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All Journal Indonesian Journal of Electrical Engineering and Computer Science Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi
Shafira, Lulu
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Computer Science & IT

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Implementasi Sistem Terdistribusi Aplikasi Mobile Rekomendasi Wisata Jawa Barat Menggunakan Framework Flutter Shafira, Lulu; Ikhsan, Ali Nur; Yuliastuti, Lulu; Faizah, Nailatul
Jurnal Ilmiah IT CIDA Vol 10 No 2: Desember 2024
Publisher : STMIK AMIKOM Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55635/jic.v10i2.213

Abstract

Indonesia negara kepulauan dengan kekayaan sumber daya alam dan budaya yang melimpah, memiliki potensi besar dalam industri pariwisata, yang berkontribusi positif terhadap perekonomian negara. Keberagaman budaya dan alamnya menarik wisatawan domestik dan mancanegara, menjadikannya sumber devisa penting. Dalam rangka memaksimalkan potensi ini, sistem rekomendasi wisata berbasis mobile menjadi solusi efektif. Aplikasi pariwisata berbasis Android dirancang untuk menyediakan informasi real-time dan cepat kepada wisatawan, menggunakan framework Flutter dan bahasa pemrograman Dart. Teknologi ini memanfaatkan API dan Firebase untuk mendukung pengembangan backend yang efisien. Penelitian ini mengembangkan aplikasi iGo, sebuah sistem rekomendasi wisata di Jawa Barat, yang menggabungkan pembelajaran mesin, API, komputasi awan, dan web service. iGo dirancang untuk membantu pengguna menentukan tujuan wisata berdasarkan rekomendasi yang diperoleh dari integrasi API dan penilaian pengunjung. Aplikasi ini menawarkan fitur yang dapat diakses online maupun offline, meskipun fitur rekomendasi hanya tersedia secara online. Implementasi sistem ini berhasil memberikan rekomendasi wisata dengan rating tertinggi, dan diharapkan dapat menjadi alat yang bermanfaat bagi pengguna dalam menentukan tujuan wisata di Jawa Barat.
Word embedding and imbalanced learning impact on Indonesian Quran ontology population Utomo, Fandy Setyo; Purwati, Yuli; Azmi, Mohd Sanusi; Shafira, Lulu; Trinarsih, Nikmah
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp603-613

Abstract

This research addresses limitations in Quranic instance classification, exceptionally high dimensionality, lack of semantic relationships in the term frequency-inverse document frequency (TF-IDF) technique, and imbalanced data distribution, which reduce prediction accuracy for minority classes. This study investigates the impact of word embedding and imbalance learning techniques on instance classification frameworks using Indonesian Quran translation and Tafsir datasets to handle previous research limitations. Four classification frameworks were built and evaluated using accuracy and hamming loss metrics. The results show that the synthetic minority oversampling technique (SMOTE) technique, TF-IDF model, and logistic regression classifier provide the best accuracy results of 62.74% and a hamming loss score of 0.3726 on the Quraish Shihab Tafsir dataset. This is better than the performance of previous classifiers backpropagation neural network (BPNN) and support vector machine (SVM) used in the previous framework, with accuracies of 59.91% and 62.26%, respectively. Logistic regression can also provide the best classification results with an accuracy of 67.92% and a hamming loss of 0.3208 using the previous framework. These results are better than the performance of the previous classifiers BPNN and SVM used in the previous framework, with accuracies of 62.26% and 66.98%, respectively. TF-IDF feature extraction outperforms word2vec in instance classification results due to its superior support under limited dataset conditions.
Co-Authors Azmi, Mohd Sanusi Faizah, Nailatul Fandy Setyo Utomo Ikhsan, Ali Nur Trinarsih, Nikmah Yuli Purwati Yuliastuti, Lulu