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All Journal Abdimas Toddopuli: Jurnal Pengabdian Pada Masyarakat Venn: Journal of Sustainable Innovation on Education, Mathematics and Natural Sciences
Kusnaeni Kusnaeni
Institut Teknologi Bacharuddin Jusuf Habibie
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Environmental Science Mathematics Physics Other

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Sosialisasi Teknologi Tepat Guna: Stainless steel Sebagai Pengganti Plastik untuk Wadah Makanan dan Minuman Bagi UPT SMAS Al Iman Uluale Putri Mutia Monica; Suriyanto Bakri; Nurmalasari Nurmalasari; Muhammad Rifki Nisardi; Syukrika Putri; Yusri Prayitna; Hartina Husain; Kusnaeni Kusnaeni; Muhammad Zhaky Putra; Dian Safitri
Abdimas Toddopuli: Jurnal Pengabdian Pada Masyarakat Vol. 7 No. 2 (2026): Volume 7, No 2, Juni 2026
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/00rq2x08

Abstract

Penggunaan wadah makanan berbahan plastik masih dominan di kalangan siswa SMA karena sifatnya yang praktis dan ekonomis. Namun, penggunaan berulang dan paparan panas berpotensi menyebabkan pelepasan mikroplastik yang berdampak pada kesehatan. Di sisi lain, stainless steel merupakan material alternatif yang memiliki ketahanan korosi tinggi, sifat higienis, serta umur pakai yang lebih panjang. Permasalahan utama yang dihadapi mitra adalah rendahnya pemahaman siswa mengenai risiko penggunaan plastik serta keunggulan stainless steel sebagai material yang lebih aman. Kegiatan pengabdian ini bertujuan meningkatkan literasi sains siswa melalui sosialisasi teknologi tepat guna. Metode yang digunakan meliputi pemaparan materi interaktif, diskusi, serta evaluasi melalui pre-test dan post-test. Hasil kegiatan menunjukkan adanya peningkatan pemahaman siswa terkait bahaya mikroplastik dan pentingnya pemilihan material yang tepat. Kegiatan ini berkontribusi dalam membentuk kesadaran siswa terhadap penggunaan material yang aman, higienis, dan berkelanjutan dalam kehidupan sehari-hari.
Perbandingan Analisis Sentimen Ulasan Produk pada Platform E-Commerce Menggunakan Algoritma Naïve Bayes dan Random Forest Afif Budi Andy B; Kusnaeni Kusnaeni; Irwan Usman; Muhammad Hidayatullah; Muh. Rifandi
Venn: Journal of Sustainable Innovation on Education, Mathematics and Natural Sciences Vol. 5 No. 3 (2026): Riset Matematika dan Pendidikan Matematika
Publisher : Pusat Studi Bahasa dan Publikasi Ilmiah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53696/venn.v5i3.458

Abstract

Sentiment analysis has become increasingly important in e-commerce because product reviews influence consumer purchasing decisions and provide feedback for sellers to evaluate product quality and improve services. The large number of online reviews on e-commerce platforms makes manual analysis inefficient and time-consuming, thereby requiring automated sentiment classification methods that are accurate and computationally efficient. This study aims to compare the performance of the Multinomial Naïve Bayes and Random Forest algorithms in classifying sentiment in Tokopedia product reviews using the PRDECT-ID dataset, which consists of 5,400 Indonesian-language reviews. The research methodology involved several preprocessing stages, including case folding, cleaning, normalization, tokenization, stopword removal, and stemming using the Sastrawi library, followed by feature extraction using the TF-IDF method. The dataset was divided using a stratified random split approach with 80% training data and 20% testing data, and the models were evaluated using accuracy, precision, recall, F1-score, and ROC-AUC metrics. The results indicate that Multinomial Naïve Bayes outperformed Random Forest, achieving an accuracy of 93.59%, precision of 91.82%, recall of 94.65%, F1-score of 93.21%, and ROC-AUC of 0.9813. In comparison, Random Forest achieved an accuracy of 90.35%, precision of 85.63%, recall of 93.67%, F1-score of 89.47%, and ROC-AUC of 0.9635. In addition to its superior classification performance, Multinomial Naïve Bayes also demonstrated greater computational efficiency with significantly faster training time. These findings suggest that Multinomial Naïve Bayes is a more effective approach for sentiment classification of Indonesian-language e-commerce product reviews.
Co-Authors Afif Budi Andy B Dian Safitri Hartina Husain Irwan Usman Muh. Rifandi Muhammad Hidayatullah Muhammad Rifki Nisardi Muhammad Zhaky Putra Nurmalasari Nurmalasari Putri Mutia Monica Suriyanto Bakri Syukrika Putri Yusri Prayitna