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All Journal JURIKOM (Jurnal Riset Komputer) Journal of Computer and Information System (J-CIS)
Heliawati Hamrul
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Optimasi Distribusi Buku menggunakan Algoritma Branch and Bound untuk Efisiensi Rute Terpendek Juliani; Heliawati Hamrul
Journal of Computer and Information System ( J-CIS ) Vol 5 No 2 (2022): J-CIS Vol 5 No. 2 Tahun 2022
Publisher : Universitas Sulawesi Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31605/jcis.v5i2.2550

Abstract

Pendistribusian buku di Polewali Mandar. Pendistribusian buku dilakukan selama jam sekolah, dari saat sekolah dibuka hingga ditutup. Namun, proses pendistribusian buku saat ini belum menggunakan strategi distribusi yang efektif, sehingga menghasilkan rute distribusi yang kurang optimal. Tujuan dari penelitian ini adalah untuk menangani masalah distribusi dengan menggunakan algoritma branch and bound untuk mengoptimalkan distribusi buku melalui metode travelling salesman problem. Setelah mengimplementasikan algoritma branch and bound, jarak rute awal yang semula 75,63 km menjadi 65,34 km, sehingga terjadi penghematan jarak sebesar 10,29 km. Selain itu, biaya pengantaran buku dapat dikurangi dari Rp 252.100 menjadi Rp 217.800, sehingga CV. Bumi Sarana Mandiri dapat menghemat biaya sebesar Rp 34.300. Dengan demikian, algoritma branch and bound dapat digunakan untuk menyelesaikan masalah distribusi buku yang lebih efektif melalui metode travelling salesman problem. Kata kunci— Distribusi Buku, Travelling Salesman Problem, Algoritma Branch and Bound
Analisis Perbandingan Pelabelan Inset Lexicon dan MBERT pada Sentimen Danantara Menggunakan SVM dengan Kernel Trick Rusmini; Heliawati Hamrul; A. Amirul Asnan Cirua
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9629

Abstract

This study aims to compare the Inset Lexicon and MBERT sentiment labeling methods for analyzing sentiment related to the Danantara issue using a Support Vector Machine (SVM) with Linear, RBF, Polynomial, and Sigmoid kernels. The main issues in this study are the suboptimal sentiment labeling methods for Indonesian-language data that can accurately capture linguistic context, as well as the uncertainty regarding the best labeling method when combined with various TF-IDF-based SVM kernels. Model evaluation uses metrics such as Accuracy, Precision, Recall, F1-score, and Cross-validation (CV). The results show that the Inset Lexicon labeling method with a Linear kernel yields the highest Accuracy of 81% with a CV of 78%, Precision of 87%, and Recall of 92%. The RBF kernel achieved an Accuracy of 78% with a CV of 76%, followed by the Sigmoid kernel at 79% Accuracy with a CV of 76%, and the Polynomial kernel at 65% Accuracy with a CV of 65%. The highest F1-score for the negative class using the Linear kernel reached 89%. Meanwhile, in MBERT labeling, the highest accuracy was achieved by the RBF kernel at 72% with a CV of 71%, followed by the Linear kernel with an accuracy of 71%, then the Polynomial kernel with an accuracy of 68%, and the Sigmoid kernel with an accuracy of 67%. The highest F1-score was found in the negative class at 79% using the RBF kernel. Overall, the negative class showed the most consistent performance, while the neutral class had the lowest Recall and F1-score values for almost all kernel types. These findings confirm that an in-depth comparative analysis between lexicon-based and deep learning-based approaches demonstrates that methods such as the Inset Lexicon can deliver better and more stable performance on Indonesian-language data.
Co-Authors A. Amirul Asnan Cirua Juliani RUSMINI