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All Journal Intellect : Indonesian Journal of Learning and Technological Innovation Indonesian Journal of Education Research (IJoER) Komunita: Jurnal Pengabdian dan Pemberdayaan Masyarakat Ngudi Waluyo Empowerment : Jurnal Pengabdian Kepada Masyarakat
Olivia Wardhani
Universitas Tidar
Arts Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

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Evaluating Risks in an Outcome-Based Education Assessment Information System: A Qualitative Case Study in Higher Education Olivia Wardhani; Pringgo Widyo Laksono
Indonesian Journal of Education Research (IJoER) Vol. 7 No. 3 (2026): June
Publisher : Cahaya Ilmu Cendekia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37251/ijoer.v7i3.2705

Abstract

Purpose of the study: This study aims to identify, categorize, and prioritize risks in an Outcome-Based Education (OBE)-based assessment information system related to learning outcome evaluation, Program Learning Outcomes–Course Learning Outcomes assessment processes, and institutional quality assurance implementation in higher education. Methodology: This study used a qualitative case study approach with the Project Management Body of Knowledge® Guide Sixth Edition Project Risk Management framework, Risk Breakdown Structure (RBS), and qualitative probability–impact matrix. Data collection methods included document analysis, direct observation, semi-structured interviews, prototype system review, and Program Learning Outcomes–Course Learning Outcomes mapping validation. Main Findings: Eighteen risks were identified across technical, data-related, operational, human resource, curriculum-related, and infrastructure categories. High-priority risks involved inconsistencies in Program Learning Outcomes–Course Learning Outcomes mapping and assessment weighting structures. Extreme risks included system integration limitations, developer dependency, and infrastructure readiness issues affecting learning outcome evaluation and assessment consistency. Novelty/Originality of this study: This study presents a structured qualitative risk analysis framework for Outcome-Based Education (OBE)-based assessment information systems by integrating educational evaluation perspectives with project risk management approaches. The study highlights how organizational, operational, and data-related risks influence learning outcome assessment validity and institutional quality assurance processes.
Pemanfaatan Pestisida Nabati Dalam Pengendalian Hama Tanaman Sayuran Di Desa Drono, Kabupaten Temanggung Olivia Wardhani; Marisa Adilah Putri; Noval Hadad
KOMUNITA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol 5 No 2 (2026): Mei
Publisher : PELITA NUSA TENGGARA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60004/komunita.v5i2.645

Abstract

Utilization of Botanical Pesticides in Controlling Vegetable Plant Pests in Drono Village, Temanggung Regency. The use of synthetic pesticides on vegetable crops is still a common practice among farmers because it is considered effective in controlling pests, but it has negative impacts on the environment and health. One environmentally friendly alternative is the use of botanical pesticides made from papaya leaves (Carica papaya L.) which contain bioactive compounds such as papain, alkaloids, flavonoids, terpenoids, and saponins. This community service activity aims to improve farmers' knowledge and skills in the manufacture and use of botanical pesticides as an effort to control aphid pests (Aphis gossypii Glover) on vegetable crops in Drono Village, Temanggung Regency. The method used is a participatory approach through the stages of observation, socialization and counseling, as well as demonstrations and direct practice. The activity evaluation was carried out using pre-test and post-test instruments for 33 participants covering six assessment aspects. The results of the activity showed an increase in the average knowledge score of participants from 32% in the pre-test to 86% in the post-test, representing an improvement of 54 percentage. In addition, most participants were able to follow and practice the stages of making botanical pesticides well.
A Review on Trends and Effectiveness of Rainfall Prediction Models for Smart Irrigation: Toward Future Development Olivia Wardhani; Rayfal Mayvandra Aurora Akbar Mayvandra Aurora Akbar; Yasabuana Athallahaufa Natawijaya
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.
Pemodelan dan Prediksi Curah Hujan Menggunakan SARIMA untuk Mendukung Perencanaan Irigasi Presisi di Kabupaten Temanggung Olivia Wardhani; Rheza Ari Wibowo; Ikhwan Alfath Nurul Fathony Fathony; Beta Estri Adiana; Yasabuana Athallahaufa Natawijaya; Rayfal Mayvandra Aurora Akbar
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 Suamanda Ika Novichasari; Beta Estri Adiana; Restu Rakhmawati; Olivia Wardhani
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
Pelatihan AI bagi Ibu Rumah Tangga: Meningkatkan Peran Orang Tua dalam Mendukung Prestasi Akademik Anak Restu Rakhmawati; Olivia Wardhani; Suamanda Ika Novochasari; Beta Estri Adiana; Danar Cahyo Prakoso; Fadhila Syahida Wibowo; Zharifa Nur Majidah
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

Kegiatan ini bertujuan mengatasi rendahnya literasi digital dan pemahaman Kecerdasan Buatan (AI) di kalangan ibu rumah tangga Dusun Jerukwangi yang berdampak pada kurang optimalnya pendampingan akademik anak, terutama pada Matematika dan Bahasa Inggris. Mereka juga menghadapi kesulitan struktural berupa tingkat pendidikan yang relatif rendah, beban peran ganda, dan kesenjangan kurikulum. Program pengabdian ini menggunakan metode pelatihan yang mengombinasikan teori dan praktik, dengan fokus pada tutorial penggunaan aplikasi AI yang mudah diakses (ChatGPT dan Photomath) pada smartphone. Untuk keberlanjutan, dibentuk komunitas belajar berbasis WhatsApp. Hasil observasi awal menunjukkan nilai akademik anak rata-rata (50-80). Evaluasi pasca-pelatihan mengonfirmasi bahwa ibu rumah tangga telah mulai memanfaatkan aplikasi AI untuk mendampingi anak mengerjakan pekerjaan rumah. Intervensi terbukti kompatibel, namun temuan menunjukkan bahwa masih ada peserta yang kesulitan implementasi, yang diyakini dipengaruhi oleh faktor struktural tersebut. Disimpulkan bahwa literasi teknologi AI dapat ditingkatkan, namun implementasi praktis memerlukan pendampingan yang lebih intensif untuk menjembatani gap pemahaman dan peran ganda ibu, demi memaksimalkan dampak pada prestasi akademik anak.
Co-Authors Beta Estri Adiana Danar Cahyo Prakoso Fadhila Syahida Wibowo Ikhwan Alfath Nurul Fathony Fathony Marisa Adilah Putri Noval Hadad Pringgo Widyo Laksono Rayfal Mayvandra Aurora Akbar Rayfal Mayvandra Aurora Akbar Mayvandra Aurora Akbar Restu Rakhmawati Rheza Ari Wibowo Suamanda Ika Novichasari Suamanda Ika Novochasari Yasabuana Athallahaufa Natawijaya Zharifa Nur Majidah