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Vynska Amalia Permadi
Universitas Pembangunan Nasional Veteran Yogyakarta
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Analisis Sentimen Menggunakan Algoritma Naive Bayes Terhadap Review Restoran di Singapura Permadi, Vynska Amalia
Jurnal Buana Informatika Vol 11, No 2: Vol 11, No 2 (2020): Jurnal Buana Informatika Volume 11 - Nomor 2 - Okober 2020
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v11i2.3769

Abstract

Abstract. Sentiment Analysis Using Naive Bayes Classifier Against Restaurant Reviews in Singapore. Various restaurant options bring up a problem for diners to pick a restaurant to dine in. Thus, visitors usually perceive the restaurant's recommendation or rating in advance to know other diners' opinions about the restaurant. Previous restaurant diners' comments can be presented in sentiment analysis to determine their satisfaction. This research investigates the Naïve Bayes Classifier algorithm's performance in classifying visitors' sentiment based on restaurant diner comments. We will group visitors' comments into two types of sentiment: positive (satisfied) and negative (unsatisfied). The results of the data classification test are analyzed to determine its accuracy. The grouping of visitor satisfaction reviews using the naïve bayes algorithm provides an accuracy score of 73%. Besides, we visualize the research classification results in the browser-based R Shiny web application through word cloud and diagrams.Keywords:restaurant review, sentiment analysis, Naïve Bayes ClassifierAbstrak. Variasi pilihan restoran yang tidak sedikit menjadi salah satu masalah bagi pengunjung ketika ingin memilih restoran. Sehingga, pengunjung biasanya melihat rekomendasi atau penilaian pengunjung lain terhadap restoran tersebut terlebih dahulu untuk mengetahui penilaian pengunjung lain terhadap restoran tersebut. Penilaian atau review pengunjung dapat disajikan dalam analisis sentimen berdasarkan komentar para pengunjung restoran sebelumnya untuk melihat kepuasan pengunjung terhadap restoran tersebut. Penelitian ini dilakukan untuk mengetahui performa algoritma Naïve Bayes Classifier dalam melakukan klasifikasi sentimen berdasarkan komentar pengunjung restoran. Penelitian dilakukan dengan mengklasifikasikan data komentar pengunjung restoran menjadi dua kategori sentimen, yaitu: positif (satisfied) dan negatif (unsatisfied). Hasil pengujian pengklasifikasian data kemudian dianalisis akurasinya. Hasil pengelompokan review kepuasan pengunjung menggunakan algoritma naïve bayes memberikan nilai akurasi sebesar 73%. Visualisasi hasil klasifikasi dari analisis kemudian ditampilkan pada aplikasi berbasis web yaitu R Shiny berupa wordcloud dan diagram. Kata Kunci: penilaian restoran, analisis sentimen, Naïve Bayes Classifier
Analisis Sentimen Menggunakan Algoritma Naive Bayes Terhadap Review Restoran di Singapura Vynska Amalia Permadi
Jurnal Buana Informatika Vol. 11 No. 2: Vol 11, No 2 (2020): Jurnal Buana Informatika Volume 11 - Nomor 2 - Okober 2020
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v11i2.3769

Abstract

Abstract. Sentiment Analysis Using Naive Bayes Classifier Against Restaurant Reviews in Singapore. Various restaurant options bring up a problem for diners to pick a restaurant to dine in. Thus, visitors usually perceive the restaurant's recommendation or rating in advance to know other diners' opinions about the restaurant. Previous restaurant diners' comments can be presented in sentiment analysis to determine their satisfaction. This research investigates the Naïve Bayes Classifier algorithm's performance in classifying visitors' sentiment based on restaurant diner comments. We will group visitors' comments into two types of sentiment: positive (satisfied) and negative (unsatisfied). The results of the data classification test are analyzed to determine its accuracy. The grouping of visitor satisfaction reviews using the naïve bayes algorithm provides an accuracy score of 73%. Besides, we visualize the research classification results in the browser-based R Shiny web application through word cloud and diagrams.Keywords:restaurant review, sentiment analysis, Naïve Bayes ClassifierAbstrak. Variasi pilihan restoran yang tidak sedikit menjadi salah satu masalah bagi pengunjung ketika ingin memilih restoran. Sehingga, pengunjung biasanya melihat rekomendasi atau penilaian pengunjung lain terhadap restoran tersebut terlebih dahulu untuk mengetahui penilaian pengunjung lain terhadap restoran tersebut. Penilaian atau review pengunjung dapat disajikan dalam analisis sentimen berdasarkan komentar para pengunjung restoran sebelumnya untuk melihat kepuasan pengunjung terhadap restoran tersebut. Penelitian ini dilakukan untuk mengetahui performa algoritma Naïve Bayes Classifier dalam melakukan klasifikasi sentimen berdasarkan komentar pengunjung restoran. Penelitian dilakukan dengan mengklasifikasikan data komentar pengunjung restoran menjadi dua kategori sentimen, yaitu: positif (satisfied) dan negatif (unsatisfied). Hasil pengujian pengklasifikasian data kemudian dianalisis akurasinya. Hasil pengelompokan review kepuasan pengunjung menggunakan algoritma naïve bayes memberikan nilai akurasi sebesar 73%. Visualisasi hasil klasifikasi dari analisis kemudian ditampilkan pada aplikasi berbasis web yaitu R Shiny berupa wordcloud dan diagram. Kata Kunci: penilaian restoran, analisis sentimen, Naïve Bayes Classifier
Ecoprint dan Pengelolaan Media Sosial Pemasaran pada Kelompok Ibu-ibu PKK RW 03/RT 01, Demangan Yogyakarta Frans Richard Kodong; Juwairiah Juwairiah; Vynska Amalia Permadi; Riza Prapascatama Agusdin
Seminar Nasional Informatika (SEMNASIF) Vol 1, No 1 (2020): Peran Digital Society dalam Pemulihan Pasca Pandemi
Publisher : Jurusan Teknik Informatika

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

Abstract

AbstractThe PKK Driving Team (Empowerment of Family Welfare) runs from the central level to the village / kelurahan level, the PKK is managed and driven by the PKK Driving Team chaired by the wife of the Regional Leadership. At the smallest level, the PKK program can be developed by groups of PKK RW and RT mothers. One of the steps to increase the empowerment of village communities is the PKK RT 01 RW 03 Demangan Yogyakarta Women's Group since 2017 has formed an environmentally friendly fabric making business, where in its implementation the business involves community members so they can carry out a productive business that utilizes or processes materials -Natural / organic ingredients to produce environmentally friendly fabric products. In accordance with the development of information and communication technology, social media can become a reliable marketing medium. This paper will discuss about community economic empowerment and also how to use social media for online marketing. The approach is carried out by stages; the process of finding solutions to existing problems, discussion of the needs of social media to PKK group managers, is carried out by providing assistance in order to increase the capacity of administrators (in particular) and members (in general) to empower the community's economy, increase quantity and quality production tools, as well as product promotion and marketing.Keywords : PKK, Bussines, Fabric, MarketingTim Penggerak PKK (Pemberdayaan Kesejahteraan Keluarga) berada di tingkat pusat sampai dengan desa/kelurahan, PKK dikelola dan digerakkan oleh Tim Penggerak PKK yang diketuai oleh isteri Pimpinan Daerah. Pada tingkat yang terkecil pun program PKK dapat di kembangkan oleh kelompok ibu-ibu PKK RW maupun RT. Salah satu langkah peningkatan pemberdayaan masyarakat desa maka Kelompok Ibu-Ibu PKK RT 01 RW 03 Demangan Yogyakarta sejak tahun 2017 telah membentuk suatu usaha pembuatan kain ramah lingkungan, dimana dalam pelaksanaannya usaha tersebut melibatkan warga masyarakat agar dapat melakukan suatu usaha produktif yang memanfaatkan atau mengolah bahan-bahan alami/organik untuk menghasilkan produk kain ramah lingkungan. Sesuai perkembangan teknologi informasi dan komunikasi, media sosial mampu menjadi media pemasaran yang handal. Paper ini akan membahas tentang pemberdayaan ekonomi masyarakat dan juga bagaimana memanfaatkan media sosial untuk pemasaran daring (online marketing). Pendekatan diakukan dengan tahap ; proses pencarian solusi terhadap permasalahan yang ada, diskusi terhadap kebutuhan media sosial kepada pengurus Kelompok ibu-ibu PKK, dilakukan dengan memberikan pendampingan dalam rangka peningkatan kemampuan kepada pengurus (pada khususnya) dan anggota (pada umumnya) untuk pemberdayaan ekonomi masyarakat, meningkatkan kuantitas dan kualitas alat produksi, serta promosi dan pemasaran produkKata Kunci : PKK, Usaha, Kain, Pemasaran
K-MEANS AND ELBOW METHOD FOR CLUSTER ANALYSIS OF ELEMENTARY SCHOOL DATA Vynska Amalia Permadi; Sylvert Prian Tahalea; Riza Prapascatama Agusdin
PROGRES PENDIDIKAN Vol. 4 No. 1 (2023): January 2023
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/prospek.v4i1.328

Abstract

This research was conducted to find the groups of elementary schools in the Special Capital Region of Jakarta, also known as DKI Jakarta. Elementary school data were selected because it is the first stage of formal education in Indonesia. This research used K-means clustering with the elbow method to determine optimal cluster numbers. The optimal cluster number is three with Cluster 2 having the most members, followed by Cluster 1 and Cluster 0. The data distribution of Cluster 2 shows that the second-most student body and public schools located in East and West Jakarta have an adequate student-to-teacher ratio based on Article 17 of Government Regulation 74, 2008.
Penerapan Aturan Asosiasi untuk Rule Mining pada Piala Dunia FIFA 2022 Sylvert Prian Tahalea; Vynska Amalia Permadi
Progresif: Jurnal Ilmiah Komputer Vol 19, No 1: Februari 2023
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v19i1.1063

Abstract

Football strategy comes from learning and evaluation of previous matche, but there are only a few research that conducted to explore the synergies between players. This research was conducted to implement the data mining method; and association’s rule in the sports data case study. Football is already embracing new technology as part of its ecosystem. This research used a data mining method to explore and analyze the passing route of a football team in the biggest football tournament, FIFA World Cup 2022, held in Qatar. The data was collected from the Sports References website and preprocessed before being mined. An association rule is constructed using an apriori algorithm by creating the item sets, calculating the support and confidence value, and defining the rules. The results showed the formation of 3 association rules with a confidence limit of 95%. Rule mining using an apriori association provides the possibility of an antecedent and consequent combination, which is a combination of the order in which the ball moves from the first player to the third player until the occurrence of a goal.Keywords: Association rule; Data mining; A priori algorithm; Soccer AbstrakPengembangan strategi sepak bola dilakukan dengan melakukan evaluasi dari pertandingan yang telah dilakukan sebelumnya, akan tetapi belum banyak penelitian yang dilakukan untuk melakukan evaluasi terhadap sinergi antar pemain pada suatu permainan. Penelitian ini dilakukan dengan tujuan untuk mengimplementasikan metode data mining pada bidang olahraga, salah satunya sepak bola, yang telah menggunakan teknologi sebagai bagian dari ekosistem permainan. Metode data mining digunakan untuk mengeksplorasi dan menganalisis operan pada tim sepak bola pada perhelatan turnamen Piala Dunia FIFA 2022 yang dilaksanakan di Qatar. Data yang dikumpulkan dari website Sport References dikenakan prapemrosesan data sebelum diolah. Penerapan aturan asosiasi dengan algoritme Apriori dilakukan dalam beberapa langkah, seperti pembentukan itemset, perhitungan nilai support dan confidence, hingga penentuan aturan atau rule. Hasil penelitian menunjukan pembentukan 3 aturan asosiasi dengan batasan confidence 95%. Rule mining menggunakan algoritme asosiasi Apriori memberikan kemungkinan kombinasi antecedent dan consequent yang merupakan kombinasi urutan perpindahan bola dari pemain pertama hingga pemain ketiga sampai dengan terjadinya goal.Kata kunci: Aturan asosiasi; Data mining; Algoritme Apriori; Sepak bola
Demand Forecasting for Improved Inventory Management in Small and Medium-Sized Businesses Dian Indri Purnamasari; Vynska Amalia Permadi; Asep Saepudin; Riza Prapascatama Agusdin
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 1 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i1.57144

Abstract

Small and medium-sized businesses are constantly seeking new methods to increase productivity across all service areas in response to increasing consumer demand. Research has shown that inventory management significantly affects regular operations, particularly in providing the best customer relationship management (CRM) service. Demand forecasting is a popular inventory management solution that many businesses are interested in because of its impact on day-to-day operations. However, no single forecasting approach outperforms under all scenarios, so examining the data and its properties first is necessary for modeling the most accurate forecasts. This study provides a preliminary comparative analysis of three different machine learning approaches and two classic projection methods for demand forecasting in small and medium-sized leathercraft businesses. First, using K-means clustering, we attempted to group products into three clusters based on the similarity of product characteristics, using the elbow method's hyperparameter tuning. This step was conducted to summarize the data and represent various products into several categories obtained from the clustering results. Our findings show that machine learning algorithms outperform classic statistical approaches, particularly the ensemble learner XGB, which had the least RMSE and MAPE scores, at 55.77 and 41.18, respectively. In the future, these results can be utilized and tested against real-world business activities to help managers create precise inventory management strategies that can increase productivity across all service areas.
Use of Hybrid Methods in Making E-commerce Product Recommendation Systems to Overcome Cold Start Problems Budi Santosa; Muhamad Azam Fuadi; Mangaras Yanu Florestiyanto; Vynska Amalia Permadi; Wilis Kaswidjanti
Telematika Vol 16, No 1: February (2023)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v16i1.2080

Abstract

The large number of users and the items offered in e-commerce make it difficult for buyers to choose the right items and sellers to offer their items to the right buyers. To overcome this problem, a system that can offer and recommend goods automatically, namely a recommendation system is needed. One of the most popular methods used to create a recommendation system is collaborative filtering, the recommendations are created based on similarities in user behavior. Unfortunately, this method has a weakness, namely cold start, where the recommendations will be inaccurate on data that has a lot of new users and items due to minimal historical data regarding user behavior. This problem will be tried to be solved in this study using a hybrid method, where this method combines more than 1 method to create a list of recommendations so that it will cover the shortcomings of each method. This study uses Amazon's e-commerce product and transaction data. The use of the hybrid method in this study can overcome the cold start problem by using switching and mixed methods, by not using the collaborative filtering model on new user recommendations or users who have little interaction. New users will receive recommendations based on the combination of popularity-based and content-based filtering models. This can be seen from the Mean Absolute Error (MAE) value of the model, where the MAE value for the data with a minimum user has at least 3 times rating is 0.566883, for the minimum 7 times, the MAE value is smaller, 0.487553.
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.
Improvement of KNN Collaborative Filtering Model in User-based Approach on Anime Recommendation System Vynska Amalia Permadi; Rezky Putratama Raharjo
Sistemasi: Jurnal Sistem Informasi Vol 12, No 2 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i2.2473

Abstract

This research aims to resolve the challenge of finding the list of recommendations that correspond to user preferences. The MyAnimeList dataset is utilized for model evaluation, accessible via Kaggle website. The outcome of this study is the development of a recommendation system based on the preferences of other users (user-based model). The suggested solution employs a collaborative filtering model based on the KNN algorithm and weighted attribute. The dataset consisted of 193,272 user ratings on anime, with the following attributes: username, anime_id, my_score, and my_status. As an extension of the KNN collaborative filtering paradigm, the rating value is weighted based on the user’s status. The determination of the weight is based on the responses of 105 respondents to a questionnaire. my_score and my_status values will be combined and adjusted using MinMaxNormalization in addition to being weighted. This work implemented the KNN algorithm with the following k parameter values: 3, 5, 9, 15, 23, 33, and 45. Variations in parameters are utilized to determine the optimal k value to employ in KNN, which uses the Pearson similarity matrix to calculate user similarity values. The model evaluation indicate that the optimal Mean Absolute Error and Root Mean Square Error values at parameter k = 5 are 0.14726 and 0.19855, respectively. This improved model’s findings further demonstrate that KNN collaborative filtering with an additional weighted parameter can predict ratings with stable and generally low error values for all k values.
Domain-Specific Fine-Tuning of IndoBERT for Aspect-Based Sentiment Analysis in Indonesian Travel User-Generated Content Perwira, Rifki Indra; Permadi, Vynska Amalia; Purnamasari , Dian Indri; Agusdin , Riza Prapascatama
Journal of Information Systems Engineering and Business Intelligence Vol. 11 No. 1 (2025): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.11.1.30-40

Abstract

Background: Aspect-based sentiment analysis (ABSA) is essential in extracting meaningful insights from user-generated content (UGC) in various domains. In tourism, UGC such as Google Reviews offers essential feedback, but the challenges associated with processing in Indonesian language, including the unique linguistic characteristics, pose difficulties for automatic sentiment, and aspect detection. Recent advancements in transformer-based models, such as BERT, have shown great potential in addressing these challenges by providing context-aware embeddings. Objective: This research aimed to fine-tune IndoBERT, a pre-trained Indonesian language model, to perform information extraction and key aspect detection from tourism-related UGC. The objective was to identify critical aspects of tourism reviews and classify their sentiments. Methods: A dataset of 20,000 Google Reviews, focusing on 20 tourism destinations in DI Yogyakarta and Jawa Tengah, was collected and preprocessed. Multiple fine-tuning experiments were conducted, using a layer-freezing method by adjusting only the top layers of IndoBERT, while freezing others to determine the optimal configuration. The model's performance was evaluated based on validation loss, precision, recall, and F1-score in aspect detection and overall sentiment classification accuracy. Results: The best-performing configuration involved freezing the last six layers and fine-tuning the top six layers of IndoBERT, yielding a validation loss of 0.324. The model achieved precision scores between 0.85 and 0.89 in aspect detection and an overall sentiment classification accuracy of 0.84. Error analysis revealed challenges in distinguishing neutral and negative sentiments and in handling reviews with multiple aspects or mixed sentiments. Conclusion: The fine-tuned IndoBERT model effectively extracted key tourism aspects and classified sentiments from Indonesian UGC. While the model performed well in detecting strong sentiments, improvements are needed to handle neutral and mixed sentiments better. Future work will explore sentiment intensity analysis and aspect segmentation methods to enhance the model's performance. Keywords: Aspect-Based Sentiment Analysis, Fine-tuning, IndoBERT, Sentiment Classification, Tourism Reviews, User-Generated Content
Co-Authors Agusdin , Riza Prapascatama Agusdin, Riza Prapascatama Anggani, Aliza Hanum Asep Saepudin, S.IP, M.Si
 Beay, Lazarus Kalvein Christy, Edwina Ayu Dian Indri Purnamasari Fadhila, Hasna Luthfiana Frans Richard Kodong Herlina Jayadianti Ilwaru, Venn Yan Ishak Juwairiah Juwairiah Mangaras Yanu Florestiyanto Muhamad Azam Fuadi Ojo, Mayowa Micheal Partoyo Partoyo Peter, Olumuyiwa James Purnamasari , Dian Indri Puspita, Alfriska Deviane Rezky Putratama Raharjo Rifki Indra Perwira Sahusilawane, Maria Engeline Santosa, Budi Sylvert Prian Tahalea Tahalea, Sylvert Prian Wilis Kaswidjanti Yopi Andry Lesnussa