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All Journal Sci-Tech Journal Adopsi Teknologi dan Sistem Informasi Journal of Empowerment and Community Service
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..  
Analisis Sentimen Ulasan Jembatan Repo-Repo di Google Maps Menggunakan Metode SVM dengan Ekstraksi Fitur BERT Ikram, Muhammad; Rudiman; Verdikha, Naufal Azmi
Adopsi Teknologi dan Sistem Informasi (ATASI) Vol. 5 No. 1 (2026): Adopsi Teknologi dan Sistem Informasi (ATASI)
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/atasi.v5i1.3588

Abstract

Penelitian ini bertujuan untuk meningkatkan akurasi analisis sentimen pada ulasan Jembatan Repo-Repo Kabupaten Tenggarong Kutai Kartanegara, Kalimantan Timur, yang diambil dari Google Maps. Penelitian sebelumnya menggunakan metode TF-IDF dan klasifikasi Naïve Bayes, namun hanya mencapai akurasi 58%. Untuk mengatasi keterbatasan tersebut, penelitian ini menerapkan ekstraksi fitur menggunakan IndoBERT dengan data yang sama dari sumber penelitian sebelumnya. Seleksi fitur digunakan dengan metode Chi-Square, dan klasifikasi menggunakan Support Vector Machine (SVM). Hasil penelitian menunjukkan bahwa seleksi fitur Chi-Square mampu mereduksi dimensi fitur dari 768 menjadi 100, serta meningkatkan akurasi klasifikasi dari 57% menjadi 65%. Selain akurasi, performa model juga meningkat pada metrik evaluasi lainnya, yaitu precision dari 55% menjadi 60%, recall dari 57% menjadi 65%, dan f1-score dari 56% menjadi 61%. Selain itu, waktu pelatihan SVM juga menjadi lebih efisien dengan penghematan sebesar 0,1092 detik. Temuan ini membuktikan bahwa kombinasi IndoBERT, Chi-Square, dan SVM efektif dalam meningkatkan performa klasifikasi sentimen berbasis teks.
Co-Authors Andromeda, Radhitya Fahrezi, Irqi Anbi Hasudungan, Rofilde Ikram, Muhammad Ilham, Muhammad Fauzan Nur Naufal Azmi Verdikha Reza, Andi Yahya, Alan