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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 40 Documents
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Search results for , issue "Vol 14, No 2 (2025): Sistemasi: Jurnal Sistem Informasi" : 40 Documents clear
Performance Comparison of ResNet50, VGG16, and MobileNetV2 for Brain Tumor Classification on MRI Images Kurniawan, Muhammad Bayu; Utami, Ema
Sistemasi: Jurnal Sistem Informasi Vol 14, No 2 (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.v14i2.5054

Abstract

Brain tumor classification using MRI images is a significant challenge in medical diagnosis, requiring models with high accuracy and efficient training. This study aims to compare the performance of three Convolutional Neural Network (CNN) models—ResNet50, VGG16, and MobileNetV2—for brain tumor classification based on MRI images. The dataset consists of four brain tumor categories: glioma, meningioma, pituitary, and no tumor, with data split into training, validation, and testing sets. Each model was evaluated using metrics including accuracy, precision, recall, F1-score, specificity, and training time to assess their effectiveness in predicting brain tumors with optimal accuracy and efficiency. Experimental results indicate that VGG16 achieved the best overall performance, with an accuracy of 94.93%, precision of 94.68%, and specificity of 98.33%, while also having the shortest training time of 47.15 minutes. MobileNetV2 demonstrated strong performance with a recall of 94.08% but required a longer training time of 79.53 minutes. ResNet50 recorded the lowest accuracy (91.67%) despite excelling in precision (91.79%), but it underperformed in recall (91.25%) and specificity (97.2%). Overall, this study confirms that VGG16 is the most efficient and effective model for MRI-based brain tumor classification.
Clove Quality Classification using the ResNet50V2 Architecture Linggama, Muhamad Nurfaizi; Ariatmanto, Dhani
SISTEMASI Vol 14, No 2 (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.v14i2.5075

Abstract

The development of artificial intelligence (AI) and computer vision has opened new opportunities in the agricultural industry, including in clove quality classification. The quality of cloves affects their market value and export standards; however, classification still faces challenges such as similarities in shape, size, and color, as well as variability in lighting and background images that can reduce the accuracy of classification models. To address these challenges, this study develops a clove quality classification system using a Convolutional Neural Network (CNN) with the ResNet50V2 architecture, which has proven to be superior in image processing. The dataset used consists of 1,250 images of cloves that were processed through stages of background removal, image cropping, and resizing to 224x224 pixels to meet the model's requirements. The data is divided into 80% for training and 20% for testing. The model is trained using deep learning techniques, with parameters optimized to enhance classification performance. The results show that the ResNet50V2 model achieves an accuracy of 98.80%, with very high precision, recall, and F1-score. The accuracy and loss graphs indicate that the model operates stably without experiencing overfitting, while the confusion matrix shows a very low prediction error rate. These results demonstrate that ResNet50V2 is effective in classifying clove quality.
Predicting Students' Academic Performance in Mathematics based on Big Five Personality Traits using Random Forest with Synthetic Minority Over-Sampling Technique Nurul Pratiwi, Annisa; Utami, Ema
Sistemasi: Jurnal Sistem Informasi Vol 14, No 2 (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.v14i2.5102

Abstract

The secondary school period is a crucial time for the development of students' academic and social performance. Educational data mining (EDM) has emerged as a strategic method capable of exploring patterns in educational data to predict academic performance based on various factors, including students' personalities. However, the imbalance in educational data remains an issue that can lead to bias in predictive models. This study aims to identify the factors contributing to the academic performance in mathematics of junior high school students, such as academic, demographic, and Big Five personality factors. The Random Forest method and SMOTE oversampling technique are employed to identify components that contribute to students' academic performance and to enhance the performance of the predictive model. The research indicates that academic factors are significant, while socio-economic and personality factors are less significant in relation to academic performance. Additionally, the application of the SMOTE technique proves effective in addressing data imbalance, and the Random Forest model demonstrates optimal performance with appropriate tuning. The combination of Random Forest, hyperparameter tuning using GridSearchCV, and SMOTE successfully develops a model with an accuracy rate of 99%.
Mapping Machine Learning Trends in Chemistry Research using LLM with Multi-Turn Prompting Yudertha, Andreo; Putri, Riski Dwimalida
Sistemasi: Jurnal Sistem Informasi Vol 14, No 2 (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.v14i2.4961

Abstract

A review of research in the field of chemistry that incorporates machine learning is essential to identify recent developments and explore its potential applications. Published research articles provide an opportunity to analyze emerging research trends. The use of natural language processing (NLP) technology not only accelerates text data analysis but also enhances accuracy in understanding the content and context of scientific articles. Previously, trend analysis in ophthalmology research had been conducted using Zero-Shot Learning. In this study, an analysis of chemistry-related articles focusing on machine learning was carried out using a multi-turn prompting technique. The process began with data collection through web scraping of abstracts containing the keywords "machine learning" and "chemistry." The retrieved data was then tabulated and analyzed using a Large Language Model (LLM) with a Multi-Turn Prompting approach, where general prompts were initially used, followed by deeper exploration based on previous responses. Additionally, statistical descriptive analysis was performed using targeted prompts. Analysis of 200 article abstracts identified seven key terms related to the use of machine learning in chemistry: chemical (138 articles), protein (119 articles), drug (107 articles), structure (100 articles), molecular (96 articles), chemistry (91 articles), and quantum (84 articles). Furthermore, three dominant research topics were found in the intersection of chemistry and machine learning: protein and molecular structure, quantum chemistry, and drug discovery. The number of articles on machine learning in chemistry began to rise in 2012 and saw a significant increase in 2019. The findings suggest that there are still many opportunities for developing machine learning applications in chemistry, particularly in quantum chemistry. This field only began to gain attention in 2013, and the number of published articles remains relatively low each year, indicating that it is still in the early stages of exploration.
Optimization of Workstation Capacity using the Theory of Constraints Approach to Increase Production Output Putri, Fadilla Umeida; Aryanny, Enny
Sistemasi: Jurnal Sistem Informasi Vol 14, No 2 (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.v14i2.5033

Abstract

A manufacturing company in Surabaya that produces various metal-based products is facing bottleneck issues, particularly in the production of steel doors and fire doors, due to an imbalance in machine capacity and cycle time. This problem has led to an accumulation of semi-finished goods and a decline in production efficiency. To address this issue, this study applies the Theory of Constraints (TOC) to redesign production planning through five key stages. Based on Rough-Cut Capacity Planning (RCCP) calculations, bottlenecks were identified at the bending workstation (SK-2), assembly workstation (SK-3), and painting workstation (SK-5). To mitigate these constraints, an additional 7 hours of overtime was implemented at these three workstations from July to November 2024. This strategy successfully increased fire door production from 89 units with a throughput of Rp178,000,000 to 167 units with a throughput of Rp334,000,000, reflecting a growth of Rp156,000,000 (46.7%). These findings demonstrate that the proposed approach is effective in optimizing throughput and maximizing production capacity utilization.
Sentiment Analysis of Fintech Application User Reviews using the CRISP-DM Framework for Product Development Prioritization Amalsyah, Muhammad Rizky; Kurniawan, Dedy; Rifai, Ahmad; Sari, Purwita
Sistemasi: Jurnal Sistem Informasi Vol 14, No 2 (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.v14i2.5064

Abstract

The rapid growth of fintech applications has increased the need for sentiment analysis to understand user perceptions of the offered products. This study focuses on sentiment analysis of user reviews for the Flip application on Google Play Store by applying the Support Vector Machine (SVM) algorithm within the CRISP-DM framework. The analysis process involves text preprocessing, sentiment labeling using a pretrained BERT model, and classification using SVM with TF-IDF feature extraction. The results indicate that the majority of users express positive sentiment (56.9%), primarily regarding cost efficiency, transaction ease, and product speed. However, negative sentiment (43.1%) is also present, mainly concerning additional fees, transaction delays, and technical issues in app usage. A topic modeling analysis using the Latent Dirichlet Allocation (LDA) method identifies key topics that highlight both Flip's strengths and challenges. The findings suggest that while Flip holds significant potential in meeting user needs, improvements are needed in product aspects, cost transparency, and app performance optimization. This study is expected to serve as a strategic foundation for fintech app developers to enhance data-driven product quality, ultimately increasing user satisfaction and loyalty.
Development of Web-App using Agile Scrum Method at PT. Stechoq Robotika Indonesia Mufreni, Sadr Lufti; Mushawwir, Ahmad
Sistemasi: Jurnal Sistem Informasi Vol 14, No 2 (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.v14i2.5083

Abstract

The increasing number of Jaminan Kecelakaan Kerja (JKK) claims over the past five years underscores the urgency of enhancing workplace safety measures. One possible solution is the adoption of Automated Guided Vehicles (AGVs) to streamline warehouse operations and minimize the risks associated with manual handling. In this study, the AGV is owned by the stakeholder, PT. Stechoq Robotika Indonesia (STECHOQ), which is responsible for AGV customization and the development of its control system through a web-based application. The web-app was developed using ReactJS, TypeScript, and Tailwind CSS, adopting the Agile Scrum methodology The web-app development followed a two-sprint approach, with each sprint lasting one week. A total of six key features were implemented Login, Dashboard, Station Management, AGV Management, Task Management, and Robot Control. Iterative Black Box Testing was conducted on these six features throughout both sprints, confirming their successful operation without any issues. Additionally, this study modified the Agile Scrum methodology by merging the sprint retrospective with the sprint review phase, enhancing efficiency while aligning with the existing workflow. The objective of this study is to develop a web-based application capable of controlling the stakeholder's AGV.
Comparison of Rating-based and Inset Lexicon-based Labeling in Sentiment Analysis using SVM (Case Study: GoBiz Application Reviews on Google Play Store) Firda, Hiliah; Putra, Pacu; Oktadini, Nabila Rizky; Sevtiyuni, Putri Eka; Meiriza, Allsela
Sistemasi: Jurnal Sistem Informasi Vol 14, No 2 (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.v14i2.4795

Abstract

Digital transformation has impacted various sectors, including Micro, Small, and Medium Enterprises (MSMEs). GoBiz, a partner platform for Gojek's GoFood service, plays a crucial role in supporting MSME digitalization, making it essential to understand user perceptions of the application. This study conducts sentiment analysis on 5,000 GoBiz user reviews from the Google Play Store. It compares two labeling methods—Rating-Based and Inset Lexicon—and evaluates them using the Support Vector Machine (SVM) algorithm. The analysis process includes data selection, text preprocessing, data transformation using TF-IDF, SVM implementation with 10-fold cross-validation, and result visualization through word clouds. The findings indicate that the Rating-Based labeling method achieved an accuracy of 87%, with a precision of 86.7%, recall of 87.1%, and an F1-score of 86.8%. Meanwhile, the Inset Lexicon labeling method outperformed it, achieving an accuracy of 89.7%, precision of 89%, recall of 89.8%, and an F1-score of 89.3%. These results suggest that the combination of the Inset Lexicon labeling method and the SVM algorithm is more effective in classifying user sentiment and providing a more accurate understanding of user perceptions regarding the GoBiz application. Sentiment analysis results indicate that users appreciate GoBiz’s ease of operation but face challenges with driver services and advertisement features, highlighting areas for improvement to enhance user satisfaction.
From Legacy Systems to Digital Solutions: Change Management in IT Transformations Zangana, Hewa Majeed; Mohammed, Harman Salih; Husain, Mamo Muhamad
Sistemasi: Jurnal Sistem Informasi Vol 14, No 2 (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.v14i2.5016

Abstract

The transition from legacy systems to modern digital solutions is a pivotal aspect of IT transformations that demands meticulous planning and execution. This study examines the role of change management in IT transformations by exploring key factors such as stakeholder engagement, risk mitigation, and alignment of technology with organizational goals. A mixed-methods research approach was employed, integrating both qualitative and quantitative methodologies. The qualitative aspect involved expert interviews and case studies from multiple industries, while the quantitative approach utilized statistical regression analysis on survey responses from IT professionals. Key performance indicators (KPIs) such as project success rates, adoption levels, and cybersecurity resilience were analyzed to assess the impact of change management strategies. The study identifies a strong correlation between agile methodologies and increased organizational adaptability, emphasizing the importance of iterative development, continuous feedback, and cross-functional collaboration. Findings highlight that integrating change management frameworks with IT project delivery enhances efficiency and reduces resistance to digital transformation. This research provides a comprehensive framework for organizations aiming to optimize their IT transition processes and maximize the benefits of digital transformation.
Kit iTCLab Application as a Learning Tool for the Internet of Things Maulana, Asrul; Rahmat, Basuki; Matalangi, Matalangi
Sistemasi: Jurnal Sistem Informasi Vol 14, No 2 (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.v14i2.5046

Abstract

The rapid advancement of the Internet of Things (IoT) has created opportunities for transformative innovations in various technology-based learning sectors, particularly through Smart Kits. This study aims to develop and implement the Internet-Based Temperature Control Lab (iTCLab) Kit as an interactive learning tool for IoT. The iTCLab application is designed to enable users to monitor and control temperature in real-time via an internet-based platform. The system utilizes temperature sensors, heating actuators, an ESP32 microcontroller, and an internet connection. Testing was conducted through the Microcontroller course in the Informatics Study Program. The results indicate that the iTCLab Kit significantly enhances students' understanding and mastery of practical IoT programming. Thus, the iTCLab Kit serves as an innovative solution to support more effective and efficient IoT learning.

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