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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
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Object Detection using YOLOv8 : A Systematic Review Megantara, Nugraha Asthra; Utami, Ema
SISTEMASI Vol 14, No 3 (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.v14i3.5081

Abstract

This study is a Systematic Literature Review (SLR) that comprehensively reviews the recent advances in YOLOv8-based object detection models and their implementations in various application fields, such as UAV aerial photography, fruit ripeness identification, road defect detection, forest fire smoke detection, and medical imaging. This study evaluates the performance of YOLOv8 based on precision, recall, F1-score, and mean average precision (mAP) metrics, and compares its advantages and limitations with previous YOLO versions and other object detection algorithms. Improvements in the YOLOv8 architecture, including attention mechanisms, improved feature extraction, and hyperparameter optimization, enable significant improvements in accuracy and computational efficiency, especially for small objects and low-light conditions. In addition, the integration of image enhancement techniques strengthens the model's performance in challenging environmental conditions. This study is expected to be an important reference for researchers and practitioners in developing YOLOv8-based object detection models for real-world applications.
Classification of Service Sentiments on the by.U Application using the Support Vector Machine Algorithm Zulkarnain, Zulkarnain; Novita, Rice; Angraini, Angraini; Zarnelly, Zarnelly
SISTEMASI Vol 14, No 4 (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.v14i4.5367

Abstract

This study aims to classify user sentiment toward the by.U application service using the Support Vector Machine (SVM) algorithm. The background of this research is based on the importance of understanding user opinions on the quality of digital services as a basis for evaluation and service improvement. Review data was collected from the Google Play Store, totaling 9,091 data points, which were then processed through preprocessing stages such as cleaning, case folding, tokenization, stopword removal, and stemming. Sentiments were categorized into three groups: positive, negative, and neutral. The training and testing process involved dividing the data into training and testing sets with an 80:20 ratio, and evaluation was conducted using metrics such as accuracy, precision, recall, and F1-score. The evaluation results showed that the SVM algorithm achieved an accuracy of 83% in classifying sentiments. The model performed best on positive sentiment (precision 84%, recall 90%, F1-score 87%) and negative sentiment (precision 81%, recall 92%, F1-score 86%), while neutral sentiment still had weaknesses with an F1-score of only 64%. This indicates that neutral sentiment classification still requires model enhancement. This study demonstrates that SVM is an effective method for automatically analyzing user opinions on digital services. These classification results can serve as a reference for developers in evaluating and improving service quality based on user feedback.
UI/UX Design of WeatherWise: A Mobile Weather Application using a Human-Centered Design Approach Pattipawae, Christin; David, Felix
SISTEMASI Vol 14, No 3 (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.v14i3.5140

Abstract

The design of User Interface (UI) and User Experience (UX) plays a crucial role in weather forecast applications, which often face issues such as unstructured layouts, inaccurate information, and delayed notifications. Using a Human-Centered Design (HCD) approach, this study aims to design the UI/UX of the WeatherWise application to enhance user comfort and satisfaction. The methodology follows the stages of inspiration, ideation, and implementation, involving data collection through observations and interviews to understand user needs, followed by prototyping for evaluation. The results are expected to produce a more intuitive and responsive design that effectively addresses existing issues, positioning WeatherWise as a more effective and preferred weather forecast application compared to others. Thus, this research not only improves the user experience in weather applications but also serves as a guide for developers in applying HCD principles to UI/UX design.
Evaluation of the Impact of the Online Game Mobile Legends on Users’ Mental Health using the Fuzzy Logic Method Kurniawansyah, Fito Cahya; Munzir, Medyantiwi Rahmawita; Mustakim, Mustakim; M. Afdal, M. Afdal; Marsal, Arif
SISTEMASI Vol 14, No 4 (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.v14i4.5311

Abstract

One of the benefits of advancing technology is its ability to serve as a medium for entertainment. The online game Mobile Legends has gained widespread popularity and attracts a broad audience, including university students. While playing Mobile Legends can offer cognitive stimulation, enjoyment, and entertainment, it can also lead to excessive addiction, which may negatively affect users’ mental health. This study applies a fuzzy logic approach, which processes input data through fuzzy rules involving fuzzification, fuzzy inference, and defuzzification. Three input variables are used: Playing Time, Emotional Level, and Stress Level, with a single output variable: Health Index, which reflects the user's mental health condition. The purpose of this study is to evaluate the impact of the Mobile Legends online game on users’ mental health using fuzzy logic implemented in MATLAB. Based on the results, the manual Mamdani fuzzy analysis yielded a defuzzification result of 44.75. Meanwhile, using MATLAB Fuzzy Mamdani Toolbox (version 2023b) with input values of Playing Time (69), Emotional Level (68), and Stress Level (65) produced an output of 52.7. Both results fall within the fuzzy domain of “Agree” [40, 60, 80], indicating that prolonged playing time of Mobile Legends can influence players’ mental health.
Development of New Student Admissions Features for the Qaryah Thayyibah Elementary School Website in Purwokerto Purwati, Yuli; Purnawati, Ely; Najmuddin, Faris Labib; Bafaqih, Mohamad Aqil; Alfazri, Rifa
SISTEMASI Vol 14, No 3 (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.v14i3.5109

Abstract

One of the key factors supporting effective school management is the quality of the new student admission (PPDB) process. At Qaryah Thayyibah Elementary School, student registration is still carried out manually by filling out a physical form or through a Google Form link, which is then submitted to the admissions committee along with other administrative requirements. Although the school already has a website, it is currently built on WordPress, and the administrators experience difficulties in managing it. The aim of this study is to optimize the school’s website by redeveloping it using the Laravel framework, incorporating UI/UX design that aligns with the school’s branding and functional needs. Additionally, the study involves developing a dedicated PPDB feature on the new website. The system development follows the Rapid Application Development (RAD) method, which includes the stages of planning, analysis, design, and implementation. The outcomes of this research include an optimized school website built using Laravel and the development of an integrated PPDB feature. The implemented system simplifies report generation by enabling direct integration between the school website and PPDB data processing. Moreover, it offers a more structured data storage system, making data retrieval more efficient and effective.
Sensor Node Network Monitoring System using RESTful Web Services in Smart Farming Technology Isnanto, Rahmat Fadli; Ubaya, Huda; Asvi, M. Fauzi; Haidar, Rosali; Sari, Purwita
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.v14i5.5220

Abstract

The agricultural sector in Indonesia heavily depends on optimal environmental conditions. This study proposes a smart farming system based on the Internet of Things (IoT), utilizing RESTful web services as a more flexible and efficient IoT platform alternative. The system is designed for real-time monitoring of various environmental parameters such as temperature, air humidity, and soil conditions, including nitrogen (N), phosphorus (P), potassium (K), electrical conductivity (EC), pH, and soil moisture—captured through a network of sensor nodes. Test results show that the developed RESTful API architecture successfully facilitates effective communication between hardware and software components, enabling flexible data access and analysis through a web-based monitoring interface. This system is expected to assist farmers in making more informed decisions regarding land management, improve agricultural productivity, and support sustainable farming practices.
Design and Implementation of a Responsive E-Commerce Web Application for Teras Thrifting Store Ulfiana, Riska Fitri; S.Kom., MT., Muhammad Najib Dwi Satria
SISTEMASI Vol 14, No 4 (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.v14i4.5242

Abstract

The rapid advancement of information technology has significantly impacted various sectors, including the trade industry. E-commerce has emerged as an innovation that enables buying and selling transactions to take place online, without limitations of location or time. However, some businesses—such as Teras Trifting, a second-hand clothing store—still rely on conventional transaction methods, such as in-store purchases or orders via social media messages. These approaches tend to be inefficient and prone to errors in recording customer and sales data. To address these challenges, this study focuses on the design and implementation of a responsive e-commerce web application aimed at improving transaction efficiency and the management of product and customer data. The system development process adopts the Extreme Programming (XP) methodology to ensure flexibility and adaptability to user needs. The application was developed using PHP and Bootstrap, with particular emphasis on user experience, system performance, and data security. Evaluation results show that the system achieved the following scores: functional suitability at 97.2%, performance efficiency at 94.8%, and usability at 93.84%, all classified as “Excellent.” Overall, the system attained a 94.82% feasibility score based on ISO 25010 standards, and is categorized as “Excellent” according to the Likert scale. In conclusion, this responsive e-commerce web application is considered to contribute positively to the business sector by enhancing competitiveness and operational efficiency within the digital ecosystem.
Comparison of LSTM and Transformer Models in Predicting NVIDIA Stock Closing Prices and the Application of Rule-based Trading Strategies Gani, Muhammad Irfan Abdul; Setyaningsih, Putry Wahyu
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.v14i5.5445

Abstract

In today’s modern financial landscape, where accuracy and speed of prediction are increasingly critical, machine learning techniques play a vital role in stock price forecasting. This study evaluates the effectiveness of two deep learning models—Long Short-Term Memory (LSTM) and Transformer—in predicting NVIDIA (NVDA) stock prices using historical data from June 7, 2021 to June 7, 2025, with an 80% training and 20% testing data split. The results show that the LSTM model achieved a Root Mean Squared Error (RMSE) of 2.7703 on the training data and 7.3796 on the testing data, while the Transformer model produced an RMSE of 5.3573 (training) and 10.8563 (testing). A hybrid model demonstrated improved prediction accuracy with an RMSE of 3.5643 (training) and 8.6727 (testing), although it still did not outperform LSTM. The model also indicated a moderately declining trend in stock prices over the projected 30-day period. Gaussian noise augmentation was applied during training to improve model generalization. This study also explores investment strategy development by analyzing rule-based trading signals, generating buy (long) and sell (short) signals based on predicted price movements. Additionally, risks such as market volatility and potential overfitting were evaluated, alongside the influence of non-technical factors such as market sentiment. The primary focus of the research is to compare the performance of the LSTM and Transformer models in forecasting NVIDIA’s closing stock prices and applying a simple rule-based trading strategy. For future work, the use of methods such as Prophet, ARIMA, and hybrid ensemble approaches is recommended to enhance prediction accuracy, improve market adaptability, and deliver a more robust stock forecasting system leveraging advanced machine learning techniques for more optimal investment decisions.
LSTM-Based NLP Chatbot for Fish E-Marketplace at BBI Cangkiran Mijen Wahyudi, Tri Agus; Putri, Riana Defi Mahadji; Arief, Ulfah Mediaty; Sulistyawan, Vera Noviana
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.v14i5.5170

Abstract

Efficient and responsive information services are essential to support the fish sales process at the Cangkiran Mijen Fish Hatchery Center (Balai Benih Ikan), Semarang City. Interviews with hatchery staff revealed that the fish trading process is still conducted conventionally, requiring buyers to visit the hatchery in person. Currently, information regarding fish sales is only available through the official Semarang City Government website and Google Maps, which provides limited and often incomplete details. To obtain more comprehensive information, the public must contact staff via WhatsApp or visit the site directly. Moreover, customer inquiries tend to be repetitive, making the information service less effective. To address these issues, this study aims to develop a web-based fish e-marketplace system integrated with a natural language processing (NLP) chatbot using the Long Short-Term Memory (LSTM) algorithm. The system is expected to provide more informative, responsive, and always-available information services without relying on staff availability. The chatbot was trained using 757 question-and-answer pairs as training data. The system was developed using the Software Development Life Cycle (SDLC) waterfall model. Testing results indicate that the system demonstrates good functionality, is compatible across multiple devices and web browsers, and received positive feedback from users regarding ease of interaction and the relevance of chatbot responses. Algorithm validation results show an accuracy of 97%, precision of 94%, and recall of 95%.
Comparison of Machine Learning Methods (Linear Regression, Random Forest, and XGBoost) for Predicting Poverty in Central Java in 2024 Pratama, Zahwa Bunga Putri; Astuti, Yani Parti
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.v14i5.5494

Abstract

Poverty is a major issue faced by Central Java Province, with rates fluctuating annually. To respond to and address this challenge more effectively, a predictive, data-driven approach is essential. This study applies machine learning techniques to forecast the number of people living in poverty in 2024 at the district/city level, utilizing socio-economic data from 2019 to 2023 provided by the Central Bureau of Statistics (BPS). Seven indicators are used as predictor variables, including the poverty line, the number and percentage of people living in poverty, the open unemployment rate, average years of schooling, the Human Development Index, and the regional minimum wage. The data were normalized using StandardScaler and split into training (80%) and testing (20%) sets. This study compares three regression algorithms—Linear Regression, Random Forest, and XGBoost—to evaluate their effectiveness in modeling the complexity of socio-economic data. The analysis reveals that XGBoost delivers the best performance, with a Mean Absolute Error (MAE) of 6,665 and an R² score of 0.978, outperforming Random Forest (MAE: 9,209; R²: 0.947) and Linear Regression (MAE: 10,917; R²: 0.896). By comparing these models, the study addresses a gap in the literature regarding the effectiveness of machine learning models for local-level poverty prediction. The findings suggest that XGBoost holds strong potential as a data-driven policy support tool, particularly in poverty alleviation planning and decision-making at the regional level.

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