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All Journal JIKSI (Jurnal Ilmu Komputer dan Sistem Informasi) Jurnal Informatika dan Teknik Elektro Terapan E-Dimas: Jurnal Pengabdian kepada Masyarakat Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Computatio : Journal of Computer Science and Information Systems Jurnal CoreIT Jurnal Eksplora Informatika SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Sebatik KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Simtek : Jurnal Sistem Informasi dan Teknik Komputer Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat ComTech: Computer, Mathematics and Engineering Applications Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Muara Sains, Teknologi, Kedokteran dan Ilmu Kesehatan JISKa (Jurnal Informatika Sunan Kalijaga) INTI Nusa Mandiri Jurnal Mnemonic Jurnal Bakti Masyarakat Indonesia Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Journal of Dinda : Data Science, Information Technology, and Data Analytics Internet of Things and Artificial Intelligence Journal Jurnal Pengabdian Pada Masyarakat Edu Komputika Journal Jurnal Serina Abdimas Jurnal Esensi Infokom : Jurnal Esensi Sistem Komputer dan Informasi
Teny Handhayani
Universitas Tarumanagara
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Journal : computatio journal of computer science and information systems

 1 
PENGENALAN KUE TRADISIONAL INDONESIA MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK Lubis, M.Kom., Chairisni; Sumarlie , Devid; Handhayani, Teny
Computatio : Journal of Computer Science and Information Systems Vol. 6 No. 2 (2022): Computatio: Journal of Computer Science and Information Systems
Publisher : Faculty of Information Technology, Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/computatio.v6i2.21098

Abstract

In Indonesia, a lot of cakes are included in the category of traditional snacks. Traditional snacks are a unique culture of the archipelago that must be preserved by Indonesians. Traditional cakes are snacks that people like because they are dense and filling. Traditional cakes have a variety of textures, shapes and colors are very diverse and some are similar to each other, so it is rather difficult to identify the cake. The problem faced by buyers is that they often do not know the name of a cake because of the many types of cakes sold in the market. Technological advances have also caused many local people to use social media to take photos of food, but to recognize these cakes, there are still many people who do not really understand traditional cakes compared to modern cakes. The above problem can be solved if a system is made to recognize the image/photo of the cake and the computer can be programmed and to classify the cake into a certain category of cake by utilizing the image of the cake using the Convolutional Neural Network (CNN) algorithm. The best test results are tests that include data augmentation during training, where VGG-16 has a higher accuracy than DenseNet121 which is 80% and DenseNet121 testing which uses k-fold cross validation with an accuracy of fold 1 which is 77% and a drastic increase up to fold 5. If without using data augmentation, the best result obtained is an accuracy of 83% achieved by DenseNet121 without transfer learning, learning rate 1e-5 and batch size 16.
Perbandingan Algoritma ANN, KNN dan Decision Tree untuk Klasifikasi Delay Airlines Jason Sunaryo; Teny Handhayani
Computatio : Journal of Computer Science and Information Systems Vol. 10 No. 1 (2026): Computatio: Journal of Computer Science and Information Systems
Publisher : Faculty of Information Technology, Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/computatio.v10i1.29905

Abstract

As time progresses, transportation methods become more sophisticated. One of the commonly used modes of long-distance travel is by air travel. However, there are occasional challenges encountered, one of which is delays. Airlines delays not only inconvenience passengers but also incur losses for airlines due to increased operational costs. Therefore, there is a need to predict whether a flight will be delayed or not. This research aims to predict delays using three algorithms: ANN, Decision Tree, and KNN. The dataset utilized in this study consists of 539,383 records. The findings of this study indicate that the most suitable algorithm for this dataset is the Decision Tree algorithm, with an average accuracy of 62.5%.
Prediksi Jumlah Penduduk Tingkat Kecamatan di Wilayah Bogor Menggunakan Metode Long Short Term Memory Owen Djoenaedi; Dyah Erny Herwindiati; Teny Handhayani
Computatio : Journal of Computer Science and Information Systems Vol. 8 No. 2 (2024): Computatio: Journal of Computer Science and Information Systems
Publisher : Faculty of Information Technology, Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/computatio.v8i2.16219

Abstract

Population growth is addition or reduction of the population which is influenced by several factors. In Indonesia, this is something that pays great attention and is monitored by the government, especially on Java Island. Worries of population increase is one of the reasons for this monitoring which can cause problems with the support power and capacity power of the environment. The purpose of this design is to predict the population and calculate population growth rate at sub-district level in the Bogor area for 2021 and 2022 using population data at different annual intervals in each areas. Prediction is done using Long Short Term Memory. The configuration parameters of the model used for training and testing is different for each areas which obtained from the results of the parameter experiment which was repeated 5 times for each configuration to obtain the best Mean Absolute Percentage Error (MAPE) average. All models for LSTM method gain an average MAPE below 10% in each areas so that the models for prediction were stated to be very good.
Co-Authors Adela Calista Adela Tania Agus Budi Dharmawan Akmal Farouqi Andre Andre Andre Andre, Andre Andrew Castello Purba Andrian, Gion Andry Winata Angelica Christina Arya Bintang Saputra Arya Dwi Saputra Arya Dwi Saputra Brando Dharma Saputra Cecillia Chung Chairisni Lubis Cherissa Aeryn Djaya Christina, Angelica Daffa Hilmi Aji Dara Kharisma Limparan Darius Andana Haris David Jansen Dayanti, Afina Putri Desi Arisandi Desi Arisandi Desi Arisandi Duncan Ariel Dwi Saputra, Arya Dyah Erny Herwindiati Ericko, Teddy Faradila Herfiyana Farhan Afrial Fawaz Gabriella Adeline Halim Georgia Sugisandhea Gion Andrian Hendryli, Janson Herfiyana, Faradila Huang, Jervis Irvan Lewenusa Irvan Lewenusa Irvan Lewenusa, Irvan Janson Hendryli Janson Hendryli Jason Jason Sunaryo Jaya, Jefri Jayadi, Bryan Valentino Jeanny Pragantha Jeanny Pragantha Jeanny Pragantha Jefri Jaya Jeremia Pinnywan Immanuel Jochsen, Erico Jordi Pradipta Kusuma Jourdan Stanley Julius Juan Karnadi, Benny Kelvin Wijaya Kelvin Wijaya Kusuma, Jordi Pradipta Lely Hiryanto Lim, Maggie Lubis, M.Kom., Chairisni Mahendra, Izam Susilo Mahendra, Izam Susilo Manatap Dolok Lauro Manatap Dolok Lauro, Manatap Dolok Manatap Sitorus Manatap Sitorus Dolok Lauro Marcel Yusuf Rumlawang Arpipi Marchel Yusuf Rumlawang Arpipi Mathew Judianto Matthew Oni Matthew Russel Paul Mikael Reichi Sopany Mohammad Faraditya Eka Putra Monica Ong Muhammad Isnaini Syaifudin Naufal Firdausyan Nicholas Eugene Supardi Nicko Kurniawan Novario Jaya Perdana Oni, Matthew Owen Djoenaedi Owen Maytrio Phratama Paulus Samotana Zalukhu Peter James Tedja Phratama, Owen Maytrio Purba, Andrew Castello Raffy Sonata Sandy Permadi Sormin Sitorus Dolok Lauro , Manatap Sopany, Mikael Reichi Sumarlie , Devid Sumarlie, Aurellia Clearesta Tanudy, Clara Tasya Syamsudin Tommy Wijaya Putra Tony Tony Veri Wasino Wasino Wasino . Wasino Wasino William William Winata, Andry Yudistira Permana Zyad Rusdi