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All Journal Jurnal Teknik Informatika JOIV : International Journal on Informatics Visualization
Nababan, Adi Pandu Rahmat
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Library & Information Science

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Analisis Sentimen Twitter Pasca Pengumuman Hasil Pilpres 2019 Menggunakan Metode Lexicon Analysis Nababan, Adi Pandu Rahmat; Lumenta, Arie Salmon Matius; Rindengan, Yaulie Deo; Pontoh, Fransisca Joanet; Akay, Yuri Vanli
Jurnal Teknik Informatika Vol 15, No 1 (2020): JURNAL TEKNIK INFORMATIKA
Publisher : Universitas Sam Ratulangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35793/jti.15.1.2020.29030

Abstract

Perkembangan yang cepat dalam bidang teknologi informasi menjadi sebuah paradigma baru. Twitter salah satu produk teknologi yang memungkinkan pengguna menyampaikan informasi peristiwa dari dunia nyata ke media sosial sehingga dapat diketahui khalayak luas lewat media internet. Salah satu peristiwa menarik adalah pengumuman hasil Pilpres oleh KPU pada tanggal 21 Mei 2019. Banyak kicauan di twitter menjadi trending topik yang berhubungan dengan kejadian tersebut. Karena itu pada penelitian ini akan dilakukan penarikan data tweet, kemudian menganalisis data dengan metode lexicon analysis. Lalu menampilkan data yang telah dianalisis. Pada penelitian ini terdapat 21 hashtag trending topik yang ditarik. Selanjutnya data tweet melalui tahap preprosesing yaitu tokenizing, normalisasi kata, filtering dan stemming. Data dianalisis menggunakan metode lexicon analisis dan dukungan kamus lexicon dan dijalankan dalam bahasa R untuk menentukan sentimen positif, negatif dan netral. Dari hasil penelitian sentimen positif   tertinggi  pada hashtag #JokowiAminSudahMenang sebanyak 54%. Sentimen negatif   tertinggi    pada   hashtag    #PrabowoBukanPemimpin sebanyak 51%, serta sentimen netral tertinggi terdapat pada #98jagademokrasi sebanyak 72%. Lexicon analysis dapat mengklasifikasikan data tweet dalam tiga kelas yaitu positif, negatif dan netral. Dan tingkat akurasi analisis sangat ditentukan oleh banyaknya jumlah kata pada kamus lexicon.
Classification of Coral Images Using Support Vector Machine with Gray Level Co-Occurrence Matrix Feature Extraction Nababan, Adi Pandu Rahmat; Haryanto, Toto; Wijaya, Sony Hartono
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2708

Abstract

This research developed a coral image classification method using Support Vector Machine (SVM) with Gray Level Co-occurrence Matrix (GLCM) feature extraction to improve the accuracy of coral reef condition monitoring. Coral images were collected in the waters of Sangihe Islands Regency and labelled by experts for healthy, unhealthy, and dead categories. Preprocessing included cropping, background removal, sharpening, and image normalization. GLCM feature extraction was performed with a distance of 1, 2, and 3 pixels and directions of 0°, 45°, 90°, and 135°. SVM uses Linear, Radial Basis Function, and Polynomial kernels with parameters set in a grid. The results indicate that the polynomial kernel with parameters C=10, degree=3, and gamma=1 achieves the highest accuracy, at 91.85%. Oversampling increased the accuracy by 2.17%, while feature selection by boxplot and model-based decreased the accuracy by 0.8% and 0.2%, respectively. On the other hand, feature selection using correlation analysis significantly decreased accuracy by 16.11%. These findings significantly contribute to coral reef conservation by offering a more accurate and efficient classification method. This method enables better and timely monitoring of coral reef conditions, thus supporting more effective conservation interventions. Integrating these research results into IoT systems can improve overall coral reef monitoring and conservation efforts.
Co-Authors Lumenta, Arie Salmon Matius Pontoh, Fransisca Joanet Rindengan, Yaulie Deo Sony Hartono Wijaya Toto Haryanto Yuri Vanli Akay