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Universitas Almuslim
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics

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Rancang Bangun Sistem Monitoring Kesehatan Pasien Berbasis IoT dengan Peringatan Dini Budi Handoyo; Teuku Muhammad Johan; Imam Muslem R; Iqbal Iqbal
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 9 No. 2 (2025): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2025
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v9i2.26317

Abstract

Keterbatasan ketersediaan serta tingginya biaya perangkat pemantauan pasien konvensional di rumah sakit mendorong perlunya solusi alternatif yang terjangkau, fleksibel, dan mampu melakukan pemantauan secara real-time. Penelitian ini menyajikan perancangan dan implementasi sistem pemantauan kesehatan pasien berbasis Internet of Things (IoT) untuk perawatan inap non-intensif. Sistem ini memantau parameter vital utama, meliputi suhu tubuh, denyut jantung, dan saturasi oksigen darah, guna mendukung deteksi dini terhadap kondisi pasien yang tidak normal. Sistem dikembangkan menggunakan mikrokontroler ESP32 Lolin Lite yang terintegrasi dengan sensor MAX30102 untuk pengukuran denyut jantung dan saturasi oksigen, sensor DS18B20 untuk pengukuran suhu tubuh, layar OLED 0,91 inci untuk visualisasi lokal, serta jaringan Wi-Fi lokal menggunakan router WRT54G. Data pasien dikirimkan ke server lokal untuk penyimpanan yang aman dan ditampilkan melalui antarmuka berbasis web yang dapat diakses oleh tenaga medis dan keluarga pasien. Kinerja sistem dievaluasi dengan membandingkan hasil pengukuran terhadap alat pemantauan medis referensi menggunakan sepuluh sampel pengujian untuk setiap parameter. Hasil pengujian menunjukkan rata-rata persentase galat sebesar 1,31% untuk denyut jantung, 0,27% untuk suhu tubuh, dan 1,02% untuk saturasi oksigen darah, yang mengindikasikan tingkat akurasi pengukuran yang baik. Sistem yang dikembangkan terbukti layak sebagai solusi pemantauan pasien berbasis IoT yang andal dan berbiaya rendah, serta berpotensi untuk dikembangkan lebih lanjut dan diintegrasikan dengan sistem rekam medis rumah sakit.
Expert System for Diagnosing Hernia Disease (Herniae) Using the Forward Chaining Method Iqbal Iqbal; Dasril Azmi; Fitriani Fitriani
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 1 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i1.26943

Abstract

This study aims to analyze and design an expert system for diagnosing hernia using the Forward Chaining and Certainty Factor methods. This system was developed to assist in the process of determining a diagnosis based on the main symptoms experienced by the patient, while also providing appropriate treatment recommendations. To address the problem of knowledge uncertainty that often arises in expert systems, this study integrates the Certainty Factor method to measure the level of confidence in the diagnosis results. Meanwhile, the Forward Chaining method is used as a reasoning mechanism that starts from facts or symptoms provided by the patient to a conclusion in the form of a disease diagnosis. The diagnosis process in this system begins with a consultation session, where the system will ask a series of relevant questions according to the symptoms experienced by the patient. Based on the answers given, the system will make inferences to produce a diagnostic decision. Based on the test results, the expert system built is able to provide a diagnosis that is close to the assessment of medical experts. The Certainty Factor testing model provides advantages in measuring the level of confidence in the diagnosis, so that the results given are not absolute, but have a more realistic probabilistic value. Thus, an expert system for diagnosing hernia is able to overcome uncertainty and produce a confidence level value for the diagnosis. Based on test results, the system demonstrated a fairly good accuracy rate, around 90%–97%, depending on the combination of symptoms selected. Thus, the CF method is effective as an aid in initial diagnosis, although it still requires further examination by medical personnel.
IoT Based Fire Early Warning System Using ESP32 and Telegram with Multi Sensor Integration Budi Handoyo; Iqbal Iqbal
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 1 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i1.27142

Abstract

Fire poses a severe threat, leading to significant loss of life, property, and environmental damage, underscoring the critical need for early detection and rapid response. This study proposes and implements an Internet of Things (IoT)-based fire early warning system utilizing an ESP32 microcontroller, integrated with multiple sensors, and Telegram as a real-time communication platform. The system continuously monitors environmental conditions through a DHT22 temperature sensor, an MQ-7 carbon monoxide (CO) gas sensor, and a flame sensor. A rule-based approach is employed to classify conditions as either normal or fire events, based on predefined threshold values, specifically temperature greater than or equal to 40 degrees Celsius and CO concentration greater than or equal to 200 ppm. To ensure comprehensive alerting, the system incorporates a dual-layer warning mechanism, providing local alerts via a buzzer and remote notifications through Telegram. An interactive Telegram bot interface is also implemented, facilitating real-time monitoring and multi-user notification management. Performance evaluation, conducted using a confusion matrix with 300 testing samples consisting of 150 normal and 150 fire conditions, demonstrated high classification efficacy. The system achieved an accuracy of 92.6 percent, a precision of 93.2 percent, a recall of 92.0 percent, and an F1-score of 92.6 percent. Furthermore, the system exhibited excellent responsiveness, with an average notification delay of 3.2 seconds, indicating near real-time performance. This integration of multi-sensor detection and Telegram-based communication significantly enhances the reliability and accessibility of fire alerts, offering an effective, low-cost, and scalable solution suitable for various early fire warning applications.
Co-Authors Armita Jaya Asmaul Husna Auna Fajriah Budi Handoyo Bulan Nuri Dasril Azmi Fitriani Fitriani Hannan Asrawi Iskandar Zulkarnaini M. Fadlinur Miftahul Jannah Mifzal Muslem Muslem Muslem R, Imam Nafira Najmuddin MA, Najmuddin Natasya Aulia Sriwinar Sriwinar Teuku Muhammad Johan Zikriana, Lissa