object detection algorithm YOLOv8n with a multi-view frame fusion approach in a manufacturing system. The multi-view frame fusion approach utilized two cameras positioned in opposite directions and sampling frames from video data to generate a multi-view visual representation without increasing system complexity. The dataset was constructed by merging multi-view images of normal and defective cans and was then used to train the YOLOv8n model. Experimental results show that the proposed system achieved a precision of 0.982, a recall of 0.909, a mean Average Precision at IoU 0.5 of 0.962, and an inference speed of up to 29 frame per second. The direct testing results demonstrate that the system could operate reliably with a low detection error rate. These results indicate that the proposed multi-view frame fusion approach combined with YOLOv8n is effective and feasible for implementation as an automated quality control system in manufacturing environments.

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