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Kab. indragiri hilir,
Riau
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 1 (2025): Sistemasi: Jurnal Sistem Informasi" : 40 Documents clear
Designing a Barcode System to Optimize Food Product Production and Distribution Management Alfi, Rizki; Maryam, Maryam; Nadiyah, Khairun; Mardesci, Hermiza
Sistemasi: Jurnal Sistem Informasi Vol 14, No 1 (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.v14i1.4955

Abstract

In the digital era, efficient supply chain and inventory management pose significant challenges for Micro, Small, and Medium Enterprises (MSMEs), particularly in the food sector. The use of barcode technology can enhance stock management and product tracking in real-time. However, the adoption of this technology among MSMEs, especially for traditional food products like rendang, remains limited. This study aims to design and implement a Code 128 barcode system for rendang products produced by MSMEs, incorporating information such as product variants, production batch, production date, and expiration date. The research employs a Research and Development (R&D) method, consisting of needs analysis, design, implementation, and evaluation stages. The findings reveal that the designed barcode system improves stock management efficiency, reduces recording errors, and facilitates integration with modern retail systems. Nonetheless, the study is limited to a small scale and encounters challenges related to infrastructure and human resources. To address these limitations, the research proposes solutions such as utilizing more affordable devices and providing training to enhance human resource skills. This study is expected to contribute to the digital transformation of food-sector MSMEs, helping them improve competitiveness in an increasingly challenging market.
Evaluation of User Satisfaction on Taxsee Driver Application using User Experience Questionnaire (UEQ) Rianda, Rahmad Didho; Rahmawita, Medyantiwi; Syaifullah, Syaifullah; Jazman, Muhammad
Sistemasi: Jurnal Sistem Informasi Vol 14, No 1 (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.v14i1.4868

Abstract

Technological advancements have made daily life more convenient, particularly in the field of online transportation services. To stay competitive in an increasingly tight market, companies like Maxim must continuously innovate to improve service quality. Maxim offers two applications: Maxim for passengers and Taxsee Driver for drivers. The Taxsee Driver app facilitates drivers in finding passengers, managing trips, and streamlining payment processes. However, the app still faces several technical issues, such as bugs during peak hours, fake orders, and GPS inaccuracies, which impact drivers' experiences and earnings. This study aims to evaluate the Taxsee Driver app using the User Experience Questionnaire (UEQ) to identify its strengths and weaknesses from the drivers' perspective. Through this analysis, key aspects such as interface design, responsiveness, security, and navigation integration can be assessed objectively. The evaluation results are expected to provide valuable insights for developers to enhance service quality, improve driver satisfaction and loyalty, and strengthen Maxim's position in the online transportation industry.
Implementation of Naive Bayes and Support Vector Machine Classification Algorithms for Sentiment Analysis of Bilingual Cyberbullying on X Application Sari, Novita; Jazman, Muhammad; Ahsyar, Tengku Khairil; Syaifullah, Syaifullah; Marsal, Arif
Sistemasi: Jurnal Sistem Informasi Vol 14, No 1 (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.v14i1.4799

Abstract

The significant increase in social media usage has contributed to the rise in cyberbullying incidents, particularly in the context of multilingual language use. This study aims to conduct sentiment analysis to detect potential cyberbullying content on the X application using a bilingual approach (Indonesian and English) and leveraging the Naive Bayes (NB) and Support Vector Machine (SVM) algorithms. Tweets are collected and processed through a pre-processing stage to extract relevant features for sentiment analysis. Both algorithms are then applied to classify tweets into positive, negative, or neutral categories and identify indications of cyberbullying. The results of the trials indicate that the NB algorithm outperformed SVM, achieving an accuracy rate of 87%. Furthermore, in identifying cyberbullying patterns in bilingual text, NB reached the highest accuracy rate for the Indonesian language at 87%. These findings suggest that this study can serve as a reference for developing more accurate and responsive cyberbullying detection systems on bilingual social media platforms.
Development of Creatorku Application for Managing Digital Influencer Portfolios using Lean Startup Approach Tamiri, Muhammad Husni; Wahyuni, Elyza Gustri
Sistemasi: Jurnal Sistem Informasi Vol 14, No 1 (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.v14i1.4997

Abstract

Influencers face significant challenges in managing their digital portfolios to present analytics data professionally. Manual processes, such as taking screenshots and compiling data, which are time-consuming, often reduce work efficiency. This study develops the Creatorku application using the Lean Startup approach with the Build-Measure-Learn cycle to ensure the product meets user needs. The minimum viable product (MVP) includes data integration from Instagram and YouTube, an analytics dashboard, automatic portfolio creation, and a shareable link feature. Test results show that Creatorku completely eliminates the need for manual data management while enhancing professionalism in the eyes of partners. Additionally, the application supports influencer marketing by providing a solution that improves work efficiency and collaboration with brands. User feedback suggests integrating with other platforms, such as TikTok, improving data visualization, and adding personalization options. With these results, Creatorku is expected to become an innovative solution to support influencers and offer new insights for technology startups.
Implementation of Web-based Electrical Appliance Installation Monitoring: TAM Testing Approach Suthirta, Kev Michael; Witno, Suwitno
Sistemasi: Jurnal Sistem Informasi Vol 14, No 1 (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.v14i1.4572

Abstract

The development of technology in the business world can have a positive impact, simplifying the way companies manage their operations and improving business performance through the implementation of systems. A web-based information system for monitoring activities within the company was designed using the Laravel Framework to address inefficiencies in the current manual monitoring process. The existing system requires on-site visits for monitoring and uses paper-based reporting, which is inefficient, prone to disorganized or lost reports, and time-consuming. The proposed solution is the development of a system using the Laravel framework and the Waterfall methodology. The system design was evaluated using the Technology Acceptance Model (TAM), focusing on three variables: Perceived Usefulness (PU), Perceived Ease of Use (PEOU), and Behavioral Intention to Use (BITU). The evaluation process involved three stages: determining the variables, defining indicators, and creating a questionnaire. A total of 54 questionnaires were distributed and processed using the SmartPLS4 application. The results revealed that perceptions of the system's ease of use significantly influence users' behavior to continue using the Web-Based Monitoring System for Electrical Equipment Installation. Additionally, the perceived ease of use impacts the perception of the system's usefulness, and the perception of usefulness also affects users' behavioral intention to repeatedly use the system.
Evaluation of the Maturity Level of the RS SIM System using the Cobit 5 Framework in the Evaluation Direct and Monitoring Domain Rezeki, Yunika Tri; Oktadini, Nabila Rizky; Putra, Pacu; Sevtiyuni, Putri Eka; Meiriza, Allsela
Sistemasi: Jurnal Sistem Informasi Vol 14, No 1 (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.v14i1.4889

Abstract

The purpose of using the COBIT 5 framework in the Evaluate, Direct, and Monitor (EDM) domain in this study is to assess the maturity level of the Hospital Information Management System (HIMS) at the Primaya Hospital Laboratory. The evaluation process focuses on three main aspects: EDM01 (Governance Framework Setting and Maintenance), EDM02 (Ensuring Risk Optimization), and EDM05 (Ensuring Transparency). Data were collected by involving the hospital’s IT team through questionnaires and interviews. Based on the analysis, the current maturity level of the HIMS ranges from level 2 to level 4, with a target of achieving level 4 (Predictable Process). A GAP analysis was conducted to compare the current system condition (as-is) with the desired condition (to-be). This study provides improvement recommendations, including the optimization of system governance and the enhancement of IT performance transparency. These recommendations are expected to support increased efficiency, effectiveness, and the strategic value of the Hospital Information Management System (HIMS) at Primaya Hospital.
Early Detection of Mental Health Disorders based on Sentiment using Stacking Method Maldini, Naufal; Utomo, Danang Wahyu; Tresyani, Rahmadika Putri
Sistemasi: Jurnal Sistem Informasi Vol 14, No 1 (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.v14i1.4842

Abstract

Mental health disorders are a serious and growing global concern, including in Indonesia. This study aims to predict mental health disorders through sentiment analysis using the Stacking Classifier approach, which combines Random Forest, Gradient Boosting Classifier, and Logistic Regression algorithms. The dataset was sourced from various social media platforms, consisting of textual data classified into seven mental health categories, such as depression, anxiety, and personality disorders. The data underwent preprocessing steps, including cleaning, balancing, and dimensionality reduction using the TF-IDF algorithm. The study results indicate that the Stacking Classifier method achieved an accuracy of 95.66%, with a precision of 95.63%, recall of 95.66%, and F1-Score of 95.64%. These results outperform the individual algorithms tested in the research. The findings demonstrate the significant potential of sentiment analysis powered by machine learning for early detection of mental health disorders, making it a valuable tool to enhance diagnosis and intervention in mental health care more effectively.
Optimized Weight Evolutionary-based Support Vector Machine (SVM) Optimization for Comment Sentiment Mulyana, Mulyana; Utomo, Wahyu
Sistemasi: Jurnal Sistem Informasi Vol 14, No 1 (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.v14i1.4762

Abstract

Comments in questionnaire feedback carry sentiment meanings, such as positive, negative, or neutral. Each review comment on training services requires prompt and accurate follow-up to improve service quality. However, sentiment classification often demands significant time and effort to determine sentiments accurately. This study aims to enhance efficiency and accuracy in sentiment classification for training questionnaires. A comparative analysis was conducted using three algorithms: Naïve Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The results indicate that SVM is the fastest and most accurate algorithm, with a training time of 3.067 seconds, 100 milliseconds faster than Naïve Bayes and 45.8 seconds faster than KNN. SVM achieved an accuracy of 60.81%, with an average sensitivity of 61%, specificity of 80%, and precision of 63%. Subsequently, this study integrated the Optimized Weight Evolutionary method to enhance SVM's accuracy and address attribute selection. Testing results showed a 2.16% improvement in SVM accuracy, bringing it to 63.10%. The training process was conducted on a dataset of 1,153 comments, with 90% of the data used for algorithm training. The combination of SVM and Optimized Weight Evolutionary proved effective in achieving more accurate sentiment classification. This study provides new insights into the application of sentiment classification, particularly for training feedback. Optimizing the algorithm can help training companies respond more effectively to comments and improve overall service quality.
Analysis of Push Pull Mooring Factors on Switching Intention in the Use of QRIS among MSMEs in Bantul Regency Sa'baniyah, Firda; Ratnasari, Asti; Heksaputra, Dadang; Rochmadi, Tri
Sistemasi: Jurnal Sistem Informasi Vol 14, No 1 (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.v14i1.4396

Abstract

Micro, Small, and Medium Enterprises (MSMEs) are the backbone of Indonesia’s economy, contributing significantly by absorbing 96.92% of the workforce. MSMEs must adapt to technological advancements, particularly in digital payment systems. The Quick Response Code (QR Code) has been introduced as a payment innovation in Indonesia, with the implementation of the Quick Response Code Indonesian Standard (QRIS) mandated by Bank Indonesia for all payment system providers starting January 1, 2020, under PADG No. 24/1/PADG/2022. Data from ASPI shows a 62.95% increase in QRIS adoption by merchants in 2022 compared to the end of 2021. The Special Region of Yogyakarta, particularly Bantul Regency, contributes significantly to the region's GDP, accounting for 15.18%. It is crucial to encourage MSMEs in Yogyakarta to embrace economic digitalization through the use of QRIS in sales transactions. This study analyzes the push-pull-mooring (PPM) factors that influence MSMEs' switching intentions to adopt QRIS. Push factors refer to negative aspects of the old service, pull factors represent positive attributes of the new service, and mooring factors are barriers to switching intention. The research employs the PPM framework and analyzes data using the SEM-PLS method through SmartPLS 4.1. The sample consists of 100 respondents who meet the criteria of MSMEs that have never used QRIS and do not keep sales transaction records in their business. The results indicate that three hypotheses show significant influence, while four are not significant. The latent variables that significantly affect switching intention are saving time, perceived lack of transaction records, and perceived trouble. The analysis reveals that pull factors have a relatively higher impact on switching intention compared to push factors. Mooring factors, represented by the latent variable of habit, show no significant effect on switching intention.
Sentiment Analysis on Android Applications using MediaPipe for Text Classification Ardika, Kadek Febry; Santoso, Joko Dwi
Sistemasi: Jurnal Sistem Informasi Vol 14, No 1 (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.v14i1.4964

Abstract

Sentiment analysis is an important process for understanding public opinion on various issues discussed on social media. This study aims to develop an Android application that utilizes MediaPipe for text classification based on sentiment. MediaPipe is used as a framework for feature extraction, which is then analyzed using machine learning models. The study employs a real-time design approach to support efficient text processing on devices with limited resources. The testing results show that the application achieves high accuracy in classifying positive, negative, and neutral text. These findings suggest that MediaPipe can be an effective solution for sentiment analysis on mobile devices. This research makes a significant contribution to public opinion analysis technology by introducing an efficient, adaptive, and scalable approach.

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