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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 20 Documents
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Search results for , issue "Vol. 14 No. 4 (2025): NOVEMBER" : 20 Documents clear
Sentiment Analysis on Ajaib App Using the SVM Method Minggow, Lingua Franca Septha; Vitianingsih, Anik Vega; Kacung, Slamet; Maukar, Anastasia Lidya; Rusdi, Jack Febrian
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.v14i4.2402

Abstract

The rapid growth of investment applications has transformed trading accessibility, yet user dissatisfaction persists, particularly regarding transaction delays, technical issues, and inadequate customer support. This study addresses a research gap in sentiment analysis, specifically in the context of the Ajaib investment application, by employing a Support Vector Machine (SVM) model combined with lexicon-based labelling. The objective is to classify user-generated Google Play reviews into positive, negative, and neutral sentiments, providing actionable insights for service improvement. The research follows a structured methodology comprising data crawling, text pre-processing (cleaning, case folding, tokenization, stopword removal, and stemming), TF-IDF feature extraction, and supervised classification with SVM. Model validation utilises a 3×3 confusion matrix to calculate accuracy, precision, and recall, thereby ensuring a robust performance evaluation. Experimental results demonstrate that the SVM classifier achieves high accuracy in sentiment polarity classification, highlighting its suitability for text-based sentiment analysis in the financial domain. The distinct contribution of this research is its implementation of SVM for sentiment classification for Ajaib, offering a replicable framework for leveraging user feedback to enhance digital investment platforms. These findings contribute to the development of automated sentiment analysis systems that support data-driven decision-making for improving customer satisfaction.
Effectiveness of Artificial Intelligence-Based Adaptive Honeypots in Cyber Threat Detection: A Systematic Literature Review and Meta-Analysis Purnama, Lukas Hadi; Prasetyo, Daniel Hary
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.v14i4.2403

Abstract

This study conducted a systematic literature review and meta-analysis on using honeypot systems enhanced by artificial intelligence to improve the effectiveness of cyber threat detection in organizational environments. The review followed the PRISMA protocol and assessed 454 articles from databases, including IEEE, ACM, Emerald, and Web of Science. After a multi-stage screening process, 62 articles met the inclusion criteria and were further analyzed. The synthesis indicated that integrating artificial intelligence into honeypot systems improved detection accuracy, expanded the system’s ability to recognize varied attack patterns, and optimized resource efficiency. A meta-analysis of 36 studies revealed consistent, significant improvements in detection performance. Quantitatively, the analysis yielded a mean effect size of 0.905, indicating a substantial improvement in detection effectiveness resulting from AI integration. These findings confirm that adopting AI-based honeypot technologies is essential for addressing increasingly complex cyberattacks and for providing a foundation for future research into the development of standardized evaluation frameworks.
Comparing CNN and GRU for Gold Price Prediction Using Deep Learning Dwi Agung Yanumatrajaya; Asmarayani , Ilham Fikri Dwi; Soetanto, Hari
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.v14i4.2406

Abstract

This research proposes a hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) to predict gold prices. The motivation stems from the volatile and complex nature of the gold market, heavily influenced by macroeconomic indicators such as the exchange rate (IDR/USD), Bank Indonesia (BI) interest rate, and inflation. In the hybrid architecture, the CNN serves as a feature extractor to identify nonlinear patterns in historical and economic data. At the same time, the GRU captures temporal dependencies, enabling the model to learn both short-term and long-term dynamics. The dataset comprises daily gold prices from January 2020 to August 2024, enriched with macroeconomic indicators to improve predictive relevance. Experimental results show rapid convergence of training and validation losses within 12 epochs. Model evaluation using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) demonstrates high predictive accuracy, with a MAPE of 1.136%. A comparative analysis with standalone CNN and GRU models reveals that the hybrid CNN–GRU architecture consistently outperforms both in terms of accuracy and prediction stability. This study contributes to financial forecasting by providing a robust, data-driven predictive tool that can support timely investment decisions in volatile market conditions.
Performance of Single-Hop and Multi-Hop Topologies in IoT-Based Wireless Sensor Networks for Environmental Monitoring Sulistyawan, Vera Noviana; Muhsin, Muhsin; Hasanah, Uswatun; Suni, Alfa Faridh; Pamungkas, Damar Purba; Santoso, Rizal Budi; Aditama, Kevin Muhammad Tegar; Fauzi, Muhamad Kurniawan
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.v14i4.2408

Abstract

This study aims to evaluate the performance of an IoT-based Wireless Sensor Network (WSN) system in monitoring temperature and humidity in a modern poultry house. Testing was conducted across two network topologies — single-hop and multi-hop — to analyze data transmission delay and sensor measurement accuracy. The methodology includes measuring the delay from sensor nodes to the sink node and analyzing sensor accuracy by comparing actual temperature and humidity values with sensor readings. The results indicate that the single-hop topology has lower and more stable transmission delays, ranging from 18 ms to 36 ms. In contrast, the multi-hop topology exhibits higher transmission delays, averaging 47.9 ms, due to additional time spent traversing intermediary nodes. In terms of accuracy, the temperature sensor shows minimal deviation from actual values, demonstrating good reliability. However, the humidity sensor exhibits greater variation, necessitating additional calibration or the use of higher-precision sensors. The evaluation using MAPE, RMSE, MSE, and MAE provides further insights into sensor error levels within the system. The uniqueness of this study lies in the comparative analysis of single-hop and multi-hop network performance in a WSN-IoT-based monitoring system. The study's implications emphasize the importance of optimizing network protocols to reduce latency in multi-hop communication and improving sensor accuracy to enhance the reliability of environmental monitoring.
Classification of University IT Helpdesk Tickets Using Support Vector Machine with Hyperparameter Optimization Yulio Ferdinand; Lubis, Muharman; Pratiwi, Oktariani Nurul
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.v14i4.2413

Abstract

The classification of IT helpdesk tickets is crucial for improving response efficiency in service management systems, particularly within academic institutions. However, the process is still mostly manual, increasing the risk of misclassification. This study explores the use of the Support Vector Machine (SVM) algorithm with four kernel functions — RBF, Linear, Polynomial, and Sigmoid — to automate the classification of user-submitted service tickets. The dataset was sourced from the Telkom University service desk application database, covering 2023 and 2024, and comprises 13,508 records across nine service categories. Preprocessing steps such as stemming, stopword removal, and TF-IDF feature extraction were applied before model training and evaluation. The RBF kernel achieved the highest accuracy at 85.04%, followed by Linear at 80.64%, Sigmoid at 75.94%, and Polynomial at 63.69%. The internet access category had the best classification performance across all kernels, with RBF and Linear achieving F1-scores of 90% and 89%, respectively. The request data category also showed consistently strong results with F1-scores above 80%. Misclassifications were mainly due to overlapping vocabulary, data imbalance, and limited semantic variation in ticket descriptions. The results indicate that the RBF kernel is most suitable for this multi-class classification task. This study highlights the effectiveness of machine learning in improving helpdesk automation and provides a basis for future enhancements, such as incorporating semantic-rich features and addressing class imbalance. Notably, this research contributes a comparative analysis of different SVM kernel performances, which has not been extensively explored in previous research.
Analysis of Contributing Factors and Prediction of Urban Waste Generation Using PSO-ANN Bariyah, Taufiqotul; Miftahurrohmah, Brina; Faria, Niswatun
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.v14i4.2425

Abstract

This research examines the factors influencing waste generation in urban areas, with a focus on East Java, which has experienced increased waste due to population growth and urbanization. Using the Spearman correlation method, it was found that unemployment (ρ = 0.87) and population (ρ = 0.865) are significantly related to waste generation. However, HDI (ρ = -0.152) and population density (ρ = -0.169) are uncorrelated with waste generation. Furthermore, waste generation predictions will be built using the Particle Swarm Optimization-Artificial Neural Network (PSO-ANN) model. The modeling results showed that the PSO-ANN architecture with one hidden layer achieved RMSE of 0.125 and MAE of 0.109, while the model with two hidden layers achieved RMSE of 0.123 and MAE of 0.105. These findings indicate that the two-hidden-layer PSO-ANN model is more effective in predicting waste generation than the single-layer model. This study recommends exploring alternative methods and additional variables to provide a more comprehensive examination and analysis of waste disposal management in the future.
Diabetes Classification Algorithm Optimization Using Particle Swarm Optimization on Naïve Bayes, C4.5 and Random Forest Maulana, Reffy; Eliyani, Eliyani
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.v14i4.2431

Abstract

The global rise in diabetes prevalence presents a significant public health concern, emphasizing the need for accurate and efficient early detection systems. This study investigates the performance of three classification algorithms—Naïve Bayes, C4.5, and Random Forest—for predicting diabetes and explores the impact of hyperparameter tuning via Particle Swarm Optimization (PSO) on model performance. The research employs the 2023 Behavioral Risk Factor Surveillance System (BRFSS) dataset from the Centers for Disease Control and Prevention (CDC), which includes a wide range of health-related and demographic variables from adult respondents across the United States. Each algorithm was tested under two conditions: with default parameters and after optimization using PSO. Experimental results demonstrate that the Random Forest algorithm, even without optimization, yielded the highest accuracy at 95.15%, whereas Naïve Bayes showed the weakest performance. However, applying PSO significantly improved the performance of initially suboptimal models, particularly Naïve Bayes and C4.5. Specifically, Naïve Bayes accuracy increased from 80.80% to 82.24% (a 1.44% increase), and C4.5 accuracy increased from 91.22% to 91.31% (a 0.09% increase). In contrast, the effect of optimization on Random Forest was minimal, showing a slight decrease in accuracy to 94.37%, indicating the model’s robustness in its default configuration. These findings underscore the importance of algorithm selection and tailored optimization strategies in enhancing the accuracy of diabetes classification systems.
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.

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