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International Journal of Information Engineering and Science
Published by Asosiasi Riset Teknik Elektro Dan Informatika Indonesia
ISSN : 30481902     EISSN : 30481953     DOI : 10.62951
Core Subject : Engineering,
Engineering
The scope of the this Journal covers the fields of Information Engineering and Science. This journal is a means of publication and a place to share research and development work in the field of technology
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 33 Documents
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Changes in the Physical and Mechanical Properties of Clay Soil Due to Stabilization with Lime Ferly Indra Putra; Kiagus Ahmad Roni; Sri Martini
International Journal of Information Engineering and Science Vol. 2 No. 2 (2025): May : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i2.295

Abstract

Clay soil stabilization is a crucial process to enhance the soil's bearing capacity and stability, making it more suitable for construction purposes. Stabilizing clay soils improves their mechanical properties, reduces swelling, and increases their load-bearing capacity, which is essential for the foundation of various structures. This study aims to investigate the effect of lime (CaO) addition and curing time on the physical properties of clay soil, particularly focusing on unconfined compressive strength (qu) and overall soil stability. The experimental methodology involved applying different percentages of lime content (ranging from 3% to 7%) and varying curing times (7, 14, and 28 days). The soil samples were tested for their unconfined compressive strength after each combination of lime content and curing duration. The results indicated that the addition of 5% lime (CaO) and curing for 14 days led to a significant improvement in the unconfined compressive strength by 153.3%, compared to the untreated clay soil. Furthermore, increasing the curing time beyond 14 days did not show substantial improvements in strength, suggesting that 14 days is the optimal curing period for this combination. The study also highlighted that the lime treatment not only enhanced the mechanical properties but also reduced the plasticity of the clay, making it more stable and easier to handle during construction. Based on these findings, it can be concluded that the appropriate combination of lime content and curing time plays a significant role in improving the stability of clay soils. This research provides valuable insights into optimizing soil stabilization techniques, offering an effective solution for enhancing soil properties for engineering applications
Grouping of Toddler Nutritional Status Based on Anthropometric Data in Pekan Kuala Village Using the K-Means Clustering Method Dita Mawarni; Relita Buaton; Kristina Annatasia
International Journal of Information Engineering and Science Vol. 2 No. 3 (2025): August : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i3.300

Abstract

Nutritional issues among toddlers continue to be a pressing public health challenge in Indonesia, including in Kelurahan Pekan Kuala, where although anthropometric data have been systematically collected through the e-PPGBM application, they have not been thoroughly explored in terms of clustering patterns that may provide deeper insights. This study seeks to classify toddler nutritional status by applying the K-Means Clustering method to anthropometric indicators such as age, weight, height, and weight-to-height index. A dataset consisting of 648 entries recorded between January and March 2025 was processed using MATLAB R2014b with cluster variations set at 5, 7, and 9. The analysis revealed that the majority of toddlers were categorized as having good nutritional status, while a portion of the sample was identified as undernourished and some at risk of overnutrition, indicating the diverse nutritional challenges faced by this community. Furthermore, testing the variance across cluster configurations demonstrated that the 9-cluster model yielded the lowest variance score of 0.20, thereby representing the most optimal solution since it produced more homogeneous, balanced, and stable clusters compared to other configurations. These outcomes highlight the importance of data-driven approaches in public health planning, as the clustering results not only provide a clearer picture of nutritional distribution among toddlers but also serve as a foundation for more evidence-based and targeted intervention strategies. By offering a more granular understanding of nutritional variations, this research is expected to support local health authorities in developing customized nutrition programs, allocating resources more effectively, and ultimately improving child health outcomes in Kelurahan Pekan Kuala and similar communities across Indonesia, where malnutrition and overnutrition risks continue to coexist.
Addition of Plastic Mixture (LDPE) for the Development of Alternative Mixtures in Concrete Blocks Abdullah, Abdullah; Erna Yuliwati; Eka Sri Yusmartini
International Journal of Information Engineering and Science Vol. 2 No. 3 (2025): August : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i3.301

Abstract

This study investigates the potential of Low-Density Polyethylene (LDPE) plastic waste as a partial substitute for sand in concrete block mixtures, focusing on its effects on compressive strength and water absorption. LDPE is a non-biodegradable plastic waste that poses significant environmental challenges. Its incorporation into construction materials offers a promising solution to reduce pollution while enhancing the performance of building components. The research employed LDPE substitution levels of 10%, 15%, 20%, 25%, and 30% by weight of sand, compared against conventional concrete blocks without LDPE. Experimental results revealed that the highest compressive strength was achieved with a 15% LDPE mixture, reaching 80.762 kg/cm² at 28 days of curing—an increase of approximately 40.8% compared to normal blocks, which recorded 57.359 kg/cm². LDPE additions up to 20% maintained favorable strength characteristics, while higher proportions (25% and 30%) led to a decline in mechanical performance. In terms of water absorption, the inclusion of LDPE demonstrated a decreasing trend, attributed to the hydrophobic nature of plastic, which enhances moisture resistance in the concrete blocks. These findings suggest that a 15% LDPE substitution represents an optimal formulation for producing eco-friendly concrete blocks with improved strength and reduced water absorption. The study highlights the dual benefits of waste management and material innovation, aligning with sustainable development goals. By repurposing plastic waste into construction applications, this approach not only mitigates environmental impact but also contributes to the advancement of green building technologies. Further research is recommended to explore long-term durability, thermal properties, and scalability of LDPE-based concrete products in real-world construction settings.
Exploring the Integration of Information Systems and Security Challenges in Afghanistan’s Current Market Sayed Zakariya Habib; Mohammad Ali Fahimi; Mir Mohammad Naim Sadat
International Journal of Information Engineering and Science Vol. 2 No. 4 (2025): November : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i4.323

Abstract

This study aims to investigate the integration of information systems and the associated security challenges within Afghanistan's current market, emphasizing the complex relationship between technological innovation, governance stability, and institutional readiness. Using the Delphi method, the study engaged experts from academia, government, and the private sector to identify key barriers and enablers shaping Afghanistan's digital transformation. Findings reveal that the country's progress in adopting information systems is hindered by fragmented policies, weak cybersecurity awareness, infrastructure limitations, and dependency on donor-funded projects. Despite growing recognition of the importance of digitalization, Afghanistan's institutional fragility continues to impede coordinated implementation and sustainable innovation. Comparative insights with other emerging markets highlight that long-term investment in digital literacy, regulatory coherence, and private sector engagement are critical to overcoming these barriers. The study highlights the importance of adopting a hybrid developmental model that harmonizes local institutional realities with internationally recognized technological standards, fostering adaptability and resilience within Afghanistan's volatile environment. It advances existing understanding by demonstrating how governance reform, human capital enhancement, and cybersecurity integration function as mutually reinforcing components of the nation's digital transformation. Sustainable progress depends on establishing a unified national vision that bridges technology, education, and governance, thereby reinforcing market integrity and institutional stability amid persistent security and economic uncertainty.
Customer Data Management Analysis for Customer Segmentation Using K-Means Clustering Method Andre Leto; Reza Aminullah; Ani Dijah Rahajoe
International Journal of Information Engineering and Science Vol. 2 No. 4 (2025): November : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i4.345

Abstract

This study aims to examine customer segmentation through K-Means clustering from a customer data management perspective, emphasizing the interpretive value of analytical results rather than solely their computational outcomes. The research addresses a critical issue in contemporary data-driven organizations, where customer analytics is often reduced to technical modeling without sufficient translation into managerial insights. To respond to this gap, the study adopts a qualitative interpretive approach embedded within a quantitative clustering process, positioning clustering as part of a broader information management cycle. The empirical analysis is based on the Mall Customers Dataset obtained from Kaggle, consisting of 200 customer records with numerical attributes representing age, annual income, and spending score. Quantitative processing using K-Means clustering was employed to identify customer segments, while qualitative interpretation was applied to analyze the managerial meaning of each cluster. Data interpretation was supported by analytical documentation, visualization outputs, and reflective analysis of cluster characteristics. The findings reveal four distinct customer segments with different behavioral and economic profiles, each carrying specific strategic implications for customer relationship management and marketing decision-making. The study demonstrates that the primary value of clustering lies not merely in segment formation, but in its ability to transform raw customer data into actionable managerial knowledge. In conclusion, this research contributes to customer analytics literature by integrating data mining techniques with qualitative interpretation, offering a more human-centered and decision-oriented framework for customer data management. Future research is encouraged to extend this approach using organizational case studies or participatory decision-making contexts.
Humanist Librarians in the AI Era : Maintaining Human Values in Information Services Azwar Azwar
International Journal of Information Engineering and Science Vol. 2 No. 4 (2025): November : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i4.327

Abstract

The development of artificial intelligence (AI) technology has brought about a major transformation in the world of libraries and information services. Automation, data analysis, and AI-based recommendation systems have increased the efficiency and accessibility of information for users. These advances also pose new challenges for librarians, particularly in maintaining human values ​​in the service process. Humanist librarians in the AI ​​era are required not only to understand technology but also to maintain an ethical, empathetic, and communicative role in interactions with users. This research uses a literature review to address the questions raised. Librarians, as mediators between technology and humans, act as bridges between digital literacy and ethical information, maintaining warmth and empathy in library services. By prioritizing human values ​​such as empathy, responsibility, and information justice, librarians can ensure that the application of AI in libraries remains oriented toward human needs and does not diminish the essence of civilized service.
Satisfaction Level Analysis QRIS Users Based on Experience and Perception Twitter Users/X Using Naive Baiyes Veri Arinal; Satria Wira Yudha; Muhammad Joko Umbaran Kharis Bahrudin; Dessyanti Ryantina
International Journal of Information Engineering and Science Vol. 2 No. 4 (2025): November : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i4.53

Abstract

QRIS (Quick Response Code Indonesian Standard) has become a widely used national digital payment standard. User satisfaction with this service needs to be monitored continuously to ensure its sustainability. This study aims to predict the level of QRIS user satisfaction based on their experiences and perceptions expressed organically on the Twitter social media platform. The method used is sentiment analysis with the Naive Bayes classification algorithm implemented using RapidMiner software. The research data was obtained from Twitter user comments collected through web scraping techniques. The text data then went through a preprocessing stage that included cleansing, stopword filtering, stemming, and tokenizing to be prepared as features ready to be processed by the model. The data was divided into training (80%) and testing (20%) subsets for model training and validation. The results showed that the Naive Bayes model was able to predict user satisfaction sentiment with an accuracy of 80.99%. These findings indicate that the model is highly accurate in identifying satisfied comments and sufficiently sensitive in detecting dissatisfaction. This study concludes that sentiment analysis of Twitter UGC data using Naive Bayes is an effective and efficient approach for predicting QRIS user satisfaction in real time. The practical implication of this study is to provide an automatic feedback system for service providers to monitor public sentiment and take targeted corrective actions.
Sentiment Analysis of the Kabur Aja Dulu Trend on X as a Basis for Designing a Public Sentiment Monitoring System Using Naïve Bayes and SVM Sutisna Sutisna; Tri Wahyudi; Dwi Swasono Rachmad; Fachrur Rozi
International Journal of Information Engineering and Science Vol. 2 No. 3 (2025): August : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i3.79

Abstract

Social media X (Twitter) has become the main platform for the Indonesian public to express opinions, including on the trend of 'kabur aja dulu' (let's just run away for a bit). This research aims to classify the sentiments of the public using the Naïve Bayes and Support Vector Machine (SVM) methods, and to compare the accuracy of both in sentiment analysis. Data was collected via the Twitter API with the hashtag #kaburajadulu, resulting in 2,067 tweets, which, after the cleansing process and manual labeling, left 385 data points. The analysis process followed the CRISP-DM stages, which include business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Model evaluation was conducted using a confusion matrix with accuracy, precision, and recall metrics. The classification results show that 82% of tweets have a positive sentiment and 18% negative. The Naïve Bayes algorithm achieved an accuracy of 86.49%, slightly lower than SVM, which reached 88.05%. In conclusion, Support Vector Machine is more effective in sentiment classification on public opinion data. This research contributes to the digital mapping of public opinion and recommends the development of automatic labeling methods as well as the exploration of advanced algorithms in the future.
Design of a Web-Based Instagram Content Management System to Support Brand Awareness for SR12 Herbal Cosmetics Products Untung Surapati; Agus Tanti Rahayu; Tatinia Arda Rizqi Amalia; Lusi Noviani
International Journal of Information Engineering and Science Vol. 3 No. 1 (2026): February : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i3.83

Abstract

PT. SR12 Herbal Cosmetics is a company engaged in the field of herbal and skin care. Founded in 2015 by Toni Firmansyah, S. Farm., Apt. and Asrianty Salam, S. Farm. This company has a vision to provide benefits to many people through the herbal and skin care products they produce. SR12 Herbal Cosmetics products are formulated based on research from certified scientists, and have been tested at the Sucofindo Laboratory, are free of mercury and hydroquinone, and have been registered with the Indonesian Food and Drug Supervisory Agency (BPOM RI). SR12 Herbal Cosmetics has several factories in West Java Province and has an extensive distribution network with hundreds of distributors and tens of thousands of partners throughout Indonesia. The goal to be achieved is to produce a management information system model including a management information system for PT SR12 Herbal Cosmetics. The research object chosen is a company in the field of cosmetics and skin care which has its head office in Gunung Sindur, West Java. This selection aims to form a management information system design model that is able to produce relevant and timely information for planning, controlling, decision making and evaluating the performance of activities. For the Web-Based Instagram Content Management Information System Design project to Support SR12 Herbal Cosmetics' Brand Awareness, I used Agile (Scrum) due to the dynamic nature of digital marketing and potential changes to the Instagram API or business needs. This allowed SR12 to get core functionality faster and provide iterative feedback, ensuring the system built was truly relevant to their brand awareness needs.
Sentiment Analysis of the Performance of the Legal System in Indonesia Based on Twitter Comments Using the Naïve Bayes Algorithm Rasiban Rasiban; Dadang Iskandar Mulyana; Muhammad Joko Umbaran Kharis Bahrudin; Nicola Marthy
International Journal of Information Engineering and Science Vol. 2 No. 2 (2025): May : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i2.84

Abstract

The development of social media, especially TWITTER, has become one of the main means for people to express opinions and criticism on various issues, including the performance of law in Indonesia. This study aims to analyze public sentiment towards the performance of law based on TWITTER user comments using the Naïve Bayes algorithm. The research data consists of 1004 comments collected from several videos related to legal topics. The analysis process includes the stages of data crawling, pre- processing (text cleaning, normalization, and tokenization), labeling sentiment into positive, negative, and neutral, and testing the Naïve Bayes model. The results show that the Naïve Bayes algorithm is able to classify sentiment with an accuracy level of 93.73%. The distribution of sentiment from 1004 comments shows that the majority of public opinion is (negative/positive/neutral), which indicates that public perception of the performance of law is still (critical/positive). These findings are expected to be input for related parties to understand public opinion and improve the quality of legal performance in Indonesia

Page 3 of 4 | Total Record : 33

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