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All Journal Jiko (Jurnal Informatika dan komputer) Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI)
Windania Purba
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Computer Science & IT

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PERANCANGAN SISTEM INFORMASI PEMESANAN TIKET ONLINE PADA KMP.IHAN BATAK BERBASIS ANDROID Windania Purba; Dilona Ujunga; Sihalohoa, Tri Wahyuni Lestari; Damanik, Jantiaman
Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Vol. 3 No. 2 (2020): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Utility Project Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9767/jikomsi.v3i2.64

Abstract

Pada KMP Ihan Batak, sistem pemesanan tiket yang berlaku saat ini masihbelum online. Sistem pemesanan tiket yang demikian memiliki kelemahanantara lain, kurangnya sistem keamanan dalam pengolahan data. Untuk itulahdalam penelitianini dibuat suatu aplikasi pemesanan tiket berbasis mobile gunamenjawab permasalahan yang dihadapi selama ini. Untuk mendapatkan hasilyang maksimal dalam pemesanan tiket maka di buatlah suatu sistem yang dapatmempermudah penumpang dalam memesan tiket, agar penumpang tidak lagiantri dalam memesan tiket. Sistem yang akan dibangun adalah teknologi mobilepemesanan tiket. Dengan adanya sistem ini maka akan jauh lebih baikdibandingkan dengan sistem manual baik dari segi keamanan, kecepatan, sertaketertiban
COMPARATIVE ANALYSIS OF PSO AND FIREFLY OPTIMIZATION FOR VIOLENCE REPORT CLASSIFICATION Ryo Wijaya; Palma Juanta; Erick Simson; Ricky Ricky; Windania Purba
JIKO (Jurnal Informatika dan Komputer) Vol 8 No 2 (2025)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i2.9721

Abstract

Cases of violence against children and women continue to increase, but the handling of reports is often hampered by the large volume of incoming reports and the lengthy manual classification process. This study aims to address these issues by developing a method for automatically classifying reports of violence using the Support Vector Machine (SVM) algorithm optimized with Particle Swarm Optimization (PSO) and Firefly algorithms. The main objective is to group types of violence accurately to facilitate faster and more effective identification and handling. The research dataset consists of 500 reports obtained from Kaggle, with stages including text pre-processing, implementation of optimization algorithms, and evaluation based on accuracy, precision, recall, and misclassification error. The experiments were conducted using Python on the Google Colab platform. The results showed that PSO-SVM achieved an accuracy of 87.00% and a recall of 80.42%, outperforming Firefly-SVM which achieved an accuracy of 86.00% and a recall of 78.75%. Although Firefly-SVM demonstrated slightly higher precision (92.63%) compared to PSO-SVM (91.53%), PSO-SVM had a lower misclassification error (13.00% compared to 14.00%). These findings indicate that PSO-SVM is more effective for applications requiring better case detection, while Firefly-SVM is more suitable for applications prioritizing precision in positive predictions.
Co-Authors Damanik, Jantiaman Dilona Ujunga Erick Simson Palma Juanta Ricky Ricky Ryo Wijaya Sihalohoa, Tri Wahyuni Lestari