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All Journal JURNAL SISTEM INFORMASI BISNIS Riau Journal of Computer Science Jurnal CoreIT JSFK (Jurnal Sains Farmasi & Klinis) Jurnal Riset Informatika Building of Informatics, Technology and Science Zonasi: Jurnal Sistem Informasi Warta Pengabdian Andalas: Jurnal Ilmiah Pengembangan Dan Penerapan Ipteks Bulletin of Computer Science Research Pharmaceutical Sciences and Research (PSR)
Rahmad Abdillah
Teknik Informatika Fakultas Sains dan Teknologi Universitas Islam Negeri Sultan Syarif Kasim Riau
Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Medicine & Pharmacology Public Health Social Sciences Other

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PENERAPAN METODE LOGISTIC REGRESSION UNTUK KLASIFIKASI SENTIMEN PADA DATASET TWITTER TERBATAS Putri, Adilah Atikah; Agustian, Surya; Abdillah, Rahmad; Pizaini, Pizaini
ZONAsi: Jurnal Sistem Informasi Vol. 7 No. 1 (2025): Publikasi artikel ZONAsi: Jurnal Sistem Informasi Periode Januari 2025
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/zn.v7i1.24804

Abstract

Kecepatan dan akurasi menjadi semakin penting dalam analisis sentimen publik, terutama di media sosial seperti Twitter, yang sering digunakan untuk menyampaikan opini terkait berbagai isu terkini. Penelitian ini mengaplikasikan metode Logistic Regression untuk klasifikasi sentimen pada dataset terbatas yang terdiri dari 300 sampel, yang dikategorikan menjadi sentimen positif, negatif, dan netral. Studi kasus mengeksplorasi respons masyarakat terhadap pengangkatan Kaesang Pangarep sebagai Ketua Umum Partai Solidaritas Indonesia (PSI) di Twitter. Data eksternal dari vaksinasi COVID-19 dan topik umum (open topic) digunakan dalam penelitian ini untuk meningkatkan proses klasifikasi. Metode TF-IDF digunakan untuk meningkatkan representasi teks. Grid Search digunakan untuk mengoptimalkan hyperparameter model. Evaluasi dilakukan menggunakan metrik F1-score untuk mengukur precision dan recall. Hasil baseline menunjukkan F1-score sebesar 40,83%, sementara berdasarkan hasil eksperimen yang dilakukan optimasi menghasilkan peningkatan hingga 52,68% dengan akurasi 61,76% pada eksperimen terbaik (C7). Penelitian ini menunjukkan bahwa metode Logistic Regression yang dioptimalkan dapat melakukan klasifikasi dengan dataset terbatas, yang relevan untuk analisis sentimen.
Utilization of Unprocessed Sugarcane Bagasse and Coffee Waste into Soap Products Rachmaini, Fitri; Abdillah, Rahmad; Gumala, Azhoma; Srangenge, Yoneta
Warta Pengabdian Andalas Vol 32 No 4 (2025)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LPPM) Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jwa.32.04.473-479.2025

Abstract

The increasing by-products from sugarcane and coffee consumption have resulted in a greater volume of sugarcane bagasse and coffee waste with potential environmental impacts. Physically and chemically, coffee and sugarcane waste can be processed into soap products without further processing. This community service activity aimed to provide education and practical skills in utilising coffee and sugarcane waste as raw materials for hand soap production. The activity was conducted at the Baitussalam Mosque in Gadut, Padang City, with 20 participants. The implementation methods included material presentation, demonstrations, and hands-on practice in liquid soap formulation. The soap formula used sugarcane and coffee waste as the main ingredients with additional components including Texapon as a surfactant, EDTA as a chelating agent, salt as a viscosity regulator, warm water, and fragrance. The findings indicated that participants comprehended the potential of utilising sugarcane and coffee waste, allowing them to produce a basic liquid soap product for daily usage. Soap made from sugarcane and coffee waste has the potential to become an organic product with antioxidant and antimicrobial benefits for the skin. This activity aims to increase public awareness of the importance of waste management and promote the development of environmentally friendly, nature-based creative enterprises.
SENTIMENT CLASSIFICATION OF PUBLIC PERCEPTIONS ON RP200 TRILLION HIMBARA STIMULUS USING NAÏVE BAYES Wan Sobri Amin; Fikry, Muhammad; Abdillah, Rahmad; Agustian, Surya
Jurnal Riset Informatika Vol. 8 No. 2 (2026): Maret 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i2.500

Abstract

The government's policy in the form of a fund stimulus of Rp200 trillion to the Himpunan Bank Milik Negara (HIMBARA) is a strategic step to maintain national economic stability and encourage real sector recovery. However, the implementation of public policy is inseparable from the response and public perception that develops on social media. This study aims to classify public sentiment towards the Rp200 trillion fund stimulus policy to Bank HIMBARA based on Instagram user comments and test the performance of the Naïve Bayes Classifier method in analyzing public policy sentiment. This study uses a quantitative approach with text mining and machine learning methods. Data in the form of 1.309 Instagram comments was collected through web scraping techniques from several online media accounts, then processed through text preprocessing and manual labeling stages into positive, neutral, and negative sentiments. Feature weighting was carried out using TF-IDF, then the data were classified using Multinomial Naïve Bayes and Complement Naïve Bayes. The results show that the Complement Naïve Bayes model achieved the best performance with an accuracy of 84%, an F1-score of 81%, and a high ROC-AUC value. These findings indicate that the majority of public sentiment toward the stimulus policy tends to be positive, and that the Naïve Bayes method is effective for social media–based sentiment analysis.
Co-Authors Azhoma Gumala Dian Ayu Juwita Dwi Afrianti, Rezy Elvia Budianita Fadhilah Syafria Fatma Sri Wahyuni Febi Yanto Fernando, Yendri Ikhlas Fitri Insani Harni, Yulia Iis Afrianty Meliani, Meliani Muhammad Fahri Muhammad Fikry Najwa, Nina Fadilah Okfalisa Okfalisa Pasiolo, Lugas Pizaini Pizaini Putri, Adilah Atikah Rachmaini, Fitri Reski Mai Candra Rifqi, Melvi Auliya Simaremare, Harris Simaremare Srangenge, Yoneta Surya Agustian Suwanto Sanjaya Wan Sobri Amin