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All Journal JAMBURA JOURNAL OF PROBABILITY AND STATISTICS Venn: Journal of Sustainable Innovation on Education, Mathematics and Natural Sciences Ininnawa: Jurnal Pengabdian Masyarakat
Muh. Rifandi
Universitas Sulawesi Barat
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Energy Engineering Environmental Science Mathematics Physics Public Health Social Sciences Other

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Analisis Faktor-Faktor Penghambat Penyelesaian Studi Mahasiswa Program Studi Matematika Universitas Sulawesi Barat Menggunakan PLS-SEM Rahmah Abubakar; Muh. Rifandi; Rahmawati Rahmawati; Fatimah Fatimah
Jambura Journal of Probability and Statistics Vol 4, No 1 (2023): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v4i1.19240

Abstract

This research aims to examine the factors that influence the completion of students' studies at the University of West Sulawesi Mathematics Study Program. The high frequency of alumni with a length of study above the expected time is a polemic that needs to be solved and followed up. The research method analyses the partial least squares structural equation model (PLS-SEM). This study involved 2016, 2017 and 2018, batch students. Data collection used an online questionnaire. The results showed that the self-control factor and the intelligence and interest factor had a significant effect on students' motivation to complete their study on time. On the other hand, environmental factors and campus instrument factors do not have a significant effect.
Meningkatkan Pengetahuan dan Pemahaman Siswa Terhadap Materi Matematika dengan Menggunakan Software Geogebra Rahmah Abubakar; Meryta Febrilian Fatimah; Muh. Rifandi
Ininnawa : Jurnal Pengabdian Masyarakat Vol. 1 No. 1 (2023): Volume 01 Nomor 01 (April 2023)
Publisher : Program Studi Manajemen FEB UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26858/ininnawa.v1i1.173

Abstract

Pengabdian kepada masyarakat ini bertujuan untuk meningkatkan pemahaman pembelajaran matematika siswa kelas VIII SMP Negeri 1 Majene khususnya pada materi sistem persamaan linier dua variabel dan tiga variabel. Hal ini didasarkan pada hasil wawancara yang telah dilakukan siswa kelas VIII SMP Negeri 1 Majene dalam memahami materi serta menggambar grafik sangat kurang, terlihat dari nilai ulangan yang diberikan oleh guru mata pelajaran. Solusi atas permasalahan tersebut adalah pemberian pelatihan pengenalan dan penggunaan software Geogebra. Pelaksanaan pengabdian kepada masyarakat ini dilakukan melalui beberapa tahap yaitu tahap persiapan dengan komunikasi dengan mitra terkait permasalahan-permasalahan yang dihadapi dalam pelaksanaan pembelajaran matematika, tahap pelaksanaan dengan memberikan penjelasan materi/teori dan melakukan praktek penggunaan software Geogebra, serta tahap monitoring dan evaluasi dengan mengukur keberhasilan pelatihan penggunaan software Geogebra terhadap siswa kelas VIII SMP Negeri 1 Majene. Adapun hasil yang dicapai dari kegiatan ini adalah meningkatnya kemampuan siswa dalam menyelesaikan permasalahan sistem persamaan linier dua variabel dan tiga variabel.
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 Fatimah Fatimah Irwan Usman Kusnaeni Kusnaeni Meryta Febrilian Fatimah Muhammad Hidayatullah Rahmah Abubakar Rahmawati Rahmawati