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All Journal Jurnal Ilmiah Teknologi dan Rekayasa Sistemasi: Jurnal Sistem Informasi Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Jurnal Teknik Komputer AMIK BSI Jurnal Khatulistiwa Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING ILKOM Jurnal Ilmiah Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Infortech Jurnal Restikom : Riset Teknik Informatika dan Komputer EVOLUSI : Jurnal Sains dan Manajemen
Fuad Nur Hasan
Universitas Bina Sarana Informatika
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Neuroscience Transportation

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Analisis Sentimen Pengguna TikTok Terhadap Postingan Tiktok Smartfrenworld Menggunakan Algoritma Logistic Regression Arga Budi Mulya; Rifky Wahyu Eka Putra; Muhammad Arif Giovanni; Adheiktyo Februarjuna Putravani; Arya Ade Putra; Fuad Nur Hasan
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 6 (2025): Desember 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i6.9945

Abstract

Abstrak - Penelitian ini bertujuan melakukan analisis sentimen pengguna TikTok terhadap postingan akun resmi Smartfrenworld menggunakan algoritma Logistic Regression. Metode ini dipilih karena keunggulannya dalam efisiensi komputasi dan efektivitasnya yang tinggi dalam menangani klasifikasi biner pada data teks, sehingga cocok untuk analisis sentimen. Data yang digunakan adalah 4.200 komentar pengguna TikTok yang dikumpulkan melalui proses scraping. Metodologi penelitian mencakup tahap pengumpulan data, prapemrosesan data yang mencakup Case folding, Normalisasi Kata Slang, dan penghapusan Stopword, serta pelabelan sentimen menggunakan metode Rule-Based. Hasil pelabelan menunjukkan bahwa sentimen negatif lebih mendominasi, yaitu sebesar 52,31% atau 2.197 sampel, dibandingkan dengan sentimen positif sebesar 47,69% atau 2.003 sampel. Evaluasi kinerja model Logistic Regression sebagai algoritma Machine Learning menunjukkan hasil yang sangat baik dan stabil, dengan nilai akurasi keseluruhan mencapai 0,9286 (92,86%). Selain itu, model ini juga mencapai nilai Presisi, Recall, dan F1-Score yang seimbang, yaitu 0,93 untuk kedua kelas sentimen. Analisis WordCloud menunjukkan bahwa sentimen negatif berkaitan dengan berbagai kendala teknis layanan seperti "sinyal", "jaringan", dan "gangguan", sementara sentimen positif berkaitan dengan interaksi dan pelayanan pelanggan yang baik, terlihat dari kata kunci seperti "teman" dan "trimz".Kata kunci: Analisis Sentimen; TikTok; Logistic Regression; Smartfrenworld; Machine Learning; Abstract - This study aims to analyze TikTok user sentiment towards Smartfrenworld's official account posts using the Logistic Regression algorithm. This method was chosen because of its advantages in computational efficiency and its high effectiveness in handling binary classification on text data, making it suitable for sentiment analysis. The data used are 4,200 TikTok user comments collected through a scraping process. The research methodology includes data collection stages, data preprocessing including Case folding, Slang Normalization, and Stopword removal, as well as sentiment labeling using the Rule-Based method. The labeling results show that negative sentiment is more dominant, namely 52.31% or 2,197 samples, compared to positive sentiment at 47.69% or 2,003 samples. The performance evaluation of the Logistic Regression model as a Machine Learning algorithm shows excellent and stable results, with an overall accuracy value reaching 0.9286 (92.86%). In addition, this model also achieves balanced Precision, Recall, and F1-Score values, namely 0.93 for both sentiment classes. WordCloud analysis shows that negative sentiment is related to various technical service issues such as “signal,” “network,” and “interference,” while positive sentiment is related to good customer interaction and service, as seen from keywords such as “friends” and “thanks”.Keywords: Sentiment Analysis; TikTok; Logistic Regression; Smartfrenworld; Machine Learning;
Pendekatan computer vision untuk analisis fitur visual dalam estimasi produktivitas tanaman kopi Jenie Sundari; Ahmad Sinnun; Fuad Nur Hasan; Mulia Rahmayu
Jurnal Ilmiah Teknologi dan Rekayasa Vol. 31 No. 1 (2026)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/tr.2026.v31i1.182

Abstract

Coffee productivity is an important factor in supporting the sustainability of smallholder plantations, particularly in the Sukabumi region. However, conventional estimation methods that rely on manual observation tend to be subjective and inefficient. Although previous studies have applied computer vision and machine learning for yield prediction, most approaches depend on large-scale datasets and expensive sensing technologies, and often do not integrate multiple visual plant features comprehensively. This indicates a research gap in the use of simple RGB-based imaging for productivity estimation. This study aims to analyze visual features of coffee plants and develop a coffe productivity estimation model using the Random Forest algorithm. The dataset consists of 10 coffee plant images collected directly from field observations, with extracted features including fruit count, fruit maturity percentage, canopy area, leaf color, and leaf texture. Model evaluation is performed using Leave-One-Out Cross Validation (LOOCV) method to optimize data utilization on a limited dataset. The results show that the model achieves a Mean Absolute Error (MAE) of 0.06, a Root Mean Square Error (RMSE) of 0.07, and a coefficient of determination (R²) of 0.91. These results indicate good predictive performance within the available dataset. Feature importance analysis reveals that fruit count and fruit maturity percentage are the most influential factors in determining coffee productivity. This study contributes to the development of a low-cost image-based estimation approach that is practical and potentially applicable for smart agriculture in smallholder coffee plantations, although the findings remain preliminary due to the limited dataset size.
Co-Authors Abdul Latif Adheiktyo Februarjuna Putravani Ahmad Sinnun Arga Budi Mulya Ari Abdilah Arya Ade Putra Dini Nurlaela Dwi Yuni Utami Elah Nurlelah Hariani Jenie Sundari Kartika Handayani Lestari Yusuf Lila Dini Utami Lisnawanty Mahmud Syarif Muhammad Arif Giovanni Muhammad Rifqi Firdaus Mulia Rahmayu Narti Narti Noor Hasan Rangga Pebrianto Rifky Permana Rifky Wahyu Eka Putra Taufik Hidayatulloh Yohana Rosalinda Situmorang