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All Journal International Journal of Advances in Applied Sciences Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Journal of Applied Engineering and Technological Science (JAETS) Jurnal Pengabdian Masyarakat dan Riset Pendidikan
Oktavia Citra Resmi Rachmawati
Politeknik Internasional Tamansiswa Mojokerto
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Physics Other

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Deep Embedded Clustering for Indonesian Protein, Fat, and Energy Availability Data Zakha Maisat Eka Darmawan; Oktavia Citra Resmi Rachmawati; Ashafidz Fauzan Dianta; Kholid Fathoni; Rizky Yuniar Hakkun; Tri Budi Santoso; Kevin Ilham Apriandy
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.8996

Abstract

Understanding disparities in regional food availability is crucial for food security policies. Most previous studies on Indonesian food availability use conventional clustering methods. These methods operate directly on the feature space and may miss complex, non-linear relationships in nutritional data. This limitation highlights the need for advanced analytical approaches to uncover deeper patterns. This study analyzes patterns of provincial food availability in Indonesia using Deep Embedded Clustering (DEC). It uses per capita indicators of energy, fat, and protein from both plant and animal sources, as well as the 2023 Food Consumption Pattern (FCP) score. DEC integrates representation learning with clustering. This allows the model to capture latent structures and nonlinear relationships that traditional clustering cannot identify. The analysis began by comparing K-Means and Hierarchical Clustering using the silhouette score to generate pseudo-labels for the DEC model. Hierarchical Clustering with Ward linkage and Euclidean distance achieved the highest silhouette score (0.3958) and was used for pseudo-label generation. Two DEC configurations were implemented, showing improved clustering performance. These achieved silhouette scores of 0.7829 (DEC-1) and 0.6385 (DEC-2). The results reveal four distinct clusters of Indonesian provinces, each with different food availability characteristics. These range from balanced, nutrient-rich regions to provinces with more limited or specific nutritional patterns. The findings show that DEC can capture complex structures in nutritional data. It produces more meaningful clusters than conventional approaches. In practice, the identified clusters provide policymakers, nutrition experts, and the food industry with useful insights for region-specific strategies. These strategies can improve food security and nutritional balance. Theoretically, this study contributes to the use of deep learning-based clustering in food availability analysis. It is especially relevant in national food security research. Future research may extend this approach by integrating time-series data and spatial analysis. This will help understand the temporal and regional dynamics of food availability in Indonesia.
Pelatihan Pemanfaatan Kecerdasan Buatan bagi Guru Sekolah Dasar di Surabaya untuk Mendukung Transformasi Pembelajaran Digital: Pengabdian Kevin Harlis Oktaviano; Kevin Ilham Apriandy; Oktavia Citra Resmi Rachmawati; Lutfia Puspa Indah Arum; Revvan Rifada Pradiza; Dwi Heru Siswantoro
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 5 No. 1 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 5 Nomor 1 (Juli 2026 -
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v5i1.6896

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan literasi dan kompetensi guru Sekolah Dasar (SD) di Surabaya dalam pemanfaatan teknologi kecerdasan buatan (AI) untuk mendukung proses pembelajaran. Kegiatan ini dilaksanakan dalam bentuk lokakarya yang diikuti oleh 102 peserta dan bertempat di SDN Keputran I/332. Metode pelaksanaan meliputi penyampaian materi, diskusi interaktif, serta praktik langsung penggunaan berbagai peralatan AI seperti NotebookLM, AI Poem Generator, dan Suno AI. Hasil kegiatan menunjukkan baiknya antusiasme peserta yang ditandai dengan partisipasi aktif selama sesi diskusi, kuis, dan praktik. Peserta mampu memahami konsep dasar AI serta mengaplikasikan teknologi tersebut untuk mendukung penyusunan materi pembelajaran, pembuatan konten edukatif, serta peningkatan produktivitas administratif kerja. Meskipun terdapat kendala seperti keterbatasan perangkat dan koneksi internet, kegiatan ini berhasil meningkatkan pemahaman awal dan kesiapan guru dalam mengadopsi teknologi AI guna menciptakan transformasi pembelajaran digital yang lebih adaptif terhadap perkembangan teknologi.
Adaptive sugarcane monitoring in Mojokerto using a hybrid powered IoT multi-sensor system and machine learning Sekar Sari; Oktavia Citra Resmi Rachmawati; Tole Sutikno
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp384-395

Abstract

This study develops a hybrid-powered IoT multi-sensor system integrated with machine learning for sugarcane monitoring in Mojokerto. Four sensors—soil moisture, pH, LM35 temperature, and LDR light—are connected to an Arduino UNO R4 WiFi microcontroller. A hybrid power supply (mains electricity and solar panels) and dual data storage (real-time transmission to Google Sheets and local SD backup) ensure resilience and reliability under field conditions. Sensor data are normalized and smoothed prior to analysis using K-Means clustering to map environmental states and a Random Forest classifier to predict crop health. Field validation demonstrates soil moisture as the most influential parameter, followed by temperature, pH, and light intensity. The Random Forest model achieved 93.01% accuracy, 93.88% precision, 99.02% recall, and a 96.38% F1-score on held-out data. By combining hybrid power, multi-sensor integration, dual storage, and machine learning, the system provides robust, data-informed monitoring that supports timely irrigation and management decisions in sugarcane cultivation.
Exploratory Data Analysis for Monitoring The Environment Variables of Sugarcane Growth Sekar Sari; Oktavia Citra Resmi Rachmawati
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i4.31360

Abstract

Sugarcane is vital to the national sugar industry and food security; however, its productivity is significantly affected by environmental factors, including temperature, light intensity, soil moisture, and pH. Fluctuations in these variables frequently lead to erratic yields and diminished sugar quality. Data obtained from IoT-based monitoring systems is often affected by noise, absent values, and outliers, complicating analysis. This research employs exploratory data analysis (EDA) on IoT-based sensor data to obtain comprehensive insights into environmental factors influencing sugarcane growth. The dataset contains 1,811 non-null entries from sensors that measure temperature, light, soil moisture, and pH. Data preparation encompassed cleansing, addressing missing values, and eliminating outliers. Univariate and multivariate analyses were conducted to evaluate variable distributions and correlations. The findings indicated that eliminating outliers improved data consistency and showed that temperature and pH had near-normal distributions, whereas light and soil moisture were skewed. A correlation study revealed moderate associations between light and pH, while regression analysis confirmed a favorable relationship between light intensity and pH. This research emphasizes enhancing the dependability and interpretability of IoT-based monitoring data through EDA, providing significant insights for precision agriculture. Future research may concentrate on predictive modeling and real-time decision-support systems to enhance farming operations.
Co-Authors Ashafidz Fauzan Dianta Dwi Heru Siswantoro Kevin Harlis Oktaviano Kevin Ilham Apriandy Kholid Fathoni Lutfia Puspa Indah Arum Pradiza, Revvan Rifada Rizky Yuniar Hakkun Sekar Sari Tole Sutikno Tri Budi Santoso Zakha Maisat Eka Darmawan