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All Journal JURNAL PENGABDIAN KEPADA MASYARAKAT JISTech (Journal of Islamic Science and Technology) JURIKOM (Jurnal Riset Komputer) JOURNAL OF SCIENCE AND SOCIAL RESEARCH Simtek : Jurnal Sistem Informasi dan Teknik Komputer CSRID (Computer Science Research and Its Development Journal) Technologia: Jurnal Ilmiah Jurnal Pendidikan dan Konseling Journal of Applied Engineering and Technological Science (JAETS) INFOKUM KLOROFIL: Jurnal Ilmu Biologi dan Terapan Syntax: Journal of Software Engineering, Computer Science and Information Technology Journal of Dinda : Data Science, Information Technology, and Data Analytics Jurnal Armada Informatika Journal of Information Systems and Technology Research Jurnal Informatika Teknologi dan Sains (Jinteks) JOMLAI: Journal of Machine Learning and Artificial Intelligence NEAR: Jurnal Pengabdian kepada Masyarakat
Yusuf Ramadhan Nasution
Universitas Islam Negeri Sumatera Utara
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Nursing Public Health Social Sciences Transportation Other

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PENGENALAN POLA PENYAKIT TANAMAN JAGUNG MENGGUNAKAN METODE PRINCIPAL COMPONENT ANALYSIS DAN K-NEAREST NEIGHBOR Citra Duvita Rahman; Yusuf Ramadhan Nasution
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 6 No 3 (2024): EDISI 21
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v6i3.4755

Abstract

Penelitian ini bertujuan untuk mengenali pola penyakit pada tanaman jagung menggunakan metode Principal Component Analysis (PCA) dan K-Nearest Neighbor (KNN). Penyakit pada tanaman jagung dapat menurunkan produktivitas dan kualitas hasil panen, sehingga diperlukan metode yang efektif untuk mengidentifikasi penyakit secara dini. PCA digunakan untuk mereduksi dimensi data yang tinggi tanpa kehilangan informasi yang signifikan, sehingga mempermudah proses pengenalan pola. Setelah itu, algoritma KNN diterapkan untuk mengklasifikasikan jenis penyakit berdasarkan pola yang telah terbentuk. Hasil penelitian menunjukkan bahwa kombinasi PCA dan KNN mampu memberikan akurasi yang tinggi dalam mengidentifikasi penyakit tanaman jagung. Implementasi metode ini diharapkan dapat membantu petani dalam mendeteksi penyakit lebih awal dan mengambil tindakan pencegahan yang tepat, sehingga meningkatkan hasil panen dan kesejahteraan petani.
Post-Election Sentiment Analysis 2024 via Twitter (X) Using the Naive Bayes Classifier Algorithm Yessi Mayasari; Yusuf Ramadhan Nasution
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 4 No 2 (2024): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v4i2.1582

Abstract

This study examines sentiment related to the topic of Twitter after the 2024 election, where the topic focused on the 2024 presidential election. Where there are a lot of public opinions and comments after the 2024 presidential election. One of them is the phenomenon when Anies-Muhaimin and Ganjar-Mahfud filed a lawsuit with the Constitutional Court (MK) to appeal over suspicions of fraud over the victory of the elected pair Prabowo-Gibran. By applying the Naïve Bayes Classifier algorithm to analyze public sentiment. Through data crawling, preprocessing, feature extraction, and sentiment classification, the study identified the dominant sentiment and its intensity among social media users. This methodology utilizes quantitative data analysis, using Twitter data linked to specific election-related hashtags. The findings reveal a mix of negative and positive sentiments, reflecting diverse public opinion about election results and related political developments. The accuracy of Naïve Bayes Classifier is highlighted, demonstrating its effectiveness in sentiment classification in the context of social media. This research contributes to understanding public sentiment in the political realm and improving methodological approaches in sentiment analysis using machine learning.
Digitalisasi UMKM Gula Merah Melalui Pemanfaatan Google Maps di Desa Pematang Serai, Kabupaten Langkat Rahmi Hidayah Nasution; Friti Sintiya Silva; Ulfi Yanti Harahap; T Zaskya Azhar Azaddin; Yusuf Ramadhan Nasution
NEAR: Jurnal Pengabdian kepada Masyarakat Vol. 5 No. 1 (2025): NEAR
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/nr.v5i1.3424

Abstract

Program pengabdian masyarakat ini dilaksanakan untuk mendukung peningkatan Usaha Mikro, Kecil, dan Menengah (UMKM) pengrajin gula merah di Desa Pematang Serai, Kabupaten Langkat melalui penerapan teknologi digital dalam kegiatan pemasaran. UMKM gula merah memiliki kontribusi besar terhadap perekonomian desa karena menjadi sumber pendapatan masyarakat, namun para pelaku usaha masih menghadapi hambatan pada aspek promosi dan akses ke pasar yang lebih luas. Melihat kondisi tersebut, dilakukan pendampingan yang berfokus pada penerapan digital marketing dengan memanfaatkan Google Maps serta platform media sosial sebagai sarana pemasaran modern. Penelitian ini menggunakan pendekatan deskriptif kualitatif melalui observasi, wawancara, dan dokumentasi selama pelaksanaan Kuliah Kerja Nyata (KKN). Temuan kegiatan menunjukkan bahwa pemanfaatan digitalisasi memberikan pengaruh positif terhadap perkembangan UMKM, seperti memperluas jangkauan promosi, meningkatkan kemudahan komunikasi dengan konsumen, dan memperkuat citra produk lokal. Penandaan lokasi usaha pada Google Maps serta promosi aktif melalui media sosial terbukti membantu produk gula merah Desa Pematang Serai lebih mudah diakses dan dikenal oleh masyarakat umum. Program ini diharapkan menjadi pijakan awal yang mampu memperkuat daya saing UMKM sekaligus mendukung pertumbuhan ekonomi desa secara berkelanjutan.
Design and Implementation of a Dual-Cloud IoT Air Quality Monitoring System Using Fuzzy Mamdani Method Fiqih Qodri Ramadani; Yusuf Ramadhan Nasution
Journal of Information Systems and Technology Research Vol. 4 No. 3 (2025): September 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i3.1326

Abstract

Air pollution continues to be a critical environmental issue that negatively impacts human health, ecosystems, and urban sustainability. Therefore, reliable air quality monitoring systems are urgently required to provide real-time and accurate information for both communities and decision-makers. This study presents the design and implementation of an Internet of Things (IoT)-based air quality monitoring system that integrates environmental sensors with an ESP32 microcontroller. A key novelty of this research is the adoption of a dual-cloud architecture, combining ThingSpeak and Blynk, to enhance data accessibility, visualization, and system reliability compared to conventional single-cloud approaches. The Fuzzy Mamdani method is applied to classify air quality levels into three categories: Good, Moderate, and Poor, using input variables of temperature, humidity, and gas concentration. Methodologically, the system was tested under multiple environmental conditions, and fuzzy membership functions and rules were carefully designed to reflect realistic thresholds. The results show that the dual-cloud system enables more robust and flexible monitoring, with faster data synchronization and higher reliability in remote visualization. Quantitatively, the system achieved a 92% expert validation score and demonstrated a 15% improvement in responsiveness compared to previous single-cloud implementations reported in the literature. The discussion highlights that dual-cloud visualization provides an effective solution to overcome downtime risks and single-point failures, while also improving user experience in accessing real-time air quality information. This research contributes to the growing body of work on IoT-based environmental monitoring and can serve as a foundation for future smart city applications.
Co-Authors Abdul Halim Hasugian Adjie Prakasa Viragupty Agung Hamdika Surya Ahmad Raja Aidil Halim Lubis Aidil Halim Lubis Aisyah Nurrahmah Siregar Ali Darta Aninda Muliani Citra Duvita Rahman Fahrul Azis Nasution Fikri Hakiki Siregar Fiqih Qodri Ramadani Friti Sintiya Silva Furqan, Muhammad Heri Santoso Ilka Zufria Jihan Ulfa Deski Khairuna Khairuna Khairunnisa Khairunnisa M Chairul Azmi M Ridho Ramadhan Mhd Furqan Mhd Ikhsan Rifki Mhd Reza Alfani Mhd. Furqan Muhammad Siddik Hasibuan Nurdianti, Taing Sapitri Rabiatul Adawiyah Hasibuan Rahmi Hidayah Nasution Raissa Amanda Putri Suhardi, Suhardi T Zaskya Azhar Azaddin Tri Allan Siddiq Ulfi Yanti Harahap Yessi Mayasari