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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 164 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%.
Chatbot Berbasis AI untuk Pengelolaan Dokumen SPBE:Pendekatan Hybrid Forward Chaining dan Transformers Ery Setiyawan Jullev Atmadji; Ricky Dwi Setyawan; Nanik Anita Mukhlisoh
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.v8i1.6347

Abstract

Background: Digital government transformation requires efficient and integrated management of SPBE documents usingartificial intelligence (AI).Objective: To develop an AI chatbot to solve the problems of manual searches, human error, and difficulties in extractingtext from complex PDF documents in SPBE.Methods: The study combined Forward Chaining (for structured questions) and the IndoBERTQA Transformer model(for open-ended questions). Text was extracted using PyPDF2. The evaluation used 150 question-context-answer datapairs and two Forward Chaining scenarios.Result: Forward Chaining produced fast and fixed responses, while IndoBERTQA provided more contextual answers(Cosine Similarity of 0.730). However, the low F1-score (0.316) shows that prediction accuracy is still a major weaknessand needs priority improvement. The system successfully sped up information access, although it still struggles withextracting tables and non-text elements.Conclusion: Integrating both methods provides a good balance between the chatbot’s speed and flexibility. Furtherdevelopment on dataset quality, model optimization, and document extraction is highly necessary to improve the system’saccuracy.
Prediksi Kelayakan Calon Penerima Beasiswa MenggunakanAlgoritma Logistic Regression Eka Fitri Handayani; Nurmalitasari; Sri Sumarlinda
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.v8i1.6520

Abstract

Background: The scholarship selection process often faces problems because of the large number of applicants that mustbe evaluated based on various criteria. This makes the manual selection process take a long time and can lead to subjectivedecisions.Objective: This research aims to build a prediction model for scholarship eligibility using the Logistic Regression algorithmwith the Bagging technique.Methods: This study uses the CRISP-DM methodology with a dataset of 2,042 student records. The attributes includeGPA, parents’ income, number of dependents, and organizational participation. The data is divided into 80% for trainingand 20% for testing to build and evaluate the model.Result: The research results show that applying the Bagging technique increases accuracy from 82.89% to 83.37%, with aROC AUC of 0.9223. The model was then implemented into a web-based prediction system using Flask.Conclusion: These results show that Logistic Regression with the Bagging technique can be used to support a scholarshipselection process that is faster, more objective, and more consistent. 

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