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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Techno.Com: Jurnal Teknologi Informasi Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) International Journal of Advances in Intelligent Informatics EMITTER International Journal of Engineering Technology KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Technomedia Journal Applied Technology and Computing Science Journal GUYUB: Journal of Community Engagement Proceeding ISETH (International Summit on Science, Technology, and Humanity) Prosiding Seminar Nasional Rekayasa Teknologi Industri dan Informasi ReTII Jurnal Informatika: Jurnal Pengembangan IT
Tessy Badriyah, Tessy
Politeknik Elektronika Negeri Surabaya
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Journal : JOIV : International Journal on Informatics Visualization

 1 
Development of a Java Library with Bacterial Foraging Optimization for Feature Selection of High-Dimensional Data Badriyah, Tessy; Syarif, Iwan; Hardiyanti, Fitriani Rohmah
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2149

Abstract

High-dimensional data allows researchers to conduct comprehensive analyses. However, such data often exhibits characteristics like small sample sizes, class imbalance, and high complexity, posing challenges for classification. One approach employed to tackle high-dimensional data is feature selection. This study uses the Bacterial Foraging Optimization (BFO) algorithm for feature selection. A dedicated BFO Java library is developed to extend the capabilities of WEKA for feature selection purposes. Experimental results confirm the successful integration of BFO. The outcomes of BFO's feature selection are then compared against those of other evolutionary algorithms, namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Ant Colony Optimization (ACO).  Comparison of algorithms conducted using the same datasets.  The experimental results indicate that BFO effectively reduces features while maintaining consistent accuracy. In 4 out of 9 datasets, BFO outperforms other algorithms, showcasing superior processing time performance in 6 datasets. BFO is a favorable choice for selecting features in high-dimensional datasets, providing consistent accuracy and effective processing. The optimal fraction of features in the Ovarian Cancer dataset signifies that the dataset retains a minimal number of selected attributes. Consequently, the learning process gains speed due to the reduced feature set. Remarkably, accuracy substantially increased, rising from 0.868 before feature selection to 0.886 after feature selection. The classification processing time has also been significantly shortened, completing the task in just 0.3 seconds, marking a remarkable improvement from the previous 56.8 seconds.
Personalized Tourism in Surabaya: A Bayesian Network Approach Faradisa, Rosiyah; Badriyah, Tessy; Maulana, Hanan Ammar; Assidiqi, Moh Hasbi
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.3376

Abstract

This study investigates the application of Bayesian Networks in developing a personalized tourist destination recommendation system focused on Surabaya, Indonesia. The research incorporates push and pulls factors alongside tourist activities as key input variables to model decision-making processes. Two distinct Directed Acyclic Graph (DAG) structures are evaluated: one proposed based on existing theoretical frameworks and another generated from empirical respondent data. The dataset comprises responses from 1,350 tourists visiting twenty-five popular attractions in Surabaya. The analysis reveals that Bayesian Networks effectively identify correlations between various influencing factors. From the tests carried out, the accuracy obtained from the two DAG structures did not significantly differ. The proposed DAG achieved 35% accuracy for the top-ranked destination recommendations, while the data-driven DAG was 25%. Both achieved 75% accuracy in the top five recommendations. The accuracy increased as the number of output states was reduced. Meanwhile, in the test with binary output, BN was able to accurately classify tourist destinations with an average accuracy of 95% for both DAGs. These findings highlight the potential of Bayesian Networks to enhance tourism decision support systems by providing nuanced insights into tourists' preferences and motivations. For further research, hybridization or feature engineering can be employed to improve model accuracy. In addition, determining more appropriate push factors and tourist activities based on the tourism case studies also needs to be done to obtain better tourist preferences. This research highlights the promising role of Bayesian Networks in improving the personalization and effectiveness of tourist recommendations.
Co-Authors Achmad Basuki Achmad Basuki Adhi P., Basik Afifah, Izza Nur Agung Muliawan Ali Ridho Barakbah Alwan Fauzi Andri Permana Wicaksono APRIANDY, KEVIN ILHAM Arna Fariza Assidiqi, Moh Hasbi Assodiky, Hilmy Bima Sena Bayu Dewantara Binti Kholifah Dadet Pramadihanto Daisy Rahmania Syarif Deyana Kusuma Wardani Dimas Bagus Santoso Faradisa, Rosiyah Fitri Setyorini Hamida, Silfiana Nur Hardiyanti, Fitriani Rohmah Hilmy Assodiky Huda, Achmad Thorikul Idris Winarno Ika Restuningtyas Ira Prasetyaningrum iwan Syarif Kholifah, Binti Maulana, Hanan Ammar Maulana, Yufri Isnaini Rochmat Muhlis Tahir Nur Sakinah Pahlevi, Muhammad Nur Riza Prasetyo Wibowo Rabiatul Adawiyah Rachmawati, Oktavia Citra Resmi Ria Rahmawati Rosiyah Faradisa Rudi Kurniawan Rulisiana Widodo Selvia Ferdiana Kusuma Sesulihatien, Wahjoe Tjatur Setiawardhana, Setiawardhana Solichul Huda Susanti, Puspasari Tri Harsono Wicaksono, Andri Permana Willy Sandhika Yufri Isnaini Rochmat Maulana