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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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Prediction of Monthly Rainfall with Using Monte Carlo Simulation in the Medan City Area Arini, Arini; Cipta, Hendra
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2307

Abstract

The climate in Indonesia, which is a tropical region, is always uncertain and makes it difficult to predict weather conditions. Weather conditions can be influenced by rainfall, air temperature, wind speed, air humidity and light radiation intensity. Rainfall is relatively high and varies throughout the year, the average monthly rainfall is around 150-300 mm in the rainy season and 50-100 mm in the dry season. There are several characteristics of rainfall, namely convective rain, frontal rain and orographic rain. For this reason, a method is needed that can solve problems in predicting monthly rainfall properties using the Monte Carlo simulation method. From this study, the results of the prediction of rainfall properties were obtained with 36 data from 2021 to 2024 which had a MAPE test result of 12.28%. The test results came from the average calculation carried out on the Monte Carlo method prediction with 5 variables.
Temperature and Humidity Monitoring in Hydroponic Cultivation Based on Internet of Things: Dataset Development for Smart Agriculture Barus, Simon Prananta; Seo, Jeriko Ichtus
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 1 (2025): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i2.2311

Abstract

This research is a continuation of the previous research, entitled "Development of Hydroponic Application based on Web and Internet of Things for the Community to Monitor pH and Total Dissolved Solids." Not only pH and Total Dissolved Solids (TDS) need to be monitored, but also temperature and humidity. This research aims to produce a temperature and humidity monitoring application (in addition to pH and TDS which already exist) in hydroponic cultivation and complete the dataset that supports smart agriculture. The research method includes literature study, hardware development using NodeMCU ESP8266 microcontroller and DHT11 sensor, web-based software development with JavaScript on the Front-End side, PHP on the Back-End side, Apache as Web Server, and MySQL as database management system (DBMS), as well as the implementation stage, integration, system testing and report writing. The results of the research show that the developed system can monitor temperature and humidity in real-time with a good level of accuracy. Not only that, this system can produce a hydroponic dataset that includes temperature and humidity parameters, which can be used for data analysis and improvement of hydroponic management. Thus, this study successfully expanded the scope of the hydroponic monitoring system by adding temperature and humidity parameters. This study contributes to optimizing the hydroponic cultivation system and supporting the development of data-based smart agriculture. Further research will integrate more monitoring parameters, conduct direct hydroponic cultivation trials, and apply artificial intelligence such as machine learning and deep learning to improve efficiency and effectiveness in hydroponic cultivation.
The Effect of Chatbot Usage on Customer Satisfaction: A Quantitative Study of Shopee, Tokopedia, and Lazada Using SmartPLS Afrina, Mira; Gumay, Naretha Kawadha Pasemah; Ariani, Ardina; Febriady, Mukhlis
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 1 (2025): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i1.2312

Abstract

With the increasing growth of e-commerce, it is important to identify the features available in e-commerce applications that can provide customer satisfaction. One of the features in e-commerce is the chatbot. Chatbots in e-commerce can provide various services to users, such as assistance in product search, ordering, product information, payment processing, customer support, and more. This research aims to analyze and understand how the response quality of each chatbot in e- commerce platforms such as Shopee, Tokopedia, and Lazada affects e-commerce user satisfaction. This study employs a quantitative methodology, integrating data analysis conducted through the SmartPLS 4.1 software. The research results show that the chatbot in Shopee platform has a impact on customer satisfaction. The same goes for chatbot in Tokopedia platform, but there are two variables that do not have a direct impact, there are information quality and waiting time. Meanwhile, chatbot in Lazada platform does not affect customer satisfaction. The findings of this research should reveal new strategies for leveraging chatbot technology to better satisfy customers in e- commerce environments, as well as lay the groundwork for further research on how artificial intelligence can shape customer experiences in the future.
Comparative Analysis of RESTful, GraphQL, and gRPC APIs: Perfomance Insight from Load and Stress Testing Chandra, Steven; Farisi, Ahmad
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 1 (2025): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i1.2315

Abstract

Backend constitutes a critical component of digital infrastructure, responsible for processing business logic, managing data, and facilitating communication between software systems. APIs serve as the interface that enables software interaction and plays a pivotal role in backend operations. This study investigates the performance of three API architectures: RESTful, GraphQL, and gRPC. The experimental approach involves the implementation of Load Testing and Stress Testing to assess the performance of these architectures. The experiment utilizes a dedicated server and client hardware to simulate real- world conditions, with parameters such as CPU usage, memory usage, response time, load time, latency, success rate, and failure rate evaluated using a dataset comprising 1,000 rows of student- related records. Result show that RESTful achieves the highest total request but exhibit greater resource consumption and a higher failure rate. GraphQL demonstrated better CPU and memory efficiency with strong stability, though it has higher latency and slower response times. gRPC strikes a balance with a moderate latency and resource usage, albeit with slightly higher memory consumption under stress. By presenting a comprehensive analysis of each API architecture, this study contributes a comprehensive performance analysis under practical testing scenarios giving developers and system architect with data-driven guidance for selecting API architecture to their application needs. RESTful is well suited for high-throughput scenarios with less critical operations, GraphQL excels in resource efficiency and stability, and gRPC offers balanced performance across diverse workloads.
VGG-16 Accuracy Optimization for Fingerprint Pattern Imager Classification Andreansyah, Agus; Supardi, Julian
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 1 (2025): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i1.2317

Abstract

A fingerprint is a unique biometric identity commonly used as evidence in court. However, the quality of fingerprints can deteriorate due to external factors such as uneven surfaces, weather conditions, or distortion. This study uses the FVC2000 dataset and applies Convolutional Neural Networks (CNNs) to enhance and classify fingerprint images, focusing on patterns such as arches, loops, radial loops, ulnar loops, and twin loops. A novel aspect of this research is the optimization of the VGG-16 model by making specific adjustments to the hyperparameters, including setting the learning rate to 0.0001, using 50 epochs, and selecting a training-to- validation data split of 80%:10%. These adjustments were made to enhance the model’s ability to classify complex and varied fingerprint patterns, which typically present challenges to standard CNN models. The results of the study show the highest accuracy of 100% on the test data with the optimized parameters.These findings demonstrate that the optimized VGG-16 model successfully classifies fingerprint images with optimal performance. The real-world implications of achieving 100% accuracy include an increase in the reliability of biometric identification systems, especially for forensic and security applications that require high accuracy to ensure accurate decisions. This study makes a significant contribution to the development of CNN-based fingerprint classification systems, offering a new approach that supports more reliable and precise biometric applications.
Classification of User Expressions on Social Media Using LSTM and GRU Models Yusadara, I Gede Putra Mas; Saryanti, I Gusti Ayu Desi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 1 (2025): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i1.2318

Abstract

Social media serves as a platform for sharing information. Through social media, users can interact with others and express their feelings and emotions. Therefore, emotion analysis plays a crucial role in understanding users' conditions regarding various issues and social events. This study aims to compare the performance of emotion classification models in analyzing and identifying users' emotions on social media. The research process includes data preprocessing, training, and model performance evaluation. The dataset used is derived from Twitter social media and is available on Kaggle. It consists of two main columns: text and label, with the latter categorized into six groups. The dataset undergoes several preprocessing techniques to ensure it is ready for model training. The model training process implements the architectures of LSTM and GRU to analyze the emotions contained within the text. The evaluation results show that the model achieves an accuracy of 93% for LSTM and 94% for GRU, indicating that the GRU model slightly outperforms the LSTM in classifying emotions in textual data. This research is expected to contribute to emotion analysis systems based on deep learning.
Business Intelligence Model of Regional Hospitals using HGOD Discovery Hengki, Hengki; Gernowo, Rahmat; Nurhayati, Oky Dwi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 1 (2025): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i1.2320

Abstract

Based on data from the Regional General Hospital in the Bangka Belitung Islands province, the Gross Death Rate (GDR) is the general death rate for every 1000 patients discharged of 108,430 compared to the health department standard of <45. The Net Death Rate (NDR) is the death rate 48 hours after being treated for every 1000 patients discharged of 67,388 compared to the health department standard of <25. TOI (Turn Over Interval) is the average turnover period of days where a bed is unoccupied from being filled to the next time it is filled of 19,832 days compared to the health department standard of 1 to 3 days. The solution offered by the researcher develops Business Intelligence (BI) optimization with a new model called the HGO (Hierarchy, Governance, Outlook) Discovery approach as a framework model for developing business intelligence for regional general hospitals in Indonesia. This model is expected to be able to solve or reduce the dimensional problems that exist in hospitals, namely the main patient management, HR Key Resources, and the quality of inpatient health services. The HGO Discovery approach is able to find patterns in a series of events called sequences by sorting the work patterns that exist in the hospital so that the business process of regional general hospitals is faster and more interactive in decision making. The Business Intelligence approach carried out by regional hospitals with HGOD is expected to make patient health services more integrated through the hierarchy of patient services, governance and outlook in decision making.
Enhancing Smart City Maturity Through Digital Transformation: A Success Factors Analysis Listianingsih, Widya; Susanto, Tony Dwi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 1 (2025): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i1.2326

Abstract

Improving smart city maturity through digital transformation is becoming increasingly crucial in facing the challenges of rapid urbanization and the need for more efficient city governance. However, the lack of a unified understanding of key drivers and challenges in this domain has limited the effectiveness of existing strategies. This study aims to explore the role of digital transformation in improving Smart City maturity by identifying key success factors and best practices adopted by cities worldwide. This study used the systematic literature review (SLR) methodology based on the PRISMA framework, which included systematic steps in selecting, collecting, and analyzing relevant literature. The study results reveal six factors influencing Smart City maturity: Information and Communication Technology (ICT) infrastructure, data integration, government policies and strategic planning, stakeholder engagement, environmental sustainability, and innovation and human resource development. Unlike previous studies, this study synthesizes global best practices and success factors, offering actionable insights for policymakers and practitioners to design inclusive, sustainable, and forward- looking digital transformation strategies. Furthermore, the study underscores the need for context-specific research to optimize implementation and drive meaningful progress in diverse urban settings.
KMS Protoype using Stohmaier Framework at Association of Indonesian Kindergarten Teachers in South Bangka Regency Raya, Agustina Mardeka; Sulaiman, Rahmat
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 1 (2025): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i1.2327

Abstract

IGTKI is a kindergarten teacher association organization that aims to realize the quality of early childhood education with a spirit of professional love and character to be able to face the era of globalization. However, this goal is not fully implemented because of the distance of each school is far, the results of the training are not socialized, the lack of knowledge to handle students with special needs so that the lack of knowledge sharing among PAUD teachers. To increase the knowledge sharing needed a Knowledge Management System application that can be a solution of knowledge sharing that is not hindered by the limitations of time and place. This study uses the Strohmaier Framework for modeling knowledge management systems. Techniques of Analysis and system design are carried out using the object oriented approach method Unifed Manipulation Language (UML). The suitability of the system to the business process was tested using the Forum Group Discussion (FGD) method which was validated using the Fit criteria from Strohmaier's theory which produced a value of 76%, this value shows that the Knowledge Infrastructure designed is in accordance with the business process. Meanwhile, user acceptance of the system was tested using User Acceptance Testing (UAT) in the form of a questionnaire which was calculated based on the Linkert scale, producing a value of 84%. From this value it can be concluded that the level of user acceptance at IGTKI is good for the KMS created.
Evaluating GRU Algorithm and Double Moving Average for Predicting USDT Prices: A Case Study 2017-2024 Rizky, Rahmat; ula, Munirul; Yunizar, Zara
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 1 (2025): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i1.2328

Abstract

The cryptocurrency market is highly volatile, requiring advanced analytical methods for accurate price forecasting. This study evaluates the effectiveness of Gated Recurrent Units (GRU) and Double Moving Average (DMA) in predicting USDT (Tether Coin) prices using historical data from 2017 to 2024, sourced from Investing.com. Implemented in Jupyter Notebook, the research explores the strengths of each method in analyzing market fluctuations and price trends. GRU, a deep learning-based recurrent neural network, processes sequential data using a gating mechanism, making it effective for capturing short-term price dynamics. DMA, in contrast, is a statistical method that filters market noise to identify long-term trends, making it more reliable for stable market conditions. Performance evaluation shows DMA achieving lower errors (MAE: 5.494, MAPE: 0.0339%) than GRU (MAE: 5.984, MAPE: 0.0369%), suggesting higher accuracy for trend-based predictions. However, GRU’s lower RMSE (8.531 vs. 8.715 for DMA) indicates better adaptability to sudden price fluctuations, making it more responsive to volatile markets. A hybrid approach combining GRU and DMA reveals their complementary strengths—DMA’s minimal bias (-0.0013% MPE) supports stable trend analysis, while GRU’s slight positive bias (0.0286% MPE) captures short-term fluctuations. Additionally, a comparison with Long Short-Term Memory (LSTM) demonstrates its superior predictive accuracy, outperforming both GRU (MAE: 5.98, RMSE: 8.53) and DMA (MAE: 5.49, RMSE: 8.72) with the lowest MAE (4.31), MAPE (0.027%), and RMSE (5.64), alongside minimal bias (MPE: 0.007%). This study highlights the need for integrating multiple forecasting techniques in cryptocurrency price prediction. While DMA is well-suited for stable trends and GRU excels in volatile conditions, LSTM outperforms both, reinforcing the effectiveness of deep learning for financial time-series forecasting.

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