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All Journal Joined Journal (Journal of Informatics Education Transformasi Jurnal Multidisiplin Pengabdian Masyarakat (JMPM)
Nugroho, Agus Susilo
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Perbandingan Algoritma Klasifikasi dalam Analisis Sentimen Opini Masyarakat tentang kenaikan harga Bbm Basedt, Ngabdul; Supriyadi, Eko; Nugroho, Agus Susilo
Joined Journal (Journal of Informatics Education) Vol 6 No 2 (2023): Volume 6 Nomor 2 (2023)
Publisher : Universitas Ivet

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31331/joined.v6i2.2893

Abstract

Kenaikan harga bahan bakar minyak (BBM) telah menjadi permasalahan yang cukup kompleks dan kontroversial . Peningkatan harga BBM memengaruhi berbagai aspek ekonomi dan sosial, termasuk inflasi, biaya produksi, dan tarif transportasi di Indonesia. Klasifikasi sentimen menggunakan algoritma Naïve Bayes, Support Vector Machine, dan K-Nearest Neighbors untuk menentukan algorimat klasifikasi sentimen manakah yang terbaik. Dengan melakukan perbangdingan metode algoritma Naïve Bayes, Support Vector Machine, dan K-Nearest Neighbors untuk menentukan algorimat klasifikasi sentimen manakah yang terbaik. Dengan melakukan perbangdingan algoritma klasifikasi sentimen menghasilkan akurasi yang paling tinggi didapatkan oleh algoritma Naive Bayes dengan akurasi sebesar 80,28%. Kedua adalah algoritma Support Vector Machine (SVM) dengan akurasi sebesar 73,89%. Algoritma yang memiliki nilai akurasi paling kecil adalah algorima K-Nearest Neighbor (KNN) dengan akurasi sebesar 50,00%.
PENGELOMPOKAN PERMINTAAN DARAH BERDASARKAN GOLONGAN DAN WAKTU DI KABUPATEN GROBOGAN DENGAN ALGORITMA K-MEANS triyono, andri; Santoso, Kartika Imam; Arum, Dhika Malita Puspita; Supriyadi, Eko; Nugroho, Agus Susilo
TRANSFORMASI Vol 21, No 1 (2025): TRANSFORMASI
Publisher : STMIK BINA PATRIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56357/jt.v21i1.418

Abstract

The availability of adequate blood supplies continues to pose a significant challenge for blood transfusion services such as the Indonesian Red Cross (PMI), particularly due to the fluctuating and uneven nature of demand across various blood groups. Incorrectly estimating blood demand can result in either critical shortages that jeopardize patient safety or an excess of supplies that are wasted due to the limited shelf life of blood. The objective of this research is to examine historical blood demand data in Grobogan Regency by applying the K-Means clustering algorithm to identify trends related to time intervals and blood group classifications. The study draws on secondary data involving blood requests across multiple blood groups over a span of several years. By implementing the K-Means method, the research identifies unique trends in demand, highlighting critical periods between 2013–2016 and 2022–2024, during which nearly all blood types showed elevated levels of demand. These insights are crucial for improving blood stock management, refining donor mobilization strategies, and enhancing distribution planning based on empirical patterns. The K-Means algorithm proves effective in handling extensive and continuous numerical data, offering valuable guidance for strategic decision-making in healthcare logistics.
Pemanfaatan Penggunaan Media Sosial Sebagai Sarana Edukasi Di Kalangan Pelajar Nugroho, Agus Susilo; Ariyanto, Arif Setia Sandi; Muryanto, Muhammad
Jurnal Arba - Multidisiplin Pengabdian Masyarakat Vol. 1 No. 1 (2024): Agustus
Publisher : Jurnal Arba - Multidisiplin Pengabdian Masyarakat

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Abstract

This article discusses using social media as a means of education among students. In this service activity, a survey was conducted to determine students' social media usage habits, followed by a workshop that provided an understanding of digital literacy and techniques for using social media for learning. The results show an increase in students' understanding of using social media as an educational tool after attending the workshop. However, the challenge of distinguishing between valid information and the temptation of entertainment content remains. The conclusion states that social media has great potential to support education if used correctly. Suggestions are given for schools, teachers, and parents to be more active in guiding and supervising students' use of social media and integrating digital literacy into the learning curriculum.
PENGELOMPOKAN PERMINTAAN DARAH BERDASARKAN GOLONGAN DAN WAKTU DI KABUPATEN GROBOGAN DENGAN ALGORITMA K-MEANS triyono, andri; Santoso, Kartika Imam; Arum, Dhika Malita Puspita; Supriyadi, Eko; Nugroho, Agus Susilo
TRANSFORMASI Vol 21, No 1 (2025): TRANSFORMASI
Publisher : STMIK BINA PATRIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56357/jt.v21i1.418

Abstract

The availability of adequate blood supplies continues to pose a significant challenge for blood transfusion services such as the Indonesian Red Cross (PMI), particularly due to the fluctuating and uneven nature of demand across various blood groups. Incorrectly estimating blood demand can result in either critical shortages that jeopardize patient safety or an excess of supplies that are wasted due to the limited shelf life of blood. The objective of this research is to examine historical blood demand data in Grobogan Regency by applying the K-Means clustering algorithm to identify trends related to time intervals and blood group classifications. The study draws on secondary data involving blood requests across multiple blood groups over a span of several years. By implementing the K-Means method, the research identifies unique trends in demand, highlighting critical periods between 2013–2016 and 2022–2024, during which nearly all blood types showed elevated levels of demand. These insights are crucial for improving blood stock management, refining donor mobilization strategies, and enhancing distribution planning based on empirical patterns. The K-Means algorithm proves effective in handling extensive and continuous numerical data, offering valuable guidance for strategic decision-making in healthcare logistics.
Co-Authors Arif Setia Sandi A. Arum, Dhika Malita Puspita Basedt, Ngabdul Eko Supriyadi Kartika Imam Santoso Muryanto, Muhammad Triyono, Andri