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All Journal Journal of Applied Engineering and Technological Science (JAETS) JUKI : Jurnal Komputer dan Informatika Jurnal Pengabdian Masyarakat dan Riset Pendidikan
Kevin Ilham Apriandy
Politeknik Internasional Tamansiswa Mojokerto
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics 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.
Analysis of WebRTC Video Streaming Scalability in a Broadcaster-Viewer Model Using Scenario-Based Load Testing Achmad Torikul Huda; Doni El Rezen Purba; Roy Nuary Singarimbun; Muhammad Chaizir; Kevin Ilham Apriandy
JUKI : Jurnal Komputer dan Informatika Vol. 8 No. 1 (2026): JUKI : Jurnal Komputer dan Informatika, Edisi Mei 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53842/juki.v8i1.2607

Abstract

This study assesses the Quality of Service (QoS) performance of a Web Real-Time Communication (WebRTC)-based video streaming system operating on a local network utilizing a broadcaster-viewer model. The system was constructed with Node.js as the local server, accompanied by broadcaster and viewer web pages that facilitate laptop camera and microphone streaming, video conferencing, camera effects, and MP4 media playback. The research strategy utilized experimental performance evaluation through a scenario-based load testing approach and Quality of Service benchmarking. Testing was performed across three scenarios: one broadcaster with one viewer, one broadcaster with two viewers, and one broadcaster with three viewers. The examined QoS characteristics encompassed throughput, packet loss, delay, and jitter, assessed utilizing Wireshark. The test findings indicated that throughput escalated with the increase in viewers, rising from 2.525 Mbps in Scenario 1 to 5.026 Mbps in Scenario 3. Packet loss remained minimal throughout all scenarios, however it increased to 0.2% in Scenario 3. Scenario 2 had the largest average delay at 751.92 ms, whilst Scenario 3 demonstrated the lowest average delay at 349.06 ms and the minimal average jitter at 1.645 ms. The findings demonstrate that WebRTC can provide multi-viewer local streaming with steady performance; yet, a rise in watchers necessitates meticulous management of bandwidth and network stability to preserve video content quality.
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.
Co-Authors Achmad Torikul Huda Ashafidz Fauzan Dianta Doni El Rezen Purba Dwi Heru Siswantoro Kevin Harlis Oktaviano Kholid Fathoni Lutfia Puspa Indah Arum Muhammad Chaizir Oktavia Citra Resmi Rachmawati Pradiza, Revvan Rifada Rizky Yuniar Hakkun Roy Nuary Singarimbun Tri Budi Santoso Zakha Maisat Eka Darmawan