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All Journal JTEV (Jurnal Teknik Elektro dan Vokasional Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Prosiding SNATIF SMATIKA Proceeding of the Electrical Engineering Computer Science and Informatics JETT (Jurnal Elektro dan Telekomunikasi Terapan) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA VOLT : Jurnal Ilmiah Pendidikan Teknik Elektro Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Qua Teknika bit-Tech Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) International Journal of Research in Vocational Studies (IJRVOCAS) BEES: Bulletin of Electrical and Electronics Engineering semanTIK Smatika Jurnal : STIKI Informatika Jurnal
Ahmad Taqwa
Politeknik Negeri Sriwijaya
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Sistem Monitoring Non-Invasif Gula Darah, Denyut Jantung, dan SpO2 Berbasis Internet of Things Dimas Andreansyah; Ahmad Taqwa; Suroso Suroso
SemanTIK : Teknik Informasi Vol. 11 No. 2 (2025): SemanTIK : Teknik Informasi
Publisher : Informatics Engineering Department of Halu Oleo University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55679/semantik.v11i2.176

Abstract

Pemantauan kesehatan secara rutin memiliki peran penting dalam mendeteksi dini penyakit kronis seperti diabetes dan gangguan jantung. Namun, metode konvensional yang umumnya bersifat invasif sering menimbulkan ketidaknyamanan dan membutuhkan waktu serta biaya yang tidak sedikit. Berdasarkan permasalahan tersebut, penelitian ini mengembangkan sistem monitoring kesehatan non-invasif berbasis Internet of Things (IoT) yang mampu mengukur kadar gula darah, denyut jantung, dan kadar oksigen (SpO₂) tanpa pengambilan sampel darah. Sistem ini dirancang menggunakan mikrokontroler ESP32 sebagai pengendali utama dan sensor GY-MAX30102 sebagai sensor optik untuk mendeteksi sinyal fotopletismografi (PPG). Data hasil pengukuran dikalibrasi dengan alat medis standar guna meningkatkan akurasi dan reliabilitas. Hasil pengujian menunjukkan tingkat akurasi sebesar 95,3% untuk kadar gula darah, 95,9% untuk denyut jantung, dan 99,3% untuk kadar oksigen (SpO₂). Seluruh data pengukuran ditampilkan melalui LCD 16x2 serta dikirim secara real-time ke website monitoring menggunakan protokol MQTT untuk kemudahan pemantauan jarak jauh serta memberikan alternatif pemantauan kesehatan yang lebih praktis, efisien, dan nyaman, memungkinkan pengguna untuk memantau kondisi tubuh secara rutin tanpa harus melalui prosedur yang bersifat invasif. Regular health monitoring plays a crucial role in early detection of chronic diseases such as diabetes and heart disease. However, conventional, generally invasive methods often cause discomfort and are time-consuming and expensive. Based on these challenges, this study developed a non-invasive Internet of Things (IoT)-based health monitoring system capable of measuring blood sugar, heart rate, and oxygen (SpO₂) levels without blood sampling. The system was designed using an ESP32 microcontroller as the main controller and a GY-MAX30102 sensor as an optical sensor to detect photoplethysmography (PPG) signals. The measurement data was calibrated with standard medical devices to improve accuracy and reliability. The test results showed an accuracy rate of 95.3% for blood sugar, 95.9% for heart rate, and 99.3% for oxygen (SpO₂). All measurement data is displayed on a 16x2 LCD and sent in real-time to the monitoring website using the MQTT protocol for easy remote monitoring and provides a more practical, efficient, and convenient health monitoring alternative, allowing users to monitor their body condition regularly without having to undergo invasive procedures.
Sistem Pemilihan Rekomendasi Produk UMKM Kopi menggunakan Metode K-Nearest Neigbors M. Ardiansyah; Ahmad Taqwa; Ade Silvia Handayani
SemanTIK : Teknik Informasi Vol. 11 No. 2 (2025): SemanTIK : Teknik Informasi
Publisher : Informatics Engineering Department of Halu Oleo University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55679/semantik.v11i2.177

Abstract

Seiring dengan meningkatnya tren konsumsi kopi di berbagai kalangan dan semakin beragamnya preferensi konsumen terhadap produk kopi, hal ini juga dipicu oleh banyaknya produk kopi bermunculan di pasaran dengan berbagai varian harga, jenis olahan, dan asal kopi yang ditawarkan oleh pelaku Usaha Mikro, Kecil, dan Menengah (UMKM). Semakin banyaknya varian produk yang beredar membuat konsumen semakin sulit menemukan produk kopi yang sesuai dengan preferensi mereka. Metode K-Nearest Neighbors (KNN) menawarkan pendekatan yang efektif untuk membantu konsumen dalam menemukan rekomendasi produk kopi. Penelitian ini mengimplementasikan metode KNN mengukur jarak kedekatan antara preferensi pengguna dan karakteristik produk menggunakan dua metrik pengukuran, yaitu Euclidean dan Manhattan. Hasil evaluasi pengujian menunjukkan bahwa metrik jarak Euclidean memberikan tingkat akurasi tertinggi sebesar 92.2%, diikuti oleh Manhattan sebesar 91.8%. Berdasarkan hasil tersebut, Euclidean merupakan pilihan optimal dalam sistem rekomendasi yang dikembangkan, terbukti mampu memberikan rekomendasi produk kopi kepada konsumen dengan tingkat akurasi mencapai 92,2%. Along with the increasing trend of coffee consumption across various demographics and the growing diversity of consumer preferences for coffee products, this is also driven by the multitude of coffee products emerging in the market with various price ranges, types of processing, and origins offered by micro, small, and medium enterprises (MSMEs). The increasing variety of products available makes it more difficult for consumers to find coffee products that match their preferences. The K-Nearest Neighbors (KNN) method offers an effective approach to help consumers find coffee product recommendations. This research implements the KNN method to measure the proximity between user preferences and product characteristics using two measurement metrics, namely Euclidean and Manhattan. The evaluation results show that the Euclidean distance metric provides the highest accuracy level of 92.2%, followed by Manhattan at 91.8%. Based on these results, Euclidean is the optimal choice in the developed recommendation system and has proven capable of providing coffee product recommendations to consumers with the best recommendation results.
Co-Authors Abdul Rakhman Abdurrachman Faiz Ade Silvia Handayani Adewasti Aditia Prathama Alpha Rabbi Sugara Altrucia Pinem Amperawan Amperawan Bayu Sanjaya Ciksadan Ciksadan Dimas Andreansyah Dina Hervita Evelina, Evelina Fatahul Arifin Felisia Talitha Aprilia Hafizh Ulwan Handava Wardana Hesti, Emilia Ihsan Mustaqiim Indri Ariyanti Irma Salamah Isa, Indra Griha Tofik Lindawati Lindawati Lusi Risky Faradila M. Ardiansyah Martinus Mujur Rose Masayu Anisah Nadia Miranda Narita Tria Almirah Rosita Efriyanti Sabilal Rasyad Sholihin Sholihin Siti Fatima Sopian Soim, Sopian Suroso Suroso Syanno Revy Widia Salsabila Yanita Fibriliyanti