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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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Analisis Sentimen Publik Terhadap Implementasi Kurikulum Merdeka: Pendekatan Algoritma Random Forest dan Support Vector Machine Yoga Abira , Senna; Ni'mah, Rifdatun; Putri Permata , Regita
eProceedings of Engineering Vol. 12 No. 5 (2025): Oktober 2025
Publisher : eProceedings of Engineering

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Abstract

Abstrak—Krisis pendidikan di Indonesia mendorong pemerintah menerapkan Kurikulum Merdeka sebagai strategi peningkatan mutu pembelajaran. Namun, respons publik terhadap implementasinya masih menuai pro dan kontra, terutama di media sosial. Penelitian ini menganalisis sentimen publik terhadap Kurikulum Merdeka menggunakan 10.066 data dari platform X yang dikumpulkan selama satu tahun (Oktober 2023–Oktober 2024). Data dianalisis menggunakan algoritma Support Vector Machine (SVM) dan Random Forest (RF), melalui tahapan pelabelan manual, praproses teks, ekstraksi fitur dengan TF-IDF, serta penambahan data sintetis ke kelas minoritas menggunakan metode SMOTE. Model dievaluasi dalam tiga tahap hyperparameter tuning. Hasil menunjukkan bahwa model SVM memberikan performa terbaik dengan akurasi 72,42% dan F1-score makro 69,67%, dibandingkan RF yang mencapai akurasi 68,49% dan F1-score makro 66,25%. Sentimen netral dan negatif lebih mendominasi opini publik, sementara sentimen positif relatif rendah. Penambahan data sintetis terbukti meningkatkan kemampuan model dalam mengenali kelas minoritas. Penelitian ini memberikan gambaran empiris mengenai persepsi publik terhadap kebijakan pendidikan, sekaligus menunjukkan potensi analisis sentimen berbasis media sosial sebagai alat evaluasi kebijakan secara real-time. Kata kunci—analisis sentimen, kurikulum merdeka, media sosial, random forest, support vector machine, X
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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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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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.
Smart Irrigation untuk Optimalisasi Pertanian Sistem Green House pada Kelompok Petani Tani Sejahtera di Desa Temuasri, Banyuwangi Mohammad Hamim Zajuli Al Faroby; Helisyah Nur Fadhilah; Regita Putri Permata; Muhammad Adib Kamali
I-Com: Indonesian Community Journal Vol 5 No 1 (2025): I-Com: Indonesian Community Journal (Maret 2025)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/icom.v5i1.6540

Abstract

Smart irrigation technology is an innovation designed to improve the efficiency of water and fertilizer management in agricultural activities, especially in a greenhouse environment. This study aims to implement and evaluate the use of smart irrigation technology in Temuasri Greenhouse, which is known as one of the centers of melon cultivation with tabulampot system (fruit plants in pots). The system relies on a Programmable Logic Controller (PLC) and Human-Machine Interface (HMI) to automate the watering process and fertilizer distribution and facilitate real-time monitoring of irrigation conditions. The program involved 16 target communities who received training on the installation, operation, and maintenance of the smart irrigation system. Based on the evaluation results, the technology was successfully implemented, providing increased efficiency in water and labor use, while reducing direct contact during the watering process. The survey showed that 84.72% of participants gave very positive feedback, stating that this activity was effective, useful, and has great potential for further development. The successful implementation of smart irrigation technology is expected to become a reference for technology-based irrigation management models that support agribusiness sustainability, while increasing the productivity and quality of agricultural products in the future.
DYNAMIC TIME WARPING-BASED FUZZY C-MEANS WITH MULTIDIMENSIONAL SCALING FOR TIME SERIES CLUSTERING Sri Hidayati; Regita Putri Permata; Fidi Wincoko Putro
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 3 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss3pp2299-2310

Abstract

Weather refers to atmospheric conditions such as temperature, humidity, air pressure, wind speed, and rainfall, all of which influence human activities. Rainfall is particularly important due to its impact on agriculture and water resource management. This study classifies regions on Java Island based on rainfall patterns using the Fuzzy C-Means algorithm. Rainfall variations are influenced by geographical, topographical, and climatic factors, requiring methods that can capture spatial and temporal changes. Fuzzy C-Means was selected for its ability to manage data uncertainty and overlapping clusters. To measure rainfall pattern similarity between regions, the Dynamic Time Warping (DTW) method was applied. Since DTW is a non-Euclidean metric and incompatible with Fuzzy C-Means, the Multidimensional Scaling (MDS) method was used to convert DTW distance matrices into Euclidean feature vectors. The study used secondary daily rainfall data from NASA (2021–2024). Clustering performance was evaluated using the Silhouette Coefficient, yielding a value of 0.413184, indicating good compactness and separation. Results identified three clusters: low rainfall (Cluster 0), moderate rainfall (Cluster 1), and high rainfall (Cluster 2). ANOVA results confirmed significant differences in average rainfall between clusters, with Tukey HSD tests showing Cluster 2 significantly differs from Clusters 0 and 1, while Clusters 0 and 1 are not significantly different. These findings demonstrate that combining DTW, MDS, and Fuzzy C-Means effectively identifies temporal rainfall patterns and produces statistically meaningful clustering. The spatial distribution of each cluster is visualized using GeoJSON and a database for clearer interpretation.
Optimizing K-Means Clustering through Distance Metric Simulation for Strategic Enrollment Segmentation in Private Universities Regita Putri Permata; Amalia Nur Alifah; I Made Wisnu Adi Sanjaya
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.33089

Abstract

K-Means clustering is a widely used unsupervised learning technique for identifying patterns and grouping data based on feature similarities. However, the effectiveness of K-Means significantly depends on the choice of distance metric. This study conducts a comprehensive simulation to evaluate and compare the performance of four distance metrics—Euclidean, Cityblock (Manhattan), Canberra, and Mahalanobis—in the context of strategic market segmentation for private universities. The dataset includes simulated and institutional data incorporating variables such as account creation, registration, graduation, student performance (social, science, and scholastic scores), income, and geographic distance. The results indicate that Euclidean and Cityblock distances yield efficient and interpretable clusters with low computational costs, whereas Mahalanobis distance, despite its capacity to model covariance, introduces computational overhead without proportional improvement in segmentation quality. Interestingly, Canberra distance produces compact clusters but offers no significant gain in separability. From the resulting segmentation, two clusters emerge as high-potential targets for marketing strategies: Cluster 0 (high-income and distant students) and Cluster 1 (diverse academic and socioeconomic profiles). The findings highlight the importance of aligning distance metric selection with specific clustering objectives and offer practical insights for data-driven strategic enrollment planning in private higher education institutions.
Rainfall Forecasting using Spatio-Temporal and Neural Network Study Case: Meteorological Data of Madura Island Ryanta Meylinda Savira; Regita Putri Permata; Amalia Nur Alifah; Yohanes Setiawan; Adzanil Rachmadhi Putra
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of 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.
Comparative Study of Hybrid ARIMA-LSTM and ARIMAX-LSTM for Bitcoin Forecasting with Data Partitioning Fikrie Hartanta Sembiring; Regita Putri Permata; Rifdatun Ni'mah
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.35118

Abstract

The extreme volatility of Bitcoin prices poses significant challenges for accurate forecasting using conventional models. While ARIMA excels at capturing linear trends, it struggles with non-linear dynamics; conversely, LSTM networks can model non-linearity but often overfit noisy data. To address these limitations, this study investigates six forecasting configurations: standalone ARIMAX, standalone LSTM, and four hybrid ARIMA/ARIMAX-LSTM models employing both single-split and two-stage split strategies. A comprehensive out-of-sample evaluation on daily Bitcoin closing prices reveals that the two-stage split hybrid ARIMA-LSTM achieves a remarkable MAPE of 2.60%, outperforming all other configurations. The results demonstrate that residual structure and strategic data partitioning critically influence hybrid model performance by enhancing residual learnability. These findings offer practical guidance for researchers and practitioners designing robust forecasting pipelines for highly volatile financial markets.
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 Fannisa Egista Naya Faradila Efaranti , Inge Fidi Wincoko Putro Fikrie Hartanta Sembiring Helisyah Nur Fadhilah Helisyah Nur Fadhilah Helisyah Nur Fadhilah Helisyah Nur Fadhilah I Made Wisnu Adi Sanjaya 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 Rifdatun Ni’mah Rindra Syaifullah Ryanta Meylinda Savira Ryanta Meylinda Savira Salsabiila, Annas Thasya Haafizhah Shefira Eka Putri Sri Hidayati Sri Hidayati Syaifullah, Rindra Taqhsya Dwiyana , Ananda Vivi Indah Rahmawati Yoga Abira , Senna Yohanes Setiawan