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All Journal Community Empowerment Intellect : Indonesian Journal of Learning and Technological Innovation Ngudi Waluyo Empowerment : Jurnal Pengabdian Kepada Masyarakat Jurnal Komtika (Komputasi dan Informatika)
Wardhani, Olivia
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Arts Humanities Computer Science & IT Education Electrical & Electronics Engineering Engineering Environmental Science Public Health Social Sciences Other

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Digital literacy training for female employees at CV Gemilang Kencana Adiana, Beta Estri; Mareta, Affix; Fathony, Ikhwan Alfath Nurul; Wardhani, Olivia
Community Empowerment Vol 10 No 6 (2025)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ce.13520

Abstract

CV Gemilang Kencana, a Micro, Small, and Medium-sized Enterprise (MSME) in the food and beverage processing sector in Wonosobo Regency, faces challenges in leveraging digital technology due to low digital literacy among its employees, particularly women. This community service initiative aimed to enhance the digital literacy skills of female employees through training that covered basic technology introduction, productivity application usage, and the utilization of social media for product marketing. The implementation method involved preparation, training delivery, and evaluation stages. The training results demonstrated a significant improvement in participants' digital understanding and skills, with the average pre-test score of 46.5% increasing to 81% post-training. Active participants were able to independently utilize social media for product marketing and contributed to improved operational efficiency and MSME competitiveness. This training proved effective in empowering women and supporting digital transformation within the MSME sector, while also enhancing participants' capabilities in navigating the increasingly digitalized era.
A Review on Trends and Effectiveness of Rainfall Prediction Models for Smart Irrigation: Toward Future Development Wardhani, Olivia; Mayvandra Aurora Akbar, Rayfal Mayvandra Aurora Akbar; Athallahaufa Natawijaya, Yasabuana
Intellect : Indonesian Journal of Learning and Technological Innovation Vol. 4 No. 1 (2025): Intellect : Indonesian Journal of Learning and Technological Innovation
Publisher : Yayasan Lembaga Studi Makwa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57255/intellect.v4i1.1364

Abstract

Rainfall prediction is critical for enabling precision irrigation, particularly in tropical agricultural regions vulnerable to climate variability. This review systematically examines 15 peer-reviewed articles published between 2019 and 2024, using the PRISMA framework to evaluate the performance and applicability of rainfall prediction models for precision agriculture. The models are categorized into statistical (e.g., ARIMA), artificial intelligence (e.g., ANN, LSTM, ELM), and hybrid approaches (e.g., Neural Prophet–LSTM, ANFIS). Quantitative synthesis based on RMSE, MAE, MAPE, and R² reveals that hybrid models generally yield the highest predictive accuracy (e.g., RMSE = 0.0633; R² = 0.98), while AI models perform well on daily, nonlinear datasets but require extensive computational resources and expertise. In contrast, ARIMA remains the most practical and reliable option for monthly forecasting in data-scarce environments, offering a balance between accuracy and operational feasibility (e.g., RMSE = 69.506; MAPE = 31.41%). Contextual factors such as data availability, digital infrastructure, and user capacity significantly influence model suitability. The review also highlights real-world implementations and practical challenges—such as sensor limitations and technical skill gaps—associated with deploying advanced models. Ultimately, this review provides a comparative perspective to guide model selection based on statistical performance and implementation readiness. It further supports national food security goals by aligning predictive modeling with the operational needs of climate-resilient agriculture in supporting climate-resilient agriculture in tropical regions. Abstrak Prediksi curah hujan merupakan komponen penting dalam mendukung irigasi presisi, terutama di wilayah pertanian tropis yang rentan terhadap variabilitas iklim. Kajian ini secara sistematis menelaah 15 artikel ilmiah terbitan tahun 2019 hingga 2024 dengan menggunakan kerangka PRISMA, untuk mengevaluasi kinerja dan relevansi model prediksi curah hujan dalam konteks pertanian presisi. Model yang dianalisis mencakup pendekatan statistik (misalnya ARIMA), kecerdasan buatan (seperti ANN, LSTM, ELM), serta model hibrida (seperti Neural Prophet–LSTM dan ANFIS). Sintesis kuantitatif berdasarkan indikator RMSE, MAE, MAPE, dan R² menunjukkan bahwa model hibrida umumnya memberikan akurasi prediksi tertinggi (misalnya RMSE = 0,0633; R² = 0,98), sementara model AI efektif untuk data harian yang kompleks namun membutuhkan sumber daya komputasi dan keahlian teknis yang tinggi. Di sisi lain, ARIMA tetap menjadi pilihan paling praktis untuk peramalan bulanan di wilayah dengan keterbatasan data dan infrastruktur, karena mampu menyeimbangkan akurasi dan kemudahan operasional (misalnya RMSE = 69,506; MAPE = 31,41%). Faktor kontekstual seperti ketersediaan data, kesiapan infrastruktur digital, dan kapasitas pengguna sangat memengaruhi kesesuaian model. Kajian ini juga mengidentifikasi tantangan implementasi nyata, termasuk keterbatasan sensor dan rendahnya literasi teknologi. Secara keseluruhan, ulasan ini memberikan panduan komparatif dalam memilih model berdasarkan performa statistik dan kesiapan penerapan, serta mendukung upaya ketahanan pangan nasional melalui pemodelan prediksi yang kontekstual dan adaptif terhadap iklim.
Implementasi Model Convolutional Neural Network dalam Aplikasi Android untuk Identifikasi Limbah Infeksius Mareta, Affix; Estri Adiana, Beta; Wardhani, Olivia; Alfath Nurul Fathony, Ikhwan
Jurnal Komtika (Komputasi dan Informatika) Vol 8 No 2 (2024)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v8i2.12693

Abstract

After the COVID-19 pandemic passed, Indonesian citizens were still strict about using masks because active cases were still found. However, not all Indonesian people are aware that masks are an infectious waste, so after use, they are still disposed of carelessly. Apart from masks, other infectious waste in the form of battery waste which contains hazardous chemicals and food waste potentially to spread infectious diseases, is also dangerous for humans. These kinds of waste are major contributors to global pollution. Research on waste classification has been carried out a lot, but especially for infectious waste has not received much attention from researchers. For this reason, this research is useful to help the public distinguish infectious waste such as used food scraps, masks, and batteries so that they are more careful in disposing of waste. The research started with collecting datasets, which came from combining several infectious waste datasets available on the internet. This is done because there is no publicly available dataset that specifically contains infectious waste. Then, a classification model is created with Convolutional Neural Network (CNN) algorithm which has an accuracy of more than 90%. This algorithm has been widely used in previous studies but has never been used as a model applied to Android applications to classify infectious waste. In this study, the CNN model is applied to Android applications. From this research, an Android application with the CNN algorithm will be produced which can help Indonesians identify infectious waste with an accuracy of 94%.
Pemodelan dan Prediksi Curah Hujan Menggunakan SARIMA untuk Mendukung Perencanaan Irigasi Presisi di Kabupaten Temanggung Wardhani, Olivia; Wibowo, Rheza Ari; Fathony, Ikhwan Alfath Nurul Fathony; Adiana, Beta Estri; Natawijaya, Yasabuana Athallahaufa; Akbar, Rayfal Mayvandra Aurora
Intellect : Indonesian Journal of Learning and Technological Innovation Vol. 4 No. 02 (2025): Intellect : Indonesian Journal of Learning and Technological Innovation
Publisher : Yayasan Lembaga Studi Makwa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57255/intellect.v4i02.1642

Abstract

Changes in rainfall patterns in tropical regions increase uncertainty in agricultural water management, particularly in rainfed areas such as Temanggung Regency, Indonesia. This condition highlights the need for data-driven rainfall prediction models to support precision irrigation planning and drought risk mitigation. This study aims to develop rainfall and rainday prediction models using the Seasonal Autoregressive Integrated Moving Average (SARIMA) method based on monthly climatological data for the period 2014–2024. The analysis follows the Box–Jenkins procedure, including seasonal pattern exploration, stationarity testing, parameter identification using ACF and PACF, parameter estimation, and diagnostic and accuracy evaluation. The results indicate that the SARIMA(0,0,1)(1,0,1,12) model provides the best performance for rainfall prediction, achieving an RMSE of 99.92 mm and an MAE of 57.84 mm, while rainday prediction exhibits relatively higher errors. The model successfully captures consistent annual seasonal patterns and generates projections for 2025, indicating higher rainfall at the beginning of the year and a significant decrease during the dry season. These findings provide a quantitative basis for developing water availability risk calendars and adjusting precision irrigation strategies at the regional level, supporting sustainable water resource management and regional food security. Abstrak Perubahan pola curah hujan di wilayah tropis meningkatkan ketidakpastian dalam pengelolaan air pertanian, terutama pada wilayah tadah hujan seperti Kabupaten Temanggung. Kondisi ini menuntut pemanfaatan model prediksi berbasis data sebagai landasan perencanaan irigasi presisi dan mitigasi risiko kekeringan. Penelitian ini bertujuan untuk membangun model prediksi curah hujan dan hari hujan menggunakan metode Seasonal Autoregressive Integrated Moving Average (SARIMA) berbasis data klimatologis bulanan periode 2014–2024. Analisis dilakukan menggunakan prosedur Box–Jenkins yang mencakup eksplorasi pola musiman dan pengujian stasioneritas. Tahapan selanjutnya meliputi identifikasi parameter melalui ACF dan PACF, estimasi parameter, serta evaluasi diagnostik residual dan akurasi model. Hasil pemodelan menunjukkan bahwa model SARIMA(0,0,1)(1,0,1,12) memberikan kinerja terbaik untuk prediksi curah hujan dengan nilai RMSE sebesar 99,92 mm dan MAE sebesar 57,84 mm, sedangkan prediksi hari hujan menghasilkan tingkat kesalahan yang relatif lebih tinggi. Model mampu merepresentasikan pola musiman tahunan secara konsisten dan menghasilkan proyeksi tahun 2025 yang menunjukkan curah hujan tertinggi pada awal tahun serta penurunan signifikan pada periode kemarau. Temuan ini memberikan landasan kuantitatif untuk penyusunan kalender risiko ketersediaan air dan penyesuaian strategi irigasi presisi pada skala regional, sehingga mendukung pengelolaan sumber daya air dan ketahanan pangan daerah.
Cerdas dan Aman di Dunia Maya: Pemberdayaan Ibu Rumah Tangga Melalui Literasi Digital Novichasari, Suamanda Ika; Adiana, Beta Estri; Rakhmawati, Restu; Wardhani, Olivia
Ngudi Waluyo Empowerment: Jurnal Pengabdian Kepada Masyarakat Vol. 4 No. 2 (2025): Ngudi Waluyo Empowerment: Jurnal Pengabdian Kepada Masyarakat
Publisher : Fakultas Komputer dan Pendidikan Universitas Ngudi Waluyo

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

Abstract

Abstrak Kegiatan pengabdian ini dilatarbelakangi oleh rendahnya tingkat literasi digital di kalangan ibu rumah tangga di Desa Wirogomo, khususnya terkait keamanan dan privasi. Fokus program ini adalah pemberdayaan Kader Posyandu dan ibu rumah tangga. Tujuan utamanya adalah meningkatkan pemahaman mengenai pentingnya menjaga keamanan data pribadi, mengenali ancaman digital seperti phishing dan penipuan , serta membangun kemampuan praktis pengelolaan akun digital. Metode pelaksanaan mencakup empat tahap: (1) identifikasi dan persiapan melalui survei awal, (2) pelaksanaan program berupa workshop interaktif dan simulasi, (3) pendampingan dan evaluasi, serta (4) penyusunan luaran. Hasil kegiatan menunjukkan peningkatan pemahaman yang signifikan , di mana evaluasi akhir (post-test) mencatat lebih dari 80% peserta mampu menerapkan praktik keamanan digital dasar secara mandiri. Program ini berhasil membentuk kesadaran baru di masyarakat terkait pentingnya keamanan digital dalam kehidupan sehari-hari
Co-Authors Akbar, Rayfal Mayvandra Aurora Alfath Nurul Fathony, Ikhwan Athallahaufa Natawijaya, Yasabuana Beta Estri Adiana Fathony, Ikhwan Alfath Nurul Fathony Ikhwan Alfath Nurul Fathony Mareta, Affix Mayvandra Aurora Akbar, Rayfal Mayvandra Aurora Akbar Natawijaya, Yasabuana Athallahaufa Rakhmawati, Restu Suamanda Ika Novichasari Wibowo, Rheza Ari