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Jurnal Sisfokom (Sistem Informasi dan Komputer)
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Atma Luhur
ISSN : 23017988     EISSN : 25810588     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
Jurnal Sisfokom merupakan singkatan dari Jurnal Sistem Informasi dan Komputer. Jurnal ini merupakan kolaborasi antara sivitas akademika STMIK Atma Luhur dengan perguruan tinggi maupun universitas di Indonesia. Jurnal ini berisi artikel ilmiah dari peneliti, akademisi, serta para pemerhati TI. Jurnal Sisfokom diterbitkan 2 kali dalam setahun yaitu pada bulan Maret dan September. Jurnal ini menyajikan makalah dalam bidang ilmu sistem informasi dan komputer.
Arjuna Subject : -
Articles 678 Documents
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Improving Oil Palm Fruit Detection under Class Imbalance Using Class-Balanced Focal Loss on YOLOv11 Suparto, Adrian; Pribadi, Muhammad Rizky
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 02 (2026): MAY
Publisher : ISB Atma Luhur

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Abstract

Accurate detection of oil palm fruit maturity levels plays a crucial role in improving harvesting efficiency and maintaining the quality of palm oil production. In practice, this task remains challenging due to the presence of severe class imbalance in real-world field datasets, where certain classes have far fewer samples than others, often leading to biased model learning and reduced detection accuracy. This study investigates the performance of several Class-Balanced Loss Function variants integrated into the YOLOv11-nano framework using a publicly available oil-palm fruit dataset for harvest estimation, which presents a significantly imbalanced class ratio. Four training configurations were evaluated: the baseline Binary Cross-Entropy (BCE), Class-Balanced Focal Loss (CB-Focal), Class-Balanced Sigmoid Loss (CB-Sigmoid), and Class-Balanced Softmax Loss (CB-Softmax). The experimental results indicate that CB-Focal achieved the highest performance with an mAP@50 of 0.783, approximately 0.5 percent higher than the BCE baseline (0.778) and 4 to 5 percent greater than YOLOv8-n and YOLOv8-s models trained on the same dataset. CB-Focal also demonstrated smoother convergence and more balanced per-class performance compared to the other loss functions. These findings suggest that integrating CB-Focal into the YOLOv11-nano framework not only improves accuracy for minority classes but also holds strong potential for supporting more accurate, efficient, and scalable automated harvest monitoring systems in real plantation environments.
Effectiveness of the RIFE Algorithm in 3D Animation Motion Interpolation : A Case Study Approach Ramdhani, Arie Rahmat; Utami, Ema
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 02 (2026): MAY
Publisher : ISB Atma Luhur

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Abstract

This study evaluates the performance of the Real-Time Intermediate Flow Estimation (RIFE) algorithm in enhancing the temporal continuity and perceptual quality of 3D-rendered animation. Using 13 animation samples from the short film “I Draw It”, the algorithm was tested at 480p, 720p, and 1080p resolutions to interpolate motion from 24 fps to 48 fps, increasing temporal density while preserving visual coherence. The assessment integrates objective measures Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and processing time with expert-based evaluations using a Likert-scale instrument to capture perceptual fidelity and motion smoothness. Experimental results indicate that higher resolutions consistently provide superior interpolation stability and detail preservation, with 1080p yielding the highest perceptual performance despite increased computational requirements. Conversely, 720p demonstrates a practical trade-off between visual quality and processing efficiency, making it suitable for preview stages in production workflows. This research addresses a gap in the literature by conducting a resolution-specific evaluation of RIFE within fully 3D-rendered animation, a context that remains underexamined in existing studies. The findings establish RIFE as a viable and reliable interpolation method for modern animation pipelines and highlight its potential for integration into real-time frameworks such as Unreal Engine and Unity, thereby supporting improved production efficiency and visually consistent output.
Integrating Quantitative Data Analytics and Qualitative Insights for Digital Marketing Strategy: A Structural Equation Modeling (SEM) Approach Suwarno, Suwarno; Susanto, Jhony; Eryc, Eryc
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 02 (2026): MAY
Publisher : ISB Atma Luhur

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Abstract

The rapid evolution of Batam digital economy presents a paradox: despite high digital channel adoption, many organizations report suboptimal returns on their online investments. This discrepancy highlights a critical research gap: The absence of an integrated, quantitative model that evaluates the collective impact of core data analytics dimensions on strategic performance within this specific context. This study examines how data analytics is pivotal for improving digital marketing outcomes. Adopting a dual-objective approach, it first quantitatively evaluates the effect of five fundamental analytics components on strategic performance, and second, qualitatively uncovers the primary obstacles and facilitators in their application. An explanatory sequential mixed-methods design was employed, commencing with a PLS-SEM analysis of survey data from 150 marketing practitioners in Batam (using a five-point Likert-scale instrument) and concluding with a thematic analysis of qualitative practitioner insights. Empirically, the results validate that all five dimensions (Website Performance, Social Media Metrics, Email Marketing Performance, Customer Data Analysis, and Customer Journey Analysis) significantly enhance the effectiveness of digital marketing strategy. Among these, Customer Journey Analysis was identified as the most powerful determinant (R² = 0.783, Q² = 0.634). The qualitative data elucidated critical implementation barriers such as talent shortages and disconnected data sources, while also highlighting vital success drivers including data-informed personalization. The study’s primary novelty lies in providing a validated, holistic SEM model that establishes the synergistic and hierarchical influence of analytics dimensions, with Customer Journey Analysis as the central pillar. Consequently, it furnishes both a theoretical advancement for scholarly discourse and a practical framework advocating for a strategic reorientation towards holistic customer journey management and the dismantling of data silos to optimize outcomes.
Development of an Indonesian Trade Forecasting Information System Based on Statistical Models and Gradient Boosting Rasyid, Muh. Ashari; Lapatta, Nouval Trezandy
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 02 (2026): MAY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i02.2586

Abstract

Current Indonesian trade forecasting relies on complex manual processes prone to inaccuracies. This study develops an Indonesian Trade Forecasting Information System integrating Statistical Models (SARIMA, Prophet) and Gradient Boosting (LightGBM, XGBoost, ExtraTrees). Using BPS data from 2012-2025, XGBoost achieves MAPE 18.64% for volatile exports while SARIMA records 7.37% for stable imports. TAM validation by 30 trade analysts shows high acceptance (PU=3.73, PEOU=3.82, BI=3.59). The system features interactive dashboards, secure authentication, and CSV/PDF exports, addressing national forecasting methodology gaps. Key contributions include dual-model integration for diverse trade patterns with user-friendly interfaces.
End-to-End Face Identification: A Comparative Study of Inception-ResNet-V1 and Swin Transformer Classifiers Muhammad, Irvan Faiz; Santoso, Dwi Budi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 02 (2026): MAY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i02.2595

Abstract

Facial recognition technologies are increasingly being applied across various fields to facilitate human activities through automated systems. However, the existing frameworks often rely on multi-stage model pipelines, escalating computational complexity. This study compares two robust deep learning architectures, namely Inception-ResNet-V1 and Swin Transformer. Both are implemented as classifiers on the CASIA-WebFace dataset, consisting of 100 identity classes. The initial detector employs a cascaded network for multi-task learning (MTCNN). The Swin Transformer has a superior precision of 97.16%, surpassing the 96.35% attained by Inception-ResNet-V1. Furthermore, the high F1-scores of 96.7% and 95.79%, respectively, highlight an equilibrium alongside a robust approach to classifying a large number of classes. Beyond accuracy, both models exhibit lower latency in GPU environments, specifically 13.91 ms for the Swin Transformer and 15.04 ms for Inception-ResNet-V1. That marks a significant practical contribution to simplifying biometric identification by eliminating the necessity for separate feature extraction and distance matching modules. These results suggest that the end-to-end method holds immense possibilities for daily situations, including high-security authentication as well as large-scale automation surveillance, where computational robustness and efficiency are critical. Nevertheless, advanced optimization remains crucial for such a demanding environment.
Flood Disaster-Induced Water Inundation Potential Monitoring System in Bengkayang Regency Based on Remote Sensing Imagery and Machine Learning Christian Cahyaningtyas; Eligia Monixa Salfarini; Egi Saputra
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 02 (2026): MAY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i02.2597

Abstract

Based on data from the Bengkayang Regency Regional Disaster Management Agency (BPBD), in early 2025, 11 sub-districts were affected by flooding, with 12,023 affected people and 3,468 homes submerged. Efforts to minimize the impact of this flood disaster require an effective data-driven monitoring system. A floodwater monitoring system in Bengkayang Regency is essential for effective disaster management, reducing losses and damage, and providing early warnings to the surrounding community. One approach that can be used is remote sensing technology, which can be a solution, especially when combined with machine learning algorithms that can accelerate and improve the accuracy of data analysis. One such machine learning algorithm is the Support Vector Machine (SVM) algorithm. This study has produced a final dataset of five variables: rainfall, slope gradient, land use, VV, and NDWI. This dataset is used for the classification process using the Support Vector Machine algorithm. After preprocessing and dividing the training data by 75% and the test data by 25% of the total 512 data sets. The image classification results using SVM demonstrated quite good performance. The resulting accuracy was 80%, with precision and recall values ​​ranging from 0.67 to 0.98. Based on these results, the model demonstrated excellent ability to identify waterlogging points. The classification results were then integrated into a web-based geographic information system that displays an interactive map of the distribution of waterlogging points.
Comparative Analysis of User Experience between Teachers and Students on Vocational School E-learning using UEQ+ Pringgadhana, I Made Lanang Putra; Putra, Rian Permana Yatmika; Putera, Hagi Semara; Dantes, Gede Rasben; Indrawan, Gede; Gunawan, I Made Agus Oka
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 02 (2026): MAY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i02.2599

Abstract

The rapid digital transformation in the Industry 4.0 era has increased the adoption of e-learning systems in vocational education, making user experience (UX) evaluation essential. Previous studies have mainly focused on student’s perspectives, while teachers UX remain underexplored. This study aims to compare the UX perceptions of teachers and students toward the e-learning system at SMK TI Bali Global Jimbaran using the User Experience Questionnaire Plus (UEQ+). A quantitative approach was employed involving 74 students and 17 teachers who had used the system for at least one year. Seven UEQ+ dimensions were analyzed, and differences were tested using the Wilcoxon Signed Rank Test. The result show that teachers rated attractiveness, intuitive use, visual aesthetics, content quality and content trustworthiness positively (mean scores >1.0). Efficiency was rated moderately (M=0.21), while social interaction received a very low score (M=-1.34). Students rated most dimensions as fair to good (mean scores around 0.5-1.0), whereas efficiency and visual aesthetics were considered acceptable but requiring improvement. Social interaction was evaluated as neutral to slightly negative. These findings demonstrate significant difference in UX perceptions and highlight the need for improved interaction features and system performance in vocational e-learning systems.
Generative Representation of Aggregate Brain Activity: A Deep Autoencoder Approach for EEG Topoplot Summarization Sulistiyo, Tobias Mikha; Bachri, Karel Octavianus
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 02 (2026): MAY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i02.2607

Abstract

This research aims to assess a Deep Convolutional Autoencoder (CAE) framework for representative EEG topoplot summarization using latent space aggregation. In order to produce representative EEG topoplot summaries while maintaining important spatial features, we suggest a Deep Convolutional Autoencoder (CAE) with latent space aggregation. Prior to group-level aggregation and image reconstruction, EEG topoplots are simplified into latent representations that resemble baseline artifacts. An adolescent EEG dataset obtained during a Go/No-Go Association Task involving addiction stimuli was used to test our methodology. The frontal-temporal predominance of normal respondents and the prominent temporal-occipital activation of at-risk respondents, primarily in those with slower responses, are caused by distinct activation patterns that are associatively aroused by attentional and memory bias. These results support the use of secure EEG topoplot summarization in addiction research using CAE-based latent space aggregation.

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