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All Journal Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) International Journal Software Engineering and Computer Science (IJSECS)
Raisah Fajri Aula
Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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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

Abstract

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

Abstract

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.
Penerapan IoT dalam Sistem Monitoring Suhu dan Kelembapan pada Lahan Bawah Tanah (Basement) Masjid Al-Barkah Tundo; Anisah Nurul Azhar; Kiki Setiawan; Raisah Fajri Aula
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 1 (2025): JANUARI-MARET 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i1.3199

Abstract

Underground areas, commonly referred to as basements, are often used for essential functions such as parking and electrical distribution spaces. However, unstable temperature and humidity levels due to poor air circulation can affect comfort and safety. Therefore, a system capable of automatically monitoring and controlling temperature and humidity is needed to optimize comfort and energy efficiency. This research employs an Internet of Things (IoT) approach using a DHT11 sensor to detect temperature and humidity in the basement. The data collected by the sensor is processed using a NodeMCU ESP32 microcontroller and then displayed in real-time on a web-based application via the cloud. The system also automatically controls the fan/blower to maintain ideal conditions in the basement. The results of this research show that the implemented IoT system demonstrates high effectiveness in monitoring temperature and humidity in real-time, providing accurate data, enabling energy savings by automatically regulating the fan/blower, and improving air quality and user comfort in the basement.
Co-Authors Aditya Zakaria Hidayat Akbar, Yuma Anisah Nurul Azhar Eka Putri Aprillia Kiki Setiawan Muhamad Zaeni Nadip Sri Lestari Tundo