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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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Implementation and Evaluation of the K – Nearest Neighbors Algorithm in Badminton Movement Classification Adiba, Fera Hidayatul; Kasih, Patmi; Dara, Made Ayu Dusea Widya
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 4 (2025): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i4.2441

Abstract

To meet the needs of automated sports analysys, this study will develop and evaluated a bandminton motion analysis system that uses the K-nearest Neighbors (KNN) algorithm. This system will detect netting, smash, and serve motions and assess whether the labels are correct and inccprrect. The system uses MediaPipe Pose to extrac keypoints from 3-5 second videos, with data normalized using StandartScaler. Evaluation result show an eccuracy of 0.8438 for netting, 0.8276 for smashes, and 0.7778 for serves. Keypoints extraction time ranges from 4.53 to 25.44 seconds, influaced by lighting conditions, while prediction time is efficient at 0.03-0.05 second. Although this system can be used for sport training, additional data and features are needed to improve performance in low-ligh conditions.
Internet of Things-Based Hidroponic Plant Monitoring System Hasanah, Herliyani; Susanto, Rudi; Lestari, Wiji
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 4 (2025): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i4.2443

Abstract

Hydroponic farming is gaining popularity in urban areas due to its efficient use of space. However, this system requires careful management of nutrients, temperature, and pH, which are vital for plant growth but complex to manage manually. This study's purpose is to enhance hydroponic management, temperature, and pH. The study uses a Node MCU ESP8266 microcontroller and sensors to collect data, and the system is integrated with Telegram for easy monitoring. The prototyping methodology used in this study includes stages from analysis to testing and implementation. Test results show that the system accurately blends and adjusts pH and nutrient levels, and is compared to digital measuring instruments. This IoT-based, efficient solution for urban farmers and hydroponic practitioners significantly improves their ability to manage nutrient and environmental conditions, enabling more innovative, sustainable urban farming practices.
Comparative Analysis of Explainable AI Models for Pneumonia Detection in Chest X-rays Using Grad-CAM Richardo, M Denny; Ermatita, Ermatita; Satria, Hadipurnawan
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 4 (2025): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i4.2450

Abstract

Pneumonia is one of the main reasons why young children die around the world, so it's essential to detect it early and make sure the methods used are straightforward to understand for doctors. This study aims to analyze and compare pneumonia detection systems based on Explainable Artificial Intelligence (XAI) using the Gradient-weighted Class Activation Mapping (Grad-CAM) technique across four Convolutional Neural Network (CNN) architectures: VGG16, DenseNet, MobileNet, and EfficientNet-B0. The dataset used consists of approximately 5,800 chest X-ray images from Kaggle, split into training, validation, and test sets. The dataset underwent preprocessing, augmentation, and filtering. Each model was trained and tested using the accuracy, precision, recall, and F1-score measures. Additionally, the models were analyzed for explainability using Grad-CAM heatmaps. The results showed that MobileNet achieved the highest classification performance, attaining 99.6% accuracy, precision, recall, and F1-score, while EfficientNet-B0 demonstrated the highest explainability in a visual evaluation by medical practitioners. Explainability was assessed through a survey distributed to four medical professionals—two radiologists, a general practitioner, and a radiology technologist—using a Likert scale (1–5) to rate aspects such as focus accuracy, heatmap clarity, consistency of the area, and interpretability. EfficientNet-B0 achieved the highest average explainability score of 41.50, followed by MobileNet at 40.50. Thus, MobileNet is recommended for accuracy, while EfficientNet-B0 is the best choice if visual interpretability is a priority. This research underscores the importance of integrating explainability into the development of AI-based disease detection systems to enhance trust and safety in clinical applications.
The Use of Artificial Intelligence in Enhancing Customer Relationship Management (CRM): A Systematic Literature Review Nugraha, Zahra Sabila; Muhammad, Maulana Asykari; Waspodo, Bayu
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 4 (2025): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i4.2454

Abstract

Customer Relationship Management (CRM) has become a key business strategy for retaining customers. As data continues to grow in variety and volume, more advanced solutions are needed. The integration of Artificial Intelligence (AI), CRM, and Big Data offers promising support in addressing modern business challenges in the era of digitalization. This study explores the application of Artificial Intelligence in Customer Relationship Management (AI-CRM) through a literature review. We adopted the Kitchenham and Charters method for conducting the review and initially identified 356 studies. Data were collected from 33 studies published in the Scopus, ScienceDirect, and IEEE databases between 2020 and 2025. The results show that supervised learning remains the most widely used AI technique, while deep learning has grown significantly in recent years, indicating a shift toward more sophisticated CRM solutions. Most applications were found in Analytical CRM, particularly for churn prediction, customer segmentation, and personalization. However, challenges related to data quality, bias, privacy, and transparency remain prevalent. Additionally, areas such as B2B and Strategic CRM remain underexplored. This review emphasizes the need for organizational readiness before adopting AI-CRM and highlights AI’s transformative potential to enhance CRM strategies and gain a competitive advantage. The findings deliver useful insights into the application of AI in data-driven CRM.
Predictive Analysis of Raw Material Stock at Puri Food and Healthy, an SME, Using the Long Short-Term Memory (LSTM) Method Bangun, Pery Chandria; Juanta, Palma; Nizam, Muhammad Fachrul
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 4 (2025): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i4.2458

Abstract

Micro, Small, and Medium Enterprises play a vital role in the Indonesian economy, yet face significant challenges in managing raw material inventories, particularly for perishable commodities such as coconut sap (nira). This study applies and optimizes the Long Short-Term Memory (LSTM) method to predict raw material stock levels for coconut sap at Puri Food and Healthy, an SME, using five years of daily historical data (January 1, 2020–December 31, 2024; 2,191 entries). A descriptive and experimental quantitative approach was employed to develop a deep learning-based predictive model, with data obtained through inventory documentation and interviews with SME managers. The research process encompassed data preparation, collection, normalization, LSTM model construction using Python and TensorFlow in Google Colab, and evaluation using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R² Score. Results show the model achieved an MAE of 5.31 and an RMSE of 6.94, indicating moderate prediction error. However, the R² value of 0.0711 suggests very low explanatory power, potentially due to underfitting or data limitations. Notably, multi-step forecasting was applied to generate projections for 2026–2027 despite having historical data only through 2024, with these extended forecasts intended as experimental. The model successfully learned seasonal patterns but requires further optimization to improve predictive accuracy. This study advances AI-based inventory management for SMEs, supporting operational efficiency, waste reduction, and risk mitigation in raw material supply chains.
Implementation of CNN-Based Computer Vision for Personal Protective Equipment Detection in the Oil and Gas Industry prayogi, soni
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 4 (2025): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i4.2459

Abstract

Due to the inherently hazardous nature of operations in the oil and gas industry, strict compliance with safety protocols, including the obligatory use of Personal Protective Equipment (PPE), is essential for all workers. Nonetheless, monitoring PPE compliance through manual observation remains inefficient and prone to error, especially in expansive, intricate work settings. To address this challenge, there is a growing demand for an intelligent system capable of accurately and instantaneously detecting PPE use. This research introduces a Computer Vision approach employing Convolutional Neural Networks (CNNs) to identify PPE usage among workers within oil and gas environments. The system leverages a comprehensive dataset of images of workers wearing various types of PPE, including helmets, safety vests, and face masks. These images are used to train a CNN model designed to distinguish and classify the safety equipment. Experimental results demonstrate that the proposed CNN model achieves an impressive 94.2% detection accuracy on the validation data and maintains reliable performance across varying lighting conditions and camera angles. Moreover, the system can identify PPE violations in under 1 second per frame, making it suitable for real-time surveillance applications. As a result, this solution offers a promising enhancement to workplace safety oversight, with the potential to markedly reduce accident rates in the industry. The findings also pave the way for future integration with IoT-based monitoring platforms and further refinement of model adaptability across diverse industrial scenarios. The primary innovation of this study lies in the optimized deployment of CNNs tailored to the challenging conditions of oil and gas sites, delivering high detection precision and rapid response times. This area has seen limited exploration in existing literature.
Performance Analysis of Gradient Boosting and Decision Tree on Distributed Denial of Service Attacks in Software Defined Networks Hayati, Lilis Nur; Hatta, Andi Muhammad Iqra Rezky; Herdianti, Herdianti
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 4 (2025): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i4.2482

Abstract

Distributed Denial of Service (DDoS) attacks remain a prominent threat to modern network infrastructures, particularly in Software Defined Networks (SDNs), which operate under a centralized control architecture. This study aims to assess the effectiveness of Gradient Boosting and Decision Tree algorithms for identifying DDoS attacks in SDN environments. To improve model performance, we applied preprocessing and feature selection to a publicly available SDN-based DDoS dataset. The feature selection process successfully reduced the number of attributes from 23 to the 10 most influential features for classification. The models were trained and evaluated using multiple data splitting ratios: 60:40, 70:30, 80:20, and 90:10. Their performance was measured through accuracy, precision, recall, F1-score, and confusion matrix analysis. Experimental results showed that Gradient Boosting achieved the highest accuracy of 95.53% on a 90:10 split, with relatively low computation time. In comparison, the Decision Tree achieved a maximum accuracy of 94.26% but required more processing time. The confusion matrix for the best-performing model showed high true-positive and true-negative rates, with a low false-negative rate, indicating reliable detection capabilities. This study contributes to the ongoing research in DDoS detection by highlighting the effectiveness of machine learning algorithms in SDN environments.
Apriori-Based Association Rule Mining Approach for Developing a Product Recommendation System in an Agricultural E-Marketplace Maulana, Handika Attha; Rohman, Arif Nur
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 4 (2025): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i4.2486

Abstract

The rapid growth of transactions in Indonesian agricultural marketplaces leaves a gap between large data volumes and suboptimal marketing strategies. As a novel contribution, this study explicitly applies the Apriori algorithm to the context of agricultural e-marketplaces in Indonesia, serving as a validated case study. The primary data used in this analysis is a collection of historical transaction data obtained from one such marketplace. The dataset described in this case study includes 10 transaction histories involving 22 product items. This study aims to transform untapped historical data into business-strategy insights. By setting minimum support and confidence to 50%, the analysis successfully identified significant association rules. The strongest rule indicated a co-purchase pattern between specific products with a confidence value of 60.6% and a Lift Ratio of 11.1, indicating a robust positive correlation. This rule was then successfully implemented into a functional recommendation feature. Validation testing demonstrated complete consistency between the system results and manual calculations. This case study demonstrates the effectiveness of Apriori and provides a benchmark for developing similar technologies to improve sales and user experience in Indonesia's digital agriculture sector.
The Role of JASTIP in E-Marketplace Adoption: A UTAUT Model Analysis with Gender as a Moderator in West Papua Arniawati, Arniawati; Inan, Dedi Iskandar; Juita, Ratna`; Indra, Muhamad
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 4 (2025): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i4.2489

Abstract

E-marketplaces play a pivotal role in Indonesia’s digital economic transformation; however, infrastructural limitations, high logistical costs, and low levels of technological literacy hinder their adoption in West Papua Province. Jasa Titip (JASTIP) represents an informal digital facilitation mechanism that enables communities to collectively and affordably access e-marketplace products. Importance–Performance Map Analysis (IPMA) is used to identify strategic intervention priorities for e-marketplace adoption in West Papua using an expanded Unified Theory of Acceptance and Use of Technology (UTAUT) framework. The moderating influence of gender in community-based social support is examined. PLS-SEM was used to analyze data from 177 respondents. Results show that enabling environments, hedonic incentives, and social influence greatly affect behavioural intention and utilization. Gender also moderates social impact and behavioural intention, highlighting its function in adoption. The model explains 62.1% of behavioural intention and 66.2% of use behaviour. Policymakers and e-marketplace stakeholders should learn from these results about the necessity of inclusive initiatives that account for gender-specific adoption habits. Moreover, they highlight the critical role of JASTIP practices in overcoming structural barriers to digital access in underserved regions
Fine-Tuned IndoBERT for Aspect-Based Sentiment Analysis of Indonesian Five-Star Hotel Reviews Apriliani, Sinta; Erfina, Adhitia; Warman, Cecep
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 4 (2025): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i4.2491

Abstract

Online reviews significantly shape public perception and play a crucial role in customer decision-making within the hospitality sector. This research aims to conduct aspect-based sentiment analysis on Indonesian five-star hotel reviews using a fine-tuned IndoBERT model. Unlike prior studies that mainly applied IndoBERT to single hotels or small-scale datasets, this study fills that gap by examining 2,499 reviews collected from five luxury hotels in Jakarta. The analysis focuses on five essential service aspects: cleanliness, service quality, room comfort, food & beverages, and core facilities. The IndoBERT-base model was fine-tuned with annotated aspect-sentiment data and assessed using accuracy, precision, recall, F1-score, and confusion matrices. Experimental results show that the model reached 95.28% accuracy with a macro F1-score of 82.44%. Positive sentiment dominated the reviews (81.4%), while neutral and negative sentiments represented 16.9% and 1.7%, respectively. Service, along with food & beverages, received the highest praise, whereas cleanliness and core facilities were more often evaluated neutrally. Aspect and sentiment annotations were carried out semi-automatically using large language models (LLMs) and later validated by human annotators to ensure reliability. These findings highlight IndoBERT’s strong capability in aspect-based sentiment classification for Indonesian hotel reviews and provide actionable insights for hotel managers to enhance service quality. Moreover, this study demonstrates both the academic and practical significance of applying fine-tuned Transformer models to real-world customer experience analysis.

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