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All Journal Sci-Tech Journal Journal of Empowerment and Community Service Buffer Informatika
Rudiman
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Religion Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Public Health Social Sciences

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Digital Business Implementation for the Development of Basreng and Sus Kering Snack Sales through Instagram and Shopee in Samarinda City: Penerapan Bisnis Digital untuk Pengembangan Penjualan Snack Basreng dan Sus Kering melalui Instagram dan Shopee di Kota Samarinda Reza, Andi; Hasudungan, Rofilde; Ilham, Muhammad Fauzan Nur; Andromeda, Radhitya; Yahya, Alan; Rudiman
Journal of Empowerment and Community Service (JECSR) Vol. 3 No. 1 (2023): November
Publisher : Wadah Inovasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53622/jecsr.v3i1.372

Abstract

The implementation of digital business has become a key strategy to enhance the competitiveness and sales of MSME products, particularly in the snack sector such as Basreng and Sus Kering in Samarinda City. This community service activity aims to develop product marketing through the utilization of digital platforms Instagram and Shopee. The methods applied include designing product visual identity, creating promotional content, and collaborating with MSME actors in packaging and product distribution processes. The results show that Instagram is effective in building brand awareness and consumer engagement, while Shopee facilitates transactions and expands market reach. The digital business implementation is also supported by the use of supporting applications such as Canva for promotional design. In conclusion, digital marketing strategies can increase exposure and sales of MSME products, providing innovative solutions to conventional marketing challenges.
Analisis Sentimen Twitter Atas Isu Hak Angket Menggunakan Pembobotan TF-IDF dan Algoritma SVM Fahrezi, Irqi Anbi; Rudiman; Nauval Azmi Verdikha
Sci-tech Journal Vol. 3 No. 2 (2024): Sci-Tech Journal (STJ) In Press
Publisher : MES Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56709/stj.v3i2.526

Abstract

Social media has become an important platform for voicing public opinion. One of the most popular and frequently used social media is Twitter. Twitter is a popular social media in Indonesia for discussions on political issues. The topic that is being discussed is the "inquiry right" because of the alleged fraud that occurred in the 2024 elections. The alleged fraud in the 2024 elections raised issues related to the rolling of the right of inquiry aimed at finding out the oddity or fraud. Therefore, a method is needed to classify the opinion whether it is classified as a positive or negative sentiment. This research uses 1113 data obtained from Twitter social media by applying crawling techniques. The data goes through several preprocessing stages then feature extraction using Term Frequency-Inverse Document Frequency, split data, and Support Vector Machine algorithms. The test results using these stages obtained an accuracy of 75%, indicating that the applied method is effective in classifying public sentiment related to the inquiry right issue..  
Optimasi Ekstraksi Fitur TF-IDF Menggunakan Genetic Algorithm Pada Metode Support Vector Machine Dalam Menentukan Opini Publik Terhadap Keberlanjutan IKN Patimah, Siti; Rudiman; Yulianto, Fendy
Buffer Informatika Vol. 11 No. 2 (2025): Buffer Informatika
Publisher : Department of Informatics Engineering, Faculty of Computer Science, University of Kuningan, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Perubahan opini masyarakat mengenai keberlanjutan Kota Ibu Kota Negara (IKN) menjadi topik penting dalam memahami respon masyarakat terhadap kebijakan pemerintah. Penelitian ini bertujuan untuk menganalisis sentimen publik berdasarkan komentar YouTube dengan menggunakan metode Term Frekuensi-Inverse Document Frekuensi (TF-IDF) sebagai teknik ekstraksi fitur dan Support Vector Machine (SVM) sebagai algoritma klasifikasi. Untuk meningkatkan akurasi prediksi, digunakan Algoritma Genetika (GA) dalam optimasi parameter SVM. Dataset yang digunakan dalam penelitian ini terdiri dari komentar masyarakat di platform YouTube mengenai keberlanjutan IKN. Proses analisis diawali dengan preprocessing teks yang meliputi pelipatan kasus, penghapusan stopword, dan stemming. Selanjutnya fitur teks diekstraksi menggunakan TF-IDF dan diklasifikasikan menggunakan model SVM. Algoritma Genetika diterapkan untuk mencari parameter optimal sehingga kinerja model dapat ditingkatkan. Hasil penelitian menunjukkan bahwa pendekatan ini mampu mengklasifikasikan sentimen masyarakat ke dalam tiga kategori utama: positif, netral, dan negatif dengan tingkat akurasi lebih tinggi dibandingkan metode SVM tanpa optimasi. Penelitian ini diharapkan dapat memberikan wawasan bagi pengambil kebijakan dalam merancang strategi komunikasi publik dan memahami persepsi masyarakat terhadap keberlanjutan
Co-Authors Andromeda, Radhitya Fahrezi, Irqi Anbi Fendy Yulianto Hasudungan, Rofilde Ilham, Muhammad Fauzan Nur Naufal Azmi Verdikha Reza, Andi Siti Patimah Yahya, Alan