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Sirojul Hadi
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Jurnal Bumigora Information Technology (BITe)
Published by Universitas Bumigora
ISSN : 26854066     EISSN : 26854066     DOI : https://doi.org/10.30812/bite
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering
Jurnal Bumigora Information Technology (BITe) is one of the journals owned at Bumigora University which is managed by the Department of Computer Science. This journal is intended to provide publications for academics, researchers and practitioners who wish to publish research in the field of information technology and computer science. BITe Journal is published in 2 (two) periods, namely in June and December. The focus and scope of the BITe journal are Fuzzy Logic Control, Internet of Things, Wireless Sensor Network, Artificial Intelligence, Machine Learning and Deep Learning, Business Intelligence, Mobile Computing and Application, Data Mining, Cloud and Grid Computing, Computer Network and Security, Computer Vision, Geographical Information System (GIS), Semantic Web
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 152 Documents
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Analysis of Rclone Implementation for File Sharing Security on OneDrive Cloud Storage Widyawati, Lilik; Azwar, Muhammad; Alafi, Hairudin; Asif, Iqra
Jurnal Bumigora Information Technology (BITe) Vol. 6 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v6i1.4082

Abstract

Background: OneDrive provides a reliable infrastructure for storing and managing user data. However, along with the benefits, the security challenges associated with storing and sharing files in the cloud are also increasing. One of the main aspects of cloud storage security management is the file-sharing process. While OneDrive offers a few security features, such as access control and two-factor authentication, there remains a need to strengthen additional layers of security, especially when sharing files with external parties.Objective: This research aims to analyze the effectiveness of Rclone in improving file-sharing security on OneDrive, especially File Backup and File Encryption.Methods: This research uses the Network Development Life Cycle (NDLC) method, which includes six stages. This research focuses on three stages: analysis, design, and simulation prototyping.Result: The application of Rclone for file sharing security on OneDrive can increase data security in cloud storage, as evidenced by three backup attempts on .doc files with an average time of 2.033 seconds and successful encryption of 4 files with an average time of 37 seconds.Conclusion: Using Rclone as a tool to manage data security on OneDrive improves data protection and provides flexibility and efficiency in file management, making it an effective solution for file sharing security needs in cloud storage
Implementation of Multi-Attribute Utility Theory (MAUT) Method in Gaming Laptop Selection S, Wahyu Ardiantito; Ramadhani, Suci; Jesika, Stacyana
Jurnal Bumigora Information Technology (BITe) Vol. 6 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v6i1.4083

Abstract

Background: This time, laptops are an essential piece of technology because these instruments can be used for various tasks, including work, play, and education.Objective: This research aims to develop a decision support system using the MAUT method for selecting gaming laptops.Methods: The method used in this research is MAUT which is applied to the selection of gaming laptops.Result: Based on calculations that have been carried out using the MAUT method, it is found that the MSI Cyborg Katana laptop (B13VGK) is the first ranked gaming laptop with a utility value of 0.7035.Conclusion: The implementation of MAUT for gaming laptop selection is proven to be applicable.
Implementation of the Multi Attribute Utility Theory (MAUT) Method for Selecting the Best Affiliate Marketing Mubarok, Wahib; Efendi, Tino Feri; Rokhmah, Siti
Jurnal Bumigora Information Technology (BITe) Vol. 6 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v6i1.4088

Abstract

Background: Digital marketing has become crucial for companies to remain relevant and grow. One common marketing strategy used is through collaboration with affiliate marketing. However, many companies still need help selecting the best affiliate marketing, often relying on subjective approaches. Objective: This research aims to develop a Decision Support System (DSS) using the MAUT method to choose the best affiliate marketing in the digital marketing industry. Method: This research method involves applying the MAUT method to identify normalization value variations among affiliate marketing options, resulting in a ranking of affiliate marketers. Result: The results of this research show that Sugiharto, Rifan Jauhari, and Nina Dwi Lestari rank the highest, with respective preference values of 0.8750 and 0.8625. Conclusion: The developed DSS successfully manages affiliate marketing data, providing valuable information for decision-making processes. This study contributes significantly to data management and marketing and has potential applications in various business contexts.
Feasibility and Performance Evaluation of Microprocessor and Fuzzy Logic-Based Overcurrent Relay in Electrical Protection Systems Parada D.P, Bima Romadhon; Megawati, Citra Dewi; Nurcahyo, Eko; Hidayat, Taufik; Setiawan, Rachmadi; Latif, Kurniadin Abd
Jurnal Bumigora Information Technology (BITe) Vol. 6 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v6i1.4101

Abstract

This research focuses on the development and testing of a microprocessor-based Overcurrent Relay (OCR) with the implementation of fuzzy logic control for adaptive setting of TMS values. OCR is a crucial component in the electricity protection system which aims to detect and respond to disturbances due to excessive current, preventing damage to electrical equipment. The aim of this research is to test the feasibility and performance of microprocessor-based OCR with fuzzy logic control in setting TMS values, and compare it with the international standard IEC 60255. The method used in this research is the implementation of the Sugeno type fuzzy logic algorithm on the Atmega 328P microcontroller. This algorithm is used to control the TMS value based on the rotating frequency of the detected overcurrent. The results of this research are a characterization of the OCR response to variations in streaming current and different PS settings. The lowest TOp deviation was observed at higher PS settings, with the best performance in the IDMT OCR type. The use of fuzzy logic control is able to increase the OCR response to overcurrent disturbances, although under normal conditions it sometimes results in a longer TOp. The conclusion of this research is that microprocessor-based OCR with fuzzy logic control can provide protection performance that is adaptive and responsive to dynamic operational environmental conditions. This implementation has the potential to increase the efficiency and leakage of the electricity protection system
Determining a Digital Marketing Strategy Using a Combination of Analytical Network Process (ANP) and Profile Matching Dakwah, Muhammad Mujahid; Roodhi, Mohammad Najib; Suprayetno, Djoko; Kusmayadi, Iwan; Abdurrahman, Abdurrahman; Hammad, Rifqi
Jurnal Bumigora Information Technology (BITe) Vol. 6 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v6i1.4125

Abstract

Background: Many MSMEs in the city of Matram are experiencing diculties in determining the digital marketing strategy to use. This is due to the many digital marketing strategies that can be used and the many factors that serve as criteria for selection.Objective: Develop a decision support system using a combination of ANP and profile-matching methods to assist MSMEs in determining the digital marketing strategy to be used.Methods: The method used in this research is a combination of ANP and Profile Matching Methods.Result: The combination of methods (ANP) and Profile Matching in determining digital marketing strategies has an accuracy of 83.33%.Conclusion: The combination of ANP and Profile Matching methods in determining digital marketing strategies has successfully recommended the best digital marketing strategy.
Improving Large Language Model’s Ability to Find the Words Relationship Alam, Sirojul; Abdul Jabar, Jaka; Abdurrachman, Fauzi; Suharjo, Bambang; Rimbawa, H.A Danang
Jurnal Bumigora Information Technology (BITe) Vol. 6 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v6i2.4127

Abstract

Background: It is still possible to enhance the capabilities of popular and widely used large language models (LLMs) such as Generative Pre-trained Transformer (GPT). Using the Retrieval-Augmented Generation (RAG) architecture is one method of achieving enhancement. This architectural approach incorporates outside data into the model to improve LLM capabilities. Objective: The aim of this research is to prove that the RAG can help LLMs respond with greater precision and rationale. Method: The method used in this work is utilizing Huggingface Application Programming Interface (API) for word embedding, store and find the relationship of the words. Result: The results show how well RAG performs, as the attractively rendered graph makes clear. The knowledge that has been obtained is logical and understandable, such as the word Logistic Regression that related to accuracy, F1 score, and defined as a simple and the best model compared to Naïve Bayes and Support Vector Machine (SVM) model. Conclusion: The conclusion is RAG helps LLMs to improve its capability well.
Classification of Atopic Dermatitis and Psoriasis Skin Diseases Using Residual Network (ResNet-50) Mekacahyani, Rakhimatulfitria; Badie’ah, Badie’ah; Much Ibnu Subroto, Imam
Jurnal Bumigora Information Technology (BITe) Vol. 6 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v6i2.4164

Abstract

Background: Atopic dermatitis and psoriasis are common skin diseases with similar symptoms, characterized by abnormally red or inflamed epidermal lesions and varying degrees of skin thickening. However, they are distinct conditions, making it crucial to understand how to differentiate between them. This understanding can help reduce stigma and the risk of comorbidities, thereby improving patients' quality of life and preventing more serious health risks. Objective: The aim of this research is to increase accuracy in classifying the skin diseases atopic dermatitis and psoriasis using the Residual Network (ResNet-50) model without overfitting, and compare it with the MobileNet model to find the best approach. Method: The method used in this study is the ResNet-50 architecture for skin disease classification, namely atopic dermatitis and psoriasis. The selection of the ResNet-50 model is based on the use of shortcut connections that allow the application of deeper networks without experiencing the problem of vanishing gradients. Result: The results showed that the best accuracy reached 92.75% for training data and 88.00% for testing data, with a data ratio of 80%:10%:10%. In addition, the confusion matrix results from the best model showed that the precision, recall, and F1 score values ​​for both diseases were between ≥80% and ≤96%. Conclusion: The ResNet-50 method in scenario 1 outperformed other scenarios, improving classification accuracy and enhancing diagnostic effectiveness and medical practice development.
Detection of Lumpy Disease in Livestock Using the MobileNetV2 Architecture Method Putra, Dion Pratama; Wahyu Wiriasto, Giri; Paniran, Paniran
Jurnal Bumigora Information Technology (BITe) Vol. 6 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v6i2.4401

Abstract

Background: Lumpy Skin Disease (LSD) causes skin lesions, decreased milk production, and death in livestock such as cows. Objective: The purpose of this study is to detect LSD disease quickly and accurately using the Convolutional Neural Network (CNN) MobileNetV2 method based on android application. Method: This study uses a quantitative method with a reuse-oriented development approach and the MobileNetV2 algorithm trained with augmentation data and LSD disease image classification. Result: The results of this study are that the MobileNetV2 classification model is able to detect LSD with an accuracy of 95.91%. The developed application makes it easier for farmers to detect diseases early so that they can accelerate preventive measures. Conclusion: The implications of this study indicate that the MobileNetV2 model can improve the effectiveness of disease detection in livestock and can be applied in animal health applications in the field.
The Klasifikasi Suara Paru-Paru Menggunakan Mel Frequency Cepstral Coefficient dan Convolutional Neural Network Halim, Sayyidis Syariful; Kanata, Bulkis; Akbar, Syamsul Irfan
Jurnal Bumigora Information Technology (BITe) Vol. 6 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v6i2.4487

Abstract

Background: Challenges in diagnosing respiratory disorders are often caused by the lack of technological tools capable of accurately recognizing lung sound patterns, thereby reducing the potential for subjective misdiagnosis by medical personnel. Objective: This study aims to develop a lung sound classification model that is able to detect respiratory disorders early and accurately. Methods: The method used includes a combination of data augmentation techniques and Mel Frequency Cepstral Coefficient (MFCC) feature extraction to improve the performance of Convolutional Neural Network (CNN) in classifying lung sounds. A total of 1,350 lung audio recordings were categorized into nine classes, including normal and abnormal sounds. The augmentation techniques applied include the addition of white noise, pitch scaling, time stretching, and random gain to enrich the variety of training data. Result: The results show that the E-CNN2D model is able to achieve an accuracy of up to 95%, surpassing the previous model, which had an accuracy range of 83-93%. Conclusion: With these results, this study has the potential to be a fast and accurate diagnostic tool solution so that it can support medical personnel in reducing the risk of subjective misdiagnosis in respiratory disorders.
KosyFinder E-Commerce System Based on Web Geographic Information System and Expert System Hardiana, Hardiana; Siaulhak, Siaulhak; Jumardi, Andi; Iriansa, Iriansa
Jurnal Bumigora Information Technology (BITe) Vol. 6 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v6i2.4493

Abstract

Background : The current problem of students and the community in searching for boarding house information only relies on information from friends, relatives or manual searches in searching for boarding house availability. Currently, the availability of information service provider platforms for boarding houses is now abundant, especially those based on websites or android-based. Boarding house information services based on websites or android-based only provide general information related to boarding house information, have not been able to provide comprehensive information. Objective : The purpose of this study is the KosyFinder e-commerce platform integrated with the Web Geographic Information System (WebGis) and Expert System which can help users in choosing a boarding as needed. Method : The research method used is Research and Development using the waterfall development model and the certainty factor (CF) method in diagnosing the health of the residential environment around the boarding house. Result : The results of this study were able to produce an e-commerce application product KosyFinder that was able to be integrated with WebGis and expert systems. The results of expert testing and user testing obtained a final value of 3.8 (very suitable), which means that the resulting system can be used. Conclusion : The platform that develops is functioning properly and as expected in accordance with the results of expert and user testing in the category is very appropriate.

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