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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
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Optimalisasi Manajemen Data Perpustakaan Sekolah Dasar di Wilayah Pedesaan Melalui Implementasi Dashboard Literasi Amalia Beladinna Arifa; Trihastuti Yuniati; Shintia Dwi Alika; Atika Ratna Dewi; Habibah Ratna Fadhila Islami Hana; Vania Noverina; Aprianti Ika Larasati; Nofrizaldi Nofrizaldi
Ahsana: Jurnal Penelitian dan Pengabdian kepada Masyarakat Vol. 4 No. 2 (2026): Juni 2026 - Ahsana: Jurnal Penelitian dan Pengabdian kepada Masyarakat
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ahsana.v4i2.437

Abstract

Pengelolaan data perpustakaan yang belum optimal merupakan tantangan yang masih umum ditemukan di sekolah dasar, khususnya di wilayah pedesaan dengan akses terhadap sumber daya digital masih terbatas. Program pengabdian kepada masyarakat ini bertujuan mengoptimalkan manajemen data perpustakaan SD Negeri 1 Tumiyang, Kecamatan Pekuncen, Kabupaten Banyumas, Jawa Tengah melalui pendekatan berbasis data, yaitu dengan mengembangkan dan mengimplementasikan “Dashboard Literasi” berbasis visualisasi data interaktif. Metode pelaksanaan meliputi observasi awal kondisi perpustakaan, pengumpulan data karakteristik koleksi buku dan pengunjung, pengembangan dashboard secara partisipatif bersama para guru dan pengelola perpustakaan, serta evaluasi berbasis kuesioner. Hasil pelaksanaan menunjukkan bahwa “Dashboard Literasi” berhasil diimplementasikan dan mampu memfasilitasi pengelolaan data perpustakaan yang lebih sistematis dan transparan. Evaluasi terhadap 14 responden menghasilkan rata-rata kepuasan yang tinggi. Implikasi dari program ini mencakup potensi peningkatan efisiensi operasional perpustakaan berdasarkan umpan balik peserta, penguatan literasi digital para pengelola, serta terbentuknya fondasi berbasis data yang memungkinkan pengambilan keputusan layanan perpustakaan yang lebih tepat sasaran.
A COMPARATIVE EVALUATION OF SARIMA AND FUZZY TIME SERIES CHEN MODELS FOR RAINFALL FORECASTING IN MAKASSAR Gavrilla Claudia; Atika Ratna Dewi; Aina Latifa Riyana Putri
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1389-1404

Abstract

High rainfall intensity in Makassar often leads to flooding. Therefore, forecasting the amount of rainfall is necessary as a reference for taking appropriate mitigation measures. This study was conducted to select the best model between the SARIMA and Fuzzy Time Series (FTS) Chen based on a comparison of their forecasting accuracy, as well as to forecast the amount of rainfall in Makassar for 2024 using the best model. For this study, monthly rainfall data covering the period from January 2014 to December 2024 were collected from the official website of the Central Statistics Agency (BPS) Makassar. Based on the analysis results, SARIMA(7,2,3)(1,1,1)12 was selected as the best model, with an MAE value of 2.654 and an RMSE value of 3.846. The contribution of this study lies in providing an empirical comparison between SARIMA and FTS Chen for rainfall forecasting in tropical regions. However, the limitation of this study is that the forecasting relies solely on historical rainfall data, without incorporating other meteorological variables that may influence rainfall patterns.
PENGEMBANGAN PROFESIONAL PKG PAUD KECAMATAN BALAPULANG DENGAN INOVASI PEMBELAJARAN BERBASIS TEKNOLOGI INFORMASI Ummi Athiyah; Atika Ratna Dewi; Shintia Dwi Alika; Trihastuti Yuniati
MADANI: Jurnal Pengabdian Kepada Masyarakat Vol 11 No 1 (2025): MADANI: Jurnal Pengabdian Kepada Masyarakat
Publisher : LPPM UPN Veteran Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53834/mdn.v11i1.10680

Abstract

Pendidikan anak usia dini (PAUD) merupakan salah satu program prioritas pemerintah dalam membangun fondasi pendidikan yang kuat bagi anak-anak prasekolah. Salah satu faktor utama dalam peningkatan kualitas PAUD adalah pengembangan profesionalisme guru, khususnya dalam literasi digital. Namun, masih banyak guru PAUD yang menghadapi kendala dalam pemanfaatan teknologi informasi dalam pembelajaran. Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan literasi digital guru PAUD di Kecamatan Balapulang melalui pelatihan pemanfaatan microsite dan wordwall sebagai media pembelajaran interaktif. Pelatihan ini dirancang untuk membekali guru dengan keterampilan membuat bahan ajar yang interaktif menggunakan wordwall dan mengelola microsite sebagai pusat distribusi materi ajar. Evaluasi kegiatan dilakukan melalui kuesioner dengan skala Likert (1–5), yang menunjukkan tingkat kepuasan tinggi di antara peserta, dengan skor rata-rata 4,3–4,7 pada berbagai aspek kepuasan dan manfaat pelatihan. Hasil kegiatan menunjukkan adanya peningkatan keterampilan guru dalam memanfaatkan teknologi informasi serta peningkatan interaktivitas dalam proses pembelajaran. Dengan adanya pelatihan ini, guru-guru PAUD menjadi lebih percaya diri dalam mengintegrasikan teknologi dalam pembelajaran, sehingga dapat meningkatkan motivasi dan keterlibatan siswa. Program ini diharapkan dapat berkontribusi dalam menciptakan ekosistem pembelajaran yang lebih modern, efektif, dan sesuai dengan tuntutan pendidikan abad ke-21.
Optimasi Hiperparameter Berbasis Particle Swarm Optimization pada Model Boosting untuk Klasifikasi Kondisi Wilayah Rawan Banjir di Jawa Barat Afan Ramadhan; Anggun Dewanti; Egy Destiar Firmandani; Atika Ratna Dewi
Jurnal DutaCom Vol. 19 No. 2
Publisher : Fakultas Ilmu Komputer Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/6yf6xk83

Abstract

Banjir merupakan bencana alam paling dominan di Indonesia yang menyebabkan kerugian jiwa dan ekonomi secara masif setiap tahunnya, sehingga dibutuhkan sistem klasifikasi kondisi wilayah yang akurat untuk mendukung peringatan dini. Penelitian ini mengintegrasikan data curah hujan harian dari 460 stasiun BMKG periode 2020 hingga 2025, rekaman kejadian banjir dari BNPB-DIBI, serta fitur geospasial turunan dari DEM GLO-30 dan citra SAR Sentinel-1 sebagai masukan model. Enam model klasifikasi dibangun dan dibandingkan secara sistematis, yaitu XGBoost, LightGBM, dan CatBoost sebagai baseline beserta ketiganya yang dioptimasi menggunakan Particle Swarm Optimization (PSO). Hasil penelitian menunjukkan bahwa CatBoost baseline tampil sebagai model terbaik sebelum optimasi, sementara PSO-LightGBM berhasil melampaui seluruh model dengan F1 Macro tertinggi sebesar 0,5433 dan Recall Macro sebesar 0,5350 setelah optimasi diterapkan. Optimasi PSO terbukti efektif meningkatkan performa LightGBM dan XGBoost, namun memberikan dampak negatif pada CatBoost yang konfigurasi dasarnya sudah mendekati optimal untuk dataset ini. Analisis SHAP mengonfirmasi bahwa Runoff Potential sebagai fitur rekayasa berbasis domain hidrologi menjadi penentu prediksi paling dominan, diikuti oleh fitur elevasi dan backscatter SAR yang membuktikan kontribusi signifikan integrasi data remote sensing terhadap kemampuan diskriminasi model.
EARLY DETECTION OF RAINFALL ANOMALIES USING LSTM AND ISOLATION FOREST Adhystira Raihannoeza Almadiva; Atika Ratna Dewi; Aina Latifa Riyana Putri
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 4 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss4pp3559-3574

Abstract

The uncertainty of daily rainfall patterns in Cilacap Regency with extreme variations makes it difficult to detect hydrological anomalies early using traditional methods. This study aims to obtain the most optimal LSTM parameters for rainfall prediction models, evaluate model performance using the Mean Squared Error (MSE) also Root Mean Squared Error (RMSE) metric, and predict rainfall anomalies for the next year using Isolation Forest. Daily BMKG data from January 2015 to December 2024 were processed through preprocessing stages, including missing data handling and time sequence creation. The Long Short Term Memory model was trained using regularization techniques to avoid overfitting, and the prediction results were analyzed using Isolation Forest to identify anomalies. The experiment showed that the best combination of hyperparameters was LSTM with 50 units and a tanh activation function, dropout 0.3, followed by a first dense layer of 50 units with ReLU activation and a single output layer. This configuration resulted in a validation MSE of 23.5247161 and RMSE 4.8502, this result identified five cases of anomalies that were validated for suitability based on rainfall data from BPBD. These results demonstrate the model's ability to reconstruct rainfall patterns and detect potential anomalies, so the system has potential to support early warning efforts for disaster mitigation and water resource management in Cilacap.
Forecasting boarding passenger volume at PT KAI Daop 5 Purwokerto using SARIMA and LSTM Fajar Tri Wahyuni; Atika Ratna Dewi
Journal of Natural Sciences and Mathematics Research Vol. 12 No. 1 (2026): June
Publisher : Faculty of Science and Technology, Universitas Islam Negeri Walisongo Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/jnsmr.v12i1.30880

Abstract

Railway transportation plays a vital role in supporting public mobility, requiring accurate passenger demand forecasting to assist operational planning, particularly during seasonal peak periods. This study aims to apply and compare the Seasonal Autoregressive Integrated Moving Average (SARIMA) and Long Short-Term Memory (LSTM) models for daily passenger boarding volume forecasting at PT Kereta Api Indonesia (KAI) Daop 5 Purwokerto. The dataset consists of daily passenger boarding records from January 1, 2023, to July 15, 2025, treated as a time series. For the SARIMA model, data preprocessing included log transformation, differencing, and seasonal differencing to achieve stationarity. Meanwhile, the LSTM model employed data normalization and sequence construction to capture nonlinear temporal dependencies. The experimental results show that the best SARIMA configuration is SARIMA(3,1,3)(0,1,1)7, while the optimal LSTM architecture uses a window size of 30 with two hidden layers of 64 and 32 units trained for 50 epochs. Performance evaluation on the test data indicates that the LSTM model outperforms SARIMA, achieving lower error values (RMSE = 2632.94; MAPE = 13.2%) compared to SARIMA (RMSE = 3399.48; MAPE = 17.9%). These findings indicate that, in this case study, the LSTM model achieved better forecasting performance than the SARIMA model on the selected hold-out test dataset (882 training observations and 45 testing observations). The obtained MAPE of 13.2% indicates good forecasting accuracy according to the adopted accuracy scale, suggesting that LSTM is a promising approach for supporting passenger demand forecasting at PT KAI Daop 5 Purwokerto.
PEMODELAN REGRESI DATA PANEL UNTUK MENGKAJI KEMISKINAN DI PULAU JAWA Atika Ratna Dewi Dewi; Ummi Athiyah
MATHunesa: Jurnal Ilmiah Matematika Vol. 14 No. 02 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v14n02.p1-9

Abstract

Poverty, a widespread socio-economic phenomenon, delineates the struggle of individuals and families to meet basic needs due to limited resources. Indonesia grapples with notably high poverty rates, especially within the ASEAN region. Java Island, home to over half of Indonesia's population, serves as a microcosm reflecting the nation's economic landscape. Analyzing Java's dynamics provides insights into Indonesia's overall poverty profile. A meticulous panel data regression analysis, drawing from authoritative sources like the Badan Pusat Statistik (BPS) and provincial government websites, elucidated poverty determinants on Java Island. Results underscored key factors: unemployment rate, average schooling length, and labor force participation, explaining variations with an R-squared of 0.901217. Such findings equip policymakers to craft targeted interventions. Leveraging empirical insights, policymakers can devise evidence-based strategies addressing socio-economic challenges fueling poverty. Through education reform, employment initiatives, and labor market interventions, policymakers aim to alleviate poverty, fostering inclusive growth and social development across Java Island and beyond.
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