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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 1,011 Documents
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Sentiment Analysis of CapCut Application Reviews using Support Vector Machine with the SMOTE Technique shefia, faridah ayu; Setiaji, Pratomo; Triyanto, Wiwit Agus
SISTEMASI Vol 15, No 2 (2026): 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.v15i2.5948

Abstract

The growing popularity of short-form video content across various social media platforms has increased the use of cross-device video editing applications, accessible through smartphones, desktops, and web-based services. CapCut is one of the most widely used applications for creating creative content, and user reviews on the Google Play Store serve as an important indicator for evaluating user experience quality. However, review datasets are often imbalanced, with positive sentiment dominating and neutral sentiment appearing in much smaller proportions, which poses challenges for sentiment classification. This study aims to analyze user sentiment toward CapCut reviews using Support Vector Machine (SVM) and applying the Synthetic Minority Over-sampling Technique (SMOTE) to address data imbalance. The data were collected by scraping reviews from the Google Play Store, resulting in 4,381 cleaned review entries after the data cleaning stage. The reviews then underwent text preprocessing, TF-IDF feature weighting, and model training. The experimental results show that the SVM model achieved an accuracy of 73.54% with a weighted F1-score of 0.736. These findings indicate that SMOTE contributes to improving model performance on minority classes. Overall, this study provides insights into user perceptions of CapCut and highlights the potential of SVM as an effective sentiment classification method for text-based application reviews.
Business Process Reengineering in Water Billing Administration: A Case Study of KPAB Gang Gedang Mas Aprilianti, Devi; Suharso, Wildan
SISTEMASI Vol 15, No 2 (2026): 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.v15i2.5827

Abstract

The water billing administration process in Gang Gedang Mas, Curungrejo Village, is still conducted manually, resulting in slow procedures, inefficiency, and a high risk of errors. This study aims to improve the efficiency and accuracy of the water billing administration process through a Business Process Reengineering (BPR) approach. The improvements focus on four main processes: water meter recording, data entry, bill calculation, and billing information distribution, involving two key actors: field officers and administrative staff. This research employed a case study method, consisting of direct observation, process modeling using BPMN notation, and the measurement of processing time efficiency and throughput within one billing cycle before and after the process redesign. The proposed solution is a digital process model design that supports workflow automation at the design level, without implementing an actual system, while still maintaining the operational role of field officers. The results indicate a significant reduction in processing time, from 684 minutes to 168 minutes, along with a 307.5% increase in administrative process throughput efficiency within one billing cycle. This study demonstrates that applying BPR through process model redesign can optimize water billing management in small-scale communities with limited infrastructure, providing an efficient solution that can be adapted to similar contexts.
Analysis and Implementation of Linear Regression and Decision Tree Methods to Predict Sales at Rayyan Bakery, Simpang Marbau Hidayah, Natari Dia Alika; Munthe, Ibnu Rasyid; Juledi, Angga Putra; Nasution, Marnis
SISTEMASI Vol 15, No 2 (2026): 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.v15i2.6124

Abstract

The development of information technology and data analytics has encouraged business actors to leverage historical data as a basis for decision-making. In the small and medium enterprise (SME) sector, particularly in the culinary field, the ability to predict sales is a crucial aspect of production planning and stock management to ensure operational efficiency. Rayyan Bakery Simpang Marbau, as a bakery SME, faces challenges due to fluctuating sales that have traditionally been managed based on experience rather than systematic data analysis. The main problem addressed in this study is the absence of a data-driven sales prediction method that can assist the business owner in estimating sales accurately. Therefore, a predictive approach that utilizes historical sales data is required to support managerial decision-making. This study employs linear regression and decision tree methods. The analyzed data consist of historical sales records of Rayyan Bakery Simpang Marbau over a specific period. Linear regression is used to model the linear relationship between sales variables, while the decision tree captures non-linear patterns and produces easily interpretable decision rules. The performance of both methods is analyzed and compared based on the accuracy of the predictions they generate. The results indicate that both linear regression and decision tree methods can be effectively used to predict sales; however, the decision tree provides greater flexibility in capturing fluctuating sales patterns. These findings are expected to assist Rayyan Bakery in production planning and stock management, as well as serve as a reference for applying sales prediction methods in similar SMEs.
Analysis of Cryptocurrency Candlestick Patterns using Gramian Angular Field and Hybrid Deep Learning Hasriadi, Hasriadi; Razak, Mashur; Jalil, Abdul
SISTEMASI Vol 15, No 2 (2026): 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.v15i2.5883

Abstract

Cryptocurrency markets such as Bitcoin, Ethereum, and Solana exhibit high volatility, making price forecasting difficult when relying solely on conventional technical analysis. This study aims to analyze cryptocurrency candlestick patterns by utilizing Gramian Angular Field (GAF) representations and to evaluate the performance of a hybrid deep learning model combining CNN–LSTM–Transformer to support investment decision-making. The proposed method involves processing daily historical Open, High, Low, and Close (OHLC) data from three major cryptocurrency assets: Bitcoin (BTC-USD), Ethereum (ETH-USD), and Solana (SOL-USD), covering the period from January 1, 2020, to September 30, 2024, obtained from Yahoo Finance. The time-series data were transformed into 64×64 pixel GAF images and used to train a baseline CNN model as well as a hybrid CNN–LSTM–Transformer model. Model evaluation was conducted across multiple forecasting horizons, including 1 day, 7 days, 30 days, 180 days, and 1 year, and was further complemented by real-time testing using the CoinGecko API in March 2025. The results indicate that the hybrid model achieved the best performance at different horizons for each asset: BTC-USD at the 30-day horizon with an R² of 0.971 and an SMAPE of 0.77%, ETH-USD at the 1-year horizon with an R² of 0.948 and an SMAPE of 0.81%, and SOL-USD at the 1-year horizon with an R² of 0.910 and an SMAPE of 4.72%. Real-time testing demonstrated that the model consistently captured the overall price movement trends despite high market volatility. It can be concluded that the integration of GAF representations and the hybrid CNN–LSTM–Transformer model has strong potential to enhance cryptocurrency candlestick analysis and can be utilized as a component of a Decision Support System for digital asset investment.
Security Mitigation Analysis of Mobile Application Using Static and Dynamic Methods with MobSF Nugraha, Fazri; Mansur, Mansur
SISTEMASI Vol 15, No 2 (2026): 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.v15i2.5916

Abstract

This study evaluates the security of the Mobile Application for the Palm Oil Harvest Information System using static and dynamic analysis through the Mobile Security Framework (MobSF). The research is motivated by the high risk of exploitation in APK-based applications and the lack of in-depth security assessments for applications that manage farmers’ operational data. Static analysis was conducted to identify structural weaknesses, including the use of debug certificates, enabled debugging mode, a low minimum SDK version (minSdkVersion), and exported components without proper protection. The initial results showed an App Security Score of 43/100 (Medium Risk), which increased to 67/100 (Low Risk) after configuration improvements were applied. Dynamic analysis was then performed to assess application security during runtime. The results indicated that the client side was relatively secure, with HTTPS-encrypted communication and no logging of sensitive data. However, dynamic analysis revealed vulnerabilities on the server side, where several backend endpoints could be accessed without authentication and without parameter validation, leading to potential risks of Broken Access Control and Insecure Direct Object Reference (IDOR). The findings confirm that static improvements are effective in strengthening the structural security of the application. Nevertheless, reinforcing authentication, authorization, and request validation mechanisms on the backend API remains essential to ensure comprehensive security before deployment in an operational environment. Unlike previous studies that generally focus only on vulnerability mapping, this study evaluates the effectiveness of security mitigation in a step-by-step manner by demonstrating improvements in static analysis scores and re-validating the results through dynamic analysis. Therefore, this research provides a more comprehensive security assessment of mobile applications by covering both client-side and backend aspects.
Selection of the Best Marketplace using SAW and WP Methods: A Case Study of Bekasi City Hadistra, Esa; Supriyanto, Raden
SISTEMASI Vol 15, No 2 (2026): 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.v15i2.4846

Abstract

In today’s digital era, e-commerce has made transactions between sellers and buyers easier by eliminating the need for face-to-face interaction. The abundance of available marketplaces often makes it difficult for consumers to choose the platform that best fits their needs. This study aims to provide recommendations for the best marketplace based on four key criteria: trust, user interface design, promotions, and product completeness. The Simple Additive Weighting (SAW) and Weighted Product (WP) methods were applied to support this decision-making process. The research was conducted on five popular marketplaces, with data collected through questionnaires distributed to 200 active respondents. Both SAW and WP methods were used to calculate the weight and score of each marketplace based on consumer preferences regarding the predefined criteria. The results show that Shopee ranked as the top marketplace, achieving the highest scores of 0.99 (SAW) and 5.77 (WP), due to its strengths in trust, promotional offers, and product variety. Tokopedia placed second, with scores of 0.98 (SAW) and 5.75 (WP), excelling in its more intuitive user interface design. Other marketplaces showed strengths in specific criteria but were unable to surpass Shopee and Tokopedia in the final scores. These findings provide valuable insights for consumers in selecting the most suitable marketplace for their needs, and for marketplace operators seeking to improve service quality based on consumer-prioritized criteria.
Optimization of Phishing Detection Performance with Variable Correlation Analysis and Imbalance Learning Arifin, Samsul; Setyo Utomo, Fandy
SISTEMASI Vol 15, No 2 (2026): 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.v15i2.4671

Abstract

Phishing is a common cyber security threat in which attackers attempt to deceive users into disclosing personal information such as passwords, credit card numbers, and other sensitive data. With the rapid advancement of technology, phishing techniques have become increasingly sophisticated and harder to detect using traditional methods. Therefore, it is essential to develop detection techniques capable of identifying phishing websites with high accuracy. This study aims to optimize phishing detection performance by integrating variable correlation analysis for feature selection and applying imbalanced learning techniques to address data imbalance. The research stages include Data Collection, Data Preprocessing, and Data Exploration, which involve correlation analysis, removal of low-correlation features, and data visualization. In the Model Building and Training phase, the dataset is split into features and labels, followed by training and the application of data balancing techniques, ending with Model Evaluation. The evaluated algorithms include Logistic Regression, Naive Bayes, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron, Decision Tree, Random Forest, Gradient Boosting, and CatBoost. The results show that the KNN algorithm delivers the best performance, achieving an accuracy of 91.25% and optimal scores in Precision (0.906943), Recall (0.927858), and F1-Score (0.922141), along with the lowest Hamming Loss at 0.0875. In contrast, the SVM algorithm recorded the lowest performance among the tested models. The implementation of this method is expected to contribute to the development of more reliable and accurate phishing detection systems in the future.
Evaluation of E-VAKU Application Usability using System Usability Scale and Cognitive Walkthrough Methods Narke, I Made Reyvinno Dirga; Nugraha, Deny Wiria
SISTEMASI Vol 15, No 2 (2026): 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.v15i2.5845

Abstract

The Central Sulawesi Provincial Government, in line with digital transformation and e-government initiatives, has implemented the E-VAKU application (Electronic Financial Accountability Verification). This digital system is designed to address bureaucratic challenges and enhance efficiency and transparency in the verification of regional financial accountability. Given that the success of an information system is strongly influenced by its usability, this study conducts a comprehensive usability evaluation of the E-VAKU application using a combination of two methods. The System Usability Scale (SUS) is employed to quantitatively measure user perceptions, while the Cognitive Walkthrough (CW) method is applied to identify specific issues within workflows and user interface interactions. The SUS evaluation produced an average score of 74.25, which falls into the “Good” category with a grade of B. Meanwhile, the CW analysis recorded a success rate of 90%, an error rate of 12%, and a time-based efficiency of 0.0205 tasks per second. The findings from both methods resulted in specific improvement recommendations, including interface redesigns for the onboarding, login, dashboard, and Payment Order (SPP) pages, as well as the addition of notification and search features. These enhancements aim to make the E-VAKU application more intuitive and user-friendly. This study is expected to serve as a practical reference for developers in refining the application to support more effective and optimal governance practices.
Mango Leaf Disease Detection using Threshold with CNN ResNet50 Architecture Baginda, Aditya Dwi; Fajriani, Alfiah; Shalihah, Rifa Atus
SISTEMASI Vol 15, No 2 (2026): 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.v15i2.6123

Abstract

Mango leaf diseases pose a significant threat to farmers’ productivity in Indonesia due to the difficulty and inaccuracy of manual diagnosis. A mango leaf disease detection system was developed by optimizing the decision threshold for classification using a ResNet50 Convolutional Neural Network (CNN). The Kaggle dataset consisted of 3,979 mango leaf images across eight classes: healthy, anthracnose, bacterial canker, gall midge, cutting weevil, dieback, sooty mold, and powdery mildew. The raw dataset was processed in Roboflow with an 80:10:10 train-validation-test split, and threefold data augmentation on the training set produced a total of 9,600 images. Decision threshold optimization using the precision-recall curve analysis identified 0.85 as the optimal threshold. At this threshold, precision reached 97.03%, while recall was 94.36%. These results provide a critical reference for agricultural applications in Indonesia, particularly considering local characteristics. The model achieved an F1-score of 95.49% after validation on the augmented dataset specifically tailored for tropical conditions.
Performance Analysis of Docker-based NFV Service Chaining Networks in a Single-Host Environment Fitroh, Rayhan Ziqrul; Ichsan, Ichwan Nul
SISTEMASI Vol 15, No 2 (2026): 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.v15i2.5898

Abstract

Network Function Virtualization (NFV) and Service Function Chaining (SFC) enable network functions to be deployed as Virtual Network Functions (VNFs) on flexible commodity servers. However, chaining multiple VNFs within a service chain may degrade data-plane performance, particularly in container-based environments. This study analyzes the performance of container-based SFC in a single-host Docker environment under three scenarios: (1) a direct client–server connection without VNFs (baseline), (2) the addition of a single Layer 3 (L3) VNF in the form of an iptables firewall, and (3) the integration of an L3 firewall VNF combined with a Layer 4 (L4) load balancer VNF based on HAProxy. Performance evaluation was conducted by measuring TCP throughput using iperf3, end-to-end latency using ping, and CPU utilization of each container using docker stats. The results indicate that adding the L3 firewall reduces throughput by approximately 33% and nearly doubles latency compared to the baseline. Meanwhile, incorporating the L4 load balancer causes throughput degradation of up to 92%. CPU utilization analysis shows that the kernel-space firewall introduces minimal additional overhead in user space, whereas the L4 VNF becomes the primary source of CPU saturation. These findings suggest that, in container-based SFC deployments on a single-host Docker environment, performance bottlenecks are primarily driven by user-space L4 VNFs rather than kernel-based L3 forwarding. Therefore, L4 VNFs require special consideration when designing service chaining architectures for resource-constrained edge nodes.

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