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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmiah Informatika Komputer Jurnal Ilmiah Teknologi dan Rekayasa Prosiding KOMMIT Majalah Ilmiah Matematika Komputer Techno.Com: Jurnal Teknologi Informasi Jurnal Ilmiah KOMPUTASI JURNAL PERTANIAN PRESISI (JOURNAL OF PRECISION AGRICULTURE) INTECOMS: Journal of Information Technology and Computer Science Teknika Jurnal Pengabdian Kepada Masyarakat Darma Saskara Advance Sustainable Science, Engineering and Technology (ASSET)
Nur Sultan Salahuddin
Program Studi Sistem Komputer, Fakultas Ilmu Komputer Dan Teknologi Informatika Universitas Gunadarma
Agriculture, Biological Sciences & Forestry Automotive Engineering 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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Social Sciences

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Ekstraksi Wawasan Dari Platform X (Twitter): Aplikasi Crisp-Dm Dan Visualisasi Tableau Dalam Analisis Sentimen Debat Capres Dan Cawapres Pertama 2024 Di Indonesia Haiban, Marastrika Farhan Nur; Salahuddin, Nur Sultan
INTECOMS: Journal of Information Technology and Computer Science Vol 8 No 1 (2025): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/intecoms.v8i1.13311

Abstract

Analisis sentimen publik terkait debat capres-cawapres pertama tahun 2024 di Indonesia menggunakan data dari Platform X (Twitter). Penelitian ini menggunakan metodologi CRISP-DM yang meliputi tahapan Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, dan Deployment. Data dikumpulkan menggunakan teknik crawling Tweet-Harvest API, kemudian diolah menggunakan teknik Natural Language Processing (NLP) untuk mengelompokkan sentimen ke dalam tiga kategori, yaitu positif, negatif, dan netral. Pemodelan dilakukan menggunakan Random Forest dengan pembagian dataset sebesar 70% untuk data training dan 30% untuk data testing. Meskipun model menunjukkan akurasi yang tinggi pada data training, namun terdapat indikasi overfitting dan oversampling pada data testing. Untuk mengatasi hal tersebut, diterapkan teknik undersampling dan hyperparameter tuning yang menghasilkan akurasi model sebesar 62%. Hasil analisis sentimen divisualisasikan menggunakan Tableau sehingga menghasilkan dashboard yang interaktif sehingga mudah dipahami oleh audiens teknis maupun non-teknis. Analisis Sentimen ini memberikan wawasan mendalam tentang dinamika opini publik selama debat presiden dan wakil presiden pertama di Indonesia pada tahun 2024, dengan rekomendasi untuk pengembangan lebih lanjut guna meningkatkan kinerja model dan kualitas visualisasi. Kata kunci— Analisis Sentimen, CRISP-DM, Tableau, Debat Capres Cawapres Pertama, X (Twitter)
Development of a Robotic System for Agricultural Pest Detection: A Case Study on Chili Plants Nur Sultan Salahuddin; Fathi Muthia Tarie; Trini Saptariani
Advance Sustainable Science Engineering and Technology Vol. 7 No. 1 (2025): November-January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i1.1152

Abstract

Chili peppers, a key agricultural commodity in Indonesia, are highly susceptible to pest infestations and diseases, leading to significant economic losses and challenges in sustainable farming. This study presents the design and implementation of a real-time pest detection system that integrates robotics, computer vision, and deep learning to enhance agricultural productivity. The system is built on a Raspberry Pi 5 and Arduino Mega Pro Mini, utilizing a camera for image capture and ultrasonic sensors for navigation. A ResNet-based model was trained on a dataset of 2,703 chili leaf images, categorized into healthy and diseased classes, achieving a detection accuracy of  91%. The system provides early warnings to farmers through a web-based interface, allowing timely intervention and reducing reliance on chemical pesticides. While promising, the system faced challenges such as environmental variability, which influenced image recognition accuracy. By automating pest detection and promoting precision farming, this innovation addresses the need for sustainable agricultural practices, contributing to global food security and reducing environmental impact.
Co-Authors A.A. Ketut Agung Cahyawan W AA Sudharmawan, AA Ahmad S S Zambarkah Ahmad S S Zambarkah Aisyah Aisyah Alvien Yuliant Anacostiana Kowanda Aviana Furi Busono Soerowirdjo Darmawan, Andrea Dhoni Setyanto Fahnun, Budi Utami Fathi Muthia Tarie Fauziah Fauziah Febrin Ludia Ramadini Haiban, Marastrika Farhan Nur Hustinawaty Hustinawaty, Hustinawaty Karjadi, Mochamad Mohammad Iqbal Musyaffa, Aqilla Rahman Nur Iramadhan Poernomo Sari, Raden Roro Sri Qisthi Al Hazmi Hidayatur Rohman Rafi, Farid Al Sandy S. Prayogo Sari, RR. Sri Poernomo Sari, Sri Peornomo Sari, Sri Purnomo Sri Poernomo Sari Sri Poernomo Sari Sri Poernomo Sari Sri Poernomo Sari Suger, Herik Sulardi . Supiani Supiani, Supiani Supriyono Supriyono Sutiawan Tresno Trini Saptariani Trini Saptariani