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All Journal Jurnal Buana Informatika JUTI: Jurnal Ilmiah Teknologi Informasi Telematika Seminar Nasional Informatika (SEMNASIF) Journal of Information Systems Engineering and Business Intelligence Sistemasi: Jurnal Sistem Informasi Knowledge Engineering and Data Science BAREKENG: Jurnal Ilmu Matematika dan Terapan MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Progresif: Jurnal Ilmiah Komputer PROGRES PENDIDIKAN Universitas Muhammadiyah Yogyakarta Undergraduate Conference Proceeding Jurnal INFOTEL
Vynska Amalia Permadi
Universitas Pembangunan Nasional Veteran Yogyakarta
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Library & Information Science Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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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.
Optimising the Fashion E-Commerce Journey: A Data-Driven Approach to Customer Retention Fadhila, Hasna Luthfiana; Permadi, Vynska Amalia; Tahalea, Sylvert Prian
Knowledge Engineering and Data Science Vol 7, No 1 (2024)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v7i12024p58-70

Abstract

A fashion e-commerce company offers a wide range of products from domestic and international brands that are popular with young people. However, there has been an increase in non-organically acquired customers, many of whom do not return to make repeat purchases. This has led to a higher customer churn rate, with a significant proportion of non-organically sourced customers failing to become repeat purchasers. Consequently, a churn analysis and prediction model were developed to address this issue. This paper employs the Recency, Frequency, and Monetary (RFM) framework for churn analysis and prediction. The framework is underpinned by three key dimensions: last purchase recency, purchase frequency, and total transaction value. Seven machine learning algorithms were evaluated to identify the optimal approach. Following a comparative analysis of these models, Random Forest emerged as the superior algorithm, demonstrating an accuracy of 0.99, precision of 0.97, recall of 0.99, ROC AUC of 0.98, and F1-score of 0.97. Consequently, this model will be utilized for churn prediction. Based on the analysis and modelling, several recommendations are offered to enhance customer retention for the fashion e-commerce platform. In addition to predicting churn, this paper provides insights into potential refinements to the churn prediction model, such as real-time monitoring, personalized customer experiences, analysis of customer feedback, and lifetime value analysis.
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
STABILITY ANALYSIS OF A MATHEMATICAL MODEL OF RABIES SPREAD WITH VACCINATION IN HUMAN AND DOG POPULATIONS, INCLUDING AWARE AND UNAWARE EXPOSED SUBPOPULATIONS Sahusilawane, Maria Engeline; Ilwaru, Venn Yan Ishak; Lesnussa, Yopi Andry; Beay, Lazarus Kalvein; Ojo, Mayowa Micheal; Permadi, Vynska Amalia; Peter, Olumuyiwa James
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp861-878

Abstract

Rabies is a zoonotic disease that causes progressive and fatal inflammation of the brain and spinal cord, which can be prevented by vaccination. This study aims to analyze the stability of a mathematical model of rabies disease spread with vaccination in human and dog populations in Maluku Province. The model uses a system of ordinary differential equations that separates the human population into six subpopulations (6 variables) and the dog population into three subpopulations (3 variables). The new variables are unaware subpopulations that we divide from aware subpopulations. The results showed that disease-free and endemic equilibrium points could be achieved, and the stability of these equilibrium points was analyzed using basic reproduction numbers Both disease-free and endemic equilibrium points are locally asymptotically stable. The Numerical simulations were also conducted to determine the characteristics of each subpopulation. This study was to provide better insight into controlling the spread of rabies in Maluku Province and it can be used as a reference in developing mathematical models for other infectious diseases.
Forecasting the Poverty Rates using Holt’s Exponential Smoothing Riza Prapascatama Agusdin; Sylvert Prian Tahalea; Vynska Amalia Permadi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.2672

Abstract

As a developing country with many provinces, Indonesia has a poverty problem that needs to be overcome. This research aimed to predict the poverty level in the Special Region of Yogyakarta using poverty data provided by the Central Statistics Agency for the Special Region of Yogyakarta. The method used in this research was Holt exponential smoothing to predict poverty levels in Yogyakarta City and four districts (Sleman, Bantul, Kulon Progo, and Gunungkidul) in this province. Three performances were measured to evaluate forecast results: sum squared error, mean squared error, and root mean squared error. The research results showed that the best configuration for the cities of Yogyakarta and Bantul is , = 0.9, 0.4; Kulon Progo and Gunungkidul are , = 0.9, 0.9; and Sleman are , = 0.9, 0.6. The forecasting results for 2022 to 2024, using a 95% confidence interval, showed that the poverty rate will increase in every city and district in the Special Region of Yogyakarta.
Musical Instruments Recommendation System Using Collaborative Filtering and KNN Puspita, Alfriska Deviane; Permadi, Vynska Amalia; Anggani, Aliza Hanum; Christy, Edwina Ayu
Proceedings of Universitas Muhammadiyah Yogyakarta Graduate Conference Vol. 1 No. 2 (2022): Engaging Youth in Community Development to Strengthen Nation's Welfare
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (484.727 KB)

Abstract

Introduction – The trend of e-commerce and online shopping has offered customers more product choices, but it also resulted in information overload. Nowadays, users are equipped with technology that allows websites to automatically deliver products that they may be interested in so that they can easily locate their favorite items from enormous options. To automate the recommendation process, recommender systems are created and built. This research creates a musical instrument recommendation system based on user reviews. Methodology/Approach – In this paper, we design and implement a recommendation system that combines the k-Nearest Neighbor (kNN) algorithm with a collaborative filtering framework. Collaborative filtering is chosen in this case because of its capability of providing new information to users by collecting information that has been obtained from the other users. Furthermore, kNN is considered as a suitable combination in this case since this method is relatively simple and able to find the similarity of objects being compared. Findings – To evaluate this study, the recommendation results are evaluated using the Root Mean Square Error (RMSE) calculation method, and the RMSE result obtained is 0.8734 for schema that divides dataset into 70% data train and 30% dataset using KNNWith Means with pearson measurements, and the MAE (Mean Absolute Error) result obtained is 0.5998 with schema 60% data train and 40% data test using KNNBasic algorithm with cosine similarity. Originality/ Value/ Implication – We present experimental results of consolidating the kNN algorithm in the collaborative filtering framework using Amazon’s musical instrument dataset. Furthermore, we can see that kNN together with a collaborative filtering algorithm performs a satisfactory outcome.
Forecasting the Poverty Rates using Holt’s Exponential Smoothing Agusdin, Riza Prapascatama; Tahalea, Sylvert Prian; Permadi, Vynska Amalia
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.2672

Abstract

As a developing country with many provinces, Indonesia has a poverty problem that needs to be overcome. This research aimed to predict the poverty level in the Special Region of Yogyakarta using poverty data provided by the Central Statistics Agency for the Special Region of Yogyakarta. The method used in this research was Holt exponential smoothing to predict poverty levels in Yogyakarta City and four districts (Sleman, Bantul, Kulon Progo, and Gunungkidul) in this province. Three performances were measured to evaluate forecast results: sum squared error, mean squared error, and root mean squared error. The research results showed that the best configuration for the cities of Yogyakarta and Bantul is , = 0.9, 0.4; Kulon Progo and Gunungkidul are , = 0.9, 0.9; and Sleman are , = 0.9, 0.6. The forecasting results for 2022 to 2024, using a 95% confidence interval, showed that the poverty rate will increase in every city and district in the Special Region of Yogyakarta.
Co-Authors Agusdin , Riza Prapascatama Agusdin, Riza Prapascatama Anggani, Aliza Hanum Asep Saepudin 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 Santoso, Bagus Jati Sylvert Prian Tahalea Tahalea, Sylvert Prian Tohari Ahmad Wilis Kaswidjanti Yopi Andry Lesnussa