Article Per Year (5 Year)
p-Index From 2021 - 2026
2.842
P-Index
This Author published in this journals
All Journal JUITA : Jurnal Informatika Panrita Abdi - Jurnal Pengabdian pada Masyarakat JTERA (Jurnal Teknologi Rekayasa) JMM (Jurnal Masyarakat Mandiri) CARADDE: Jurnal Pengabdian Kepada Masyarakat MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Infotekmesin Community Empowerment Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Informatika: Jurnal Pengembangan IT Pengabdian Deli Sumatera
Dwi Intan Af’idah
Politeknik Harapan Bersama
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering Public Health Social Sciences Other

Published : 18 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 18 Documents
Search

 1   2 
Aspect-Based Sentiment Analysis for Indonesian Tourist Attraction Reviews Using Bidirectional Long Short-Term Memory Dwi Intan Af'idah; Puput Dewi Anggraeni; Muhammad Rizki; Aji Bagus Setiawan; Sharfina Febbi Handayani
JUITA: Jurnal Informatika JUITA Vol. 11 No. 1, May 2023
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v11i1.15341

Abstract

The tourism sector in Indonesia experienced growth and made a positive contribution to the national economy, but this growth has yet to reach its target. Therefore, the government of Indonesia has implemented a sustainable tourism development program by establishing ten priority tourism destinations. Aspect-based sentiment analysis (ABSA) towards tourist attraction reviews can assist the government in developing potential goals. The ABSA process compares with two deep learning models (LSTM and Bi-LSTM), which are considered to obtain good performance in text analysis. The shortcomings of previous ABSA research should have examined the performance of the aspect classification and sentiment classification models sequentially. This makes the performance obtained from the ABSA task invalid. Thus, this study is conducted to determine the version of the aspect classification model and the sentiment classification model individually and simultaneously. This study aims to develop an aspect-based tourist attraction sentiment analysis as an intelligent system solution for sustainable tourism development by applying the binary relevance mechanism and the best deep learning model from LSTM or Bi-LSTM. The test results showed that Bi-LSTM was superior in aspect and sentiment classification individually and simultaneously. Likewise, the aspect classification and sentiment classification test results sequentially Bi-LSTM outperformed that of LSTM. The average accuracy and f1 score of Bi-LSTM are 92.22% and 71,06%. Meanwhile, LSTM obtained 90,63% of average precision and 70,4% of f1 score.
Pengaruh Parameter Word2Vec terhadap Performa Deep Learning pada Klasifikasi Sentimen Dwi Intan Af'idah; Dairoh Dairoh; Sharfina Febbi Handayani; Riszki Wijayatun Pratiwi
Jurnal Informatika: Jurnal Pengembangan IT Vol 6, No 3 (2021): JPIT, September 2021
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v6i3.3016

Abstract

The difficulty of sentiment classification on this big data can be overcome using deep learning. Before the deep learning training and testing process is carried out, a word features extraction process is needed. Word2Vec as a word features extraction is often used in sentiment classification pre-training because it can capture the semantic meaning of the text by representing a similar vector for each word that has a close meaning. Word2Vec has three parameters that affect the model learning process namely architecture, evaluation method, and dimensions. This study aims to determine the effect of each Word2Vec parameter on deep learning performance in sentiment classification. The accuracy results of the deep learning model were evaluated to determine the effect of the Word2Vec parameter. The results of this study indicate that the three Word2Vec parameters have an influence on the performance of the deep learning model in sentiment classification. The combination of Word2Vec parameters that produces the highest average accuracy include CBOW (Continuous Bag of Word) architecture, Hierarchical Softmax evaluation method, and a dimension of 100. CBOW produces better performance, because it has slightly better accuracy for words that often appear and in this research dataset there are many words that often appear. Hierarchical Softmax shows better results because it uses a binary tree model which makes words that occur rarely will inherit the vector representation above them. The dimension with a value of 100 produces better accuracy because it is in line with the number of datasets of 10,000 reviews.  
APLIKASI PANDUAN UNTUK MENGENAL TANAMAN OBAT BERBENTUK RIMPANG BERBASIS IMAGE PROCESSING MENGGUNAKAN METODE VGG16 Aldi Budi Rianta; Dwi Intan Af'idah; Ardi Susanto
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 5 No 4 (2023): EDISI 18
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v5i4.3691

Abstract

Biodiversity in Indonesia, especially in medicinal plants with rhizome forms, is the focus of this research. With diverse geographical conditions, Indonesia possesses a wealth of traditional medicinal plant species. Previous studies have indicated limitations in manual plant identification, prompting the need for technology to support plant recognition. This research develops a guide application based on image processing using the VGG16 method to identify medicinal plants with rhizome forms. The methodology involves dataset collection, image preprocessing, VGG16 implementation, model evaluation using a confusion matrix, and application development. Black box testing of the application demonstrates success in plant identification, interface responsiveness, and data security. Results of image analysis and application development show optimal performance. Thus, this application can be considered successful in supporting the recognition of medicinal plants with rhizome forms.
PENGEMBANGAN APLIKASI PEMANDU WISATA GUCI BERBASIS MOBILE MELALUI PRINSIP USER-CENTERED DESIGN (UCD) Muhammad Fikri Hidayattullah; Dwi Intan Af’idah; Sharfina Febbi Handayani; Putri Ajeng Imamah
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 5 No 4 (2023): EDISI 18
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v5i4.3694

Abstract

Pariwisata memainkan peran krusial dalam ekonomi Indonesia, dengan pertumbuhan sektor ini mencapai 7,2% per tahun, melebihi rata-rata dunia sebesar 4,7%. Objek Wisata Guci di Kabupaten Tegal, Jawa Tengah, menarik perhatian sebagai destinasi potensial. Namun, kesulitan wisatawan dalam mendapatkan informasi fasilitas umum di tempat tersebut menjadi tantangan. Penelitian ini mengembangkan aplikasi pemandu wisata "Guci Explore" berbasis Android, menerapkan metode User-Centered Design (UCD) untuk meningkatkan pengalaman pengguna. Dengan melibatkan pemandu wisata digital, aplikasi ini memberikan informasi akurat dan terkini untuk mengatasi kebingungan wisatawan. Pengujian menunjukkan kepuasan pengguna mencapai skor 81, memperkuat kategori Grade Scale "A" dan Acceptability Ranges "Acceptable". Aplikasi ini diharapkan dapat meningkatkan efisiensi perjalanan, memberikan manfaat ekonomi lokal, dan mendukung pertumbuhan sektor pariwisata Indonesia.
Sistem Informasi Manajemen Legalisir Online Berbasis Website Dairoh Dairoh; Riszki Wijayatun Pratiwi; Dwi Intan Af’idah; Sharfina Febbi Handayani; Ferian Andhika Toarsi
Infotekmesin Vol 15 No 1 (2024): Infotekmesin: Januari, 2024
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v15i1.1778

Abstract

Management and submission of legalization at the Harapan Bersama Polytechnic have not been computerized. The legalization process is carried out face-to-face by leaving legalized documents and returning them when the legalized documents are ready. This process is problematic because going to campus requires quite a long time, and there is no certainty or travel history from the management of legalized documents. The research objective is to build a legalization system to facilitate the process of implementing legalization in BAA units using the website-based waterfall method. This system validates alumni data in the form of NIM at registration, processes payments through payment gateways, pays for selected shipments, and tracks travel history from the submitted legalized documents. This system is called Simaleja, and there are two actors involved. As a result, the system runs according to the functions of the actors involved and has been tested. As for the results of the black box (actor) test, the system has been running according to the function of each actor, and the UI/UX usability results were obtained for 38 users, 82% of whom fall into the very good category.
Klasifikasi Opini Publik di Twitter Terhadap Bakal Calon Presiden Indonesia Tahun 2024 Menggunakan LSTM Secara Realtime Berbasis Website Muhammad Rizki; Muhammad Fikri Hidayattullah; Dwi Intan Af'idah
Infotekmesin Vol 14 No 2 (2023): Infotekmesin: Juli, 2023
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v14i2.1908

Abstract

The analysis of public opinions from Indonesian netizens regarding the potential presidential candidates for Indonesia in 2024 on Twitter is challenging. Human-based classification of the candidates on Twitter has limitations as it requires expertise and a considerable amount of time to process the data. Therefore, a system that provides realtime visualization of public opinion classification is necessary. Previous research only focused on model evaluation, while this study aims to implement the best model on a website. The objective of this research is to develop a system for monitoring the Twitter-based public opinion classification of the potential presidential candidates for Indonesia in 2024 within specific time frames. The training process utilizes the LSTM method, resulting in a model with an accuracy of 76%. Parameters such as batch size, dropout, and learning rate were tested. The data used in this study was obtained by crawling Twitter using the keywords Ganjar Pranowo, Anies Baswedan, and Prabowo Subianto. The LSTM model was then implemented in a website-based system that generates a dashboard with features such as a color-coded map displaying the highest levels of positive sentiment for each candidate in each province, the overall classification count for each candidate, and filters for sentiment classification based on province and specific time frames.
Sentimen Ulasan Destinasi Wisata Pulau Bali Menggunakan Bidirectional Long Short Term Memory Dwi Intan Af'idah; Dairoh Dairoh; Sharfina Febbi Handayani; Riszki Wijayatun Pratiwi; Susi Indah Sari
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 3 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i3.1402

Abstract

Pemerintah dan pelaku industri pariwisata mengalami permasalahan dalam menentukan prioritas pengembangan suatu destinasi wisata. Karena itu, diperlukan identifikasi objek wisata yang diminati namun banyak mendapat ulasan buruk melalui ulasan dari masyarakat yang tersebar di internet. Penelitian ini bertujuan melakukan analisis sentimen terhadap ulasan objek wisata di Pulau Bali menggunakan Bi-LSTM dan Word2Vec, sehingga diperoleh model terbaik yang dapat digunakan untuk mengidentifikasi objek wisata potensial namun mendapat ulasan buruk. Bi-LSTM merupakan deep learning yang menawarkan akurasi yang lebih baik daripada LSTM biasa. Sedangkan Word2Vec merupakan pretraining yang dipilih karena dapat menangkap makna semantik teks. Penelitian ini menggunakan data ulasan objek wisata di Pulau Bali yang berasal dari situs tripadvisor.com. Penelitian dimulai dari pengumpulan data, perancangan alur program, preprocessing, pretraining Word2Vec, pembagian data uji dan data latih, pelatihan dan pengujian, serta evaluasi penentuan model terbaik. Akurasi terbaik dihasilkan oleh kombisasi Word2Vec terdiri dari CBOW, Hierarchical Softmax, dimensi 200, Bi-LSTM dengan dropout sebesar 0,5 dan learning rate sebesar 0,0001. Kombinasi tersebut menghasilkan akurasi tertinggi dari keseluruhan 108 kombinasi yaitu sebesar 96,86%, precission sebesar 96,53%, Recall sebesar 96,31%, F1 Measure sebesar 96,41%. Akurasi yang baik tersebut membuktikan bahwa kombinasi parameter Bi-LSTM dan Word2Vec cocok digunakan untuk analisis sentimen ulasan objek wisata di Pulau Bali.
Pelatihan Digitalisasi di Era Teknologi Bagi Kader PKK Desa Lebakgowah Siska Aulia Utami; Yulia Trisnawati; Farda Nur Jihan; Michael Klose; Dwi Intan Af’idah
Pengabdian Deli Sumatera Vol 2 No 2 (2023): Artikel Riset Pengabdian vol. 2 no. 2 Juli 2023
Publisher : LLPM Universitas Deli Sumatera

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Dalam era yang semakin terhubung dan didukung oleh teknologi informasi dan komunikasi yang maju, penguasaan teknologi digital menjadi suatu keharusan untuk memperkuat dan meningkatkan efektivitas program-program dalam PKK. Tujuan dilaksanakannya Pengadian Pada Masyarakat ini untuk mengenalkan dan mengembangkan kemampuan ibu-ibu PKK dalam berorganisasi. Pelatihan serta praktikum menjadi metode yang digunakan dalam Pengabdian Pada Masyarakat di Desa Lebakgowah. Pemberian materi mengenai Google Form, Spreadsheet, Microsoft Word, Canva dan Capcut disampaikan secara berkala selama empat hari berturut-turut. Melalui pelatihan yang telah dilaksanakan, terdapat peningkatan yang signifikan oleh ibu-ibu PKK dalam penguasaan materi dan pratikum.
Co-Authors Achmad Miftahudin Aji Bagus Setiawan Aldi Budi Rianta Amaliyah Gian Ardi Susanto Arief Rachman Arief Rachman Dairoh Dairoh Dairoh Dairoh Dairoh, Dairoh Doni Cahya Adi Saputra Dyah Apriliani Farda Nur Jihan Ferian Andhika Toarsi Fina Yuniarti Ginanjar Wiro Sasmito Irfan Triadi Saputra Irfan Triadi Saputra Irvan Akbar Koandres Koandres M Fikri Hidayatullah Michael Klose Muhamad Zaim Zamzami Muhammad Fikri Hidayattullah Muhammad Rizki Muhammad Rizki Nurul Arifiah Gunarsih Nurul Arifiah Gunarsih Puput Dewi Anggraeni Putri Ajeng Imamah Ratono Ratono Ratono Ratono Riszki Wijayatun Pratiwi Riszki Wijayatun Pratiwi Riszki Wijayatun Pratiwi Riszki Wijayatun Pratiwi Riszki Wijayatun Pratiwi Riszki Wijayatun Pratiwi Sharfina Febbi Handayani Sharfina Febbi Handayani Siska Aulia Utami slamet Wiyono Slamet Wiyono Susi Indah Sari Susi Nurindah Sari Susi Nurindah Sari Susi Nurindahsari Syahrul Adi Mustofa Very Kurnia Bakti, Very Kurnia Yulia Trisnawati