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All Journal IPTEK The Journal for Technology and Science JSI: Jurnal Sistem Informasi (E-Journal) JURNAL E-DUMATH Journal of Information Systems Engineering and Business Intelligence KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal Komputasi Indonesian Journal of Artificial Intelligence and Data Mining Prosiding Seminar Nasional Teknoka Jurnal Teknik Informatika (JUTIF) Jurnal Pengabdian Kepada Masyarakat (JPKM) Tabikpun Jurnal Siger Matematika SWARNA: Jurnal Pengabdian Kepada Masyarakat
Dian Kurniasari
Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Lampung, Indonesia
Religion Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Other

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Pelatihan Canva Dalam Pembuatan Media Pembelajaran Bagi Guru-Guru SMK Di Bandar Lampung Fitriani Fitriani; Ahmad Faisol; Wamiliana Wamiliana; Notiragayu; Siti Laelatul Chasanah; Dian Kurniasari
Jurnal Pengabdian Kepada Masyarakat (JPKM) TABIKPUN Vol. 3 No. 3 (2022)
Publisher : Faculty of Mathematics and Natural Sciences - Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jpkmt.v3i3.96

Abstract

Canva merupakan suatu tools desain grafis untuk membantu dalam pembuatan desain kreatif yang mudah digunakan. Dalam kegiatan ini, dilaksanakan pelatihan pembuatan media pembelajaran menggunakan Canva bagi guru-guru SMK di Bandar Lampung. Kegiatan ini bertujuan untuk meningkatkan kemampuan peserta dalam pembuatan media pembelajaran menggunakan Canva. Kegiatan ini terdiri dari 3 tahapan yaitu perencanaan, pelaksanaan serta evaluasi dan pelaporan. Tahap pelaksanaan dilakukan dengan metode ceramah interaktif dan praktik. Pemaparan materi dilanjutkan dengan praktik pembuatan media pembelajaran. Kegiatan ini diikuti oleh 31 guru-guru SMK se-Bandar Lampung. Setelah pelatihan, semua peserta mampu membuat media pembelajaran. Berdasarkan survei, 16.13% sangat setuju, 45.16% peserta setuju, 29.03% kurang setuju dan 9.68% tidak setuju menggunakan Canva dalam pembuatan media pembelajaran. Kegiatan pelatihan ini sangat bermanfaat dan menambah wawasan peserta dalam pembuatan media pembelajaran.
IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK (ANN) USING BACKPROPAGATION ALGORITHM BY COMPARING FOUR ACTIVATION FUNCTIONS IN PREDICTING GOLD PRICES Dian Kurniasari; Ranti Vidia Mahyunis; Warsono Warsono; Aang Nuryaman
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 10, No 1 (2023)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v10i1.587

Abstract

The trend in global currency values is speedy and fluctuating due to the recession caused by the Covid-19 pandemic. That causes investors to flock to buy gold assets. Therefore, it is necessary to predict the price of gold from a business and academic perspective to obtain a reasonable gold price prediction model. This study applies the Backpropagation Algorithm by determining the best ANN model structure based on four activation functions: Sigmoid, Tanh, ReLU, and Linear, as well as learning rate values, namely 0.01 and 0.001. The results are the best ANN model structure with four nodes in the input layer, four nodes in the hidden layer and the output layer using the Linear activation function and a learning rate of 0.01. Based on the structure of the model, the MSE value is 0.00051, the MAPE value is 1.9798%, and the accuracy is 98%.Keywords: Artificial Neural Network, Backpropagation, Gold Price Prediction, Activation Function, Model Structure Trend nilai mata uang global sangat cepat dan fluktuatif akibat terjadinya resesi yang disebabkan oleh pandemi Covid-19. Hal ini menyebabkan, para investor berbondong-bondong untuk membeli aset emas. Oleh sebab itu, perlu dilakukan prediksi harga emas, baik dari perspektif bisnis maupun akademis agar memperoleh model prediksi harga emas yang baik. Penelitian ini menerapkan Algoritma Backpropagation dengan menentukan struktur model ANN terbaik berdasarkan empat fungsi aktivasi yaitu, Sigmoid, Tanh, ReLU, dan Linear serta nilai learning rate, yaitu 0,01 dan 0,001. Hasil yang diperoleh berupa struktur model ANN terbaik dengan empat node pada input layer, empat node pada hidden layer dan output layer dengan menggunakan fungsi aktivasi Linear dan learning rate sebesar 0,01. Berdasarkan struktur model tersebut, diperoleh nilai MSE sebesar 0.00051, nilai MAPE sebesar 1,9798% dan akurasi sebesar 98%.Kata Kunci: Artificial Neural Network, Backpropagation, Prediksi Harga Emas, Fungsi Aktivasi, Struktur Model
IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK WITH BACKPROPAGATION ALGORITHM FOR RATING CLASSIFICATION ON SALES OF BLACKMORES IN TOKOPEDIA Dalfa Habibah Nurul Aini; Dian Kurniasari; Aang Nuryaman; Mustofa Usman
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 2 (2023): JUTIF Volume 4, Number 2, April 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.2.539

Abstract

The rating assessment classification contains feedback from consumers, which is given in the form of stars which aims to assess a product. However, the amount of data in the classification process often have differences in each class or is called an imbalanced dataset. These problems can affect the classification results. An imbalanced dataset can be overcome by applying random oversampling. To classify the rating assessment, this study proposes the Neural network method, which has a good accuracy level with the backpropagation algorithm and applies random oversampling to overcome the unbalanced amount of data. The results indicate that the neural network method with the backpropagation algorithm can classify the available data with an accuracy level of 85%. The application of resampling data using random oversampling and determining the amount of distribution of training data, testing data, number of epochs and the correct number of batch sizes affect the results obtained.
Prediction of COVID-19 Using the Artificial Neural Network (ANN) with K-Fold Cross-Validation Nur Alifiah; Dian Kurniasari; Amanto Amanto; Warsono Warsono
Journal of Information Systems Engineering and Business Intelligence Vol. 9 No. 1 (2023): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.9.1.16-27

Abstract

Background: COVID-19 is a disease that attacks the respiratory system and is highly contagious, so cases of the spread of COVID-19 are increasing every day. The increase in COVID-19 cases cannot be predicted accurately, resulting in a shortage of services, facilities and medical personnel. This number will always increase if the community is not vigilant and actively reduces the rate of adding confirmed cases. Therefore, public awareness and vigilance need to be increased by presenting information on predictions of confirmed cases, recovered cases, and cases of death of COVID-19 so that it can be used as a reference for the government in taking and establishing a policy to overcome the spread of COVID-19. Objective: This research predicts COVID-19 in confirmed cases, recovered cases, and death cases in Lampung Province Method: This study uses the ANN method to determine the best network architecture for predicting confirmed cases, recovered cases, and deaths from COVID-19 using the k-fold cross-validation method to measure predictive model performance. Results: The method used has a good predictive ability with an accuracy value of 98.22% for confirmed cases, 98.08% for cured cases, and 99.05% for death cases. Conclusion: The ANN method with k-fold cross-validation to predict confirmed cases, recovered cases, and COVID-19 deaths in Lampung Province decreased from October 27, 2021, to January 24, 2022.   Keywords: Artificial Intelligence, Artificial Neural Network (ANN) K-Fold Cross Validation, COVID-19 Cases, Data Mining, Prediction.
Application of Artificial Neural Network Method using Hyperparameter Tuning for Predicting of Euro Exchange Rupiah Dian Kurniasari; Amelia Fallizia Putri; Warsono Warsono; Notiragayu Notiragayu
Jurnal Sistem Informasi Vol 15, No 1 (2023)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/jsi.v15i1.19867

Abstract

The Covid-19 pandemic has significantly impacted the economic decline in many countries, such as Italy, the United States and the European Union. Indonesia, also affected by Covid-19, was not spared from economic turmoil, especially in the foreign exchange market, where the rupiah exchange rate against the Euro experienced significant fluctuations in early 2020, hampering international trade and investment activities. Therefore, an appropriate method is needed to predict changes in the rupiah exchange rate against the Euro to minimize the obstacles. This study uses the ANN model to predict the Rupiah (Rp) exchange rate against the Euro (€). The best model is obtained through the hyper-tuning process. The optimal parameter values obtained are the input layer with 10 nodes, 2 hidden layers with 19 nodes and 13 nodes, the output layer, dropout of 0.2, 32 batch sizes, 100 epochs, and the Tanh activation function in the distribution scheme of 90% training data and 10 % testing data. Based on the MAPE value of 0.0042% and 0.0041% obtained, the prediction results on the selling and buying rates of the Rupiah against the Euro, it can be concluded that the model has good predictive ability with an accuracy value of 99.996%.Keywords: Currency Exchange Rates, Data Mining, Machine Learning, Artificial Neural Networks 
Forecasting The Value of Indonesian Oil-Non-Oil and Gas Imported Using The Gated Recurrent Unit (GRU) Dian Kurniasari; Sulistian Oskavina; Wamiliana Wamiliana; Warsono Warsono
Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 1 (2023): Maret 2023
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v6i1.20651

Abstract

In Indonesia, various factors play a role in economic development. Oil-non-oil and gas imports are one of the main factors. However, the value of oil-non-oil and gas imports in Indonesia fluctuates monthly. Therefore, an appropriate method is required to monitor changes in the value of oil-non-oil and gas imports in Indonesia so that the government can make the right choices. This study uses the GRU method to estimate the amount of oil-non-oil and gas imports in Indonesia. The best model for forecasting over the next two years has an optimum structure of 32 GRU units, 16 batch sizes, and 100 epochs, with a dropout of 0.2 and uses 80% training data and 20% test data. The MAPE value obtained is 0.999955%, with an accuracy of 99.000044%. Forecast results suggest an improvement from June 2022 to July 2024.
Workshop Pembuatan Media Pembelajaran dan Pengolahan Nilai bagi Guru SMK Nurul Huda Pringsewu Widiarti Widiarti; Asmiati Asmiati; Dian Kurniasari; Notiragayu Notiragayu; Warsono Warsono; Wenty Okzarima; Indah Suciati
SWARNA: Jurnal Pengabdian Kepada Masyarakat Vol. 3 No. 6 (2024): SWARNA: Jurnal Pengabdian Kepada Masyarakat, Juni 2024
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi 45 Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/swarna.v3i6.1315

Abstract

Sekolah Menengah Kejuruan (SMK) Swasta Nurul Huda Pringsewu memiliki empat jurusan, yaitu Asisten Perawat, Farmasi, Multimedia, dan Teknik Kendaraan Ringan dengan jumlah peserta didik sebanyak 205 Siswa dan guru sebanyak 30 orang. Berdasarkan data yang diperoleh hanya sekitar  28% lulusan SMKS Nurul Huda Pringsewu yang melanjutkan pendidikannya ke PT. Dengan demikian diperlukan adanya pembinaan terintegrasi untuk meningkatkan keinginan para siswa melanjutkan pendidikan ke PT.  Pengabdian ini menitik beratkan pada pembinaan komprehensif guru. Pembinaan guru meliputi pelatihan pembuatan media pembelajaran menggunakan Canva dan pengolahan nilai menggunakan Microsoft Excel. Kegiatan pengabdian ini mendapat sambutan  dan hasil yang baik dari para guru di SMK Swasta Nurul Huda Pringsewu. Berdasarkan hasil analisis statistik dengan menggunakan Uji T (T Test) menggunakan R Studio diperoleh bahwa nilai pre-test sebelum dilakukan workshop berbeda sangat signifikan dengan nilai post-test setelah dilakukan workshop.
Co-Authors . Wamiliana Aang Nuryaman Ahmad Faisol Amanto Amanto Amelia Fallizia Putri Anis Mahfud Al’afi Anisa Fitriyani Asmiati Asmiati Dalfa Habibah Nurul Aini Dian Ekawati Dolly Yudhistira, Dolly Evita Fitriasari Fatkur Rokhman Fitriani Indah Suciati Jevri Setia Nugraha Kartika Candra Buana Kurnia Muludi Muhammad Taufiq Sofrizal Mustofa Usman Mustofa Usman Mustofa Usman Noferdis Setiawan Notiragayu Notiragayu Notiragayu Notiragayu Notiragayu Nur Alifiah Rahman Cahyadi, Rahman Ranti Vidia Mahyunis Siti Laelatul Chasanah Sulistian Oskavina Wamiliana Wamiliana Warsono Warsono Warsono Warsono Warsono Warsono Warsono Warsono Wenty Okzarima Widiarti Widiarti Widiarti Widiarti Yeftanus Antonio