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INDONESIA
Jurnal Mandiri IT
Published by Institute of Computer Science
ISSN : 23018984     EISSN : 28091884     DOI : https://doi.org/10.35335/mandiri
Core Subject : Science, Education,
Computer Science & IT Library & Information Science Mathematics
The Jurnal Mandiri IT is intended as a publication media to publish articles reporting the results of Computer Science and related research.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 233 Documents
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Web-based project material inventory information system at PT XYZ using the waterfall method Cut Zahra Malahayatinur; Antika Zahrotul Kamalia; Candra Naya
Jurnal Mandiri IT Vol. 15 No. 1 (2026): Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v15i1.550

Abstract

PT XYZ is an enterprise operating in the building contractor sector with a high intensity of material movement. Currently, the warehouse inventory management procedure in the company still relies on conventional methods using a manual physical Logbook. This mechanism triggers various operational obstacles, such as a high risk of handwriting errors (human error), stock discrepancies between physical availability and written balances, and a slow cascade of report recapitulation for management. This study aims to design and develop a Web-Based Project Material Inventory System to digitalize the entire workflow of stock management. The system development method implemented is the Waterfall model, which encompasses requirement analysis, design, implementation, testing, deployment, and maintenance. This computerized application is built using the PHP programming language with the Laravel framework, a MySQL database, and supported by a Tailwind CSS-based interface. System functionality testing was conducted through the Black Box Testing method to ensure all operational modules run according to the system requirements. The final result of this research is a centralized information system capable of managing master data (items, vendors, units, projects), processing incoming and outgoing item transactions with automatic stock calculation, and presenting inventory mutation reports in real-time. The implementation of this system has been proven to reduce warehouse administrative burdens, eliminate the risk of physical damage to paper documentation, and accelerate managerial decision-making processes at PT XYZ.
Sentiment analysis of public opinion on the non-cash food assistance (BPNT) program on platform x using naive bayes Nurul Asma As-shidiq; Donny Maulana; M. Zubair Abdurrohman
Jurnal Mandiri IT Vol. 15 No. 1 (2026): Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v15i1.551

Abstract

The Non-Cash Food Assistance (BPNT) program is one of the Indonesian government's social assistance policies that frequently receives public attention and discussion on social media. This study aims to analyze public sentiment toward the implementation of the BPNT program based on comments posted on Platform X. The dataset consisted of 1,758 Indonesian-language comments collected through web crawling between October 2025 and January 2026. The collected data were processed through several preprocessing stages, including case folding, cleaning, tokenization, normalization, stopword removal, and stemming. Furthermore, TF-IDF was applied to transform textual data into numerical features, and sentiment classification was performed using the Multinomial Naive Bayes algorithm. The dataset was divided into training and testing data using an 80:20 ratio. The results showed that neutral sentiment dominated public discussions with 51.48%, followed by positive sentiment with 33.90% and negative sentiment with 14.62%. Performance evaluation using a Confusion Matrix achieved an accuracy of 79.545%. These findings indicate that the Naive Bayes approach can effectively classify public sentiment regarding the BPNT program and provide useful insights for evaluating social assistance policies.
Attention-enhanced hybrid deep learning for skin cancer diagnosis with hierarchical feature fusion Ahmad Sanmorino; Rendra Gustriansyah; Shinta Puspasari
Jurnal Mandiri IT Vol. 15 No. 1 (2026): Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v15i1.552

Abstract

Skin cancer is one of the most prevalent cancers worldwide, making early and accurate diagnosis essential for improving patient outcomes and reducing mortality. However, automated skin lesion classification remains challenging due to high inter-class similarity, class imbalance, and variations in lesion appearance. This study proposes an attention-enhanced hybrid deep learning for skin cancer diagnosis with hierarchical feature fusion. The proposed framework integrates channel and spatial attention with hierarchical feature fusion to enhance discriminative feature learning and improve classification robustness. Experiments were conducted on the PAD-UFES-20 dataset using image preprocessing and data augmentation. The proposed model achieved approximately 98% training accuracy, 95% validation accuracy, and a validation loss below 0.5, outperforming DRMv2Net, DenseNet201, ResNet101, and MobileNetV2 while demonstrating faster convergence and stronger generalization. These findings demonstrate the potential of hybrid attention and hierarchical feature fusion to improve the reliability and robustness of artificial intelligence-based diagnostic systems in dermatology, supporting more effective clinical decision-making and early skin cancer screening.

Page 24 of 24 | Total Record : 233

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