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INDONESIA
Sistemasi: Jurnal Sistem Informasi
Published by Universitas Islam Indragiri
ISSN : 23028149     EISSN : 25409719     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Sistemasi adalah nama terbitan jurnal ilmiah dalam bidang ilmu sains komputer program studi Sistem Informasi Universitas Islam Indragiri, Tembilahan Riau. Jurnal Sistemasi Terbit 3x setahun yaitu bulan Januari, Mei dan September,Focus dan Scope Umum dari Sistemasi yaitu Bidang Sistem Informasi, Teknologi Informasi,Computer Science,Rekayasa Perangkat Lunak,Teknik Informatika
Arjuna Subject : -
Articles 878 Documents
 84   85   86   87   88 
Development of a Patient Safety Incident Reporting System using the Agile Development Method Anam, Muhammad Khoirul; Astuti, Yani Parti; Mubarok, Ahmad Hasan
SISTEMASI Vol 14, No 5 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i5.5280

Abstract

One of the critical aspects of healthcare services is patient safety. However, the reporting of patient safety incidents in Indonesia remains low due to the use of inefficient manual systems and the limited participation of healthcare professionals. This study aims to develop a web-based patient safety incident reporting system using the Agile Development method. Agile was chosen for its ability to progressively adapt to user needs through fixed-duration iterations (sprints). The uniqueness of the system lies in the integration of the Agile approach with the Laravel architecture, which enables rapid, modular, and participatory development. Data were collected through interviews, questionnaires, and literature reviews, and analyzed using the PIECES framework. The system was developed using the Laravel framework and evaluated through User Acceptance Testing (UAT) with 24 respondents from the Temanggung District General Hospital (RSUD Kabupaten Temanggung). The testing results showed that 91% of respondents strongly agreed on the system's ease of use and effectiveness. The system has proven to enhance efficiency, accuracy, and user engagement in the incident reporting process. It also offers practical implications for other hospitals aiming to build more integrated and adaptive reporting systems to support improved healthcare service quality.
Implementation of Indoor 4G Signal Interference Detection using USRP B210 and RTL-SDR Ciksadan, Ciksadan; Thabroni, Feri; Suroso, Suroso
SISTEMASI Vol 14, No 5 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i5.5450

Abstract

Signal interference is one of the main challenges in maintaining the quality of 4G network services, particularly in indoor environments with complex propagation characteristics. This study aims to develop a 4G signal interference detection system based on Software Defined Radio (SDR), utilizing a USRP B210 device as the transmitter and two RTL-SDR units as receivers. The system is designed to monitor signals in real time at frequencies of 800 MHz and 1700 MHz, and to analyze Received Signal Strength Indicator (RSSI) and Signal-to-Noise Ratio (SNR) parameters from each RTL-SDR to identify potential signal interference. The test results show that RTL1 consistently received signals of higher quality compared to RTL2. At 800 MHz, the SNR difference between the two receivers reached 21.06 dB, while at 1700 MHz it was 15.46 dB. Although no foreign signals were visually detected in the spectrum, the significantly lower SNR values on RTL2 indicate the presence of non-spectral interference, likely caused by differences in propagation conditions such as multipath effects or physical obstructions. These findings demonstrate that the proposed system is capable of detecting hidden interference through a quantitative comparison between two receivers operating simultaneously. This approach proves effective for indoor signal monitoring and can be further developed to support automation using machine learning techniques.
Design of the Feeding Frog Cognitive Game for Early Childhood using the Mechanics, Dynamics, and Aesthetics (MDA) Framework and Greenfoot Hayat, Cynthia
SISTEMASI Vol 14, No 5 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i5.5213

Abstract

This study aims to design a cognitive game that stimulates early childhood motor development while introducing knowledge about animals and their types of food. The development of the game utilizes the Mechanics, Dynamics, and Aesthetics (MDA) framework along with the Greenfoot software. The mechanics component includes genre, objectives, platform, rating, levels, and artistic concept. The dynamics component covers storyline, gameplay controls, challenges, and game rules, while the aesthetics component emphasizes sensation, narrative, engagement, and fantasy. Players interact with the game by feeding animals based on their appropriate diets. The storyline centers on various animal habitats, where each level represents an environment with increasing difficulty—basic, intermediate, and advanced—while also providing motor stimulation. Participatory observation was conducted in several playgroups in West Jakarta to gain insights into the game's impact and identify the key elements that capture players’ interest. Evaluation of user responses showed that out of seven assessed statements, five received a "Very Good" rating, while the remaining two were rated as "Good" by 45 respondents. In terms of percentage, the highest achievement reached 90%, indicating that the Feeding Frog game demonstrates high clarity and ease of understanding, and holds potential as a tool to assist parents in stimulating children’s cognitive development.
Implementation of a Web-Based Skincare Decision Support System using the Simple Additive Weighting Method Trisdiatin, Sausan; Wahyuni, Elyza Gustri
SISTEMASI Vol 14, No 5 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i5.5505

Abstract

The challenge of selecting skincare products that match individual skin conditions often leads to confusion among users, especially due to the vast array of products available on the market. This study aims to assist users in choosing the most suitable local skincare products based on several criteria: skin concerns, skin type, price range, number of items in a set, and sales ratings. The method used is the Simple Additive Weighting (SAW) technique, which calculates the suitability level of each product alternative against the defined criteria to generate a ranked list of the most appropriate products. The system was developed using the Rapid Application Development (RAD) approach, which emphasizes rapid prototyping and iterative implementation. It was built as a web-based application using PHP, MySQL, HTML, CSS, JavaScript, and Bootstrap 5. Skincare product data were collected through web scraping from the Shopee platform, including information such as price, set completeness, and sales ratings. All data were verified for validity using BPOM certification and halal labels. The results of the study indicate that the decision support system can recommend up to three top local skincare products tailored to user preferences, complete with pricing, images, and brief descriptions—without displaying numeric scores. Functional testing confirmed that all features operated correctly, and a User Acceptance Test (UAT) yielded an average satisfaction score of 94%, indicating strong user acceptance. In conclusion, this system serves as a valuable digital tool to accelerate decision-making in skincare selection, reduce the risk of product mismatch, and improve both the efficiency and adoption potential of local skincare products based on user preferences in an online environment.
Optimization of CNN Activation Functions using Xception for South Sulawesi Batik Classification Aswan, Aswan; Puspaningrum, Eva Yulia; Asrul, Billy Eden William
SISTEMASI Vol 14, No 5 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i5.5281

Abstract

Batik motifs from South Sulawesi such as the Pinisi boat, Lontara script, Tongkonan house and Toraja combinations embody rich cultural narratives but are difficult to identify automatically. Automatic classification supports cultural preservation and education and empowers tourism and digital heritage applications. This study improves the performance of convolutional neural networks for South Sulawesi batik classification by optimizing activation functions within the Xception architecture which exploits depthwise separable convolutions for efficient and detailed feature extraction. A balanced dataset of 1400 labeled images in four classes was divided into eighty percent for training, ten percent for validation and ten percent for testing. Images were resized to 224 by 224 pixels, converted to grayscale and augmented through zoom, flip and rotation. With identical hyperparameters including a learning rate of 0.001, a batch size of 64 and training for 100 epochs using the Adam optimizer, ReLU, ELU, Leaky ReLU and Swish activation functions were compared. Evaluation metrics comprised accuracy, precision, recall, F1 score and cross entropy loss. ELU achieved the highest test accuracy of 98.57 percent, precision of 0.9864, recall of 0.9857 and F1 score of 0.9857, outperforming ReLU and Leaky ReLU with 97.86 percent accuracy and Swish with 97.14 percent accuracy. The results demonstrate that selecting an optimal activation function substantially enhances convolutional neural network classification of complex batik patterns. The findings offer practical guidance for development of resource aware batik identification systems in support of cultural digitization and education initiatives.
Payment Status Classification Invoice Bank Using Logistic Regression and Random Forest Putri, Farah Anindia; Kartikasari, Mujiati Dwi
SISTEMASI Vol 14, No 5 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i5.5470

Abstract

Payment management is an essential aspect of a bank’s financial operations, particularly in ensuring the smooth execution of procurement transactions for goods and services. The invoice, as an official document, plays a role in determining whether a transaction can be processed promptly or experiences a delay. Despite its central role, empirical research exploring the factors influencing invoice payment status remains limited, especially within the context of banking institutions. This study aims to analyze the factors that affect invoice payment status based on company type, procurement type, and invoice value. The methods employed include logistic regression and random forest to compare the classification performance of both approaches. The analysis reveals that procurement type and invoice value significantly influence payment status, with invoice value emerging as the most dominant variable based on the smallest p-value. In the random forest model, invoice value also ranks highest in terms of variable importance. In terms of accuracy, the random forest model outperforms logistic regression, achieving an accuracy of 94.47% compared to 59.30%. Although both methods yield similar precision (approximately 97%), random forest demonstrates a substantially higher recall (97.41%) and F1-score, whereas logistic regression records a recall of only 69.19%. These findings suggest that random forest is a more effective method for predicting payment status and holds strong potential for supporting data-driven decision-making in bank payment management systems
Analysis of Internet Quality Improvement from HFC to FTTH in Housing X Elfrida, Carissa; Fauzi, Ahmad; Setyowati, Endah
SISTEMASI Vol 14, No 5 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i5.5222

Abstract

The demand for fast, stable, and reliable internet services continues to grow, particularly in densely populated residential areas. One of the ongoing issues is the limitations of Hybrid Fiber Coaxial (HFC) networks, which often result in low speeds, high latency, and unstable connections. This study aims to analyze the improvement in internet quality resulting from the migration from HFC to Fiber to the Home (FTTH) in Residential Area X, East Jakarta. A quantitative approach was employed, involving direct measurements on 100 homepasses before and after the migration. The parameters analyzed include download and upload speeds, latency, jitter, packet loss, Power Link Budget, Optical Power Budget, Round Trip Time (RTT), and Quality of Service (QoS) metrics. The results show a significant improvement in internet performance after the migration to FTTH. The average download speed increased from 16.75 Mbps to 128.79 Mbps, while the upload speed rose from 2.99 Mbps to 54.53 Mbps. Latency decreased from 66.23 ms to 5.73 ms, jitter dropped from 12.37 ms to 2.55 ms, and packet loss was reduced from 3.06% to 0.47%. Although FTTH has a lower power budget compared to HFC, the Power Link Budget analysis indicates that the network quality delivered by FTTH is more stable and reliable. These findings confirm that FTTH provides a significantly more stable and dependable internet connection than HFC.
Sentiment Analysis of User Reviews of the KitaLulus Application on Google Play Store using the Support Vector Machine (SVM) Algorithm Agil Rafsanjani, Ahmad Syaifudin; Fithri, Diana Laily; Supriyono, Supriyono
SISTEMASI Vol 14, No 5 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i5.5519

Abstract

The advancement of digital technology has driven the increasing use of job search applications such as KitaLulus. User reviews on the Google Play Store serve as a crucial source for evaluating service quality and user satisfaction. This study aims to analyze user sentiment toward the KitaLulus application using the Support Vector Machine (SVM) algorithm, combined with the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance in sentiment data. The research process includes collecting 1,000 user reviews through web scraping, text preprocessing, rating-based labeling, data transformation using TF-IDF, splitting the dataset into 80% training and 20% testing, applying SMOTE, training the SVM model, and evaluating its performance. The results show that SVM trained with SMOTE-balanced data achieved an accuracy of 89%, precision of 90%, recall of 89%, F1-score of 90%, and an AUC of 0.93. This study contributes a practical implementation of the SVM-SMOTE combination, demonstrating its effectiveness in text-based sentiment classification, particularly in handling imbalanced review data from mobile applications.
Integration of Uptime Kuma Application Monitoring and MikroTik Router System using Prometheus and Grafana Defani, Refalia; wijayanto, Danur
SISTEMASI Vol 14, No 5 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i5.5326

Abstract

A network monitoring system plays a crucial role in ensuring service availability and optimizing network infrastructure performance. This study aims to integrate the Uptime Kuma application monitoring system and MikroTik router devices using Prometheus and Grafana at Life Media Company. The developed monitoring system is designed to provide real-time monitoring of services and network devices through a centralized dashboard that is more efficient and informative. The system was developed using the PPDIOO methodology, which includes the phases of Prepare, Plan, Design, Implement, Operate, and Optimize. The implementation results indicate that the integrated monitoring system offers significant improvements over the previous system in terms of access stability, monitoring visualization completeness, and the availability of more detailed technical information. The technical data successfully displayed include bandwidth usage, CPU load, memory usage, service latency, and service availability levels. The system also utilizes a 10-second scraping interval to collect data periodically, enabling faster identification of potential connectivity issues. This study contributes to the development of adaptive, open-source network monitoring systems by demonstrating that it can flexibly monitor various devices and services, while also being efficient—requiring no additional hardware and capable of displaying real-time technical data through a centralized dashboard without relying on commercial systems. In the future, the system can be further enhanced by implementing automated notifications and alerting features to strengthen its overall monitoring capabilities.
Rainfall Prediction based on Historical Weather Data using Naive Bayes Classification Model in Southeast Sulawesi Samudin, Ayustina; Saputra, Rizal Adi; Agsaria, Fabelina; Judanto, Nurendro Hardjo; Badarudin, Ade Syifa
SISTEMASI Vol 14, No 5 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i5.3882

Abstract

Southeast Sulawesi is one of the provinces in Indonesia characterized by diverse topography and climate, making it challenging to accurately identify and predict rainfall patterns. The aim of this study is to enhance our understanding of weather behavior in Southeast Sulawesi and provide a foundation for developing more advanced and region-specific weather prediction methods. The data used in this research consists of historical weather records obtained from the official BMKG (Meteorology, Climatology, and Geophysics Agency) website, containing features that significantly contribute to rainfall prediction. The method employed in this study is the Naive Bayes classification model, which involves several stages including data collection, pre-processing, and preparation for the modeling phase, ultimately generating rainfall prediction outputs. The results of the study yielded a rainfall prediction accuracy of 74.66%. For the rainfall class (0.0), the model achieved a precision of 82%, recall of 66%, and F1-score of 73%. Meanwhile, for the rainfall class (1.0), the model achieved a precision of 69%, recall of 84%, and F1-score of 76%. Despite some prediction errors, these findings indicate that the Naive Bayes method can serve as a solid foundation for the development of more sophisticated and tailored weather prediction models for the Southeast Sulawesi region.

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