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All Journal Innovative: Journal Of Social Science Research Jurnal Informatika: Jurnal Pengembangan IT
Panggabean, Trisatin
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Humanities Computer Science & IT Law, Crime, Criminology & Criminal Justice Public Health Other

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Pemanfaatan Ikan Kembung Sebagai Bahan Baku Otak-Otak untuk Meningkatkan Daya Saing UMKM di Desa Indrayaman Prayogi, Agung; Sagala, Ismaniar Hasanah; Sari, Linda; Basyir, Muhammad Khalidin; Muin, Mohd Iqbal Abdul; Panggabean, Trisatin; Siagian, Yudi Maulana
Innovative: Journal Of Social Science Research Vol. 5 No. 1 (2025): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v5i1.18018

Abstract

Setiap orang menginginkan kehidupan yang sejahtera untuk menjalani kehidupan sehari-hari dengan memenuhi kebutuhan ekonominya akan pangan, sandang, dan papan. Berbagai upaya dilakukan untuk mencapai tujuan masyarakat. Salah satu cara untuk mencapai hal tersebut dengan menciptakan usaha mikro, kecil dan menengah (UMKM). Tujuan dari penelitian ini untuk meningkatkan daya saing UMKM di desa Indrayaman dengan memanfaatkan ikan kembung sebagai bahan baku otak-otak. Metode penelitian yang digunakan yaitu metode kualitatif dengan pengumpulan data melalui observasi, wawancara, dan pelatihan. Hasil penelitian menunjukkan peningkatan minat masyarakat desa Indrayaman terhadap konsumsi ikan kembung dan kontribusi positif terhadap pendapatan UMKM setempat. Meskipun masih terdapat tantangan dalam hal standarisasi produk, kegiatan ini berhasil mempromosikan potensi ekonomi lokal dan meningkatkan pengetahuan mengenai manfaat ikan bagi kesehatan. Upaya ini diharapkan dapat memberikan kontribusi pada peningkatan daya saing UMKM dan pengembangan produk pangan lokal. Program ini juga memberikan pelatihan diversifikasi produk perikanan, yang berdampak pada peningkatan penjualan lokal.
Analisis Emosi Komentar Pengguna TikTok terhadap Film Jumbo Menggunakan Metode Naive Bayes Panggabean, Trisatin; Putri, Raissa Amanda
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 1 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i1.10036

Abstract

TikTok has become a widely used social media platform where users actively express opinions through comment features. This study aims to classify the emotions contained in TikTok user comments on the Indonesian animated film Jumbo using the Naive Bayes Classifier method. The dataset consisted of 1,341 comments collected from the official Visinema Pictures account using the Apify Web Scraper. The collected data were processed through several preprocessing stages, including case folding, tokenization, normalization, stopword removal, and stemming using the Sastrawi library. Emotion labeling was performed based on the Indonesian NRC EmoLex lexicon by categorizing comments into three emotional classes: angry, happy, and sad. Feature extraction was conducted using the TF-IDF weighting method to generate relevant text representations and identify dominant terms in each emotional category. The dataset was divided into 80% training data and 20% testing data to evaluate the model performance. The experimental results show that the Naive Bayes model achieved an accuracy of 78.81%. The emotion distribution indicates that anger was the most dominant class with 904 comments, followed by happy with 415 comments, and sad with 22 comments. The model demonstrated the best performance in the anger class, achieving 100% recall, 75% precision, and an F1-score of 85.71%. However, the classification performance for minority classes, particularly happy and sad, still requires improvement. This research contributes to the development of text mining-based emotion analysis and provides insights into audience emotional responses that may support film evaluation and marketing strategies.
Co-Authors Basyir, Muhammad Khalidin Muin, Mohd Iqbal Abdul Prayogi, Agung Raissa Amanda Putri Sagala, Ismaniar Hasanah Sari, Linda Siagian, Yudi Maulana