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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 5 Documents
 1 
Search results for , issue "Vol. 13 No. 4 (2025): April: Computer Science and Field." : 5 Documents clear
Implementation of e-crm (electronic customer relationship management) in improving the quality of service at 3 Saudara motorcycle wash Amran, Ali; Muawanah, Siti; Wati, Mala Ayu Setia; Nisa, Yustia Fitrotul; Aprilia, Wahyu Nur
Jurnal Mandiri IT Vol. 13 No. 4 (2025): April: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i4.374

Abstract

The purpose of this research effort is to critically examine the application of electronic customer relationship management (E-CRM) practices in improving service quality in Micro, Small, and Medium Enterprises (MSMEs). The methodological approach of this research is qualitative, using data collection techniques such as observation, interviews, and data collection. shows that the implementation of E-CRM can improve service quality through features such as online reservations, customer data management, and feedback systems. Key findings show that E-CRM facilitates queue management, speeds up service processes, and increases customer loyalty. The implications of this study highlight the importance of technology-based customer relationship management for SMEs to increase competitiveness and customer satisfaction.
Classification of mushroom types based on digital image processing using convolutional neural network Sari, Ira Puspita; Elvitaria, Luluk
Jurnal Mandiri IT Vol. 13 No. 4 (2025): April: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i4.387

Abstract

In this research, a classification of mushroom types based on digital image processing using a Convolutional Neural Network (CNN) is conducted. The method employs the EfficientNet-B4 architecture as the base model utilizing transfer learning and fine-tuning processes. The dataset consists of 3000 types of mushrooms, each categorized into 10 classes with 300 images per class. The CNN model is implemented using the Python programming language on Google Colab editor. Performance evaluation is carried out using accuracy, precision, recall, and F1-Score metrics to measure the model's performance. A comparison is made between all models with various training parameters, including identical and different settings. Additionally, the ratio of data splits, whether identical or different, is considered. Model 1, which utilizes a custom freeze layer and a data split ratio of 80% for training, 10% validation, and 10% testing, achieved the highest accuracy (90.00%), precision (90.09%), recall (89.63%), and F1-Score (89.59%) compared to other models. Therefore the implementation of a custom freeze layer to reduce the$ number of trainable parameters significantly impacts the accuracy level of the trained and tested model. Moreover, the determination of the data split ratio also slightly influences the accuracy level of the trained and tested model.
Integration of artificial intelligence in cyber security systems to counter quantum computing threats Ekowati, Maria Atik Sunarti; Poernomo, Moyo Hady; Nindyatama, Zefanya Permata
Jurnal Mandiri IT Vol. 13 No. 4 (2025): April: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i4.388

Abstract

With the rapid advancement in quantum computing, threats to cybersecurity systems are increasingly complex, especially in terms of encryption and data protection. The integration of artificial intelligence (AI) into cybersecurity systems is essential to address these challenges. This study aims to examine the potential of AI in improving the detection and mitigation capabilities of threats arising from the quantum computing revolution. The urgency of this research is driven by the prediction that existing cryptographic algorithms will be easily cracked by quantum computers, raising the need for more adaptive and dynamic security systems. The method used in this study is a simulation approach using machine learning algorithms to model and identify cyber threat patterns specific to quantum computing. The results show that AI-based systems can detect attacks faster and with higher accuracy compared to conventional systems. The output of this research is the development of a security system prototype that combines artificial intelligence and post-quantum security technologies, which can be implemented in various cyber applications to ensure more effective data protection in the quantum computing era.
Comparison of decision tree and naive bayes methods in glioma classification based on clinical and molecular factors Dewi, Ni Wayan Emmy Rosiana; Putra, I Made Suwija; Simanungkalit, Erwinsyah; Manoppo, Franky Gerald Cliford
Jurnal Mandiri IT Vol. 13 No. 4 (2025): April: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i4.389

Abstract

This study compares the performance of Decision Tree and Naive Bayes classifiers in classifying gliomas based on clinical and molecular factors. The dataset consists of 839 patient records with features including Grade, Gender, Age, Race, and gene mutation status. The evaluation showed that the Decision Tree classifier achieved 98% accuracy on the training data and 76% on the test data, while the Naive Bayes classifier obtained 74% and 71% accuracy, respectively. Both models demonstrated strong predictive ability, with feature importance analysis highlighting the IDH1 gene mutation as a significant factor in glioma classification. This study aims to identify the most effective method for supporting clinical decision-making in glioma diagnosis. It contributes to the development of medical decision support systems and provides insight into the application of machine learning models, particularly in utilizing molecular markers such as IDH1.
Facial image protection with visual cryptography and random least significant bit (LSB) steganography Karo Karo, Panser; Simarmata , Simon; Faizah, Novianti Madhona; Fabrianto, Luky
Jurnal Mandiri IT Vol. 13 No. 4 (2025): April: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i4.392

Abstract

The confidentiality of sensitive data—such as personal information of individuals who may pose security threats to client assets—must be strictly maintained. This data includes personal details such as name, ID number, address, date of birth, occupation, and photographs (images). The data protection process involves combining textual data (ID number, name, date of birth) with a photo into a single image, which is then processed using visual cryptography. The visual cryptography technique applied is the (k, n) scheme with a 2-out-of-k configuration. To enhance data security and confidentiality through dual-layer protection, the output from the visual cryptography process is further secured using steganography with the random LSB (Least Significant Bit) method, applied to one of the shares obtained from the previous step. The best result achieved during testing was a PSNR of 71.9977 and an MSE of 0.0041. It is expected that the combination of visual cryptography and steganography methods will significantly enhance the security of data storage to protecting it from unauthorized access.

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