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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 1,045 Documents
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Exploration of Data Augmentation in Xception for Waste Classification Ikhlas, Ariza; Arlis, Syafri
Sistemasi: Jurnal Sistem Informasi Vol 15, No 4 (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.v15i4.5709

Abstract

The increasing volume of waste worldwide has led to significant challenges related to pollution, waste management, and recycling. These issues require innovative solutions to enhance the waste management ecosystem, such as the implementation of Smart Waste Management, which leverages information technology and artificial intelligence. This study aims to implement the Xception Convolutional Neural Network (CNN) model for waste classification, explore various data augmentation techniques, and identify optimal model configurations for this task. The research methodology consists of several stages, including data preparation, model building and training, model adaptation for classification tasks, model evaluation, iterative experimentation, and saving and reloading the trained model. The dataset used in this study is the TrashNet dataset obtained from Kaggle, consisting of 2,527 images across several classes: cardboard, glass, metal, paper, plastic, and trash. Based on the optimization process, the selected hyperparameters include a batch size of 32, 64 convolutional filters, the Adam optimizer (learning rate = 0.0001), and a dropout rate of 0.25. After training for 100 epochs, the model achieved a training accuracy of 99% with a loss of 0.7%, and a validation accuracy of 87% with a validation loss of 52%. Evaluation on the test dataset yielded an accuracy of 76%, precision of 79%, recall of 75%, and an F1-score of 75%. The application of data augmentation techniques—such as scaling, translation, and color space transformation—resulted in performance improvements, increasing accuracy by 13%, precision by 11%, recall by 13%, and F1-score by 12%. This study contributes by implementing the Xception model on the TrashNet dataset for waste classification and proposing several data augmentation methods that provide empirical evidence to support or challenge existing approaches. The findings offer practical insights for the development of Smart Waste Management systems, enrich the literature through experimental results, and provide a comparative analysis of data augmentation techniques suitable for the TrashNet dataset.
Mountain Hiking Route Recommendation System using Content-based Filtering with SBERT and Cosine Similarity Putra Wijaya, Rama Danadipa; Rahman, Arif Nur
Sistemasi: Jurnal Sistem Informasi Vol 15, No 4 (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.v15i4.6087

Abstract

Mountain hiking activities in Indonesia have increased significantly; however, information on hiking routes is often dispersed informally and remains subjective, increasing the risk of a mismatch between hikers’ abilities and route characteristics. This study develops a hiking route recommendation system using a content-based filtering approach powered by Sentence-BERT (SBERT) to extract semantic features from narrative route descriptions, which are integrated with numerical GPX attributes. Similarity is computed using cosine similarity. Experimental evaluation on 39 hiking routes on Java Island shows an average Precision@5 of 0.60, outperforming TF-IDF (0.25) with a 140% improvement. The system proves effective in capturing implicit semantic relationships, thereby providing more relevant and context-aware recommendations.
Improving Cloud Task Scheduling in Cloud Sim Plus using Demand-Aware VM Selection Sultan, Nagham Ajeel
Sistemasi: Jurnal Sistem Informasi Vol 15, No 4 (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.v15i4.6253

Abstract

Deep reinforcement learning and metaheuristics are becoming popular ways to study cloud task scheduling to make things like makespan and completion time better; however, these methods often require a lot of computing power and can make it harder to understand and repeat the results. This paper suggests a simple Demand-Aware VM Selection policy for CloudSim Plus (simulated tasks) that improves the basic scheduling process by better matching tasks with virtual machines based on task needs, while leaving the rest of the simulation the same. The method was tested against the default time-shared baseline with three workload sizes (200, 500, and 1000 cloudlets) and six random seeds (42-47). The experimental results demonstrate that the proposed method drastically shortened the overall time of task completion: it raised the average time by 35.65% for 200 cloudlets (A computational task/work unit submitted for execution is CloudSim abstraction) and 28.86% for 500 cloudlets. In terms of average completion time, the newly planned lowered the average by 26.68% at 200 cloudlets and 28.53% at 1000 cloudlets; on the other hand, the 500-cloudlet scenario showed very high variability and even a slightly negative average improvement (-1.42%), indicating that completion-time averaging was sensitive under that workload regime even though there was a strong makespan gain. The results show that demand-aware binding is a clear, repeatable, and easy-to-add improvement for scheduling in CloudSim Plus, which can serve as a better starting point or as part of more advanced optimization and learning systems.
Analysis of User Acceptance and use of the GKI Salatiga+ Application using the UTAUT Model Marisa, Yohana Jenny; Maria, Evi
Sistemasi: Jurnal Sistem Informasi Vol 15, No 4 (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.v15i4.6277

Abstract

The advancement of digital technology has encouraged religious institutions to adopt information systems to enhance services for their congregations; however, user acceptance is not always optimal. This study aims to analyze the factors influencing behavioral intention to use the GKI Salatiga+ application by applying the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The study employs a quantitative explanatory approach using Partial Least Squares–Structural Equation Modeling (PLS-SEM). Data were collected through a questionnaire administered to 95 active users of the application. The results indicate that performance expectancy, effort expectancy, and facilitating conditions have a positive and significant effect on behavioral intention, with effort expectancy emerging as the most dominant factor. In contrast, social influence does not play a significant role in explaining the intention to use technology in this context. These findings suggest that, within a voluntary religious community context, the intention to use technology is more strongly influenced by perceived ease of use and system support than by social pressure. This study contributes by demonstrating that the relationships among constructs in the UTAUT model are context-dependent, particularly in community-based and voluntary technology adoption settings.
Analysis of User Satisfaction and Loyalty in the BRI Mobile E-Banking Application using a Data Mining Approach (K-Means Clustering and Decision Tree) Bahri, Sri Mawarni; Harahap, Syaiful Zuhri; Irmayanti, Irmayanti; Bangun, Budianto
Sistemasi: Jurnal Sistem Informasi Vol 15, No 4 (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.v15i4.6305

Abstract

The development of information technology has driven digital transformation in the banking sector, particularly through e-banking services that provide convenience, speed, and flexibility for users in conducting financial transactions. The BRI Mobile (BRImo) application, as a digital service of Bank Rakyat Indonesia, plays an important role in improving service quality for customers. However, the emergence of negative perceptions regarding transaction notifications, along with varying user experiences, may affect user satisfaction and loyalty levels. The main problem in this study is that the patterns of user satisfaction and loyalty, as well as the most influential factors affecting both aspects, have not been clearly identified. This study aims to analyze the level of user satisfaction and loyalty toward the BRI Mobile application and to identify the dominant factors influencing them, namely ease of use, security, trust, and system performance. The research employs a quantitative approach using data mining techniques, specifically the K-Means Clustering algorithm to group users based on satisfaction levels, and a Decision Tree to determine the most influential factors affecting user loyalty. Data were collected through questionnaires distributed to 100 active BRImo users. The results show that users can be grouped into several clusters with different satisfaction characteristics. In addition, the Decision Tree analysis reveals that system performance is the most dominant factor influencing user satisfaction and loyalty, followed by ease of use, security, and trust. Overall, the combination of K-Means Clustering and Decision Tree methods provides a more comprehensive understanding of user satisfaction and loyalty patterns, and can serve as a basis for strategic decision-making to improve the quality of the BRI Mobile application services.
Open Government Data Analytics of Tourist Visits In West Java 2014–2024: A Data Science and Philosophy of Science Perspective Nugraha, Ucu; Sitorus, Hernalom; Handayani, Sri Titi; Nursikuwagus, Agus; Ishaq, Usep Mohamad; Darmayadi, Andrias
Sistemasi: Jurnal Sistem Informasi Vol 15, No 4 (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.v15i4.6175

Abstract

Open Government Data (OGD) in tourism provides opportunities for data-driven analytics to support destination management policies. In policy practice, tourism OGD is often accepted at face value as a direct representation of real-world conditions, even though such data are constructed through definitions, recording procedures, and measurement choices. Therefore, a philosophy of science perspective is essential in data governance. This article analyzes an Open Data Jabar dataset on the number of tourist visits by visitor type and district/city in West Java Province for the period 2014–2024 (n = 565; 27 districts/cities; two visitor categories: domestic and international). The data science approach includes data quality auditing (completeness and consistency), time-series aggregation, spatial concentration measurement using the Gini coefficient, and a comparison of shock–recovery patterns in tourist visits before and after the pandemic. The results indicate a decline in total visits of -50.6% in 2020 compared to 2019, with international visits experiencing the sharpest drop (-82.8%). By 2024, total visits reached 64,517,298, dominated by domestic tourists (63,963,443; international share 0.9%). Spatial concentration in 2024 is reflected by a Gini coefficient of 0.429, with the top five regions accounting for 44.2% of total visits. The discussion emphasizes that visitor counts are epistemic representations shaped by definitions, reporting practices, and data cleaning processes. Therefore, policy recommendations should be accompanied by data provenance, metadata, and explicit uncertainty annotations to avoid the reification of indicators.
Analysis of Government Employees’ Information Security Awareness: A Case Study of Pusinfowas BPKP Nasution, Basrah; Yazid, Setiadi; Sucahyo, Yudho Giri
Sistemasi: Jurnal Sistem Informasi Vol 15, No 4 (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.v15i4.6259

Abstract

The utilization of information technology as a tool that is widely believed to facilitate business processes within organizations is inseparable from challenges related to information security threats. According to the 2024 cyber incident report issued by the National Cyber and Crypto Agency (BSSN), data exposure threats accounted for 58.34%, with most incidents originating from the government administration sector. Humans are considered the weakest link in information security; therefore, the primary effort to improve security can begin with measuring the level of security awareness. Among the various work units within BPKP, Pusinfowas, as the central information technology management unit, is considered an appropriate sample for evaluation and is expected to contribute to improving information security awareness across other units. This study employs the Human Aspects of Information Security Questionnaire (HAIS-Q) model to measure the level of information security awareness among employees at Pusinfowas. The HAIS-Q model consists of three dimensions—knowledge, attitude, and behavior—and seven focus areas: password management, email use, internet use, social media use, mobile device use, information handling, and incident reporting. The results indicate that employees’ information security awareness is at a “Good” level, with scores ranging between 80% and 100% across all HAIS-Q dimensions and focus areas.
Accessibility Evaluation of University Websites in Indonesia based on WCAG 2.2 Guidelines Suryasih, Ketut Dian; Pratiwi, Putu Yudia; Pradnyana, I Made Ardwi
Sistemasi: Jurnal Sistem Informasi Vol 15, No 4 (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.v15i4.5824

Abstract

Universities use websites as their main portal and main source of information. However, some university websites in Indonesia still show accessibility issues and do not meet the Web Content Accessibility Guidelines (WCAG) 2.2 standards. This research aims to evaluate the accessibility of five university websites in Indonesia based on the Similarweb platform. The evaluation focused on color blindness, attention deficit, and limited movement. These three disabilities are considered to have a significant impact on web accessibility. The method used in this study was WCAG-EM by using a combination of automated and manual evaluation. The automated evaluation used WAVE and Siteimprove Accessibility Checker tools. Meanwhile, manual evaluation adopted the evaluation steps from the Accessibility Insights tool by Microsoft. The results of the automated evaluation showed that the accumulated issues obtained from all university homepages were 188 issues. There are 7 types of issues identified through manual evaluation. The most common accessibility issue found is components with low contrast between the foreground and background. These results indicate that university websites in Indonesia, based on Similarweb rankings, still do not meet the international standards WCAG.
Enhancing the Diagnosis of Cerebral Vascular Occlusion in Brain MRI Images using Advanced Image Processing Techniques AL-Safar, Alhan Younis Anwr; Al-Asa'ad, Intesar
Sistemasi: Jurnal Sistem Informasi Vol 15, No 4 (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.v15i4.6084

Abstract

The paper presents an automated and computationally lightweight system that aims at diagnosing the presence of the ischemic stroke in the brain based on the MRI images and prioritizes mathematical clarity over the overpowering complexity. Workflow starts with AWCES algorithm to improve the quality of images, which is applied based on adaptive windowing determined by the entropy, and then proceeds to Watershed segmentation based on markers to word out the areas that are suspected of being vascularly blocked. The texture descriptors are then obtained using the Local Binary Patterns (LBP) and sent to a Random Forest classifier which differentiates between the damaged and the healthy brain tissue. The training stage has been integrated with the SMOTE technique to address the problem of class imbalance that is severe in the dataset. With a stratified five-fold cross-validation, the system demonstrated an AUC of 0.99, a specificity of 94% and a recall of 70%. These results indicate that the classical methods based on open mathematics can be competitive with deep learning networks, providing a high-quality and quick diagnostic methodology that could be used as a primary solution in the diagnosis of a stroke and as a complement to the exploratory three-dimensional visualization.
Development of a Web-based Library Information System Integrated with a Webcam-based Barcode Scanner Lestari, Nabilah; Fauzi, Ahmad
Sistemasi: Jurnal Sistem Informasi Vol 15, No 4 (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.v15i4.6238

Abstract

The process of borrowing and returning books in some school libraries is still carried out manually, resulting in slower service times and a higher risk of data recording errors. Therefore, a library information system is needed to improve transaction efficiency and data management accuracy. This study aims to develop a web-based library information system integrated with a webcam-based barcode scanning feature, eliminating the need for additional scanning devices. The research employs a Research and Development (R&D) approach using the Waterfall model, which includes the stages of requirements analysis, system design, implementation, and testing. System evaluation was conducted using Black Box Testing, transaction time efficiency analysis, data accuracy measurement, assessment of lighting intensity on webcam-based barcode scanning performance, and usability testing using the System Usability Scale (SUS). The results indicate that all system functions operate properly. The system reduces the average transaction time from 72.06 seconds to 24.48 seconds (66% efficiency improvement) and increases data recording accuracy from 60% to 100%. Webcam-based barcode scanning performs optimally under lighting conditions of ≥150 lux. The usability evaluation achieved a SUS score of 85.8 (excellent). However, system performance is still influenced by lighting conditions and camera quality. Future research may focus on enhancing the system by incorporating image processing techniques or alternative identification technologies to further improve scanning accuracy.

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