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Sirojul Hadi
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sirojulhadi@universitasbumigora.ac.id
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jurnal.bite@universitasbumigora.ac.id
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Jalan Ismail Marzuki, Nomer 22, Cilinaya, Cakranegara, Mataram, NTB
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INDONESIA
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 6 Documents
 1 
Search results for , issue "Vol. 7 No. 2 (2025)" : 6 Documents clear
Pengujian Efektivitas Intrusion Detection Systems (IDS) Snort,Suricata, dan Zeek terhadap Serangan SYN Flood tection System Snort, Suricata, dan Zeek dalam Mendeteksi Serangan SYN Flood pada Windows Server 2022 Arya Wirianda, I Nyoman Bagus; Huwae, Raphael Bianco; Jatmika, Andy Hidayat
Jurnal Bumigora Information Technology (BITe) Vol. 7 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v7i2.5226

Abstract

Background: Network security is an essential aspect of IT infrastructure management, with the main threat beingDenial-of-Service (DoS) attacks, particularly SYN Flood attacks.Objective: The purpose of this study is to evaluate the effectiveness of three Intrusion Detection Systems (IDS), namelySnort, Suricata, and Zeek, in detecting TCP SYN Flood attacks. The testing environment uses Windows Server 2022 asthe target system to simulate real-world conditions on a production network.Methods: This study employs an experimental method comprising the following stages: problem identification, analysis,design/development, implementation, testing, and results analysis.Result: This study shows that Snort performs best in attack detection, with an average of 68.25%, followed by Suricata at61.08% and Zeek at 55.77%. In terms of CPU usage, Snort also leads with an average of 16.3%, while Suricata and Zeekuse 24.5% and 21.7%, respectively. For RAM usage, Snort recorded an average of 18.2%, followed by Zeek at 16.6% andSuricata at 24.5%.Conclusion: This study concludes that Snort is superior in network detection and CPU efficiency. At the same time,Zeek is more efficient with RAM usage, while Suricata has average performance and the highest resource usage.   
Pengukuran Tingkat Kesadaran Keamanan Informasi pada Mahasiswa di Lingkungan Universitas Mataram dengan Metode HAIS-Q Shelbila, Fisabil Arum; Ariyan, Zubaidi; Raphael, Bianco Huwae; Arum, Shelbila Fisabil; Zubaidi, Ariyan; Huwae, Raphael Bianco
Jurnal Bumigora Information Technology (BITe) Vol. 7 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v7i2.5430

Abstract

Background: Information security is an important issue in the digital age, especially among students who actively usetechnology in both academic and non-academic activities.Objective: This study aims to determine students’ level of awareness of information security at Mataram University.Methods: The study uses the Human Aspects of Information Security Questionnaire (HAIS-Q) model, which coversthree main dimensions: knowledge, attitude, and behavior, as well as six focus areas of information security. Data wascollected through an online questionnaire distributed to active students. Analysis was conducted by calculating the averagepercentage for each dimension and domain.Result: The results indicate that the overall level of awareness among students falls into the “moderate” category. Theattitude dimension achieved the highest score at 80.01%, followed by behavior at 79.52%, and knowledge at 79.49%. TheInformation Handling and Password Management areas demonstrated strong awareness, while Internet Use had the lowestscore, particularly in the knowledge aspect.Conclusion: These findings indicate that while students have positive attitudes, their application in daily life remainssuboptimal. 
Prediksi Penjualan Jamur Tiram Menggunakan Regresi Linear Sederhana Abdilah, Hilman Fauji; Effendi, Diana
Jurnal Bumigora Information Technology (BITe) Vol. 7 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v7i2.5572

Abstract

Background: One of the primary challenges in oyster mushroom cultivation is the imbalance between production capacityand market demand, as business owners struggle to forecast sales accurately.Objective: This study aims to test the predictive power of simple linear regression for white oyster mushroom sales at theSME level.Methods: This study uses a simple linear regression model, using 20 months of historical sales data, split into trainingand test sets at 80:20, with time as the predictor variable.Result: The evaluation resulted in a Mean Absolute Error (MAE) value of 775,203, Root Mean Squared Error (RMSE)of 813,411, and Mean Absolute Percentage Error (MAPE) of 13.05%, which is categorised as good.Conclusion: This study contributes to the literature on agricultural commodity forecasting, particularly oyster mushrooms,by demonstrating the relevance of simple linear regression. These findings have implications for accurate productionplanning and reducing the risk of overproduction.
Media Augmented Reality Berbasis Permainan Tradisional untukMeningkatkan Literasi Numerasi Siswa Putri, Iin Karmila; Nur Asri, Muh Fitra; Anas, Aswar
Jurnal Bumigora Information Technology (BITe) Vol. 7 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v7i2.5706

Abstract

Background: Elementary school learning still faces challenges in providing meaningful, contextual learning experiencesthat foster students’ reasoning skills.Objective: This study aimed to design and develop an augmented reality–based mathematics learning medium thatintegrates traditional Luwu games, Madende and Maguli, to enhance elementary students’ numeracy literacy.Methods: A needs analysis was conducted through classroom observations, teacher interviews, and student questionnaires.Numeracy elements were identified from the rules of the traditional games. The learning media were designed andimplemented using Unity with image-based tracking technology. Expert validation and a limited trial were conducted with25 fifth-grade students.Result: The developed product was rated as highly feasible, with an average validity score of 85%. Students’ mean scoresincreased from 62.4 to 80.6, yielding a normalized gain score of 0.48. User responses were positive, and teachers assessedthe media as suitable for classroom use.Conclusion: The integration of Augmented Reality with local cultural contexts supports numeracy learning and has thepotential for broader adoption following performance optimization and further comparative testing. 
Analisis Sentimen Ulasan Mobile Banking Syariah Berbasis NLPuntuk Evaluasi dan Peningkatan Kualitas Layanan Digital Assani, Moh. Yushi; Rudiyan, Ari; Radiyah, Ummu
Jurnal Bumigora Information Technology (BITe) Vol. 7 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v7i2.5943

Abstract

Background: The development of Islamic mobile banking services in Indonesia requires improvements in the qualityof digital service management that focuses on user experience. User reviews on application platforms are an importantsource of data for understanding user perceptions, satisfaction, and problems encountered. The use of Natural LanguageProcessing enables systematic and automatic sentiment analysis to support the evaluation of Islamic banking servicequality.Objective: This study aims to analyze the sentiment of user reviews of sharia mobile banking applications and comparethe performance of the Na¨ıve Bayes sentiment analysis model as a classic model and IndoBERT as a transformer-basedmodel.Methods: Data was obtained through web scraping of 1,052 user reviews of the Bank NTB Syariah Mobile Bankingapp from the Google Play Store. Preprocessing steps included case folding, cleaning, stopword removal, stemming, andtokenization. Sentiment labeling was based on user rating scores with three categories: positive, neutral, and negative.Model performance was evaluated using accuracy and F1-score metrics.Result: The test results show that the Na¨ıve Bayes model achieved an accuracy of 78% with an F1-score of 0.74. Meanwhile,the IndoBERT model achieved an accuracy of 88% and an F1-score of 0.86. 
Peningkatan Kinerja Klasifikasi Scabies Sapi MenggunakanEdited Nearest Neighbours (ENN) pada Model Random Forestdan XGBoost Ihsan, M. Khaerul; Maulana, Muhammad; Tanwir, Tanwir; Mas’ud, Abi; Hanif, Naufal; Resmiranta, Dading Oktaviadi
Jurnal Bumigora Information Technology (BITe) Vol. 7 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v7i2.6055

Abstract

Background: Scabies disease in cattle causes significant economic losses for farmers due to declines in the animals’physical condition and productivity.Objective: This study aims to evaluate the effectiveness of the Edited Nearest Neighbours (ENN) method in improvingclassification performance for scabies in cattle.Methods: This research employs machine learning methods, including Random Forest and XGBoost. A dataset of 600clinical symptom samples was converted to numerical data and cleaned of noise using the ENN technique.Result: Applying ENN significantly improved the accuracy of both the Random Forest and XGBoost models, increasing itfrom around 0.60 to 0.91. In addition, both models achieved a perfect recall of 1.00, indicating maximum capability todetect positive cases.Conclusion: This study concludes that noise reduction using ENN can produce a more accurate and reliable diagnosticsystem. This method is highly recommended to optimize the performance of classification algorithms on animal clinicaldata with high levels of inconsistency. 

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