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All Journal Jurnal Komputer Terapan BAREKENG: Jurnal Ilmu Matematika dan Terapan JITER-PM (Jurnal Inovasi Terapan - Pengabdian Masyarakat)
Elviyenti, Mona
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Health Professions Mathematics Mechanical Engineering Physics Social Sciences Transportation

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ON THE TOTAL VERTEX IRREGULARITY STRENGTH OF SERIES PARALLEL GRAPH sp(m,r,4) Marzuki, Corry Corazon; Utami, Aminah; Elviyenti, Mona; Muda, Yuslenita
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0213-0222

Abstract

his study aims to determine the total vertex irregularity strength on a series parallel graph for and . Total labeling is said to be vertex irregular, if the weights for each vertices are different. Determination of the total vertex irregularity of series parallel graph is done by obtaining the largest lower bound and the smallest upper bound. The lower bound is obtained by analyzing the structure of the graph to obtain the largest minimum label of k and the upper bound is analyzed by labeling the vertices and edges of the graph, where the largest label is k and the values for each vertices weight is different. The result obtained for the total vertex irregularity strength of a series parallel graph is .
Memperkuat Kelembagaan Vokasi dalam Mengoptimalkan Kemitraan Bersama Dunia Usaha dan Dunia Industri Melalui Kegiatan Business Matching and Public Discussion Elviyenti, Mona; Perdana, M. Alkadri; Trisnadoli, Anggy; Noviar, Andri; Hardiayanto; Rahmi, Elvi; Nasari, Fina; Yuliska
JITER-PM (Jurnal Inovasi Terapan - Pengabdian Masyarakat) Vol. 2 No. 3 (2024): JITER-PM
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/jiter-pm.v2i3.6513

Abstract

Pendidikan vokasi berperan penting dalam menyiapkan tenaga kerja yang kompeten dan siap menghadapi tantangan industri. Kegiatan Business Matching and Public Discussion merupakan upaya strategis untuk memperkuat kelembagaan vokasi melalui kemitraan dengan dunia usaha dan dunia industri (DUDI). Kegiatan ini diselenggarakan oleh Tim Penguatan Ekosistem Kemitraan Vokasi Provinsi Riau, yang terdiri dari Politeknik Bengkalis, Politeknik Caltex Riau, dan Politeknik Kampar, pada Selasa, 9 Juli 2024, bertempat di Gedung Serba Guna Politeknik Caltex Riau, Pekanbaru. Tujuan utama kegiatan ini adalah membangun jejaring kemitraan antara lembaga vokasi dan industri, mengidentifikasi kebutuhan tenaga kerja, serta menyelaraskan kurikulum vokasi dengan tuntutan pasar kerja. Rangkaian kegiatan meliputi sesi pembukaan, pemaparan dari dunia industri, diskusi panel, serta penandatanganan nota kesepahaman (MoU) antara industri dan lembaga vokasi. Hasil dari kegiatan ini menunjukkan bahwa kolaborasi antara vokasi dan industri dapat mempercepat implementasi program magang, rekrutmen tenaga kerja, serta peningkatan kualitas lulusan vokasi. Oleh karena itu, kegiatan serupa perlu dilakukan secara berkala guna memastikan kesinambungan kerja sama yang lebih erat antara pendidikan vokasi dan dunia industri dalam rangka meningkatkan daya saing tenaga kerja Indonesia khususnya di Provinsi Riau.
KLASIFIKASI SUARA JANTUNG MENGGUNAKAN DEEP LEARNING INTEGRASI AI DALAM APLIKASI WEB UNTUK DETEKSI DINI GANGGUAN KARDIOVASKULAR nengsih, warnia; Elviyenti, Mona; Fadhli, Mardhiah; Aleanda, Galih; Utama, Nuradila
Jurnal Komputer Terapan Vol 11 No 2 (2025): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/jkt.v11i2.6516

Abstract

Heart disease is one of the leading causes of death worldwide, making early detection crucial to prevent more serious complications. One of the methods that can be used is heart sound analysis, which contains important information related to the physiological and pathological conditions of the heart. However, the manual analysis process by healthcare professionals requires specialized skills and may result in interpretation errors. Therefore, this research aims to develop an artificial intelligence-based system using Convolutional Neural Networks (CNN) to automate heart sound classification. This system allows users to upload heart sound recordings, which will then be processed and classified as Normal or Abnormal. The research process consists of several main stages, including data collection and preprocessing of heart sounds, development and training of the CNN model, implementation of the model into a web application, and testing and evaluation of the system using metrics such as accuracy, precision, and recall. The outcome of this research includes a deep learning model for heart sound classification. The developed system is expected to enhance the accuracy and efficiency of heart disease detection, reduce reliance on manual analysis, and serve as an artificial intelligence-based solution that can be integrated into healthcare services.
Co-Authors Aleanda, Galih Anggy Trisnadoli Corry Corazon Marzuki Hardiayanto Mardhiah Fadhli, Mardhiah Nasari, Fina Noviar, Andri Perdana, M. Alkadri RAHMI, ELVI Utama, Nuradila Utami, Aminah Warnia Nengsih Yuliska Yuslenita Muda