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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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An Automated System for Detecting and Improving Academic Text Politeness Using IndoBERT and IndoT5 Ariesty, Belina Eka; Ratnasari, Chanifah Indah
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.v15i2.6125

Abstract

The increasing use of digital communication in academic interactions between students and lecturers is often not accompanied by consistent application of language politeness norms, potentially affecting the effectiveness of academic interactions. To date, efforts to enhance language politeness have predominantly relied on manual and subjective evaluation. This study aims to develop an automated system for detecting and improving politeness in Indonesian academic text communication. The proposed approach integrates IndoBERT as a classification model to identify levels of text politeness and IndoT5 as a generative model to transform sentences identified as impolite into more appropriate academic forms. The dataset consists of 6,230 labeled sentences collected through Google Forms, TikTok, and additional synthetic data generated using ChatGPT. Experimental results show that the IndoBERT model achieves an accuracy of 97.11% in classifying academic text politeness, while IndoT5 is capable of transforming impolite sentences into more appropriate academic expressions, as demonstrated by evaluations using BLEU, ROUGE, and METEOR metrics. This study results in an integrated deep learning–based system capable of automatically detecting and improving academic text politeness within a unified processing framework.
Analysis of User Satisfaction with LIVIN’ by Mandiri as User Feedback in the Domain of Continual Service Improvement Gam, Talmi; Latuperissa, Rudi
Sistemasi: Jurnal Sistem Informasi Vol 15, No 3 (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.v15i3.6093

Abstract

The rapid development of digital banking services necessitates continuous service quality improvement to ensure that user experiences remain aligned with evolving needs and expectations. This study aims to analyze user satisfaction with the Livin’ by Mandiri application and to identify priority areas for improvement using the ITIL framework within the Continual Service Improvement (CSI) domain. This research adopts a descriptive quantitative approach with purposive sampling, involving 100 active users. Data were collected through questionnaires and analyzed using validity and reliability tests with the assistance of SPSS. The results indicate that the research instrument demonstrates excellent reliability (Cronbach’s Alpha = 0.934), and all questionnaire items are valid. The key Findings identify four factors influencing user satisfaction: interface design, reliability, responsiveness, and personalization. In addition, items X3.6 and X4.4 exhibit the lowest correlation values, indicating that they should be prioritized for improvement. From the CSI perspective, although the service demonstrates good quality and maturity, continuous improvement efforts are still necessary. These efforts should be carried out through iterative cycles of data collection, gap analysis, implementation of improvements, and evaluation to ensure sustained enhancement of service quality.
Aspect-based Sentiment Analysis of Public Opinions on Integrated Islamic Schools using Lexicon based and Machine Learning Approaches Muttakin, Fitriani; Savra, Daffa Takratama
Sistemasi: Jurnal Sistem Informasi Vol 15, No 3 (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.v15i3.5848

Abstract

This study aims to examine public perceptions of Integrated Islamic Schools through aspect-based sentiment analysis by integrating Latent Dirichlet Allocation, Lexicon-Based approach, and Deep Neural Networks. LDA is employed to extract topic structures that represent the semantic context of public reviews, Lexicon Based method is used for sentiment analysis, while DNN infers sentiment orientation based on the extracted representations. This approach seeks to combine the strengths of probabilistic topic modeling and deep learning to obtain a more comprehensive understanding of public opinion. The analysis was conducted on a collection of 2,280 online reviews, which after preprocessing resulted in 1,438 reviews processed using the LDA–DNN combination. The results demonstrate that this approach is capable of identify in opinion dimensions in a more contextual manner and enhancing the interpretability of the analysis outcomes. Empirical evaluation shows that the proposed model achieved an accuracy of 63.89% for aspect classification and 93.06% for sentiment classification, outperforming the K-Means–LSA and K-Means–PCA approaches, which achieved 45.14% and 31.94% accuracy for aspect classification and 92.36% accuracy for sentiment classification, respectively. These findings confirm the superiority of probabilistic topic modeling in capturing complex semantic relationships and provide a methodological contribution to the development of sentiment analysis in the context of integrated Islamic education.
The Effect of Social Media Promotion on Customer Loyalty Mediated by Brand Equity at Dekabeads rahmania, sherli; Putra, Apriansyah
Sistemasi: Jurnal Sistem Informasi Vol 15, No 3 (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.v15i3.6188

Abstract

The increasingly dynamic competition in the handmade accessories retail sector encourages SMEs to manage promotional strategies more effectively in order to retain customers. Social media has become one of the primary channels for shaping brand perception and value, which ultimately influence customer loyalty. This study aims to examine the effect of social media promotion on customer loyalty using the Brand Equity framework proposed by Aaker and Keller, with a case study of Dekabeads. The research adopts a quantitative approach using Partial Least Squares–Structural Equation Modeling (PLS-SEM) with SmartPLS. The respondents consist of 169 customers who have previously made purchases. The results indicate that social media promotion has a significant effect on customer engagement, brand equity, customer satisfaction, and customer loyalty. Directly, social media promotion is proven to enhance customer loyalty. In addition, there are indirect effects through increased customer engagement, strengthened brand equity, and improved customer satisfaction as mediating variables. The R-square value of 0.748 indicates that the model explains 74.8% of the variance in customer loyalty. These findings highlight that optimizing social media promotion in alignment with brand equity development plays a crucial role in fostering sustainable customer loyalty.
Analysis of User Acceptance of the McDonald’s Application using the UTAUT Model Tarika, Laurencia Christine; Maria (SCOPUS ID: 57093633500), Evi
Sistemasi: Jurnal Sistem Informasi Vol 15, No 3 (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.v15i3.6135

Abstract

The rapid advancement of information technology has driven increased adoption of digital applications in the fast-food industry, including the McDonald’s application, which is designed to support ordering and promotional information. However, in practice, some users still experience difficulties in using the application, highlighting the need to analyze the factors influencing its acceptance. This study aims to examine user acceptance of the McDonald’s application using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The research adopts a quantitative approach employing Partial Least Squares Structural Equation Modeling (PLS-SEM) for data analysis. Data were collected through an online questionnaire from 85 users of the McDonald’s application. The results indicate that performance expectancy and facilitating conditions have a significant effect on behavioral intention, while effort expectancy and social influence do not show a significant impact. Furthermore, the analysis reveals that the model demonstrates strong predictive power in explaining users’ intention to use the application. These findings suggest that perceived usefulness and the availability of technical support and facilitating resources are the key factors in enhancing user acceptance of the McDonald’s application.
Improving DCT-based JPEG Steganography using Adaptive LSB Matching for Resistance to Entropy-based Detection Utami, Intan; Destya, Senie; Putro, Miko Kastomo
Sistemasi: Jurnal Sistem Informasi Vol 15, No 3 (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.v15i3.6002

Abstract

Data transmission security has become a critical issue in the digital era, where steganography plays an important role in concealing confidential information within digital media. A key limitation of conventional Discrete Cosine Transform (DCT)-based steganography in JPEG images is its vulnerability to statistical detection through entropy analysis, as well as the risk of significant degradation in visual quality. This study aims to enhance DCT-based steganography techniques to minimize entropy-based detection while maintaining an optimal Peak Signal-to-Noise Ratio (PSNR). The proposed method employs an Adaptive LSB Matching approach by embedding messages into low-to-mid frequency coefficients using an adjustment mechanism (x±1). The performance of this method is then compared with the standard DCT approach. Experimental results show that the proposed method is able to preserve visual quality, achieving an average PSNR of 40.41 dB under maximum payload conditions, while reducing the entropy difference (ΔH) to 0.00251. These findings demonstrate that the developed technique is more robust against statistical steganalysis attacks and provides better visual fidelity compared to conventional methods.
Aspect-based Sentiment Analysis: A Bibliometric Review using Bibliometrix to Map Research Trends and Algorithm Methods Saputri, Eliana; Aini, Qurrotul; Sugiarti, Yuni
Sistemasi: Jurnal Sistem Informasi Vol 15, No 3 (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.v15i3.6153

Abstract

This study presents a bibliometric overview of research trends and algorithmic models in Aspect-Based Sentiment Analysis (ABSA). Data were collected from the Scopus database, resulting in a dataset of 2,344 journal articles published between 2021 and early 2026. The analysis was conducted using the Bibliometrix and Biblioshiny packages in R to perform number of publications per year, source’s production over time, country production over time, keyword co-occurrence, thematic mapping and evolution of research themes. The results show that ABSA research has experienced rapid growth with an annual publication increase of more than 30%. This study identifies BERT algorithmic models and Graph Convolutional Networks (GCN) as the most dominant supporting tools in the research literature. Thematic maps show that transformer-based techniques and attention mechanisms have emerged as key driving themes in this field. Furthermore, thematic evolution maps reveal a shift in focus from technical aspect extraction to online public opinion analysis, reinforced by the sharp surge in the use of Large Language Models (LLMs) in recent years. The findings provide a structured overview of the intellectual landscape of ABSA, clarifying dominant research clusters, methodological trajectories, and emerging themes. By highlighting the central role of transformer architectures, graph-based neural networks, and LLM integration, this study offers methodological guidance for future model development. Furthermore, the bibliometric insights reduce research fragmentation and identify underexplored directions, offering valuable insights for researchers to identify research gaps and develop more advanced ABSA models in future studies.
Optimization of XOR Cryptographic Keys using a Hybrid Genetic Algorithm and Simulated Annealing Muzakki, Naufal; Astuti, Nur Rochmah Dyah Puji
Sistemasi: Jurnal Sistem Informasi Vol 15, No 3 (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.v15i3.6170

Abstract

The security level of basic encryption algorithms, such as XOR, is highly dependent on the randomness and bit distribution pattern of the applied key. The use of stochastic optimization approaches, such as Genetic Algorithm (GA), in key generation often faces challenges due to premature convergence, a condition in which the search halts at a local optimum before achieving maximal entropy. This study proposes a sequential hybrid algorithm strategy that combines GA with Simulated Annealing (SA) to address fitness stagnation in PDF document encryption. The strategy is implemented through a two-phase mechanism: GA performs global exploration to identify potential solution regions, followed by SA performing local exploitation with a perturbation mechanism guided by the Metropolis probability. The algorithm’s performance is evaluated through a comparative study between conventional GA and the hybrid GA-SA. Experimental results indicate that the hybrid strategy successfully increases the average fitness value by 4.86%, achieves a Shannon entropy of 7.8952, and attains an NIST test P-value of 0.5299. These improvements demonstrate that the integration of SA effectively enhances the final solution quality of GA, producing cryptographic keys with more uniform bit distribution, passing statistical randomness tests, and exhibiting robustness against pattern analysis.
Comparison of Filter and Wrapper Feature Selection Methods for Heart Disease Risk Classification using K-Nearest Neighbors (k-NN) Kuswandani, Deni; Herman, Herman; Umar, Rusydi
Sistemasi: Jurnal Sistem Informasi Vol 15, No 3 (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.v15i3.5989

Abstract

Feature selection plays a crucial role in improving the effectiveness of medical classification models. This study compares two feature selection approaches—filter and wrapper methods—in developing a k-Nearest Neighbors (k-NN) model for heart disease risk classification. The dataset consists of patients’ demographic data, lifestyle factors, and clinical indicators. In this study, the filter method was applied by considering data types: Pearson Correlation was used for numerical features, while the Chi-Square test was applied to categorical features. The selected features from both techniques were then combined, reducing the initial 20 features to four key variables considered most relevant for heart disease risk classification: BMI, homocysteine level, blood pressure, and stress level. This approach achieved high computational efficiency; however, it resulted in only a modest accuracy improvement (76.8%) and a low recall for the minority class (0.07). In contrast, the wrapper method using Sequential Forward Selection (SFS) produced a more informative subset of 11 features, achieving higher accuracy (80.00%) and a ROC-AUC of 0.657, indicating better discrimination capability for the minority class. These findings suggest that while the filter method excels in simplicity and computational efficiency, the wrapper method is more effective in improving classification performance. This study provides empirical insights into selecting appropriate feature selection strategies based on analytical objectives, particularly for clinical decision support systems.
Application of Market Basket Analysis with the Apriori Algorithm to Discover Consumer Behavior Patterns Through Transaction Data S.Kom., M.Kom (SCOPUS ID=ID: 57201646662), Nurdin; Abdurraafi, Muthrib; Ar-Razi, Ar-Razi
Sistemasi: Jurnal Sistem Informasi Vol 15, No 3 (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.v15i3.3905

Abstract

Market Basket Analysis (MBA) examines itemsets that are purchased together by customers in a single transaction and is commonly used to analyze consumer behavior patterns based on transaction data. Kaffah Mart is a supermarket that sells daily necessities and household products. However, the store has not yet identified consumer shopping patterns within customers’ shopping baskets. This study aims to identify product association patterns formed through the application of Market Basket Analysis and to determine appropriate marketing strategies based on the generated association rules using the Apriori algorithm. The findings of this research are expected to support the development of more effective marketing strategies, thereby increasing product sales profitability at Kaffah Mart. The research methodology consists of the following stages: data collection, system flowchart design, implementation of the Apriori algorithm, and system deployment. The results show that, for the 3-itemset rules, customers who purchase sweet soy sauce and chili sauce are also likely to purchase instant noodles. Similarly, customers who buy a toothbrush and mouthwash are also likely to purchase toothpaste, with a confidence value of 100%. For the 2-itemset rule, customers who purchase shampoo are also likely to purchase bath soap, with a confidence value of 96.87%.

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