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All Journal Jurnal Ilmu Komputer dan Informasi Wahana Matematika dan Sains Journal of Natural Sciences and Mathematics Research BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Jambura Journal of Mathematics JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Progresif: Jurnal Ilmiah Komputer JISKa (Jurnal Informatika Sunan Kalijaga) Madani : Indonesian Journal of Civil Society Majalah Ilmiah Matematika dan Statistika (MIMS) Jurnal Statistika dan Aplikasinya Journal of Applied Data Sciences MATHunesa: Jurnal Ilmiah Matematika Society: Jurnal Pengabdian Masyarakat Duta.com : Jurnal Ilmiah Teknologi Informasi dan Komunikasi IJCOSIN : Indonesian Journal of Community Service and Innovation Safari : Jurnal Pengabdian Masyarakat Indonesia JUPADAI : Jurnal Pengabdian Kepada Masyarakat Madani : Jurnal Pengabdian Kepada Masyarakat Teknikom: Teknologi Informasi, Ilmu Komputer dan Manajemen Ahsana: Jurnal Penelitian dan Pengabdian kepada Masyarakat Jurnal INFOTEL Jurnal Pengabdian Sosial Parameter: Jurnal Matematika, Statistika dan Terapannya Limits: Journal of Mathematics and Its Applications
Atika Ratna Dewi
Telkom University
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture 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 Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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ANALYSIS AND SIMULATION OF THE SIR MODEL ON THE SPREAD OF COVID-19 BY CONSIDERING THE VACCINATION FACTOR Dewi, Atika Ratna; Ananda, Ridho; Rifanti, Utti Marina; Anggraeni, Nadia Putri; Ardian, Miko
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/barekengvol18iss1pp0303-0312

Abstract

Covid-19 is a serious respiratory disease that can be fatal for those affected. Governments have tried various strategies to conquer the Covid-19 pandemic. One of them is to vaccinate people with 6 years old and over. The vaccination program aims to form herd immunity so that the number of confirmed positive cases can be reduced. The purpose of this research is to form a mathematical model of the SIR (Susceptible-Infected-Recovery) spread of Covid-19 by considering vaccination factors. The SIR model is combined with a vaccination factor to forestall the unfold of Covid-19. The research method includes deriving models of nonlinear differential equation systems, solving qualitative models, deriving the basic reproduction ratio ( ), analysis of equilibrium points, and building simulation models. This model has an asymptotically stable disease-free equilibrium point. At the same time, the endemic equilibrium point is unstable. Model simulation is obtained by using different parameter values. This is proven through the outcomes of the model analysis vaccination coverage is a key parameter that can be controlled to reduce so that the pandemic ends soon.
PELATIHAN PENGGUNAAN MICROSITE UNTUK GURU SMK POLITEKNIK YP3I BANYUMAS Yuniati, Trihastuti; Dewi, Atika Ratna; Prasetyo, Novian Adi; Saputra, Wahyu Andi
JUPADAI : Jurnal Pengabdian Kepada Masyarakat Vol. 3 No. 1 (2024): Volume 3 Nomor 1 2024
Publisher : Asosiasi Dosen Akutansi Indonesia, KEPRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64795/jupadai.v3i1.138

Abstract

SMK YP3I Banyumas sebagai bentuk komitmen untuk meningkatkan kualitas pendidikan dan wujud implementasi digitalisasi pendidikan, mewajibkan setiap guru untuk memiliki akun microsite, selain sebagai branding juga untuk memudahkan dalam penyampaian bahan pembelajaran. Namun sebagian besar guru di SMK YP3I Banyumas tidak memahami bagaimana cara membuat akun microsite dan pemanfaatannya. Oleh karena itu, tim dosen dan mahasiswa ITTP mengadakan pelatihan pembuatan microsite kepada 26 guru di SMK Politeknik YP3I Banyumas. Pada pelatihan tersebut, peserta diperkenalkan dengan teknologi microsite, fitur yang dimiliki, serta langkah pembuatannya. Peserta juga didampingi dalam pembuatan akun microsite, serta bagaimana menyematkan tautan dokumen materi, kuis, atau penugasan dari Google Docs dan Google Form. Hasil kuesioner menunjukkan 73,7% peserta merasa puas dan pelatihan yang diselenggarakan sesuai dengan harapan, 78,9% peserta menyatakan bahwa pelatihan yang diberikan sesuai dengan kebutuhan, menambah pengetahuan, dan adanya tindak lanjut yang baik terhadap permasalahan yang dihadapi peserta, serta 68,4% peserta menyatakan bahwa pelatihan ini menambah keterampilan, memberikan dampak perubahan pada diri peserta, dan berharap adanya pelatihan lanjutan. Hasil dari pelatihan ini setiap guru memiliki akun microsite masing-masing, sehingga kinerja guru semakin meningkat, yang nantinya dapat berdampak terhadap kepuasan masyarakat selaku pengguna jasa pendidikan yang semakin meningkat.
Peningkatan Kapasitas Penjualan Pada Kader Pemberdayaan Masyarakat Desa Melalui Pelatihan Pemasaran Digital Athiyah, Ummi; Alika, Shintia Dwi; Dewi, Atika Ratna; Habiburrahman, Muhammad Quthb; Sa’adah, Oktavia Jazilatus; Arif Wirawan Muhammad
Madani : Indonesian Journal of Civil Society Vol. 6 No. 2 (2024): Madani : Agustus 2024
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/madani.v6i2.2193

Abstract

Empowering rural communities is essential for sustainable development, especially in the economic sector. This community service program aims to increase the sales capacity of the Sunyalangu Village Community Empowerment Cadres (KPMD) through digital marketing training. The main problems include simple packaging, conventional marketing methods, and poor business management practices. This program uses a community service method, Service Learning (SL), which involves practical steps such as product packaging training and digital marketing strategy workshops. This project significantly improved participants' skills in using sealer machines and promoting products online, especially on platforms like Shopee. The method of implementing strategic digital marketing communication training was carried out with a structured and interactive approach over two meetings. The results showed the importance of digital literacy in rural areas to achieve maximum business potential and improve economic sustainability. This training has successfully introduced participants to the world of online trading and provided them with practical skills in utilizing digital platforms to market processed products from the community.
Forecasting the Stock Price of PT Unilever Indonesia Using the ARCH-GARCH Model with the Application of Kalman Filter Sausan Sausan; Atika Ratna Dewi; Aminatus Sa’adah
JURNAL INFOTEL Vol 17 No 4 (2025): November
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i4.1408

Abstract

PT Unilever Indonesia experiences significant stock price volatility driven by both internal and external factors. This volatility underscores the need for accurate forecasting methods to support investment decision-making and risk management. This study aims to forecast the company’s stock prices using ARCH-GARCH models, enhanced with the Kalman Filter to improve predictive performance. Daily historical stock price data were obtained from the yfinance library. The research methodology consists of several stages, including literature review, data collection, exploratory data analysis (EDA), data preprocessing, forecast modelling, and evaluation. Among the evaluated models, the GARCH(1,2) with a skewed Student’s t error distribution was identified as the best-fitting model, achieving an AIC value of -5.476981. The initial forecast using the GARCH model produced a MAPE of 49.47%, RMSE of 45.56%, and MAE of 37.16%. After applying the Kalman Filter, the model’s forecasting performance improved substantially, with MAPE decreasing to 6.04%, RMSE to 6.01%, and MAE to 5.02%. These results demonstrate the effectiveness of the Kalman Filter in reducing noise, dynamically updating predictions, and enhancing the model’s responsiveness to market fluctuations.
Bridging the Digital Divide: Enhancing Teacher Competencies for Student-Centered Learning Through Microsite Development Dewi, Atika Ratna; Yuniati, Trihastuti; Arifa, Amalia Beladinna; Sari, Dian Kartika; Alika, Shintia Dwi
Society : Jurnal Pengabdian Masyarakat Vol. 5 No. 1 (2026): Januari
Publisher : Edumedia Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55824/bn5jwa48

Abstract

Elementary school teachers at Tumiyang 1 Public Elementary School face significant challenges in adopting digital technology effectively with 73% having no prior knowledge of microsites and limited skills in creating interactive digital learning media. This digital literacy gap, common in rural educational contexts, results in conventional teaching methods that fail to engage today’s digital generation effectively. To address these challenges, a two-day intensive microsite creation training program was implemented using a participatory approach through hands-on training, mentoring, and product-based evaluation. The program consisted of three main stages, preparation, implementation, and evaluation and monitoring. Teachers learned to create web-based learning media using Google Sites and S.Id, integrating various digital resources, including Google Drive, Google Forms, YouTube, and Canva. Post-training evaluation revealed universally positive outcomes that most of the participants gained understanding of microsites, with all teachers successfully creating functional, content-rich microsites tailored to their subject areas. All participants expressed interest in further learning and rated the training as effective with clear and accessible materials. This program successfully bridged the digital competence divide, empowering rural teachers as agents of change capable of creating adaptive, interactive learning environments and contributing to educational equity between urban and rural areas.    
Spatial Analysis of Ensemble Learning Models for Agricultural Drought Early Warning Sudianto, Sudianto; Ni'amah, Khoirun; Dewi, Atika Ratna; Ramadhan, Afan; Aprilia, Jeti; Tiyaswening, Arsita Wiwit; Anataya, Syalaisha Nisrina
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1108

Abstract

Drought poses a serious threat to rice production and local food security, triggered by climate anomalies such as El Niño. This study aims to evaluate and compare the performance of Ensemble Learning Models in classifying drought levels and analyze its correlation with periods of climate anomalies. This study uses Landsat 9 image data in the simulation period from June 2024 to July 2025, which is processed with HSV-based pan-sharpening and spectral index extraction (NDVI, NDWI, NDDI, EVI, LST). The modeling process applied undersampling to address class imbalance and hyperparameter tuning optimization using Optuna. The models compared included Random Forest, LightGBM, AdaBoost, XGBoost, and Gradient Boosting. The results showed that Gradient Boosting excelled with a train accuracy of 96,85% in original dataset with split dataset 70:30, whereas rise to 98.98% after tuning. Spatial validation was conducted in other rice field plots, however its steadfastly on research area with same treatment. The classification map shows the dominance of the moderate category, which temporally coincides with the period of rainfall decline associated with El Niño, although a direct causal relationship requires further investigation. These findings confirm that remote sensing combined with machine learning is effective for drought monitoring, with the caveat that the application of undersampling and limited spatial validation that is, confined solely to the research area; needs to be considered in the interpretation of results.
Model Matematika COVID-19 dengan Sumber Daya Pengobatan yang Terbatas Utti Marina Rifanti; Atika Ratna Dewi; Nurlaili
Limits: Journal of Mathematics and Its Applications Vol. 18 No. 1 (2021): Limits: Journal of Mathematics and Its Applications Volume 18 Nomor 1 Edisi Me
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Coronavirus 2019 (COVID-19) merupakan penyakit menular yang disebabkan oleh Severe Acute Respiratory Syndrome Coronavirus 2. Hingga Desember 2020, terdapat 617 ribu kasus terkonfirmasi positif COVID-19 dengan total 18 ribu kematian karena COVID-19 di Indonesia. Pada penelitian ini, kami menggunakan model kompartemen Susceptible- Exposed-Infected-Recovered (S EIR) untuk analisis dampak sumber daya pengobatan yang terbatas dan memprediksi dinamika penyebaran COVID-19 di Indonesia. Metode yang digunakan adalah penurunan angka rasio reproduksi dasar dan titik ekuilibrium menggunakan analisis sistem dinamik dalam bentuk persamaan diferensial non linier yang diperoleh dari model awal. Kemudian, kami menganalisis angka rasio reproduksi dasar dan titik ekuilibrium, serta memprediksi kondisi pandemi COVID-19 menggunakan kasus nyata di Indonesia sejak 2 Maret hingga 30 Nopember 2020. Dari hasil penelitian ini, diperoleh bahwa jika perubahan kasus terinfeksi terhadap waktu kurang dari 2640 kasus, maka angka rasio reproduksi dasar menjadi kurang dari nol dan nilai semakin mendekati nol saat mulai memasuki bulan Maret 2021. Hal tersebut berarti, jika rata-rata kasus positif terkonfirmasi harian masih di bawah kapasitas maksimal sumber daya pengobatan, yaitu 2640 kasus, maka dari hasil analisis model diprediksikan bahwa penyakit akan mulai menghilang pada bulan Maret 2021. Sebaliknya, jika kasus positif terkonfirmasi harian di atas 2640 kasus, maka diperkirakan penyakit akan mulai menghilang pada Juni 2021.
MULTIVARIATE TIME SERIES MODELING USING VECTOR AUTOREGRESSION FOR RICE PRICE PREDICTION IN INDONESIA Atika Ratna Dewi; Andreas Rony Wijaya; Mirza Ghanimi; Talitha Veda Azaria Ramadhani
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/barekengvol20iss3pp2195-2212

Abstract

This study analyzes the dynamic relationship between rice prices and selected economic variables using a Vector Autoregression (VAR) model. The analysis utilizes daily data from January 2022 to December 2023, encompassing rice prices, chicken meat prices, chicken egg prices, the Rupiah-to-USD exchange rate, inflation, and crude oil prices. The estimated VAR model is stable, as all eigenvalues lie within the unit circle. Residual diagnostics based on the Portmanteau (Ljung–Box) test indicate no residual autocorrelation across all equations (LB statistics with df = 1, p-values > 0.05), confirming the adequacy of the model specification. The model demonstrates good predictive performance for the rice-price series, achieving a Mean Absolute Percentage Error (MAPE) of 0.42% over the out-of-sample testing period (the last 20% of observations). Empirical results suggest that rice prices are influenced by dynamic interactions within the system, particularly through their relationships with chicken meat prices and the Rupiah–USD exchange rate. These findings offer valuable policy insights for maintaining rice price stability, a crucial component of national food security.
Comparison of SARIMA Method, Holt-Winters Exponential Smoothing Method and Prophet Method in Inflation Data Forecasting Atika Ratna Dewi; Desty Mayang Pratiwi; Aina Latifa Riyana Putri
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 5 No 1 (2026): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv5i1pp165-180

Abstract

This study discusses inflation forecasting in Indonesia using three time series methods, namely SARIMA, Holt-Winters Exponential Smoothing and Prophet, with monthly inflation data from January 2014 to October 2024. Inflation forecasting is important to maintain economic stability and support decision making in the monetary, fiscal, and investment sectors. The SARIMA method was chosen because of its ability to handle complex seasonal data, Holt-Winters Exponential Smoothing is used to accommodate seasonal patterns through alpha, beta, and gamma smoothing parameters, while Prophet was chosen because of its flexibility in handling nonlinear trends, seasonality, and special events such as holidays. The research steps include literature study, data collection, exploratory analysis, preprocessing, modeling with the three methods, and accuracy evaluation using Mean Absolute Percent Error (MAPE). The evaluation results show that the SARIMA(2,1,2)(1,0,1)^6model has a MAPE of 8.11%, better than Holt-Winters Exponential Smoothing of 11.75% and Prophet of 52.85%. Thus, SARIMA was chosen as the best model to forecast Indonesian inflation from November 2024 to April 2025. The prediction results were 6.19%, 5.40%, 4.96%, 4.94%, 4.57%, and 4.58%, respectively. This model is expected to be a reference in formulating strategic policies to maintain economic stability and improve public welfare.
A Comparative Study of PCA-Based Dimensionality Reduction and Best Subset Selection in Disease Classification Andreas Rony Wijaya; Atika Ratna Dewi; Muhammad Bayu Nirwana; Respatiwulan Respatiwulan; Sri Sulistijowati Handajani
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 3 (2026): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i3.38265

Abstract

Real-world datasets often contain many variables, some of which may be irrelevant or redundant. To build an effective classification model, it is important to simplify the data by keeping only the most influential features. One common approach that can be used for selecting the most influential variables is feature selection. However, when dealing with many variables, removing some may result in the loss of information. Hence, it is also necessary to consider methods that can simplify the model while retaining most of the information from the original variables. Dimensionality reduction is one such approach that effectively addresses this issue. This study employs a comparative quantitative research approach to evaluate the effectiveness of principal component analysis (PCA) as a dimensionality reduction method and best subset selection as a feature selection method in improving classification performance. The study utilizes a heart disease dataset from the UCI Machine Learning Repository consisting of 303 observations and 13 predictor variables as a case study. Both approaches are applied to reduce the number of predictor variables and make the model more interpretable. After applying both methods, three classification models — logistic regression, naïve Bayes, and linear discriminant analysis — are trained and evaluated using accuracy, recall, precision, and F1-score, and the results are further illustrated through ROC curves. Feature selection using best-subset selection yields seven variable combinations with the most significant predictors, whereas PCA requires eight principal components to explain 80% of the total variation.  The best classification performance was obtained using the feature-selected dataset, achieving an accuracy of 87% and an AUC of 0.93, outperforming both the original dataset model and the PCA-reduced dataset model. These results show that feature selection using best subset selection provides a better balance between simplicity and classification performance. Furthermore, the models obtained after feature reduction, both from best subset selection and PCA, still maintain good predictive ability as indicated by their relatively high AUC values.
Co-Authors 'Ashifa, Natasya Syafila Adhystira Raihannoeza Almadiva Afan Ramadhan Aina Latifa Riyana Putri Alika, Shintia Dwi Amalia Beladinna Arifa Aminatus Sa’adah Anataya, Syalaisha Nisrina Andreas Rony Wijaya Andreas Rony Wijaya Andreas Rony Wijaya Anggraeni, Nadia Putri Anggun Dewanti Aprianti Ika Larasati Aprianti Ika Larasati Aprilia, Jeti Ardian, Miko Arif Wirawan Muhammad Arif Wirawan Muhammad Briandoko, Singgih Desty Mayang Pratiwi Dewi Erla Mahmudah Dewi Erla Mahmudah Dewi Erla Mahmudah, Dewi Erla Dian Kartika Sari, Dian Kartika Egy Destiar Firmandani Elisabeth Angeline W B Fajar Tri Wahyuni Gavrilla Claudia Gushelmi Habibah Ratna Fadhila Islami Hana Habibah Ratna Fadhila Islami Hana Habiburrahman, Muhammad Quthb Hapsari, Santika Tri Jausha, Dill Thafa Joko Purnomo Joko Purnomo Kirana, Nahila Shofie Kusuma, Dewa Adji Maifuza Binti Mohd Amin Martiyaningsih, Dwi Puspa Miftahul Huda Mirza Ghanimi Muhammad Akbar Setiawan Muhammad Akbar Setiawan, Muhammad Akbar Muhammad Bayu Nirwana Muhammad Quthb Habiburrahman Nazila, Putri Ella Ni'amah, Khoirun Nofrizaldi Novian Adi Prasetyo Nur Alfi Ekowati Nuragustin, Ika Wida Nurlaili Nurlita, Laksmi Dyah Oktavia Jazilatus Sa’adah Puspita, Olivia Intan Ramadhan, Afan Ramadhani, Rima Dias Respatiwulan Respatiwulan Riana Safitri Rianti Yunita Kisworini Rianti Yunita Kisworini, Rianti Yunita Ridho Ananda Sa’adah, Oktavia Jazilatus Safitri, Riana Sausan Sausan Shintia Dwi Alika Shintia Dwi Alika Singgih Briandoko Sri Handini Sri Sulistijowati Handajani Sudianto, Sudianto Sulistiyasni . Sunaryono Sunaryono Surya Adi Widiarto Talitha Veda Azaria Ramadhani Tiyaswening, Arsita Wiwit Trihastuti Yuniati Trihastuti Yuniati Trihastuti Yuniati Ulya, Fadilla Zundina Ummi Athiyah Ummi Athiyah Utti Marina Rifanti Vania Noverina Vania Noverina Wahyu Andi Saputra Wahyuni, Fajar Tri Wika Purbasari Wika Purbasari, Wika Winesti, Alifia Zahra Wiyono, Brian Nugraha