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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi BAREKENG: Jurnal Ilmu Matematika dan Terapan J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika KOMPUTA : Jurnal Ilmiah Komputer dan Informatika M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika Jurnal Varian International Journal of Advances in Data and Information Systems Majalah Ilmiah Matematika dan Statistika (MIMS) Jurnal Abdimas Indonesia : Jurnal Abdimas Indonesia International Journal of Data Science, Engineering, and Analytics (IJDASEA) Indonesian Community Journal eProceedings of Engineering
Regita Putri Permata
Telkom University
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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RAINFALL FORECASTING WITH AN INTERMITTENT APPROACH USING HYBRID EXPONENTIAL SMOOTHING NEURAL NETWORK Permata, Regita Putri; Muhaimin, Amri; Hidayati, Sri
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0457-0466

Abstract

Rainfall forecasting is crucial in agriculture, water resource management, urban planning, and disaster preparation. Traditional approaches fail to capture complicated and intermittent rainfall patterns. The “Hybrid Exponential Smoothing Neural Network” is introduced in this study to handle intermittent rainfall forecasting issues. Exponential Smoothing, an established approach for discovering underlying patterns and seasonal fluctuations in time series data, is combined with Neural Networks, which are good at capturing complex linkages and nonlinearities. Using these two methods, this model hopes to deliver a complete rainfall forecasting solution that accounts for short-term changes and long-term patterns. This research uses residuals from the exponential smoothing model and is modeled using a Neural Network. The residual input is transformed using rolling mean. The results show that the hybrid model is able to capture patterns well, but there are still patterns that experience time lag. Experimental results obtained reveal that the hybrid methodology performs better than the model exponential smoothing, implying that the proposed model hybrid synergy approach can be used as an alternative solution to the rainfall time series forecasting. The results show that the Hybrid method can form patterns better than individual exponential smoothing models or neural networks. The RMSSE values for all areas are 1.0185, 1.55092, 1.0872.
Comparative Analysis Of Neural Network Model Selection And Data Transformation For Rainfall Forecasting Permata, Regita Putri; Dani, Andrea Tri Rian
Mathline : Jurnal Matematika dan Pendidikan Matematika Vol. 10 No. 3 (2025): Mathline : Jurnal Matematika dan Pendidikan Matematika
Publisher : Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/mathline.v10i3.763

Abstract

The selection of input models in neural networks significantly influences predictive accuracy in time series forecasting. This study evaluates different input models for neural networks in rainfall prediction using data from the Wonorejo Reservoir, Surabaya. The neural network inputs are determined based on significant lags identified through the Partial Autocorrelation Function (PACF) and ARIMA models. Simulation results indicate that the best Feed Forward Neural Network (FFNN) model utilizes PACF-derived input lags and is trained using the Rprop+ algorithm with a logistic activation function. Meanwhile, the optimal Deep Learning Neural Network (DLNN) model employs the Rprop- algorithm with a logistic activation function. The best-performing model for rainfall forecasting, based on the lowest Root Mean Squared Error of Prediction (RMSEP), is the FFNN model with an (8,4,1) architecture. To further refine the model, we applied a stepwise selection process to eliminate non-significant lag inputs. However, results show that this optimization had no substantial impact, as RMSEP increased after the stepwise procedure.
Rainfall Forecasting using Spatio-Temporal and Neural Network Study Case: Meteorological Data of Madura Island Savira, Ryanta Meylinda; Permata, Regita Putri; Alifah, Amalia Nur; Setiawan, Yohanes; Putra, Adzanil Rachmadhi
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.35091

Abstract

Rainfall forecasting is crucial in meteorological studies due to its significant impact on sectors such as agriculture, which is the main livelihood on Madura Island. This study aims to forecast rainfall on Madura Island using a hybrid approach that combines the Generalized Space-Time Autoregressive-X (GSTARX) model and Neural Network (NN). The data used consist of daily rainfall records from Bangkalan, Sampang, Pamekasan, and Sumenep, covering the period from January 2013 to December 2023. Data from January 2013 to September 2023 were used for training, while data from October to December 2023 were used for testing. The GSTARX model was employed to capture spatio-temporal patterns, while the NN was applied to learn the non-linear relationships in the residuals. The results show that the GSTARX model effectively captures rainfall patterns, though some differences remain compared to the actual data, with RMSE values of Bangkalan (1.514), Sampang (0.256), Pamekasan (0.477), and Sumenep (0.127). Meanwhile, the hybrid GSTARX-FFNN model achieved improved forecasting performance in Sampang (0.392), Pamekasan (0.679), and Sumenep (0.412), although Bangkalan recorded a higher RMSE (1.359). Overall, the GSTARX model proved more effective in forecasting rainfall on Madura Island, delivering smaller and more consistent prediction errors.
Implementasi BERT dan SNA dalam Sistem Tanya Jawab untuk Survei Demam Berdarah Dengue Nadifah, Rofiatun; Syaifullah, Rindra; Almaasah Zatri, Nasywaa; Putri Permata, Regita
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 2 (2025): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v14i2.16488

Abstract

As a global public health challenge, Dengue Hemorrhagic Fever (DHF) necessitates an analysis of public understanding and perception. For controlling the spread of DHF, survey data is instrumental in uncovering these perceptions. This study was conducted to perform an in-depth analysis of the public's understanding of DHF by analyzing textual responses from surveys. The analyzed data consists of answers from 33 respondents to five key questions concerning the definition, symptoms, causes, transmission, and prevention of DHF. The methods used were semantic similarity analysis with a pre-trained BERT model, cosine similarity calculation, and Social Network Analysis (SNA) to identify key respondents as opinion leaders and to map the patterns of understanding dissemination. In this model, a highly consistent level of understanding was obtained, with an F1-Score for the "Very Similar" category reaching 0.94 and a highest cosine similarity value of 0.995.
Daily Rainfall Forecasting with ARIMA Exogenous Variables and Support Vector Regression Permata, Regita Putri; Ni'mah, Rifdatun; Dani, Andrea Tri Rian
Jurnal Varian Vol. 7 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i2.3202

Abstract

There is a seasonal element every year, with the dry season often lasting from May to October and the rainy season lasting from November to April. However, climate change causes the changing of the rainy and dry seasons to be erratic, so it is necessary to anticipate weather conditions. Prediction of rainfall is used to see natural conditions in the future with time series modeling. The rainfall modeling method at the six Surabaya observation posts used is the Autoregressive Integrated Moving Average with exogenous variables (ARIMAX) and Support Vector Regression. The exogenous variable used is the captured seasonal pattern of rainfall. The SVR model uses input lags from the ARIMAX model and parameter tuning uses the Kernel Radial Based Function. Selection of the best model uses the minimum RMSE value. The results showed that the average occurrence of rain at the six rainfall observation posts occurred in January, February, March, April, November and December. The ARIMAX method in this study is well used to predict rainfall in Gubeng and rainfall in Wonorejo. The SVR input lag ARIMAX method is good for predicting rainfall for Keputih, Kedung Cowek, Wonokromo and Gunung Sari. Nonparametric methods are better used to forecast rainfall data because they are able to capture data patterns with greater volatility than parametric methods, one of which is the SVR method.
Peramalan Data Kualitas Udara Menggunakan Multivariat LSTM di Wilayah Kota Surabaya Faradila Efaranti , Inge; Putri Permata, Regita; Ni'mah, Rifdatun
eProceedings of Engineering Vol. 12 No. 5 (2025): Oktober 2025
Publisher : eProceedings of Engineering

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Abstract

Abstrak — Peningkatan polusi udara di wilayah perkotaan, termasuk Kota Surabaya, mendorong perlunya pengembangan model peramalan kualitas udara yang akurat dan adaptif. Penelitian ini bertujuan untuk menganalisis hubungan antara parameter meteorologi dan kualitas udara, serta membangun model peramalan menggunakan metode Long Short-Term Memory (LSTM) berbasis data multivariat. Data yang digunakan diperoleh dari Stasiun Pemantauan Kualitas Udara (SPKU) Kebonsari periode Januari 2022– Desember 2024, dengan parameter suhu udara, kelembapan, dan kecepatan angin sebagai input, serta PM10 dan CO sebagai target.Analisis korelasi dilakukan untuk meng identifikasi pengaruh antar parameter, dan hasilnya menunjukkan hubungan signifikan yang dapat dimanfaatkan dalam peramalan. Model LSTM dibangun dengan pendekatan time series dan dilatih menggunakan arsitektur jaringan yang mampu menangkap pola temporal antar variabel. Evaluasi kinerja mo del dilakukan dengan metrik Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), dan Symmetric Me an Absolute Percentage Error (SMAPE). Hasil evaluasi menunjukkan bahwa model menghasilkan MSE sebesar 113.6211, RMSE sebesar 10.6593, MAPE sebesar 49.45%, dan SMAPE sebesar 28.17%, yang mengindikasikan performa peramalan yang cukup baik. Dengan hasil tersebut, model multivariat LSTM memiliki potensi untuk digunakan sebagai alat bantu dalam pemantauan dan pengendalian kualitas udara oleh instansi terkait di Kota Surabaya. Kata kunci— CO, Kualitas Udara, LSTM, Multivariat, PM10, Peramalan
Segmentasi Mahasiswa Berdasarkan Kesiapan Karir menggunakan Algoritma K-Means dan Visualisasi Interaktif di Telkom University Surabaya Taqhsya Dwiyana , Ananda; Putri Permata, Regita; Ni'mah, Rifdatun
eProceedings of Engineering Vol. 12 No. 5 (2025): Oktober 2025
Publisher : eProceedings of Engineering

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Abstract

Abstrak — Tingginya angka keraguan mahasiswa semester akhir terhadap motivasi dan kompetensi kerja mereka menunjukkan pentingnya evaluasi terhadap kesiapan karir mahasiswa. Pra-survei yang dilakukan di Telkom University Surabaya mengungkap bahwa 78% mahasiswa merasa tidak yakin terhadap motivasi internal mereka, dan 83% meragukan kemampuan mereka untuk bersaing di dunia kerja. Berdasarkan hal tersebut, penelitian ini bertujuan untuk mengelompokkan mahasiswa berdasarkan tingkat kesiapan karir menggunakan algoritma K-Means, serta menyajikan hasilnya dalam bentuk dashboard interaktif. Lima faktor utama yang dianalisis meliputi motivasi, kematangan pribadi, kematangan sosial, sikap kerja, dan kompetensi kerja. Data dikumpulkan melalui kuesioner skala Likert dan dianalisis secara langsung menggunakan algoritma K-Means untuk membentuk kelompok mahasiswa dengan karakteristik kesiapan karir yang serupa. Setelah klaster terbentuk, dilakukan reduksi dimensi menggunakan Principal Component Analysis (PCA) guna memvisualisasikan hasil klaster dalam ruang dua dimensi. Validasi jumlah klaster optimal dilakukan menggunakan metode Elbow dan Silhouette Score. Penelitian ini menghasilkan tiga klaster utama yaitu klaster Siap Kerja, klaster Menuju Siap Kerja, dan klaster Butuh Pembinaan. Visualisasi interaktif melalui Looker Studio membantu dalam memahami karakteristik tiap klaster secara lebih dinamis. Hasil penelitian ini mendukung pengambilan keputusan berbasis data oleh Career Development Center (CDC) dalam merancang program pengembangan karir yang lebih tepat sasaran. Kata kunci— Kesiapan karir, K-Means, segmentasi mahasiswa, PCA visualisasi, dashboard interaktif
Comparative Analysis of ARIMA and Fourier Series Methods for Air Temperature Forecasting in Surabaya Salsabiila, Annas Thasya Haafizhah; Permata, Regita Putri; Hidayati, Sri
International Journal of Advances in Data and Information Systems Vol. 6 No. 3 (2025): December 2025 - International Journal of Advances in Data and Information Syste
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i3.1415

Abstract

Urban climate change, particularly rising temperatures and the Urban Heat Island (UHI) phenomenon, poses challenges for cities like Surabaya, Indonesia. This study compares the forecasting performance of ARIMA and ARIMA-Fourier models using daily air temperature data from 2020 to 2024. The analysis involved stationarity testing, model estimation, and evaluation across four forecasting horizons. ARIMA models (especially ARIMA(0,1,1) and ARIMA(1,1,0)) showed reliable short-term forecasts, but were less effective in capturing seasonal patterns. To address this, Fourier terms were integrated into the ARIMA framework. The ARIMA-Fourier model achieved better accuracy and higher R² values in short- and medium-term forecasts, particularly with an oscillation parameter of k = 150. However, its performance declined in long-term predictions due to overfitting risks. Overall, the ARIMA-Fourier model is more adaptive for capturing complex temperature seasonality and can support more accurate urban climate forecasting in Surabaya.
Passenger and Revenue Estimation for New Rail Transit Lines Under Construction: A Demographic Approach Alifianti, Tarisma Dwi Putri; Ni’mah, Rifdatun; Permata, Regita Putri
International Journal of Advances in Data and Information Systems Vol. 6 No. 3 (2025): December 2025 - International Journal of Advances in Data and Information Syste
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i2.1420

Abstract

This study proposes a data-driven approach to estimate passenger volume and revenue for new rail transit lines under construction, addressing the challenge of limited historical data. Principal Component Analysis (PCA) was used to reduce 29 demographic variables into three principal components, which collectively captured up to 85% of the variance. These components informed a Fuzzy C-Means (FCM) clustering process that grouped new stations with existing ones based on demographic similarity. The clustering yielded a Fuzzy Partition Coefficient (FPC) of 0.913, indicating high cluster validity and low overlap between clusters. Transition probabilities of passenger flows between stations were modeled using Markov Chains. The expanded transition matrix, incorporating new stations through demographic analogy, demonstrated rapid convergence to a stationary distribution within 5–10 iterations, validating the model’s stability. Simulation results project a 57% increase in weekday passengers and a 74% increase in weekend passengers, with estimated daily revenue peaking at Rp1.216 billion. The evaluation results confirm the robustness and reliability of the combined FCM–Markov model for long-term passenger and revenue forecasting in new transit infrastructure planning.
Co-Authors Adzanil Rachmadhi Putra Alifah, Amalia Nur Alifianti, Tarisma Dwi Putri Almaasah Zatri, Nasywaa Amri Muhaimin Andrea Tri Rian Dani Astikhatul Mufaidah Dani, Andrea Tri Rian Faradila Efaranti , Inge Helisyah Nur Fadhilah Helisyah Nur Fadhilah M. Fathurahman Mohammad Hamim Zajuli Al Faroby Muhaimin, Amri Muhammad Adib Kamali Nadifah, Rofiatun Ni'matuzzahroh, Ludia Ni’mah, Rifdatun Putra, Fachrian Bimantoro Rifdatun Ni’mah Rindra Syaifullah Ryanta Meylinda Savira Salsabiila, Annas Thasya Haafizhah Sanjaya, I Made Wisnu Adi Savira, Ryanta Meylinda Sembiring, Fikrie Hartanta Shefira Eka Putri Sri Hidayati Syaifullah, Rindra Taqhsya Dwiyana , Ananda Yohanes Setiawan