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All Journal Jurnal Informatika Telematika : Jurnal Informatika dan Teknologi Informasi International Journal of Advances in Intelligent Informatics Seminar Nasional Informatika (SEMNASIF) Proceeding of the Electrical Engineering Computer Science and Informatics ILKOM Jurnal Ilmiah MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Tekno Insentif SEINASI-KESI Science in Information Technology Letters Jurnal INFOTEL
Herlina Jayadianti
Prodi Sistem Informasi, Jurusan Teknik Informatika, Fakultas Teknik Industri Universitas Pembangunan Nasional ”Veteran” Yogyakarta
Computer Science & IT Electrical & Electronics Engineering

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Sentiment analysis of Indonesian reviews using fine-tuning IndoBERT and R-CNN Herlina Jayadianti; Wilis Kaswidjanti; Agung Tri Utomo; Shoffan Saifullah; Felix Andika Dwiyanto; Rafal Drezewski
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1505.348-354

Abstract

Reviews are a form of user experience information on a product or service that can be used as a reference for potential consumers’ preferences to buy, use, or consume a product. They can be also used by business entities to find out public opinion about their product or the performance of their business products. It will be very difficult to process the review data manually and it will take a long time. Therefore, sentiment analysis automation can be used to get polarity information from existing reviews. In this study, IndoBERT with Recurrent Convolutional Neural Network (RCNN) was used to automate sentiment analysis of Indonesian reviews. The data used was a sentiment analysis dataset obtained from IndoNLU with sentiment consisting of negative sentiment, neutral sentiment, and positive sentiment. The results of the test showed that IndoBERT with the Recurrent Convolutional Neural Network (RCNN) had better results than the IndoBERT base. IndoBERT with Recurrent Convolutional Neural Network (RCNN) obtained 95.16% accuracy, 94.05% precision, 92.74% recall and 93.27% f1 score.
IMPLEMENTASI MAPPING OTOMATIS DARI DATABASE MYSQL 5.6 KE PROTEGE 4.3 DENGAN TURTLE ONTOLOGY, D2RQ, JENA, DAN NETBEANS 7.4 Widiatminingsih Widiatminingsih; Herlina Jayadianti; Heru Cahya Rustamaji
Seminar Nasional Informatika (SEMNASIF) Vol 1, No 1 (2017): “e-Defense : Menjaga keamanan data menghadapi cyber warfare untuk memperkokoh ke
Publisher : Jurusan Teknik Informatika

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Abstract

The problem of changing  information into the level of knowledge is still being a big problem. Manualy mapping process  from MySql to protege is still running by user/ programmers if they wants to build web  3.0 generation. This is a waste of time. By using D2Rq Turtle the transfer process from information into the form of knowledge becomes faster.
Essay auto-scoring using N-Gram and Jaro Winkler based Indonesian Typos Herlina Jayadianti; Budi Santosa; Judanti Cahyaning; Shoffan Saifullah; Rafal Drezewski
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 22 No 2 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i2.2473

Abstract

Writing errors on e-essay exams reduce scores. Thus, detecting and correcting errors automatically in writing answers is necessary. The implementation of Levenshtein Distance and N-Gram can detect writing errors. However, this process needed a long time because of the distance method used. Therefore, this research aims to hybrid Jaro Winker and N-Gram methods to detect and correct writing errors automatically. This process required preprocessing and finding the best word recommendations by the Jaro Winkler method, which refers to Kamus Besar Bahasa Indonesia (KBBI). The N-Gram method refers to the corpus. The final scoring used the Vector Space Model (VSM) method based on the similarity of words between the answer keys and the respondent’s answers. Datasets used 115 answers from 23 respondents with some writing errors. The results of Jaro Winkler and N-Gram methods are good in detecting and correcting Indonesian words with the accuracy of detection averages of 83.64% (minimum of 57.14% and maximum of 100.00%). In contrast, the error correction accuracy averages 78.44% (minimum of 40.00% and maximum of 100.00%). However, Natural Language Processing (NLP) needs to improve these results for word recommendations.
LSTM forecast of volatile national strategic food commodities Herlina Jayadianti; Vynska Amalia Permadi; Partoyo Partoyo
JURNAL INFOTEL Vol 15 No 4 (2023): November 2023
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v15i4.1037

Abstract

Using the Long Short-Term Memory (LSTM) forecast, this study suggested a short-term projection model for national critical food pricing commodities. The model was trained using historical time-series data from each commodity price over the previous three years. The results demonstrated that the proposed LSTM architecture model was generalizable to all commodities and performed well in the majority of cases. This result indicates that the model is resilient and can be used to forecast commodity prices and offer accurate forecasts for most of the ten volatile national strategic foods, with an error value of less than 0.01 and an accuracy value of >95%. The model, however, failed to recognize the pricing pattern in cooking oil and beef commodities, both of which had increasing trend patterns. This shows that the model may be unable to effectively estimate commodity prices in the face of fast price fluctuations. The magnitude and quality of the dataset hampered the investigation. The time period selected also influenced the study. Future research should employ a more extensive and diversified dataset to increase the model's performance, allow it to learn more patterns and make more accurate predictions, and could use a more extended lookup date to improve forecast accuracy. This would enable the model to account for more recent pricing changes. Despite the limitations, the results of this study are promising and could be used to develop a more accurate and reliable food price prediction model. Policymakers and stakeholders could use the model to make informed food prices and inflation decisions.
Evaluasi Kualitas Website Republika.co.id Menggunakan Metode WebQual 4.0 dan Importance Performance Analysis (IPA) Putra, Naufal Rizki; Kraugusteeliana, Kraugusteeliana; Wirawan, Rio; Lawi, Arwin; Jayadianti, Herlina
PROSIDING SEINASI-KESI Vol 1, No 1 (2022): SEMINAR NASIONAL INFORMATIKA, SISTEM INFORMASI, DAN KEAMANAN SIBER
Publisher : Fakultas Ilmu Komputer UPN Veteran Jakarta

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Abstract

Di Indonesia sendiri, perusahaan media yang memanfaatkan teknologi internet telah terjadi peningkatan dengan pesat, yaitu dengan penggunaan website sebagai media online, salah satunya adalah Republika.co.id. Republika.co.id mengubah penayangan berita menjadi sebuah portal berita online dengan variasi berita yang jauh lebih luas dibandingkan dengan pada saat awal didirikan. Untuk membantu pengembangan website Republika, maka dilakukan penelitian evaluasi kualitas website menggunakan Webqual 4.0 dan Importance Performance Analysis (IPA). Pengujian data yang dilakukan dari hasil penelitian yaitu uji validitas, uji reliabilitas dan uji paired sample t – test, lalu dilanjutkan dengan menganalisis nilai kesenjangan (Gap Analysis) antara penilaian kinerja serta penilaian harapan, Webqual Index (WQI) untuk mendapatkan nilai dari kualitas website, serta menghitung nilai Importance Performance Analysis (IPA). Hasil analisis yang didapatkan bahwa website Republika termasuk dalam kategori baik dengan nilai WQI yaitu 0,76, namun masih terdapat beberapa indikator yang perlu dilakukan perbaikan.
ANALISIS PERANCANGAN SISTEM KANTIN ONLINE UNIVERSITAS XYZ Sayidina Az Zahwa, Shahnaz Gladys; Khairani, Fatia; Anandar, Rafika; Fitri, Nurul Ainil; Jayadianti, Herlina; Kraugusteeliana, Kraugusteeliana
PROSIDING SEINASI-KESI Vol 2, No 1 (2023): SEMINAR NASIONAL INFORMATIKA, SISTEM INFORMASI, DAN KEAMANAN SIBER
Publisher : Fakultas Ilmu Komputer UPN Veteran Jakarta

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

Abstract

Kantin merupakan salah satu fasilitas di lingkungan kampus yang dapat memberikan kenyamanan bagi mahasiswa, staf, dan karyawan serta berperan sebagai sumber pendapatan utama bagi Universitas XYZ dan juga para pedagang berjualan di dalamnya. Tujuan dari penelitian ini adalah merancang sistem informasi kantin online untuk memfasilitasi pemesanan makanan dan minuman di Universitas XYZ. Sistem ini mencakup tahap pemilihan menu, konfirmasi pemesanan, pembayaran, dan pengambilan pesanan. Metode yang digunakan adalah metode waterfall, yang mencakup perencanaan, analisis, desain, dan implementasi. Metode tersebut dipilih karena memiliki langkah-langkah terstruktur sehingga sistem ini dapat berfungsi sebagai platform pemesanan online yang memungkinkan pengguna untuk melakukan transaksi tanpa harus berkunjung fisik ke kantin. Selain itu juga dalam menganalisis permasalahan, peneliti menggunakan metode PIECES. Hasil dari penelitian ini berupa beberapa diagram UML seperti, use case diagram, activity diagram, sequence diagram, dan class diagram serta terdapat perancangan antarmuka yang bertujuan untuk perancangan sistem informasi kantin online. Hasil penelitian ini diharapkan akan mengoptimalkan kontribusi kantin sebagai aset kampus yang berharga, meningkatkan efisiensi dalam proses pemesanan makanan, serta memberikan fleksibilitas bagi pengguna dalam menikmati layanan kantin. Sistem ini mendukung kebutuhan mahasiswa, staf, karyawan, dan pedagang kampus serta memberikan kontribusi positif terhadap pengelolaan kampus dan inisiatif akademik dan non-akademik.
Improving sentiment analysis on PeduliLindungi comments: a comparative study with CNN-Word2Vec and integrated negation handling Jayadianti, Herlina; Arianti, Berliana Andra; Cahyana, Nur Heri; Saifullah, Shoffan; Dreżewski, Rafał
Science in Information Technology Letters Vol 4, No 2 (2023): November 2023
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v4i2.1184

Abstract

This study investigates sentiment analysis in Google Play reviews of the PeduliLindungi application, focusing on the integration of negation handling into text preprocessing and comparing the effectiveness of two prominent methods: CNN-Word2Vec CBOW and CNN-Word2Vec SkipGram. Through a meticulous methodology, negation handling is incorporated into the preprocessing phase to enhance sentiment analysis. The results demonstrate a noteworthy improvement in accuracy for both methods with the inclusion of negation handling, with CNN-Word2Vec SkipGram emerging as the superior performer, achieving an impressive 76.2% accuracy rate. Leveraging a dataset comprising 13,567 comments, this research introduces a novel approach by emphasizing the significance of negation handling in sentiment analysis. The study not only contributes valuable insights into the optimization of sentiment analysis processes but also provides practical considerations for refining methodologies, particularly in the context of mobile application reviews.
Co-Authors A H Pratomo Agung Tri Utomo Alisya Amalia Putri Hasanah Anandar, Rafika Angelica Amartya Putri Arianti, Berliana Andra Bambang Yuwono Baptista Sousa Pinto, Carlos Alberta Cahyadi, Tedy Agung Carlos Alberta Baptista Sousa Pinto Carlos Sousa Pinto Dessyanto Boedi Prasetyo Dreżewski, Rafał Dyah Ayu Irawati E T Paripurno Ervina Amelia Malik Felix Andika Dwiyanto Fitri, Nurul Ainil Hafsah Hafsah Hasna Nur Hanani Herry Sofyan Herry Sofyan Heru Cahya Rustamaji Heru Cahya Rustamaji Indra Aji Setyo Wicaksono J D Peasetyo Judanti Cahyaning Juwairiah Juwairiah Khairani, Fatia Kraugusteeliana Kraugusteeliana Lawi, Arwin Lukito Edi Nugroho M F Pitayandanu Moh. Wahyu Septyanto Muhammad Fahim Rasyid Muhammad Fathurrahman Pitayandanu Nava'atul Fadillah Novrido Charibaldi Nur Ali Amri Nur Heri Cahyana Oliver Samuel Simanjuntak Partoyo Partoyo Paulus Insap Santosa Putra, Naufal Rizki Rafal Drezewski Rafal Drezewski Ruth Damayanti S P Tahalea Saifullah, Shoffan Santosa, Budi Sayidina Az Zahwa, Shahnaz Gladys Tri Setia Yoga Nugroho Vynska Amalia Permadi Wahyu Widayat Wahyu Widayat Wahyu Widayat Wahyu Widayat Widayat, Wahyu Widiatminingsih Widiatminingsih Wilis Kaswidjanti Wirawan, Rio Yanu Ramdhani Saputra Yuli Fauziah Yusuf Suryo Baskoro