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All Journal Jurnal SITECH : Sistem Informasi dan Teknologi bit-Tech
Eka Putri, Siti Oktavia
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Computer Science & IT

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Analisis dan Perancangan Sistem Pick-Up Barang Menggunakan Metode Iconix Process Rismawati, Sinta Ayu; Eka Putri, Siti Oktavia; Ramadhani, Nasywa Zahira; Ana Wati, Seftin Fitri; Fitri, Anindo Saka
Jurnal SITECH : Sistem Informasi dan Teknologi Vol 6, No 1 (2023): JURNAL SITECH VOLUME 6 NO 1 TAHUN 2023
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/sitech.v6i1.9307

Abstract

CV. Kreasi Wisata adalah sebuah organisasi yang bergerak di bidang logistik sekaligus penyedia jasa kurir yang bekerja sama dengan PT. Lion Parcel Express. CV. Kreasi Wisata ternyata masih menggunakan metode yang kurang efektif pada proses bisnisnya, seperti penyampaian informasi pick-up lewat chat pribadi ke admin lalu diteruskan ke kurir tanpa penjadwalan yang terbilang kurang efektif. Oleh karena itu, perlu adanya sebuah sistem baru yang dapat memudahkan para aktor (Customer, Admin, dan Kurir) selama proses pick-up barang. Dalam artikel ini dibahas mengenai analisis dan perancangan sistem pick-up barang dengan menggunakan iconix process. Pengumpulan data dilakukan dengan metode observasi dan wawancara langsung ke pihak yang bersangkutan, ditambah dengan literature review dari jurnal yang berkaitan. Perancangan sistem yang diusulkan digambarkan dengan UML (Unified Modelling Language). Hasil dari analisis dan perancangan ini berupa desain sistem informasi yang dapat melakukan penjadwalan pick-up barang dari order yang dilakukan customer secara otomatis dan dilengkapi dengan fitur tracking. Untuk selanjutnya, diharapkan sistem ini dapat dilanjutkan ke tahap pengembangan dan diimplementasikan pada CV. Kreasi Wisata.
Convolutional Neural Network Approach for Aspect-Based Sentiment Analysis of Tourism Reviews Eka Putri, Siti Oktavia; Amalia Anjani Arifiyanti; Abdul Rezha Efrat Najaf
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2582

Abstract

The tourism industry is a key economic sector in Indonesia, with East Java ranking highest in tourist visits. This study aims to enhance tourism development by applying aspect-based sentiment analysis (ABSA) using convolutional neural networks (CNN) to analyze online reviews. CNN was selected for this study due to its proven efficiency in capturing local n-gram features and its relatively lower computational cost compared to other deep learning model. Reviews from TripAdvisor and Google Maps were collected focusing on four aspects: attraction, amenities, access, and price. Five different models were developed in this research: one multilabel aspect classifier designed to identify multiple aspects mentioned within each review, and four sentiment classifiers focused on evaluating the sentiment polarity for each specific aspect. These models were trained and evaluated using various combinations of word embeddings, including static embeddings like Word2Vec, and contextualized embeddings such as BERT and IndoBERT. Additionally, the impact of preprocessing through stemming was investigated to understand how simplifying word forms affects model performance. Results indicate that IndoBERT-CNN attains the best overall sentiment classification, reaching F1-scores up to 0.71 for attraction and 0.93 for amenities, while Word2Vec-CNN with stemming leads multilabel classification. Meanwhile stemming improves performance for static embeddings like Word2Vec by simplifying word forms, it reduces effectiveness in transformer-based models like BERT and IndoBERT that rely on natural language context. These findings highlight the benefit of choosing appropriate embeddings and preprocessing for different tasks, thus providing practical insights for improving tourism services through better tourist reviews analysis.
Co-Authors Abdul Rezha Efrat Najaf Amalia Anjani Arifiyanti Ana Wati, Seftin Fitri Fitri, Anindo Saka Ramadhani, Nasywa Zahira Rismawati, Sinta Ayu