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All Journal JURNAL SISTEM INFORMASI BISNIS IT JOURNAL RESEARCH AND DEVELOPMENT Pena Justisia: Media Komunikasi dan Kajian Hukum TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Journal of Law, Poliitic and Humanities Malcom: Indonesian Journal of Machine Learning and Computer Science Science, Technology, and Communication Journal Scientific Journal of Informatics INOVTEK Polbeng - Seri Informatika
Okfalisa Okfalisa
Department of Information Technique, FST, UIN SUSKA
Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Journal : INOVTEK Polbeng - Seri Informatika

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Application of ARIMA and ARIMAX Methods to Predict the Number of Visitors to Hotel XYZ Pekanbaru Vernando, Julio; Insani, Fitri; Okfalisa, Okfalisa; Kurnia, Fitra
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/enrfna19

Abstract

Predicting the number of visitors to Hotel XYZ is one of the steps that can be taken by the hotel to find out how many visitors will increase in each upcoming holiday season. The purpose of this study is to forecast the number of visitors to Hotel XYZ from June 2023 to July 2024 using the ARIMA and ARIMAX comparison methods. The research methodology encompasses problem identification, data collection, data processing, and ARIMA and ARIMAX analysis, which involves testing the parameters (p, d, q) selected based on the ACF and PACF using the AIC Model. Based on the test results, ARIMAX (5, 0, 3) has the lowest AIC, which is 3495.2, followed by ARIMAX (3, 0, 5), which has a slightly higher AIC. The results showed that the ARIMAX (5, 0, 3) model is the most accurate model for predicting data (eg the number of hotel guests, room demand, or income), with an RMSE value of 15.80% and a MAPE of 18.90%. Therefore, research that applies the ARIMAX model can provide real benefits in supporting operational efficiency, resource management, and hotel business strategy, ultimately increasing the competitiveness and profitability of the hotel.
Application of ADASYN Technique in Classification of Stroke Disease using Backpropagation Neural Network zikrillah aulia, said rizki; okfalisa, okfalisa; haerani, elin; oktavia, lola
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/jdhv9s39

Abstract

The high prevalence of stroke in Indonesia and the challenge of imbalanced medical record data are major obstacles to the development of an accurate early detection system. This research aims to build a reliable stroke classification model by applying the ADASYN (Adaptive Synthetic Sampling) oversampling technique to address class imbalance before the data is processed using the Backpropagation Neural Network (BPNN) algorithm. The ADASYN technique is applied with the goal of reducing the bias that arises from the imbalanced data distribution between the majority and minority classes. Testing was conducted through various data splitting scenarios (70:30, 80:20, 90:10) and hyperparameter variations to find the optimal configuration. The best results were obtained with the 90:10 data split scheme, using an architecture of 29 neurons and a learning rate of 0.01, which successfully achieved peak performance with an accuracy of 90.46% and an F1-score of 91.03%. This study demonstrates that the combination of ADASYN and BPNN is a highly effective approach for producing a stroke prediction model that is not only accurate but also sensitive to the minority class, thus having great potential as an early detection support tool in the healthcare sector.
Co-Authors Ahmad, Sayyidina Anshari Al-Sa’ban, M Dafa Alfitri, Rahmat Aslati Aslati Azhar Azhar Belantara, M Abyan Che Hussin, Ab Razak Delifah, Nur Elin Haerani Erlina, Marni Fachry Abda El Rahman Fadhilah Syafria Fahruddin Fahruddin Finaldhi, Harry Fitri Insani Haerani, Elin Haerani Iswavigra, Dwi Utari kurnia, fitra Libra, Robert Luh Kesuma Wardhani Maulana, Muhammad Rizky Muslimin, Al’hadiid Najwa, Nina Fadilah Nazruddin Safaat H Ningrum, Melia Vivi Oktavia, Lola Pizaini Pizaini Pranggono, B. Pratama, M Farhan Aulia Rahmad Abdillah Robert Libra Ruri Hartika Zain Rusnedy, Hidayati Saktioto Saktioto Santana, Qistan Alif Setiaji, Haris Setiawan, Adil Simaremare, Harris Simaremare Siti Ummi Masruroh Surya Agustian Tarmizi, Veci Cahyono Vernando, Julio Yusra, Yusra zikrillah aulia, said rizki