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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 1,146 Documents
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Implementation of Agile Methods in the Development of a Mobile-based Hybrid Learning Management System Application Wiliramayanti Wiliramayanti; Hoiriyah Hoiriyah; Moh. Aminollah Hamzah; Rofiuddin Rofiuddin
SISTEMASI Vol 15, No 7 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i7.6597

Abstract

The rapid advancement of digital technology in higher education has increased the demand for learning systems that not only support academic activities but are also easily accessible through smartphones, the devices most frequently used by university students. Although Learning Management Systems (LMSs) have been widely adopted, most existing implementations remain web-based, limiting the optimization of mobile learning experiences. This study aims to develop a mobile-based Hybrid Learning Management System (HyLMS) that integrates synchronous and asynchronous learning within a single platform. The system was developed using the Agile Development methodology to facilitate iterative development and continuous adaptation to user requirements. The development process consisted of requirements gathering, system design, implementation, testing, and evaluation. The resulting application provides essential features, including user registration, authentication, course management, learning material access, assignment submission, discussion forums, notifications, and assessment. Black Box Testing confirmed that all system functionalities operated as expected. Furthermore, user experience was evaluated using the User Experience Questionnaire (UEQ) involving 35 respondents, with positive results across all evaluation dimensions. The highest score was achieved in the Stimulation dimension (1.52), indicating a high level of user engagement and motivation. These findings demonstrate that the proposed mobile-based HyLMS effectively supports hybrid learning by providing a more flexible, integrated, and user-centered learning environment for higher education institutions.
Implementation of the Game Development Life Cycle for an Android-based Color Theory Educational Game Raditya Wardhana; Muhammad Tofa Nurcholis; Buyut Khoirul Umri
SISTEMASI Vol 15, No 7 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i7.6479

Abstract

Students often experience difficulties in understanding color theory, particularly when it is taught through conventional theoretical instruction without interactive or visual learning support. Although they pay attention to the instructional content, approximately 65% of students still struggle to apply color theory concepts in graphic design. This study aims to develop an Android-based educational game to facilitate students' understanding of color theory through an interactive and visual learning experience. The game was developed using the Game Development Life Cycle (GDLC), which consists of six phases: initiation, pre-production, production, testing, beta, and release. Construct 2 was used as the game development platform. The game incorporates two-dimensional animations covering key topics, including color theory, color meanings, color temperature, color dimensions, and color schemes. The learning materials are presented through engaging visual storytelling to enhance comprehension. The game was evaluated through alpha and beta testing, while user responses were assessed using a Likert-scale questionnaire. The evaluation results indicate that the game is functional, engaging, enjoyable, and effective in delivering educational content. Therefore, the proposed educational game provides an alternative and interactive approach to teaching color theory to students.
Analysis of User Acceptance of the SATUSEHAT Application using the Extended UTAUT 2 Model Ari Heriyadi; Apriansyah Putra; Rizka Dhini Kurnia; Putri Eka Sevtiyuni
SISTEMASI Vol 15, No 7 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i7.6705

Abstract

The advancement of information technology in the healthcare sector has encouraged the Indonesian government to introduce the SATUSEHAT application as an integrated digital health platform. User acceptance of the SATUSEHAT application is influenced by various factors, including perceived usefulness, ease of use, trust, and privacy protection. This study aims to investigate the factors influencing user acceptance of the SATUSEHAT application using an Extended UTAUT2 model by incorporating Trust and Perceived Privacy as additional constructs. A quantitative research approach was employed by distributing an online questionnaire to SATUSEHAT users, and the collected data were analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS) with SmartPLS software. The results indicate that Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Hedonic Motivation, Habit, Trust, and Perceived Privacy have positive and significant effects on Behavioral Intention. Furthermore, Facilitating Conditions, Habit, and Behavioral Intention were found to have positive and significant effects on Use Behavior. In contrast, Price Value was not found to have a significant influence on users' Behavioral Intention. These empirical findings confirm the explanatory capability of the Extended UTAUT2 model in understanding user acceptance of digital health technology. The results further suggest that the adoption of the SATUSEHAT platform is driven primarily by perceived usefulness, ease of use, social support, habitual use, trust, and confidence in data privacy protection, rather than by economic cost considerations.
Enhancing Hate Speech and Offensive Language Detection using CatBoost with RoBERTa-based Contextual Embeddings Muhammad Elfarizi; Surya Agustian; Fitra Kurnia; Suwanto Sanjaya; Fitri Insani
SISTEMASI Vol 15, No 7 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i7.6637

Abstract

The widespread dissemination of hate speech and offensive content on social media platforms has become a critical societal issue, highlighting the need for reliable automated detection systems. This study proposes a hybrid approach that leverages frozen embeddings from the pre-trained language model cardiffnlp/twitter-roberta-base-offensive as a high-level semantic feature extractor, combined with the CatBoost gradient boosting algorithm as the final classifier. The proposed method was evaluated on the HASOC 2021 English dataset through six experimental scenarios and compared with a TF-IDF baseline using CatBoost's default hyperparameters. Experimental results demonstrate that the proposed approach achieved a Macro F1-score of 0.7924 for the binary classification task (Task 1A) and 0.6113 for the multiclass classification task (Task 1B), outperforming the TF-IDF baseline, which achieved scores of 0.7724 and 0.5798, respectively. The proposed system demonstrated a clear performance improvement and achieved results comparable to those of the top-ranked teams on the official HASOC 2021 leaderboard, while avoiding the computational cost associated with fine-tuning large pre-trained language models.
Provincial Clustering using GARCH-based Chili Price Volatility Features Yogata Rama Guninta; Bety Wulan Sari; Yoga Pristyanto
SISTEMASI Vol 15, No 7 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i7.6521

Abstract

Bird's eye chili is a strategic food commodity in Indonesia whose prices are highly susceptible to interregional fluctuations due to differences in distribution systems and supply chain conditions. These fluctuations often occur over short time horizons, making analyses based on monthly or annual data less capable of capturing short-term price spikes that have the greatest impact on consumers' purchasing power and price stabilization policies. Previous studies have generally clustered regions based on nominal or average prices, which do not adequately represent the dynamics of daily price movements. This study aims to cluster Indonesian provinces according to the volatility characteristics of bird's eye chili prices by proposing a clustering approach that utilizes GARCH(1,1)-based conditional volatility features to represent daily price dynamics, combined with the K-Means algorithm for cluster formation. Daily bird's eye chili price data from 34 provinces covering the period from April 2024 to April 2026 were obtained from the National Strategic Food Price Information Center (PIHPS). The price data were transformed into daily returns and modeled using GARCH(1,1) to estimate the average conditional volatility of each province, which was subsequently used as the clustering feature. The optimal number of clusters was determined using the Elbow Method and the Silhouette Score. The evaluation results identified five as the optimal number of clusters, achieving a silhouette score of 0.65 and classifying the 34 provinces into five volatility categories: very high, high, moderate, low, and very low. The findings reveal that provinces with higher price levels do not necessarily belong to the highest volatility cluster, indicating that a volatility-based approach provides additional insights into price dynamics beyond those captured by nominal price-based clustering. These results can support regional food price volatility monitoring and serve as a reference for developing data-driven decision support systems for food price management.
Prediction of Air Pollution in the Sultanate of Oman using Machine Learning Approaches Shamssa Abdullah Al-Rahbi; Mohd Alodat
SISTEMASI Vol 15, No 7 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i7.6260

Abstract

Air pollution has become a major environmental and public-health concern worldwide, and understanding its behaviour is essential for effective monitoring and management. This study investigates air-quality patterns across four regions in the Sultanate of Oman—Al Khuwair, Salalah, Al Khoud, and Bediya—using a combination of statistical modelling and machine-learning techniques. Hourly data for 2023, including pollutant concentrations and key meteorological variables, were obtained from the Environment Authority of Oman, cleaned, and pre-processed to construct region-specific datasets. Air Quality Index (AQI) values were calculated for each pollutant and classified into three categories (Good, Moderate, and Unhealthy). Kernel Support Vector Machine (KSVM) and Gaussian Process Regression and models were trained using a 70/30 temporal split to classify AQI levels. Results showed that KSVM achieved the highest accuracy in Salalah (96.97%), Al Khoud (94.33%), and Bediya (93.37%), while Gaussian Process Regression performed best in Al Khuwair (70.32%). In conclusion, this research demonstrates that advanced kernel-based classifiers can effectively model non-linear environmental data, providing a scalable solution for regional environmental management.
Analysis of WhatsApp Business Features on the Effectiveness of Marketing Communications for Craft MSMEs in Jambi City Rudi Nata; Ari Andrianti; Miranty Yudistira; Oki Dahwanu; Rahmad Ashar; Renaldi Yulvianda
SISTEMASI Vol 15, No 7 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i7.6177

Abstract

This study analyzes the influence of WhatsApp Business features (Broadcast Message, Quick Reply, Catalog, and Labeling) on marketing communication effectiveness among creative SMEs in Jambi City. Using a mixed methods approach, the results indicate that WhatsApp Business features collectively have a significant effect on marketing communication effectiveness (R² = 0.596, p = 0.003), explaining 59.6% of the variance. Among the four features, only the Catalog feature showed a significant positive influence (β = 0.903, p = 0.034), highlighting its critical role in enhancing customer communication and engagement. The novelty of this research lies in its focus on creative SMEs producing cultural heritage based products in Jambi and its simultaneous evaluation of four key WhatsApp Business features. The findings suggest that SMEs should prioritize optimizing Catalog features through high quality visual content that effectively communicates product value and local cultural identity, while policymakers should support targeted digital literacy programs for heritage based businesses.
Analysis and Development of Transformer (DistilBERT)-LSTM and Reinforcement Learning Models for Adaptive Phishing Email Detection Farizal Herry Saputra; Kahfi Heryandi Suradiraja; Abu Khalid Rivai
SISTEMASI Vol 15, No 7 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i7.6432

Abstract

Phishing detection faces two major challenges: performance degradation caused by domain shift and a heavy reliance on costly labeled data. This study proposes an adaptive phishing email detection model that integrates a hybrid DistilBERT–LSTM architecture with a Proximal Policy Optimization (PPO)-based Reinforcement Learning agent. The proposed methodology employs a multi-stage transfer learning framework using three datasets: Enron as the source domain, Phishing_Validation for supervised domain adaptation, and CEAS_08 to simulate an unlabeled data stream through pseudo-labeling. Experimental results demonstrate excellent performance on the source-domain dataset (Enron), achieving an F1-score of 0.9935. However, the model's performance declined on the Phishing_Validation dataset (F1-score = 0.9067), confirming the impact of domain shift. By incorporating the PPO agent, the proposed model autonomously recovered its performance on the CEAS_08 dataset, achieving an F1-score of 0.9516, an accuracy of 0.9468, and a ROC–AUC of 0.9915. The stability of the adaptation process was validated by the convergence of the Kullback–Leibler (KL) divergence to 0.000173, although a minor overconfidence of approximately 5% was observed between the model's confidence estimates and the ground-truth labels. These findings demonstrate the effectiveness of PPO in mitigating domain shift within an unsupervised adaptation environment. Future research should focus on improving model calibration and exploring multimodal feature integration to further strengthen cybersecurity defenses.
Feasibility Classification of Free Nutritious Meal Kitchen Partners Using C4.5 for Food Safety Abdul Kholiq; Rizaldi Rizaldi; Dewi Anggraeni
SISTEMASI Vol 15, No 7 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i7.6598

Abstract

The Free Nutritious Meal Program requires a rigorous selection process for kitchen partners because kitchen quality, sanitation, clean water availability, human resources, production capacity, and food distribution are directly associated with food safety assurance. This study aims to develop a C4.5-based classification model to replicate the operational feasibility assessment rules used for evaluating kitchen partners in the Free Nutritious Meal Program based on field survey data. The dataset comprised 200 kitchen partners, including micro, small, and medium enterprises (MSMEs) and catering providers, located in Asahan Regency, North Sumatra, Indonesia, and was collected through structured field observations. Each partner was evaluated using ten assessment criteria: legal compliance, location, facilities and infrastructure, sanitation, clean water availability, human resources, production capacity, food safety, distribution, and risk history. Individual criterion scores were converted into a weighted composite score, after which feasibility labels were assigned according to an operational decision rule based on a minimum threshold score of 80 and the presence of critical failure criteria, defined as mandatory indicators that automatically disqualify a partner regardless of whether the minimum score threshold is achieved. Consequently, the class labels were not derived from independent expert audits or official institutional decisions but were generated from policy-based operational rules and used as the ground truth for model training. The dataset was divided into training and testing sets using an 80:20 ratio, resulting in 160 training instances and 40 testing instances. The results showed that 48 partners were classified as Feasible, while 152 were classified as Not Feasible. The C4.5 model achieved an accuracy of 95.00%, with 90.00% precision, 90.00% recall, and a 90.00% F1-score. The most influential predictor was the number of critical failure criteria (92.97%), followed by the clean water score (7.03%). These findings demonstrate that the C4.5 algorithm can effectively extract and replicate operational feasibility assessment rules, providing a transparent, consistent, and interpretable decision-support tool for selecting kitchen partners while supporting food safety assurance.
Comparison of Machine Learning Algorithms for Sentiment Analysis of Trans Jogja on Social Media Putri Muryanti Setyowati; Yoga Pristyanto; Arif Nur Rohman
SISTEMASI Vol 15, No 7 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i7.6494

Abstract

The social media platform X has become an important channel for the public to express opinions and share experiences regarding public services, including Trans Jogja. User-generated content from this platform provides valuable insights into public perceptions of service quality. However, because these data consist of unstructured text, sentiment classification techniques based on machine learning are required to analyze them effectively. This study aims to compare the performance of several machine learning algorithms for sentiment classification, including Naïve Bayes, Support Vector Machine (SVM), Random Forest, Neural Network, Logistic Regression, and Decision Tree, in classifying user sentiment toward Trans Jogja on the X platform. Data were collected through a web crawling process using Tweet Harvest with keywords related to Trans Jogja, covering the period from January 1, 2025, to June 10, 2026, resulting in a dataset of 3,035 tweets. The preprocessing stage included data cleaning, case folding, tokenization, normalization, stopword removal, and stemming. Text representation was performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method. The dataset was then divided into training and testing sets using five train–test split ratios: 90:10, 85:15, 80:20, 75:25, and 70:30. Model performance was evaluated using a confusion matrix and the corresponding accuracy, precision, recall, and F1-score metrics. The experimental results demonstrate that the Support Vector Machine (SVM) consistently outperformed the other algorithms across different data split ratios. At the 85:15 train–test split, the SVM achieved an accuracy of 91%, precision of 91%, recall of 91%, and an F1-score of 91%, indicating that it is the most effective algorithm for sentiment classification of Trans Jogja users on the X platform.

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