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All Journal Jurnal Statistika Universitas Muhammadiyah Semarang Jiko (Jurnal Informatika dan komputer) Jurnal Onoma: Pendidikan, Bahasa, dan Sastra bit-Tech Jurnal Sistem informasi dan informatika (SIMIKA) International Journal of Advances in Data and Information Systems Journal of Advanced in Information and Industrial Technology (JAIIT) Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Nusantara Science and Technology Proceedings Jurnal Teknik Informatika (JUTIF) Djtechno: Jurnal Teknologi Informasi International Journal of Data Science, Engineering, and Analytics (IJDASEA) Jurnal Impresi Indonesia Journal of Renewable Energy, Electrical, and Computer Engineering Buletin Sistem Informasi dan Teknologi Islam Journal of Information Systems and Technology Research Kohesi: Jurnal Sains dan Teknologi Parameter: Jurnal Matematika, Statistika dan Terapannya Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Komtika (Komputasi dan Informatika)
Damaliana, Aviolla Terza
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Hybrid Holt Winter-Prophet method to forecast the num-ber of foreign tourist arrivals through Bali's Ngurah Rai Airport Damaliana, Aviolla Terza; Hindrayani , Kartika Maulida; Fahrudin, Tresna Maulana
IJDASEA (International Journal of Data Science, Engineering, and Analytics) Vol. 3 No. 2 (2023): International Journal of Data Science, Engineering, and Analytics Vol 3, No 2,
Publisher : Universitas Pembangunan Nasional Veteran Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijdasea.v3i2.8

Abstract

The Indonesian is an archipelago rich in culture and natural resources. The Government of Indonesia utilizes this wealth by maximizing the tourism potential to earn sizeable foreign exchange. As a major destination, the Indonesian government needs a strategy to ensure foreign tourists continue to increase in terms of health, cleanliness, a sustainable environment and infrastructure. When we can forecast the number of foreign tourists, it is hoped that the government can establish appropriate policies to develop tourism. Based on this, an appropriate forecasting method is needed. This study will use a hybrid model with the Holt-Winter and the Prophet method. The data used is the number of foreign tourists to Bali through Ngurah Rai Airport from January 2009 to December 2019. This study will use stages based on the OSEMN Framework. These stages are Obtain, Scrub, Explore, Model, and Interpret. The result of this study is that the MAPE value for the Hybrid Method is 2.5880%. This result means the Hybrid Holt Winter-Prophet is better than the Holt Winter Method
Modelling of Return of S&P 500 Using the Non Linear Generalized Autoregressive Conditional Heteroscedasticity (NGARCH) Model Trimono, Trimono; Damaliana, Aviolla Terza; Putri, Irma Amanda
Nusantara Science and Technology Proceedings 8th International Seminar of Research Month 2023
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2024.4110

Abstract

ARIMA Box-Jenkins is one of the most popular forecasting methods. ARIMA modeling requires a non-heteroskedastic care that shows constant residual variants. Awake, meaning residual residue from heteroscedastic ARIMA modeling (not constant). To overcome the problem of residual heteroskedasticity ARIMA used modeling volatility that is Generalized Autoregressive Conditional Heteroscedasticity (GARCH). GARCH is used to model the ARIMA residual variant which means symmetric. Some financial data has an asymmetric nature caused by the influence of good news and bad news. To accommodate these asymmetric properties, we use the Non-Linear Generalized Autoregressive Conditional Heteroscedasticity (NGARCH) volatility model which is the development of the GARCH model. This research applies NGARCH model using S & P 500 share price index data from January 1, 2019, until July 31, 2023 during active day (Monday-Friday). The purpose of this study, to find the best model NGARCH. The best model generated for S & P 500 stock price index data is ARIMA (1,0,1) NGARCH (1,1) because it has small AIC.
FORECASTING THE OCCUPANCY RATE OF STAR HOTELS IN BALI USING THE XGBOOST AND SVR METHODS Damaliana, Aviolla Terza; Muhaimin, Amri; Prasetya, Dwi Arman
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 12, No 1 (2024): Jurnal Statistika Universitass Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.12.1.2024.24-33

Abstract

The hotel occupancy rate indicator has become a concern in recent years as it goes hand in hand with the rapid growth of the global tourism industry. A way to maintain or even improve this indicator is to carry out managerial planning using forecasting methods. The forecasting methods used in this research are XGBoost and SVR. The advantage of this modelling is that it achieves high accuracy and processing speed. Meanwhile, the benefit of SVR is that it will produce good prediction because can overcome overfitting. The steps in this research are exploring data, separating training data and testing data, transforming data, modelling data, forecasting data, and evaluating forecasting results using RMSE, MAE, and MAPE. The results show that MAPE value from both methods is smaller than 10%, which means that both methods can predict the occupancy rate of star hotels in Bali very accurately. Apart from that, the SVR method has smaller values for all model evaluation criteria than the XGBoost method, which means that the SVR method is better than XGBoost for predicting the occupancy rate of star hotels in Bali.
Sentiment Analysis on Digital Korlantas POLRI Application Reviews Using the Distilbert Model Putri, Nabila Rizky Amalia; Trimono, Trimono; Damaliana, Aviolla Terza
Journal of Renewable Energy, Electrical, and Computer Engineering Vol. 4 No. 2 (2024): September 2024
Publisher : Institute for Research and Community Service (LPPM), Universitas Malikussaleh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jreece.v4i2.17197

Abstract

The implementation of digitalization in public services by Korlantas Polri has facilitated faster administration, wider access, and improved service quality. The Korlantas Polri Digital app has garnered more than 5 million downloads on the Google Play Store, with a rating of 3.7 and around 110 thousand reviews. Given that an app's reputation can be significantly affected by criticism, sentiment analysis becomes very important to categorize user reviews as positive, negative, or neutral, thus assisting developers in identifying app shortcomings. This study uses DistilBERT, a deep learning model distilled from BERT, to assess the effectiveness of sentiment analysis on reviews. Data was collected from user reviews on the Google Play Store between September 1, 2023 and May 31, 2024, resulting in 8,752 reviews retained for analysis. Model performance was evaluated at three data ratios: 60:40, 70:30, and 80:20, with the best performance results seen at a ratio of 80:20, achieving 88% accuracy. Increasing the training data ratio from 60:20 to 80:20 has a positive impact on the model, suggesting that the model can learn better with larger training data.
COMPARISON OF DECISION TREE AND RANDOM FOREST METHODS IN THE CLASSIFICATION OF DIABETES MELLITUS Maulidiyyah, Nova Auliyatul; Trimono, Trimono; Damaliana, Aviolla Terza; Prasetya, Dwi Arman
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 2 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8316

Abstract

Diabetes mellitus is a deadly disease caused by the failure of the pancreas to produce enough insulin. Indonesia ranks fifth in the world with the number of people with diabetes in 2021 at around 19.47 million, and this number continues to increase. One of the main challenges in diabetes management is to make the right classification between type 1 and type 2 diabetes, as misdiagnosis can result in inappropriate treatment and worsen the patient's condition. This study uses a machine learning approach to compare Decision Tree and Random Forest methods in classifying type 1 and type 2 diabetes mellitus. The goal is to identify the most effective model in predicting the type of diabetes based on medical record data. The comparison was done using k-fold cross validation and confusion matrix. The results showed that Random Forest provided an average accuracy of 94%, while Decision Tree reached 93% during cross validation testing. Although both models were able to perform well in classification, Random Forest showed a more stable performance and a slight edge in accuracy over Decision Tree. Evaluation with the confusion matrix showed that the Decision Tree model achieved 93% accuracy compared to Random Forest's 91%. In addition, the Decision Tree model also had a lower number of prediction errors, 7, compared to 9 for Random Forest. The most influential variables in classification also differed between the two models, showing the unique advantages and characteristics of each approach.
EMPLOYEE VOLUNTARY ATTRITION PREDICTION AT PT.XYZ: ENSEMBLE MACHINE LEARNING APPROACH WITH SOFT VOTING CLASSIFIER Bey Lirna, Cagiva Chaedar; Trimono, Trimono; Damaliana, Aviolla Terza
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2007

Abstract

This research addresses the complexity of employee attrition challenges at PT.XYZ. The main objective is to develop a predictive system for potential voluntary employee attrition by focusing on an in-depth analysis of the factors contributing to attrition at PT.XYZ. The research utilizes data containing information on the job history of PT.XYZ employees from 2018 to 2023. The method employed in the research is a soft voting ensemble classifier model, incorporating SVM, decision tree, and logistic regression, supported by relevant literature. Analysis and exploration of historical data of PT.XYZ employees are conducted to identify key factors influencing employees' decisions to leave the company. Careful data preprocessing is carried out to ensure dataset quality before applying it to the soft voting classifier model. The results of the soft voting classifier modeling used in this research achieve excellent accuracy in both training and testing datasets with respective accuracy percentages of 99% and 98%. Based on the final results of applying the soft voting classifier model, it is expected to provide deep insights and solutions to enhance employee retention at PT.XYZ.
Penerapan Metode T2 Hotelling untuk Menganalisis Faktor Jumlah Penduduk Miskin Jawa Timur Kurniawan, Muhammad Erlangga; Ananta, Aditya Putra; Anugrah, Muhammad Cahya Raka; Wara, Shindi Shella May; Damaliana, Aviolla Terza
Kohesi: Jurnal Sains dan Teknologi Vol. 7 No. 8 (2025): Kohesi: Jurnal Sains dan Teknologi
Publisher : CV SWA Anugerah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.3785/kohesi.v7i8.12215

Abstract

Kemiskinan merupakan permasalahan utama di kehidupan masyarakat di suatu wilayah, khususnya di Jawa Timur. Penelitian ini bertujuan untuk menganalisis faktor yang memengaruhi jumlah penduduk miskin di Jawa Timur dengan menggunakan metode T2 Hotelling, pendekatan statistika multivariat yang membandingkan perbedaan dua kelompok data. Data yang digunakan adalah Tingkat Partisipasi Angkatan Kerja (TPAK), Indeks Pembangunan Manusia (IPM), dan Jumlah Penduduk Miskin (JPM) pada tahun 2022 dan 2023, yang diperoleh dari Badan Pusat Statistika (BPS) Jawa Timur. Sebelum analisis dilakukan, kami menguji normalitas, homogenitas, dan independensi untuk memastikan bahwa data memenuhi asumsi dalam analisis multivariat. Hasil uji menunjukkan bahwa uji normalitas belum terpenuhi, sehingga dilakukannya transformasi box-cox agar data dapat memenuhi asumsi yang dibutuhkan. Uji T2 Hotelling mengindikasikan bahwa adanya perbedaan signifikan antara data tahun 2022 dan 2023, yang menunjukkan bahwa adanya faktor yang memiliki dampak terhadap perubahan jumlah penduduk miskin. Kemudian, uji Paired t-test menunjukkan bahwa TPAK 2022 menunjukkan perbedaan paling signifikan dari variabel lainnya. Hasil penelitian ini menunjukkan bahwa TPAK dan IPM berperan utama dalam perubahan jumlah penduduk miskin di Jawa Timur. Oleh karena itu, diperlukan evaluasi kebijakan yang berfokus dalam peningkatan akses pendidikan, ketenagakerjaan, serta peningkatan kualitas hidup masyarakat agar dapat menurunkan angka kemiskinan secara efektif.
ANALISIS KEJAHATAN DI INDONESIA PADA DATA TAHUN 2022-2023 MENGGUNAKAN UJI T² HOTELLING’S Kaffi, Laisal; Maulana, Mohammad Hikmal; Wiyono, Farhan Syah Putra; Wara, Shindi Shella May; Damaliana, Aviolla Terza
Kohesi: Jurnal Sains dan Teknologi Vol. 7 No. 8 (2025): Kohesi: Jurnal Sains dan Teknologi
Publisher : CV SWA Anugerah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.3785/kohesi.v7i8.12216

Abstract

Kejahatan adalah suatu perbuatan atau tindakan yang dapat menimbulkan masalah-masalah dan mengakibatkan dampak negatif bagi kehidupan masyarakat. Tingginya angka kejahatan di suatu wilayah dapat memengaruhi stabilitas sosial serta ekonomi, sehingga diperlukan analisis mendalam untuk memahami pola kejahatan yang terjadi. Penelitian ini menggunakan data Statistik Kriminal 2024 dari Badan Pusat Statistik (BPS) yang mencakup laporan kejahatan di Indonesia pada tahun 2022 dan 2023. Dengan menerapkan Uji T² Hotelling’s dan Uji t-Paired, analisis dilakukan untuk mengidentifikasi perubahan signifikan antar tahun pada berbagai kategori kejahatan. Hasil penelitian menunjukkan adanya peningkatan signifikan dalam kejahatan kesusilaan, hak milik dengan kekerasan, narkoba, serta penipuan dan korupsi, sementara kejahatan terhadap nyawa mengalami sedikit penurunan. Secara keseluruhan, rata-rata jumlah kejahatan di berbagai kategori mengalami kenaikan, mencerminkan peningkatan tingkat kejahatan di beberapa wilayah. Studi ini menunjukkan bahwa analisis multivariat dapat menjadi pendekatan alternatif dalam memahami tren kejahatan secara komprehensif, sehingga dapat membantu penegak hukum dalam merumuskan kebijakan yang lebih strategis dan efektif.
PREDIKSI PERMINTAAN DARAH DI UTD KOTA SURABAYA MENGGUNAKAN METODE HYBRID ARIMA-ANFIS Oktaviani, Sheny Eka; Trimono, Trimono; Damaliana, Aviolla Terza
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 6 No. 1 (2025): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v6i1.938

Abstract

Blood supply is a crucial aspect for UTD which must meet the demand for blood for those who need it. UTD Surabaya City faces challenges in meeting blood needs caused by the uncertainty of blood demand which varies and is individualized according to the recipient's clinical condition which has an impact on the quality of UTD Surabaya City services, thus creating challenges in meeting blood needs optimally. Therefore, it is necessary to predict blood demand to assist UTD Surabaya City in ensuring adequate blood stock, planning the blood stock needs that will be requested, and avoiding stock overstocks and stock shortages. To overcome this, blood demand is predicted using the Autogressive Moving Average (ARIMA) and Adaptive Neuro Fuzzy Inference System (ANFIS) approaches. This combination of the ARIMA-ANFIS method combines the advantages of ARIMA in capturing linear patterns and ANFIS in handling non-linear patterns from ARIMA residuals. The prediction results from the ANFIS model will be added to the prediction results from the ARIMA model to obtain a hybrid ARIMA-ANFIS model. The ARIMA-ANFIS model is used to predict the number of blood requests by combining ARIMA predictions and residuals modeled using ANFIS. This process includes stationarity analysis, selecting the best ARIMA model, residual modeling with ANFIS, as well as performance evaluation using MAPE to ensure prediction accuracy. The best ARIMA (6,1,0) model was obtained with the lowest AIC value of -153.838, then from the ARIMA modeling results the residuals were obtained as input for ANFIS modeling. Analysis shows that the ARIMA-ANFIS hybrid model has better performance, with a MAPE value of 5.28%, compared to the ARIMA model which only achieved a MAPE of 6.21%.
Implementing GCV and mGCV to Determine Optimal Knot in Spline Regression for East Java Life Expectancy Lestari, Amanda Ayu Dewi; Damaliana, Aviolla Terza; Prasetya, Dwi Arman
International Journal of Advances in Data and Information Systems Vol. 6 No. 2 (2025): August 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i2.1379

Abstract

Life Expectancy is a vital indicator for evaluating population’s overall welfare and health status within a specific region. According to data published by Badan Pusat Statistik (BPS) National, East Java Province ranks 10th nationally in terms of life expectancy in 2024, with male life expectancy recorded at 70.39 years and female life expectancy at 74.4 years. This research focuses on examining four key factors that are believed to influence life expectancy in East Java during the 2024 including the Percentage of the Poor Population (X1), the Percentage of Individuals Aged 5 and Above Who Regularly Smoke Tobacco (X2), the Expected Years of Schooling (X3), and the Open Unemployment Rate (X4). To determine the optimal knot points in the nonparametric truncated spline regression model, the study utilizes Generalized Cross-Validation (GCV) and the modified Generalized Cross-Validation (mGCV) methode by minimizing their respective error values. The findings indicate that all four variables significantly impact life expectancy. Among the methods applied, the mGCV approach demonstrates good performance, achieving the lowest error value of 0.100 and a coefficient of determination of 82.91%.
Co-Authors Agustina, Fadlila Amri Muhaimin Ananta, Aditya Putra Anas Ahmadi Anugrah, Muhammad Cahya Raka Bey Lirna, Cagiva Chaedar Diyasa, I Gede Susrama Mas Dwi Arman Prasetya Fahrudin, Tresna Maulana Febriyanti, Dea Putri Pascha Hindrayani , Kartika Maulida idhom, Mohammad Illah, Ibnu Zahy' Atha Ilmatus Sa'diyah Isyanto, Aisyah Kirana Putri Jauharis Sapu, Wahyu Syaifullah Kaffi, Laisal Kurniawan, Muhammad Erlangga Lestari, Amanda Ayu Dewi Maulana, Mohammad Hikmal Maulidiyyah, Nova Auliyatul Mohammad Idhom Muhaimin, Amri Muhammad Nasrudin Muizzadin, Muizzadin Nabilah Selayanti Nugroho, Muhammad Rafli Feandika Nurhalizah, Cesaria Deby Oktaviani, Sheny Eka Permadani, Citra Amelia Intan Pradipta, Alvino Hadiyan Putri, Adinda Rusdianti Maulani Putri, Irma Amanda Putri, Maretta Fairuz Luthfia Winoto Putri, Nabila Rizky Amalia Putri, Shafira Amanda Ramadhani, Dafinah Riyantoko, Prismahardi Aji Rizky Mahendra, Rakha Trimono Trimono, Trimono Wara, Shindi Shella May Windyadari, Chrysilla Citra Wiyono, Farhan Syah Putra Zahwa, Aniq Farichatus