Article Per Year (5 Year)
p-Index From 2020 - 2025
2.681
P-Index
This Author published in this journals
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

Published : 19 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 19 Documents
Search

 1   2 
Urban Village Clustering in Surabaya City based on Live Birth Rate using K-Means with Principle Component Analysis Regita Putri Permata; Rifdatun Ni’mah; Amri Muhaimin
Internasional Journal of Data Science, Engineering, and Anaylitics Vol. 2 No. 2 (2022): International Journal of Data Science, Engineering, and Analytics Vol 2, No 2,
Publisher : International Journal of Data Science, Engineering, and Analytics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijdasea.v2i2.41

Abstract

Pregnancy and childbirth are important times in a mother's life. Mothers and children are vulnerable so their health efforts should be prioritized. The health level is a useful indicator to see the health efforts achievement or success of an area. The Surabaya City Government is very concerned about the health and safety of mothers and babies problem. Therefore, this study aims to map and classify urban villages in Surabaya based on the number of live births and pregnant women using the K-Means algorithm and feature reduction techniques using Principal Component Analysis. Two main components can be formed as the result of the variable reduction. The most optimal grouping of urban villages in the city of Surabaya is 3 groups/clusters. Based on the number of live births and pregnant women, those consisted of 3 clusters, in which cluster 0 consisted of 99 villages, cluster 1 consisted of 42 villages, and cluster 2 consisted of 12 villages
Analisis Regresi Logistik Biner Multilevel pada Status Kemiskinan di Pulau Jawa menggunakan Algoritma MCMC Metropolis-Hasting Regita Putri Permata; Rifdatun Ni'mah
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 16 No 1 (2023): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol16.no1.a6578

Abstract

Pulau Jawa adalah pulau paling padat penduduk di Indonesia. Namun, ada beberapa provinsi di Pulau Jawa yang mengalami masalah kemiskinan. Provinsi Jawa Tengah memiliki tingkat kemiskinan sebesar 11,32% pada tahun 2018, lebih tinggi dari persentase kemiskinan pulau Jawa, yang merupakan akumulasi dari kemiskinan di semua kabupaten dan kota di provinsi tersebut. Model regresi logistik multilevel mempunyai struktur data hirarki yang terdiri dari satu variabel prediktor yang diukur pada level paling bawah (level 1) dan satu variabel penjelas yang diukur pada setiap level atau level selanjutnya. Struktur hirarki data kemiskinan mengindikasikan bahwa data tersebut berasal dari beberapa level, dimana level yang lebih rendah yaitu Kabupaten/Kota tersarang pada level yang lebih tinggi yaitu Provinsi. Data persentase kemiskinan daerah diubah ke dalam bentuk biner menjadi variabel status kemiskinan sehingga metode pendugaan parameter dilakukan dengan pendekatan model regresi logistik biner hirarki dengan algoritma Metropolis-Hasting. Pemodelan ini membantu pemerintah dalam mengambil kebijakan terhadap kelompok kabupaten/kota kategori miskin berdasarkan nilai Indeks Pembangunan Manusia (IPM). Analisa pemodelan menunjukkan bahwa variabel IPM memberikan dampak yang sama saja bagi kecenderungan status kemiskinan kabupaten/kota dengan asumsi parameter lain konstan. Variabel interaksi antara IPM dan dana program Bantuan Pangan Non Tunai memberikan dampak kecenderungan kabupaten/kota di Pulau Jawa berstatus tidak miskin sebesar 1,07 kali daripada miskin.
Pemanfaatan Fitur Facebook sebagai Upaya dalam Meningkatkan Penjualan Produk UMKM di Ujungpangkah Kabupaten Gresik Regita Putri Permata; Amalia Nur Alifah; Helisyah Nur Fadhilah
I-Com: Indonesian Community Journal Vol 3 No 4 (2023): I-Com: Indonesian Community Journal (Desember 2023)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/icom.v3i4.3241

Abstract

Masih rendahnya kemampuan pelaku usaha dalam mengelola dan mengembangkan produk usahanya di Pangkah Wetan Gresik disebabkan terbatasnya pengetahuan tentang digitalisasi dan kemampuan dalam memasarkan produk secara online. Hal ini menjadi permasalahan yang dihadapi akibat terbatasnya literasi digital dalam menunjang kegiatan wirausaha berbasis potensi unggulan local. Kegiatan pengabdian ini bertujuan untuk meningkatkan literasi digital bagi para pelaku usaha UMKM dengan memanfaatkan fitur sosial media salah satunya Facebook Marketplace sebagai lapak jual online. Penjualan secara online dapat meningkatkan jumlah pelanggan dan meningkatkan omset. Metode pengabdian dilaksanakan melalui kegiatan pelatihan dengan pendekatan kooperatif-partisipatif. Hasil pengabdian menunjukkan foto produk olahan diunggah dan dijual melalui Facebook Marketplace. Hasil penelitian menunjukkan bahwa sebanyak 62,5% sudah menginstall dan mencoba membuka dan mempraktekkan Facebook Marketplace serta rata-rata postest meningkat dari 5.4 menjadi 6.5 dari skor rata-rata pretest, artinya pemahaman peserta meningkat mengenai Facebook Marketplace setelah diadakan sosialisasi literasi digital. Kegiatan ini diharapkan untuk meningkatkan penjualan dan jangkauan dari pemasaran produk UMKM.
Daily Rainfall Forecasting with ARIMA Exogenous Variables and Support Vector Regression Regita Putri Permata; Rifdatun Ni'mah; Andrea Tri Rian Dani
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.
Penyesuaian distribusi proses keberangkatan sepeda motor dari lahan parkir saat waktu puncak Ni'mah, Rifdatun; Permata, Regita Putri
Majalah Ilmiah Matematika dan Statistika Vol. 23 No. 2 (2023): Majalah Ilmiah Matematika dan Statistika
Publisher : Jurusan Matematika FMIPA Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/mims.v23i2.38203

Abstract

The study aims to model the distribution of motorcycle departures from the parking lot at peak times with the Poisson process approach. This process involves a discrete number of departures and continuous interdeparture time. The probability distribution candidate was selected to model the data according to the data nature, the stochastic process, and the empirical observation of the departure process. Parameters are estimated using the Maximum Likelihood Estimation (MLE) method and the bootstrapping procedure to construct confidence intervals for the parameter. The goodness-of-fit test is applied to select the best probability distribution that matches empirical data. Inferences to the distribution parameters suggest that Weibull's distribution is more appropriate for describing the motorcycle's inter-departure time. The number of motorcycle departures fits significantly into a negative binomial distribution. The results of the study concluded that the Poisson process applied was a case of overdispersion, with the motorcycle departure rate decreasing over time.Keywords: Bootstrapping, departure, distribution, goodness-of-fit, PoissonMSC2020: 60E05
Smart Irrigation untuk Optimalisasi Pertanian Sistem Green House pada Kelompok Petani Tani Sejahtera di Desa Temuasri, Banyuwangi Al Faroby, Mohammad Hamim Zajuli; Fadhilah, Helisyah Nur; Permata, Regita Putri; Kamali, Muhammad Adib
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

Teknologi irigasi pintar adalah inovasi yang bertujuan meningkatkan efisiensi penggunaan air dan pupuk dalam pertanian, khususnya di lingkungan greenhouse. Salah satu implementasinya dilakukan di Temuasri Greenhouse, pusat budidaya melon dengan sistem tabulampot, yang kini memanfaatkan smart irrigation untuk mengoptimalkan proses irigasi. Sistem ini bekerja dengan mengandalkan Programmable Logic Controller (PLC) dan Human-Machine Interface (HMI) yang memungkinkan penyiraman dan distribusi pupuk berjalan otomatis serta dapat dipantau secara real-time. Untuk memastikan sistem ini dapat diterapkan dengan baik, sebanyak 16 peserta dilibatkan dalam pelatihan mengenai instalasi, pengoperasian, dan pemeliharaan teknologi ini. Hasil evaluasi menunjukkan bahwa penerapan smart irrigation mampu meningkatkan efisiensi penggunaan air hingga 40% dan menghemat waktu kerja manual hingga 50%, sekaligus mengurangi kontak langsung selama proses penyiraman. Survei juga mencatat 84,72% peserta memberikan umpan balik sangat positif terhadap program ini. Dengan keberhasilan tersebut, implementasi smart irrigation diharapkan menjadi model pengelolaan irigasi berbasis teknologi yang berkelanjutan, membantu meningkatkan produktivitas, serta mendukung kualitas hasil pertanian di masa depan.
Peningkatan Literasi Data Siswa Melalui Pelatihan Visualisasi Data Di SMK Negeri 6 Surabaya Ryanta Meylinda Savira; Astikhatul Mufaidah; Shefira Eka Putri; Rindra Syaifullah; Regita Putri Permata; Helisyah Nur Fadhilah
Jurnal Abdimas Indonesia Vol. 5 No. 1 (2025): Januari-Maret 2025
Publisher : Perkumpulan Dosen Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34697/jai.v5i1.1440

Abstract

Perkembangan teknologi informasi dan komunikasi (TIK) telah mendorong pentingnya pemanfaatan data dalam dunia pendidikan, termasuk pada tingkat sekolah menengah kejuruan. Namun, keterbatasan pemahaman dan keterampilan siswa dalam mengolah serta memvisualisasikan data masih menjadi tantangan, khususnya di lingkungan SMK. Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan kemampuan siswa SMK Negeri 6 Surabaya dalam analisis dan visualisasi data menggunakan alat bantu seperti Microsoft Excel. Pelatihan dilaksanakan melalui tiga tahapan utama, yaitu sesi teori, praktik, dan diskusi studi kasus yang dirancang agar siswa memahami konsep dasar pengolahan data dan mampu membuat visualisasi yang informatif. Evaluasi dilakukan melalui pre-test dan post-test serta survei kepuasan peserta, dengan hasil menunjukkan peningkatan pemahaman dan antusiasme siswa terhadap pemanfaatan data. Program ini diharapkan tidak hanya meningkatkan literasi data siswa, tetapi juga memperkuat kesiapan mereka dalam menghadapi tantangan dunia kerja berbasis teknologi dan data.
Optimizing K-Means Clustering through Distance Metric Simulation for Strategic Enrollment Segmentation in Private Universities Permata, Regita Putri; Alifah, Amalia Nur; Sanjaya, I Made Wisnu Adi
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.v10i2.33089

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.
Comparative Study of Hybrid ARIMA-LSTM and ARIMAX-LSTM for Bitcoin Forecasting with Data Partitioning Sembiring, Fikrie Hartanta; Permata, Regita Putri; Ni'mah, Rifdatun
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.v10i2.35118

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.
Mengeksplorasi Masalah Kejahatan dari POV Statistik dengan Regresi Binomial Negatif Dani, Andrea Tri Rian; Fathurahman, M.; Ni'matuzzahroh, Ludia; Putri Permata, Regita; Putra, Fachrian Bimantoro
Jurnal Varian Vol. 8 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i2.4445

Abstract

Criminality is a complex issue in Indonesia that is very important to the government, law enforcement agencies, and society. The underlying causes of Indonesia's crime problem are complex and impacted by various circumstances. The aim of this research is to model the crime problem in Indonesia and determine the influencing factors.  The method used in this research is Negative Binomial Regression. The results of the study show that the negative binomial regression model can be used to model criminal problems because the variance value is more significant than the average. Based on the parameter significance test results, both simultaneously and partially, the open unemployment rate, Gini ratio, average years of schooling, and prevalence of inadequate food consumption significantly affect the crime rate, with an Akaike’s Information Criterion Corrected (AICc) value of 698,098. These findings suggest that addressing economic inequality, unemployment, education, and food security could help reduce crime in Indonesia. Policies aimed at improving job opportunities, reducing income disparity, and enhancing education and food security are crucial in mitigating crime. This study provides valuable insights for policymakers and law enforcement agencies, offering a foundation for more targeted and effective crime prevention strategies. Future research could employ the robust Poisson Inverse Gaussian Regression method to avoid the overdispersion problem. 
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