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Kab. indragiri hilir,
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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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House Price Prediction using the Random Forest Regression Algorithm Balqis, Fika Halimah; Aini, Qurrotul
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.v15i2.5726

Abstract

House price prediction is a complex problem because it is influenced by various factors such as building quality, location, and living area size. As a result, conventional methods often lack accuracy in estimating housing prices. This study aims to apply the Random Forest Regression (RFR) algorithm to predict house prices using the House Prices – Advanced Regression Techniques dataset from Kaggle, which contains 1,460 property records. The SEMMA (Sample, Explore, Modify, Model, Assess) methodology was adopted due to its systematic workflow and structured focus, which improves the reliability of the developed model. In the modeling stage, RFR was implemented because it is capable of handling non-linear patterns and maintains stable performance even with a large number of features. Based on the evaluation results, the model achieved a Root Mean Squared Error (RMSE) of 28,452.75 and a coefficient of determination (R²) of 89%. This was followed by a robustness test with an RMSE of 30,665.40, indicating the stability of the model. Feature importance analysis also revealed that OverallQual had the greatest influence on house price prediction. These findings confirm that Random Forest Regression is a reliable method for predicting house prices and has strong potential to be further developed for price recommendation systems, automated property valuation, and integration into digital platforms within the real estate industry.
Sentiment Analysis of Money Lover App Reviews using Random Forest and Naïve Bayes Bila, Nanda Aulia Salsa; Triyanto, Wiwit Agus; Setiaji, Pratomo
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.v15i2.5859

Abstract

This study aims to analyze user sentiment toward the Money Lover application and to compare the performance of two different machine learning algorithms, Random Forest and Naïve Bayes, in binary classification of review data. A total of 3,000 comments were collected using web scraping techniques and then classified into positive and negative sentiment categories. The preprocessing stage included text cleaning, normalization, tokenization, stopword removal, and stemming. In the next stage, term weighting was performed using TF-IDF to convert the text into numerical vector representations. The results provide insights into the overall sentiment tendencies of users toward the Money Lover application and demonstrate the effectiveness of both algorithms in processing textual reviews within the financial domain. Based on model evaluation, the Random Forest algorithm achieved superior average performance, with an accuracy of 94%. Meanwhile, the Naïve Bayes algorithm showed slightly lower performance, achieving an accuracy of 92%. These findings were supported by cross-validation results and ROC curve analysis, which indicated that Random Forest consistently outperformed Naïve Bayes. The performance difference suggests that an ensemble-based approach such as Random Forest is better able to handle textual variation in review data, resulting in more stable and accurate sentiment classification.
Rice Plant Disease Detection System based on Leaf Image using Web-based CNN Algorithm Menden, Lisa; Santa, Kristofel; Kumajas, Sondy
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.v15i2.6067

Abstract

Rice (Oryza sativa) plays a crucial role as a major staple food commodity. However, diseases such as Bacterial Blight, Brown Spot, and Leaf Blast can cause significant crop losses. Current manual identification methods have limitations due to high subjectivity and long diagnosis time. This study proposes a web-based automatic detection system using a Convolutional Neural Network (CNN). The dataset was obtained from Kaggle and consisted of 2,800 images evenly distributed across four classes (700 images per class). The data were split using an 80:20 ratio for training and validation sets, followed by preprocessing steps including resizing to 224×224 pixels and data augmentation. The CNN architecture was designed with four convolutional blocks and optimized using the Adam optimizer. Training for 50 epochs achieved an accuracy of 77.50%, precision of 82.98%, recall of 77.50%, and an F1-score of 72.84%. Based on the confusion matrix analysis, the model performed very well in detecting Bacterial Blight and Brown Spot but still faced difficulties in identifying the Leaf Blast class. Overall, the developed system has the potential to serve as a decision-support tool for farmers, although further performance improvements are required, particularly for detecting specific disease variants.
Design and Development of the Restaurant X Reservation Application on the iOS Platform using App Clip Rochella, Megan; Tileng, Kartika Gianina
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.v15i2.5953

Abstract

The food and beverage industry in Indonesia has shown significant growth, with 4.85 million business units in 2023. However, many restaurants still rely on manual reservation systems, which hinder operational efficiency. Despite internet penetration reaching 79.5%, with smartphones as the primary access device (83.39%), mobile application adoption faces barriers due to friction in the installation process. This study aims to design and implement an iOS-based restaurant reservation application using App Clips technology, integrated with a real-time admin dashboard. The system was developed using the MVVM architecture, with Swift and SwiftUI for the user interface, Golang for the backend, and PostgreSQL for the database. The system includes a customer-facing reservation app, a restaurant-side reservation management app, advance payment via QRIS displayed through the app, table selection based on customer ambience preferences, an automatic overbooking prevention mechanism, and finalization of reservations once the allotted time is complete. Development evaluation was conducted using task-based usability testing with seven respondents (four admins and three customers). The results showed a 100% task completion rate on both interfaces, exceeding the benchmark average of 78%, while App Clip access successfully demonstrated its effectiveness as a quick-access method without installation. This study contributes to the documentation of App Clip implementation in mobile reservation systems and presents an integrated reservation management solution that can be adapted to other sectors within the hospitality industry.
Analysis and Improvement of an Agribusiness Web Information System Security using Grey-Box and White-Box Testing Halil, M. Isma; 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.v15i2.5939

Abstract

This study aims to analyze and improve the security of the SawitGoDigi Palm Oil Harvest Recording Information System using grey-box and white-box testing approaches. The system is used by farmers, agents, drivers, and administrators to manage land data, harvest results, distribution, and transaction records, which makes it highly exposed to security risks if vulnerabilities are present. The security testing process was conducted based on the OWASP Web Security Testing Guide (WSTG) v4.2 and the OWASP Risk Rating Methodology. The testing stages included reconnaissance, automated scanning using OWASP ZAP, manual exploitation, risk evaluation, implementation of security improvements, and retesting. The results revealed several significant vulnerabilities, including SQL Injection in the search feature, weak session management through the trusted_device cookie, and the absence of a rate-limiting mechanism that enabled brute-force attacks during the login process. The risk assessment indicated that SQL Injection and session hijacking were classified as High risk, while brute-force attacks were categorized as Medium risk. Security improvements were implemented through the use of prepared statements, strengthening cookie attributes, adding security headers, and implementing rate limiting. Retesting results confirmed that all identified vulnerabilities were successfully mitigated and reduced to a Low-risk level. This study demonstrates that a comprehensive security testing approach, which includes exploitation, remediation, and verification through retesting, can significantly enhance the security of agribusiness web applications. Furthermore, the findings show that before remediation, the system contained four vulnerabilities with High and Medium risk levels, namely SQL Injection, Session Hijacking, Brute-Force Login, and Security Misconfiguration. After the remediation and retesting process, all High- and Medium-risk vulnerabilities were successfully reduced to Low risk or marked as Closed, indicating that the system is secure for operational use.
Evaluation of Information Technology Governance Maturity Level using COBIT 2019 in the Academic Information System at Nahdlatul Ulama University of Lampung Saputra, Hadi Nurma Dwi; Wasilah, Wasilah
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.v15i2.5817

Abstract

The rapid development of Information Technology (IT) has significantly facilitated human activities in carrying out work and various other tasks, including in the education sector. The utilization of IT plays an important role in supporting administrative processes and academic activities; therefore, proper governance is required to ensure its effective and efficient use. This study employed a descriptive quantitative approach. Data were collected through observation and the distribution of questionnaires to students. The sample size was determined using the Slovin formula with a 10% margin of error, resulting in a minimum required sample of 94 respondents. In total, this study successfully collected data from 108 student respondents. This research aimed to evaluate the maturity level of IT governance in the Academic Information System (SIAKAD) at Nahdlatul Ulama University of Lampung using the COBIT 2019 framework. The results indicated that the maturity level of SIAKAD governance was at level 4 (Predictable), meaning that the processes have been consistently implemented and well documented. The highest gap was found in the APO07 domain (Manage Human Resources), while the BAI03, DSS01, DSS02, and MEA01 domains showed stable performance in accordance with student expectations.
School Profile Website using the K-Means Algorithm at the Minahasa Regency Education Office Pongmangatta, Tiara; Santa, Kristofel; Kumajas, Sondy
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.v15i2.6048

Abstract

Background: The Education Office often manages school profiling with minimal and fragmented data, such as school name, district, level, total students, and total teachers. This situation makes planning reactive and error-prone, highlighting the need for a lightweight yet reliable workflow to transform aggregated data into actionable evidence. Method: This study developed a web-based profiling tool that integrates a user interface designed using a User-Centered Design (UCD) approach with a transparent K-Means clustering algorithm. Development was carried out through iterative prototyping, with features standardized using z-scores and cluster validity assessed using the silhouette method along with other internal validity indices. Results: Data entry features with header previews and numeric checks effectively reduced rework. The silhouette value peaked at $k=2$, producing two interpretable segments (moderate vs. high staffing load), with an optional $k=3$ for exploratory purposes. Usability evaluation using the System Usability Scale (SUS) yielded a score of approximately 82, indicating good user acceptance, with system response times measured in seconds. Conclusion: The system provides a streamlined and sufficient workflow for routine planning and establishes a foundation for future longitudinal developments, such as tracking the number of study groups and accreditation per period.
Development of an Internet of Things (IoT)-based Air Quality Monitoring System in the Environment Sutrisno, Lourensius Andrew; Kristianto, Budhi
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.v15i2.5835

Abstract

This study developed an Internet of Things (IoT)-based air quality monitoring system to measure temperature, humidity, and carbon dioxide (CO₂) levels in real time. The study employed a prototyping method consisting of problem identification, requirements analysis, system design and development, testing, and result analysis. The system utilizes a NodeMCU V3 as the microcontroller, an MQ-135 sensor for gas detection, a DHT11 sensor for temperature and humidity measurement, and an OLED LCD for local display. Measurement data are transmitted and stored on the ThingSpeak platform and can be accessed through an Android application. Testing was conducted under three conditions: a normal environment, a closed room without ventilation, and a polluted condition with cigarette smoke exposure. The results show that the system is able to responsively detect changes in air quality, with CO₂ levels recorded at 76 ppm under normal conditions, 183 ppm in the closed room, and 729 ppm in the polluted condition. The system operates stably and provides real-time data visualization, making it suitable for low-cost implementation in household environments and small communities.
Performance Comparison of Naïve Bayes, Random Forest, and Support Vector Machine Algorithms in Sentiment Analysis of the Weverse Application Fitriyani, Anisa; Ibrahim, Ichsan
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.v15i2.5893

Abstract

Comparison of Support Vector Machine and K-Nearest Neighbor Algorithms on the Effectiveness of a Free Lunch Program Farahdinna, Frenda; Prabawati, Pipit
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.v15i2.5999

Abstract

The Free Lunch Program is a government initiative aimed at ensuring adequate nutrition for the public. This study aims to examine public perceptions of the program through sentiment analysis and to compare the effectiveness of Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) models. A total of 6,532 public comments were collected from Twitter, YouTube, and TikTok. After preprocessing, including normalization, stopword removal, and stemming, features were extracted using Term Frequency–Inverse Document Frequency (TF-IDF), resulting in 5,992 clean data points. The dataset was split into 80% training and 20% testing sets. Model training was conducted with hyperparameter tuning using 3-fold GridSearchCV. The results indicate that negative sentiment dominated at 42.7%. In the model comparison, SVM with a linear kernel significantly outperformed K-NN, achieving an accuracy of 72%, while K-NN (k=3) reached only 48%. These findings suggest that the SVM algorithm is more effective in classifying public opinion sentiment on high-dimensional data compared to K-NN.

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