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INDONESIA
Journal of Information Systems and Informatics
Published by Asosiasi Doktor Sistem Informasi Indonesia
ISSN : 26565935     EISSN : 26564882     DOI : 10.63158/journalisi
Core Subject : Science,
Computer Science & IT
Journal-ISI is a scientific article journal that is the result of ideas, great and original thoughts about the latest research and technological developments covering the fields of information systems, information technology, informatics engineering, and computer science, and industrial engineering which is summarized in one publisher. Journal-ISI became one of the means for researchers to publish their great works published two times in one year, namely in March and September with e-ISSN: 2656-4882 and p-ISSN: 2656-5935.
Arjuna Subject : -
Articles 653 Documents
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NLP-Based Sentiment Analysis of Alfagift and Klik Indomaret Application Reviews: A Comparative Study Fuji Lestari, Nur Laili Indah; Naraya, Tri Vani Diah; Anggraini, Handari Niken; Fahmi, Faisal
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1178

Abstract

Amid competition for online shopping applications, Alfagift and Klik Indomaret compete for the same market share. This study aims to analyze and compare user reviews of both applications using sentiment analysis based on Natural Language Processing (NLP) with the E-Servqual approach, focusing on Efficiency and System Availability indicators, to determine the advantages and disadvantages of each application and provide a basis for service improvement, strategic decision making, and reference for users in choosing online shopping applications that suit their needs. Methods include data collection, data grouping, data processing, selecting analyzed samples with consensus, and data analysis to describe user perceptions of the quality of service of each application. The results showed that on the positive side, both apps experienced an increase in efficiency although not significant, with gradual improvements in user experience. Alfagift showed improvements in technical responsiveness and ease of use, while Klik Indomaret was relatively stable with a simple user experience. On the negative side, efficiency issues still arise consistently and impact user perception. Alfagift often faces access and login issues, while Klik Indomaret tends to be slow when accessing various features. These findings reflect that despite year-on-year improvements, both apps still face technical challenges that need to be resolved to improve the overall quality of digital services.
Enhancing News Similarity with Chunking Strategy and Hyperparameter Setting on Hybrid SBERT - Node2Vec Model Permadi Supriyo, Reza Ananta; Setijohatmo, Urip Teguh; Maspupah, Asri
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1180

Abstract

The proliferation of online news necessitates accurate article similarity systems to combat information overload, yet models based solely on semantic content often ignore crucial structural context like news source and publication date. This research proposes and evaluates a hybrid embedding model that integrates semantic representations from Sentence-BERT (SBERT) with structural representations from Node2Vec. A series of quantitative experiments were conducted on the challenging, multilingual SPICED dataset to determine the optimal model configuration. Using Mean Squared Error (MSE) for evaluation, the results show that a per-paragraph chunking strategy yielded the best performance. This strategy's effectiveness was validated by the identical performance of an optimal fixed-size chunk (450 characters with a 64 overlap), a value that aligns closely with the dataset's average paragraph length. Furthermore, a community-focused (BFS-like) Node2Vec configuration (p=1.0, q=2.0, l=60) was identified as optimal for the structural component. Significantly, the final hybrid model (MSE = 0.1434) proved superior to both the purely semantic (MSE = 0.1449) and purely structural models (MSE = 0.2512). This study concludes that the fusion of content and context provides the most comprehensive and accurate representation for news similarity detection.
An Integrated Random Forest for Analyzing Public Sentiment on the “Makan Bergizi Gratis” Program Ramadhan, Nur Ghaniaviyanto; Khoirunnisa, Azka
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1184

Abstract

The “Makan Bergizi Gratis” (MBG) Program is a public policy aimed at improving the nutritional quality of the community, particularly vulnerable groups. However, the success of this program is heavily influenced by public sentiment and perception. This research analyzes public sentiment toward the MBG program thru the social media platform X using an ensemble-based machine learning approach. The proposed framework integrates the Random Forest algorithm and compares it with four other ensemble models: AdaBoost, XGBoost, Bagging, and Stacking. A total of 3,417 tweets were analyzed using the TF-IDF method, both with and without stemming. The Random Forest model showed the best performance with an accuracy of 91.15% and an ROC-AUC of 95.46% on the data without stemming, consistently outperforming the other models. Additionally, a visual analysis of word frequency provides a strong indication of public opinion. These findings demonstrate the effectiveness of Random Forest in managing unstructured sentiment data and provide valuable insights for policymakers to monitor public responses and improve program implementation with greater precision.
Challenges In Implementing Integrated Electronic Health Records (EHRS) in Namibia’s Public Health Sector Shonghela, Victoria Mwetunyenena; Kamati, Etuna
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1185

Abstract

The study was aimed at investigating the challenges of implementing integrated Electronic Health Records (EHR) in the Namibian Public Health Care Sector. The study employed qualitative research approach. An exploratory design was used in this study to engage IT Personnel. The study used the purposive sampling technique to select twenty respondents, particularly focusing on the IT department. The study discovered that the Ministry of Health and Social Services (MoHSS) have isolated Electronic Health Record Systems (EHRS) such as the DHIS2 and Ptracker. The MoHSS had attempted to implement integrated EHRS, however it experienced various challenges. This study discovered challenges such as lack of network infrastructure, computer literate personnel, inadequate IT personnel, lack of policies and project documentation to implement the health records. Another challenge that hindered the addressing of issues such as supply of all up-to-date computer devices and software; having proper filing system and improving the slow connection due to poor network infrastructures is budget constraints. The study further discovered a lack of interoperability and standardization, the absence of unique identifiers for patients and the lack of data warehousing to be the main barriers for the full implementation of the integrated electronic health records system. Some of the recommendations of the study are that the MoHSS develops national policies and implementation frameworks to guide the implementation of EHRS, secure adequate funds specifically for the implementation of EHRS, develops and implements training framework for IT staff, administrative and health professional, implements unique patient identifier system and utilize open standards to enable system interoperability for implementation of the e-Health Record System. The study also recommends that MoHSS consider partnering with private service providers to enter into network infrastructure sharing agreements.
LoDaPro: Combining Local Detail and Global Projection for Improved Image Quality Assessment Using Efficient-Net and Vision Transformer Sackitey, Peter; Sackitey, Patrick
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1186

Abstract

Image Quality Assessment (IQA) is crucial in fields like digital imaging and telemedicine, where intricate details and overall scene composition affect human perception. Existing methodologies often prioritize either local or global features, leading to insufficient quality assessments. A hybrid deep learning framework, LoDaPro (Local Detail and Global Projection), that integrates EfficientNet for precise local detail extraction with a Vision Transformer (ViT) for comprehensive global context modelling was introduced. Its balanced feature representation makes it easier to do a more thorough and human-centered evaluation of image quality. Assessed using the KonIQ-10k and TID2013 benchmark datasets, LoDaPro attained a validation SRCC of 91% and PLCC of 92%, exceeding the predictive accuracy of prominent IQA methods. The results illustrate LoDaPro's capacity to proficiently learn the intricate relationship between image content and perceived quality, providing strong and generalizable performance across various image quality contexts.
Enhancing Coffee Leaf Rust Detection with DenseNet201Plus and Transfer Learning Karia, Adrian Jackob; Ally, Juma S; Leonard, Stanley
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1191

Abstract

Coffee leaf rust (CLR) is a disease of coffee leaves caused by the fungus Hemileia Vastatrix, posing a major threat to global coffee production. Early and accurate detection is crucial for sustainable farming practices and disease management. This study proposes a novel deep learning approach that integrates DenseNet201Plus, an enhanced version of DenseNet201, with transfer learning to improve the accuracy and efficiency of CLR detection. DenseNet201Plus incorporates fine-tuned layers and optimized hyperparameters designed for plant disease classification, while transfer learning utilizes pre-trained weights from large-scale image datasets, enabling the model to adapt the characteristics of CLR images with limited training data. The model was evaluated on two datasets: the newly collected, high-quality Mbozi CLR dataset and the publicly available ImageNet CLR dataset, using accuracy, precision, recall, and F1-score. Results demonstrate that DenseNet201Plus achieved an accuracy of 99.0% on the Mbozi dataset, surpassing 97.78% obtained by the ImageNet Public dataset, with corresponding gains across all performance metrics. Results confirm that integration of DenseNet201Plus with transfer learning on the high-quality dataset significantly enhances CLR detection. The method outperformed several other baseline methods. The proposed approach offers a scalable, real-time detection solution for field deployment, supporting precision agriculture, enabling timely and targeted interventions.
Multi-Criteria Evaluation Based on MOORA for Improving Water Treatment Operations Prasasti, Gayung; Darmanto, Eko; Supriyono, Supriyono; Tomya, Stella Putri
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1192

Abstract

Access to clean and sustainable drinking water continues to be a significant concern, especially in areas with considerable variability in source quality. This study used the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) approach to evaluate and rank 22 drinking water sources in Central Java, Indonesia, according to several physicochemical characteristics. The study process starts with the entry of sub-district, village, time, and laboratory result data, subsequently leading to the establishment of assessment criteria and their corresponding weights. Subsequent to the MOORA computations, rankings are produced and compiled into a detailed report. The results indicate that sources X21, X19, and X18 got the best ratings, signifying excellent water quality conditions, whereas X12 rated lowest, underscoring the necessity for focused action. In contrast to conventional evaluation methods, MOORA provides computational efficiency, clear prioritizing, and less subjectivity, facilitating consistent and reproducible multi-criteria evaluations. The results offer practical suggestions for enhancing water treatment processes, prioritizing resource distribution, and directing future incorporation of Internet of Things (IoT) monitoring for real-time assessment and adaptive management. This method integrates technical evaluation with pragmatic policy formulation, enhancing operational efficiency and promoting long-term sustainability in water delivery systems.
Impact of UI/UX on Shopee User Acceptance: A TAM Approach Maulidia, Syaqilla; Camila, Naina; Husein, Fadhil; Purnama, Diki Gita
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1193

Abstract

In the digital era, e-commerce platforms such as Shopee must continually improve their user interface (UI) and user experience (UX) to enhance user acceptance and competitiveness. This study analyzes the impact of UI/UX on user acceptance of the Shopee application using the Technology Acceptance Model (TAM), incorporating four variables: Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Behavioral Intention to Use (BIU), and Actual System Use (ASU). A quantitative approach was applied, collecting data via questionnaire from a purposive sample of 90 active Shopee users in RT 002/07, Pela Mampang. Data were analyzed using SPSS 26, including validity, reliability, and hypothesis testing. The results show that PEOU significantly influences PU, while both PU and PEOU have a strong and significant effect on BIU, with PU demonstrating a slightly stronger influence. BIU also significantly affects ASU. These findings indicate that ease of use and perceived benefits are key drivers of user intention and actual usage behavior. The results provide practical implications for Shopee's design and development teams to prioritize enhancing ease of navigation, feature intuitiveness, and visual clarity to increase user engagement and system usage.
Securing EEG-based Brain-Computer Interface Systems from Data Poisoning Attacks Tom, Joshua Joshua; Ekpar, Frank Edughom; Adiqwe, Wilfred
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1195

Abstract

Electroencephalogram (EEG)-based brain computer interface (BCI) is a widely used access technology to aid human-computer interactions. It enables communication between the human brain and external devices directly without the need for actuators such as human hands and legs. The BCI system acquires brain signals from an EEG device and uses machine learning (ML) algorithms to analyze and interpret the signals into actionable commands. However, EEG-based BCI systems are vulnerable to data poisoning attacks, which compromises the accuracy and security of the BCI system, and user safety. The objective of this paper is to protect the BCI systems against backdoor data poisoning attacks for reliable system operations. In this paper, a backdoor detect-and-clean mechanism, code named Bkd-DETCLEAN, to secure EEG-based BCI systems against data poisoning (backdoor) attacks is proposed using the Random Forest Classifier. Two models were designed, trained and validated on both clean and poisoned dataset respectively. The results of experiments on two benchmark EEG datasets shows that our solution achieves a detection accuracy of 98.5%, effectively identifying poisoned samples with a little below 5% false positive rate. Continued data cleaning iterations restored the poisoned training set, resulting in an overall system accuracy improvement from 78.9% to 93%. Based on these results, the proposed model sustained high detection and cleaning efficiency with different poisoning rates, underscoring the effectiveness of the machine learning driven proposed model in ensuring that brain signal integrity is not compromised. The proposed mechanism is also applicable in other areas including healthcare and medical data protection, protecting fraud detection models in financial systems, ensuring the integrity of sensor data in industrial control systems, protecting against user data manipulation in recommender systems, etc.
Performance Comparison of Sentiment Classification Algorithms on SIGNAL Reviews Using SMOTE Anadia, Qothrunnada Wafi; Meiriza, Allsela
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1196

Abstract

Public service apps like SIGNAL are widely used to provide public access to information and vehicle tax payments. However, diverse user reviews highlight the need to evaluate public perception through sentiment analysis. Selecting an appropriate classification algorithm is crucial to ensure accurate results, particularly when dealing with imbalanced review data. Therefore, This study examines the comparative performance of four algorithms Naïve Bayes, Random Forest, Decision Tree, and SVM in analyzing the sentiment of 36,000 user feedback obtained from Google Play Store. The dataset underwent preprocessing, feature extraction using TF-IDF, and class balancing using SMOTE. Model evaluation was conducted using accuracy, precision, recall, and F1-score. The findings indicated that Random Forest performed the best overall performance (accuracy 91.04%, F1-score 94.80%), followed by Naïve Bayes (accuracy 89.89%, F1-score 93.38%), SVM (accuracy 89.22%, F1-score 93.02%), and Decision Tree (accuracy 88.40%, F1-score 92.31%). These findings indicate that Random Forest is highly effective for balanced datasets, while SVM and Naïve Bayes offer competitive precision for applications prioritizing accuracy in positive class detection. The output of this study can be applied practically by developers and related institutions in optimizing public service applications and by applying Random Forest algorithm to gain actionable insights for optimizing features and aligning services more closely with user needs.

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