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Contact Name
Sirojul Hadi
Contact Email
sirojulhadi@universitasbumigora.ac.id
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+6287852771884
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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 162 Documents
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Analisis Prediktif Pembatalan Reservasi Hotel MenggunakanRandom Forest Berbasis PySpark untuk Mendukung KeputusanIndustri Perhotelan Vellisya Afifa Qonita; Siti Laila Nurjannah; Indira Sistamarien; Silvia Ariani Daulay; Gema Parasti Mindara; Aditya Wicaksono
Jurnal Bumigora Information Technology (BITe) Vol. 8 No. 1 (2026): In-Press
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v8i1.6492

Abstract

Background: Hotel booking cancellation may cause financial losses and reduce hotel operational effectiveness.Objective: This study aims to analyze the factors influencing hotel reservation cancellations and develop a cancellationprediction model to support decision-making in the hospitality industry.Methods: The research method used is CRISP-DM (Cross Industry Standard Process for Data Mining) with a Big Dataand Machine Learning approach based on PySpark. The dataset used is the Hotel Booking Demand Dataset consisting of 119,390 reservation records. Data processing stages include data cleaning, duplicate data removal, categorical data encoding, feature assembling, and model development using Spark MLlib. The algorithms used in this study are Logistic Regression as a baseline model and Random Forest Classifier as the main prediction model.Result: The results show that Random Forest achieved the best performance with an Accuracy of 78.42%, an F1 Scoreof 76.83%, and a ROC AUC of 80.04%. Based on feature importance analysis, the most influential factors affectingreservation cancellation are lead time, market segment, and total special requests.Conclusion: The developed model can be used as a basis for implementing reservation risk scoring, enabling hotels to identify high-risk bookings and formulate more effective cancellation mitigation strategies
Sistem Pakar Deteksi Dini Preeklamsia Menggunakan Hybrid Fuzzy Tsukamoto Dan Certainty Factor Fachruddin Fahma Khoiri; Fatimatuzzahra; Khen Dedes; Zilvanhisna Emka Fitri; Nadzirotul Fitriyah
Jurnal Bumigora Information Technology (BITe) Vol. 8 No. 1 (2026): In-Press
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v8i1.6579

Abstract

Background: Preeclampsia is one of the pregnancy complications that remains a leading cause of maternal and infant mortality in Indonesia, making its early detection critically important. However, manual screening for preeclampsia still faces limitations in terms of time, availability of medical personnel, and subjectivity in symptom interpretation. Objective: This study aims to develop a web-based expert system to detect the risk of preeclampsia in pregnant women. Methods: This study applying a hybrid method combining Fuzzy Tsukamoto and Certainty Factor (CF). The Fuzzy Tsukamoto method is used to process numerical clinical data, such as systolic and diastolic blood pressure and urine protein levels, while the Certainty Factor method is used to represent the confidence level of subjective symptoms reported by patients, such as severe headache and visual disturbances. Result: Testing results show that the system is able to produce diagnoses consistent with expert  assessments across all tested case studies. For instance, in one case study, the system produced a diagnosis of Severe Preeclampsia with a confidence level of 82.04%, closely matching the expert's confidence level of 80%, with a difference of only 2.04%.

Page 17 of 17 | Total Record : 162

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