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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Applied Information System and Management Teknika
Vincentius Riandaru Prasetyo
University of Surabaya
Computer Science & IT Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media

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Gift Recommendations Based on Personality Using Fuzzy and Big Five Personality Test Susana Limanto; Vincentius Riandaru Prasetyo; Ni Wayan Gitaputri
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i6.4507

Abstract

Gifts are usually given to someone to strengthen a relationship or to motivate someone. However, givers often need help determining the appropriate gift for the potential recipient. On the other hand, many recipients are disappointed with the gifts received. This event can result in the relationship between the giver and recipient being disrupted or the motivational goal not being achieved. This research aims to develop a system to recommend gifts based on the recipient's personality. Gift recommendation is determined based on the recipient's personality because the recipient highly values gifts that match the recipient's personality. The system is built using the Fuzzy method, and the personality measurement tool used is the Big Five Personality Test. Fifteen pairs of respondents validated the system. The validation results show that 80% of respondents as gift-givers strongly agree that the system helps determine the appropriate gift for someone. In addition, 73.33% of respondents as gift recipients strongly agree that the gifts recommended by the system do not disappoint them.
Comparison of Extreme Learning Machine Methods and Support Vector Regression for Predicting Bank Share Prices in Indonesia Williem Kevin Setiadi; Vincentius Riandaru Prasetyo; Fitri Dwi Kartikasari
Teknika Vol. 13 No. 2 (2024): Juli 2024
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v13i2.856

Abstract

Investing is the practice of postponing current consumption to obtain more significant value in the future. One profitable form of investment is stock investment, where investors buy company shares to benefit from appreciation in share value or dividend payments. Before investing in shares, investors need to pay attention to movements in the Composite Stock Price Index (IHSG), which reflects the performance of the Indonesian stock market. The Indonesian Stock Exchange (BEI) recorded around 740 companies listed in 2021. The BEI also compiled the LQ45 list of 45 stocks with the largest market capitalization, including the four largest banks in Indonesia. However, investing in bank shares only sometimes produces profits due to share price fluctuations. Stock price analysis and price movement predictions are important steps before investing. Extreme Learning Machine (ELM) and Support Vector Regression (SVR) methods are techniques used to predict time series data. This research compares the performance of the two methods in predicting stock prices of the big 4 Indonesian banks. The dataset used in this research comes from the Yahoo Finance site, which was taken since the market crash recovery period due to the Covid-19 pandemic. Based on the evaluation conducted, both the ELM and SVR methods are effective for predicting the share prices of the big four Indonesian banks. In terms of accuracy, the SVR method outperforms the ELM method due to its superior MAPE value. However, when considering computing time, the ELM method is more efficient than the SVR method.
Leveraging Deep Learning for Cultural Preservation: A Mobile Application for Padang Cuisine Njoto Benarkah; Vincentius Riandaru Prasetyo; Andreas Bayu Prakarsa
Applied Information System and Management (AISM) Vol. 8 No. 2 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i2.46680

Abstract

Padang cuisine, originating from West Sumatra, Indonesia, is recognized as one of the most widespread traditional food types due to its prevalence in restaurants across the country. Despite the increasing interest in classifying Indonesian food using artificial intelligence, there have been limited studies that have explicitly focused on classifying Padang dishes using deep learning approaches. This study aimed to develop an intelligent mobile application capable of identifying various Padang dishes from images using transfer learning-based convolutional neural networks (CNNs). Four pre-trained CNN architectures—EfficientNetV2M, MobileNetV2, VGG19, and ResNet152V2—were fine-tuned and evaluated on a dataset of Padang food images. This dataset comprised a total of 1,108 images, categorized into nine distinct Padang dishes, collected from both publicly available repositories and original photographs taken for this study. Among these models, ResNet152V2 achieved the best performance after optimization, with a validation loss of 0.4142 and a test accuracy of 91.33%. The optimized model was converted to TensorFlow Lite and deployed as a mobile application, enabling real-time recognition of Padang dishes. This study presented a deep-learning-based mobile solution for recognising nine traditional Padang dishes with high accuracy, demonstrating the potential of AI-driven applications to support culinary heritage preservation and promote cultural tourism in Indonesia.
Co-Authors Andreas Bayu Prakarsa Fitri Dwi Kartikasari Ni Wayan Gitaputri Njoto Benarkah Susana Limanto Williem Kevin Setiadi