Garuda - Garba Rujukan Digital

Public Sentiment Analysis on the Inauguration of President Prabowo Subianto on Platform X Using the Support Vector Machine (SVM) Algorithm Rosalina Saputri; Sri Lestari
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 1 (2025): APRIL 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i1.3787

The inauguration of President Prabowo Subianto emerged as a pivotal political event that captured significant public interest and sparked a wide array of reactions across social media, particularly on the X platform (formerly known as Twitter). This research aims to categorize and analyze public sentiment regarding this historic moment by utilizing the Support Vector Machine (SVM) algorithm, a robust machine learning approach for classification tasks. A dataset comprising 1,000 tweets was initially gathered through targeted searches related to the inauguration. Subsequently, the data underwent a rigorous preprocessing phase, which included tokenization to break down text into individual components, stopword removal to eliminate irrelevant terms, filtering to exclude special characters and noise, and Term Frequency-Inverse Document Frequency (TF-IDF) transformation to convert textual data into a numerical format suitable for algorithmic processing. After preprocessing, 909 data points were prepared for further analysis. The dataset was then divided into two subsets: 80% allocated for training the model (727 data points) and 20% reserved for testing its performance (182 data points). The results of sentiment classification indicated that, among the test data, 653 tweets conveyed a positive sentiment toward the inauguration, whereas 74 tweets expressed a negative sentiment. Performance evaluation of the model demonstrated a commendable accuracy rate of 89.82%, alongside a precision of 89.82%, a recall of 100%, and an F1-score of 94.63%. These metrics highlight the model’s strong capability to accurately discern and classify public opinions related to political developments. The elevated recall rate, in particular, signifies the model’s ability to identify all instances of positive sentiment without omission. However, the precision score suggests some room for refinement in reducing misclassifications. The findings underscore the effectiveness of the SVM algorithm in dissecting and interpreting consumer sentiment toward significant political events. This provides a reliable tool for such analyses. Moreover, the outcomes of this study are anticipated to offer a valuable reference point for stakeholders and policymakers in leveraging data-driven approaches to gauge public opinion and monitor economic trends in Indonesia. This research also lays the groundwork for future investigations into sentiment analysis within the digital sphere. This could guide strategic communications and policy formulation based on real-time societal feedback

Comparison of Classification of Songket Fabric Types Using AlexNet and VGG19 (Visual Geometry Group) Method Sri Lestari; Nida Apipah
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 1 (2025): APRIL 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i1.3815

This study aims to evaluate and compare the performance between deep learning models AlexNet and VGG19 in Songket fabric classification. Due to its complex patterns and subtle differences, Songket classification must be accurate. The datasets in this study are various types of Songket images and all datasets are classified by type for easy analysis. After intensive learning and evaluation, VGG19 is a superior classifier than AlexNet. The highest performance is achieved by the VGG19 method in terms of performance measure accuracy, precision, recall, and F1 score, which may be due to the increase in depth and better extraction of some detailed visual features from complex images. Although these results have substantial practical implications, some issues need to be further discussed before optimizing the results. Hyperparameters, such as learning rate or batch size, can be changed to optimize the speed and accuracy of the model. In addition, the diversity of the data should be increased by using data augmentation techniques to ensure that the model generalization to market conditions is possible. More complex additions (lighting changes, texture distortion simulation, or others) can also contribute to improving the robustness of the trained model to these disturbances. The conclusion of the research is the importance of improving the accuracy and usefulness of single fabric classification. This will result in its application in heritage preservation and textile development.

Automatic Purchase Order Classification Using SVM in POS System at Skus Mart Sri Lestari; Muhamad Zaeni Nadip; Yuma Akbar; Aditya Zakaria Hidayat; Raisah Fajri Aula
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.4564

In retail business processes, decision-making regarding Purchase Order PO submissions often remains manual and subjective, creating risks that impede procurement efficiency. The study develops an automatic classification model to predict PO approval status using Support Vector Machine SVM algorithm integrated within Point of Sale POS systems. Historical purchase transaction data was obtained from SKUS Mart POS database containing 133 entries, including attributes such as item quantity, purchase price, previous stock levels, and total purchase amounts. The research applies CRISP-DM methodology, encompassing business understanding, data exploration, preprocessing normalization using StandardScaler, model training, evaluation, and deployment phases. The model was trained using linear kernel and validated through holdout technique with 80:20 ratio for training and testing. Test results demonstrate that the SVM model achieves 76.69% accuracy, 82.21% precision, 76.69% recall, and 78.51% F1-score. The model was implemented in a web-based POS system CodeIgniter 3 combined with Python scripts to generate automatic classifications displayed directly in the user interface. Although the model demonstrates adequate performance, the study has not compared its effectiveness against other machine learning algorithms such as Random Forest or K-Nearest Neighbor. These findings establish initial groundwork for machine learning integration to support decision automation in procurement systems.

Implementation of a Chatbot Using the Waterfall Method to Improve Helpdesk Service Efficiency at IT Consulting Companies Sri Lestari; Eka Putri Aprillia; Raisah Fajri Aula
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5207

PT XYZ is a company engaged in information and communication technology services, supporting customers' digital transformation. The effectiveness of helpdesk services plays a crucial role in maintaining operations and fostering customer relationships. However, the issue reporting process is still handled manually through platforms such as WhatsApp and email, causing several problems, including inefficient ticket management, delays in ticket number assignment, and limited historical data. This study developed a chatbot based on Microsoft Copilot Studio to automate ticket creation, supported by Power Apps to address the lack of two-way communication features, aiming to support Customer Relationship Management (CRM) efforts. The system was developed using Waterfall methodology. The results showed significant improvements in service efficiency: the previous average initial response time of 2 days, 19 hours, and 13 minutes was eliminated due to automatic ticket number assignment; the average issue resolution time decreased from 5 days, 6 hours, and 20 minutes to 42 minutes; and ticket history search time improved from 14 minutes to 2 seconds. The chatbot successfully accelerated the reporting process, enhanced data recording, and reduced the workload of the helpdesk team. This solution significantly improved helpdesk efficiency and strengthened customer engagement.

Implementation of a Chatbot Using the Waterfall Method to Improve Helpdesk Service Efficiency at IT Consulting Companies Sri Lestari; Eka Putri Aprillia; Raisah Fajri Aula
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5207

PT XYZ is a company engaged in information and communication technology services, supporting customers' digital transformation. The effectiveness of helpdesk services plays a crucial role in maintaining operations and fostering customer relationships. However, the issue reporting process is still handled manually through platforms such as WhatsApp and email, causing several problems, including inefficient ticket management, delays in ticket number assignment, and limited historical data. This study developed a chatbot based on Microsoft Copilot Studio to automate ticket creation, supported by Power Apps to address the lack of two-way communication features, aiming to support Customer Relationship Management (CRM) efforts. The system was developed using Waterfall methodology. The results showed significant improvements in service efficiency: the previous average initial response time of 2 days, 19 hours, and 13 minutes was eliminated due to automatic ticket number assignment; the average issue resolution time decreased from 5 days, 6 hours, and 20 minutes to 42 minutes; and ticket history search time improved from 14 minutes to 2 seconds. The chatbot successfully accelerated the reporting process, enhanced data recording, and reduced the workload of the helpdesk team. This solution significantly improved helpdesk efficiency and strengthened customer engagement.

Implementation of an AppSheet-Based Sales and Inventory Information System at Wijaya Baru Store Sri Lestari; Yara Raslita; Mesra Betty Yel
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 3 (2026): DECEMBER 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i3.8233

Modern small and medium retail enterprises increasingly rely on digital information systems to streamline operational workflows and improve data accuracy. Toko Wijaya Baru previously conducted sales recording, inventory tracking, and financial reporting using conventional manual paper ledgers. This manual approach created substantial operational vulnerabilities, including calculation errors, delayed information retrieval, stock discrepancy risks, and potential data loss. This study aims to design, develop, and implement an integrated sales and inventory information system utilizing the AppSheet no-code platform to enhance operational efficiency, data reliability, and administrative accuracy. Data collection was conducted through qualitative observation, in-depth interviews, and document analysis, while the system development followed an iterative prototyping methodology to align functional modules with actual operational requirements. The system integrates an AppSheet user interface with Google Sheets as a cloud-based relational database backend, incorporating core features for master product management, sales transaction processing, restocking administration, automated real-time stock recalculation via dynamic formulas, and automated report generation. System validation through Black Box Testing demonstrated that all functional modules operate successfully without discrepancies. The post-implementation findings confirm that the system eliminates manual record redundancies, accelerates transaction throughput, prevents stock miscalculations, and provides structured sales visibility. Ultimately, this implementation demonstrates that an AppSheet-based no-code architecture serves as a practical, cost-effective, and scalable digital transformation solution for retail store management.

Implementation of a Web-Based Inventory System to Support Operations at Hanbai Photocopy Nandini Ladivina Sari; Sri Lestari; Mesra Betty Yel
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 3 (2026): DECEMBER 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i3.8308

Hanbai Photocopy is a micro-enterprise providing photocopying and office stationery retail services that requires structured inventory management to maintain seamless daily operations. Previously, inventory tracking relied entirely on paper-based ledger records, which frequently resulted in recording discrepancies between physical stock and written logs, slow search retrieval, and time-consuming report compilation. This study aims to design and implement a web-based inventory management system to support operations at Hanbai Photocopy through centralized and automated data administration. The research adopted a qualitative case study methodology, collecting operational data through direct field observation, semi-structured interviews with the business owner, and literature review. System development followed the classical Waterfall lifecycle, encompassing requirements analysis, system design, implementation, testing, and maintenance. The application was constructed using Google Apps Script as the execution platform and Google Sheets as the relational cloud database, enabling a cost-effective serverless architecture without independent hosting requirements. Functional validation was conducted through black box testing across key functional requirements, including master data management, transaction logging, automated stock validation, role-based access control, and activity auditing. The results demonstrate that all planned test scenarios executed successfully, effectively preventing invalid stock deductions and enabling real-time inventory adjustments. The operational deployment facilitates structured stock tracking, mitigates manual recording errors, and streamlines periodic reporting, thereby improving operational efficiency at Hanbai Photocopy.

Customer Segmentation Analysis Using the K-Means Method on Order Data from Aisyah Catering MSME Aisha Yulia; Sri Lestari; Mesra Betty Yel
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 3 (2026): DECEMBER 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i3.8324

Aisyah Catering is a Micro, Small, and Medium Enterprise (MSME) in the catering service sector that has conventionally recorded order transactions manually. Consequently, the business owner faces difficulties in comprehensively understanding customer order patterns to formulate targeted marketing strategies. This study aims to segment Aisyah Catering's customers based on order volume using the K-Means algorithm. The research methodology follows the CRISP-DM framework, initiating with the collection of 197 transaction records digitized from physical receipts, followed by data preprocessing using Altair AI Studio to yield 187 valid records. In the modeling phase, K-Means was applied to the order quantity (number of portions) attribute as the sole quantitative variable in the clustering process. The determination of the optimal number of clusters was conducted through the evaluation of values from the Elbow Method and the Davies-Bouldin Index (DBI). The results demonstrate that k=3k=3 was selected as the optimal number of clusters with a DBI value of 0.449. The three resulting segments are: the small-scale segment (49.2% of customers, averaging 360.11 portions), the medium-scale segment (33.7% of customers, averaging 717.78 portions), and the large-scale segment (17.1% of customers, averaging 1,045.31 portions). A notable finding emerges regarding volume contribution: the large-scale segment, despite having the fewest members, accounts for 29.9% of the total order volume—nearly equal to the contribution of the small-scale segment which has nearly three times as many members. Thus, the results of this segmentation serve as a foundational basis for Aisyah Catering in designing marketing strategies, raw material procurement planning, and tailored service delivery aligned with the characteristics of each customer segment.

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