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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 Jurnal Esensi Infokom: Jurnal Esensi Sistem Informasi dan Sistem Komputer Jurnal Informatika Progres Internet of Things and Artificial Intelligence Journal Jurnal Pengabdian Pada Masyarakat KOMPUTASI Jurnal Serina Abdimas
Teny Handhayani
Universitas Tarumanagara
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Journal : Computatio : Journal of Computer Science and Information Systems

 1 
Prediksi Jumlah Penduduk Tingkat Kecamatan di Wilayah Bogor Menggunakan Metode Long Short Term Memory Djoenaedi, Owen; Herwindiati, Dyah Erny; Handhayani, Teny
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.
Clustering Data Meteorologi Wilayah Indonesia Timur Menggunakan Metode K-Means Andrian, Gion; Teny Handhayani; Desi Arisandi
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.v8i2.27127

Abstract

Peran meteorologi dalam memahami pola iklim dan dampak perubahan iklim global menjadi fokus untuk mendeteksi dini perubahan iklim, terutama dampak seriusnya pada kehidupan manusia dan sektor ekonomi di kota-kota seperti Jakarta, Semarang, dan Surabaya. Studi ini difokuskan pada wilayah Indonesia timur, termasuk Papua, Maluku, dan Nusa Tenggara, dengan tujuan mengidentifikasi pola perubahan iklim menggunakan metode clustering, khususnya K-Means. Toleransi missing value sebesar 40% memiliki pengaruh besar dengan silhouette score mencapai 0.509. Penggunaan Z-Score dan penghapusan variabel arah angin maksimum juga terbukti efektif. Hasil analisis dua cluster membentuk kelompok berbeda, terutama Cluster 0 yang hanya memiliki satu kota. Perbedaan signifikan terlihat pada suhu, kelembaban, curah hujan, lama penyinaran matahari, dan kecepatan angin antar cluster, menggambarkan pola iklim yang konsisten namun keragaman kondisi meteorologi di wilayah tersebut
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.
Co-Authors Adela Calista Adela Tania Adithya Putra, Farhan Afrial, Farhan Andre Andre, Andre Andrian, Gion Andry Winata Angelica Christina Arya Bintang Saputra Arya Dwi Saputra Brando Dharma Saputra Cecillia Chung Chairisni Lubis Cherissa Aeryn Djaya Christina, Angelica Daffa Hilmi Aji Dara Kharisma Limparan David Jansen Dayanti, Afina Putri Desi Arisandi Desi Arisandi Djoenaedi, Owen Duncan Ariel Dwi Saputra, Arya Dyah Erny Herwindiati Dyah Erny Herwindiati Ericko, Teddy Faradila Herfiyana Fawaz Georgia Sugisandhea Hendryli, Janson Herfiyana, Faradila Huang, Jervis Irvan Lewenusa Irvan Lewenusa, Irvan Janson Hendryli Janson Hendryli Jason Jaya, Jefri Jayadi, Bryan Valentino Jeanny Pragantha Jeanny Pragantha Jeremia Pinnywan Immanuel Jochsen, Erico Jong, Fenny Jordi Pradipta Kusuma Jourdan Stanley Julius Juan Karnadi, Benny Kelvin Wijaya Kusuma, Jordi Pradipta Lely Hiryanto Lim, Maggie Lubis, M.Kom., Chairisni Mahendra, Izam Susilo Mahendra, Izam Susilo Manatap Dolok Lauro, Manatap Dolok Manatap Sitorus Marchel Yusuf Rumlawang Arpipi Mathew Judianto Matthew Oni Matthew Russel Paul Mohammad Faraditya Eka Putra Monica Ong Muhammad Isnaini Syaifudin Nicko Kurniawan Novario Jaya Perdana Owen Maytrio Phratama Paulus Samotana Zalukhu Phratama, Owen Maytrio Purba, Andrew Castello Putra, Tommy Wijaya Sandy Permadi Sormin Sitorus Dolok Lauro , Manatap Sopany, Mikael Reichi Sumarlie , Devid Sumarlie, Aurellia Clearesta Tanudy, Clara Tasya Syamsudin Tedja, Peter James Tony Tony Veri Wasino Wasino Wasino Wasino, Wasino William William Winata, Andry Zyad Rusdi