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All Journal INOVTEK Polbeng - Seri Informatika Indonesian Journal of Informatic Research and Software Engineering PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Kurniawan, Viki
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Penerapan Algoritma Eclat Untuk Mencari Pola Hubungan Antar Barang Pada Data transaksi Penjualan: Application of the Eclat Algorithm to Find Relationship Patterns Between Items in Sales Transaction Data Setiawan, Andre; Kurniawan, Viki; Novita, Rita
Indonesian Journal of Informatic Research and Software Engineering (IJIRSE) Vol. 4 No. 1 (2024): Indonesian Journal of Informatic Research and Software Engineering
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijirse.v4i1.1348

Abstract

Penumpukan data penjualan dari toko Amanah Mart yang tidak diolah lebih lanjut menjadikan tidak diketahuinya informasi tersembunyi yang seharusnya dimanfaatkan sebagai sarana untuk membuat toko lebih mendapat keuntungan. Hal ini menjadikan proses penjualan kurang maksimal mengenai produk yang dijual. Untuk meningkatkan strategi penjualan perlu dilakukan penggalian informasi lebih lanjut pada data transaksi penjualan Amanah Mart guna mendapatkan informasi tersembunyi untuk pengambilan keputusan. Untuk membantu meningkatkan penjualan dan menghindari terjadinya penumpukan stok barang pada Amanah Mart dapat dilakukan dengan teknik Data Mining dengan menggunakan metode Asosiasi. Penelitian ini menerapkan Algoritma Equivalence Class Transformation (ECLAT) untuk menghasilkan aturan asosiasi item pada Amanah Mart. Selain itu mencari pola aturan asosiasi pada data transaksi penjualan untuk memberikan informasi yang membantu dalam mengatur ketersediaan stok barang pada Amanah Mart. Berdasarkan Association Rule yang didapatkan, terdapat 4 jenis produk yang paling sering dibeli yaitu Wals Magma, Wals Pelangi, Es Krim Feast Vanila 65 ML, dan Es Krim P.Pop Trico 60 ML. Association Rule yang terbentuk pada data ini dengan nilai minimum support 10% dan nilai minimum confidence 50%. Tidak didapatkan hasil yang memenuhi nilai support dan confidence. Pada data ini didapatkan hasil 6 rules dengan nilai support yang tidak mencapai 1% dan nilai confidence 10%.
Implementasi The Concurrent Development Model Untuk Membangun Learning Management System Novita, Rice; Munzir, Medyantiwi Rahmawita; Kurniawan, Viki
Jurnal Inovtek Polbeng Seri Informatika Vol 9, No 1 (2024)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v9i1.3879

Abstract

Technology plays an important role in the educational process. The weakness of the current educational process is that there is no media that helps store data, share data and monitor data properly. Learning Management System (LMS) is a web-based software program that has five main elements for management, documentation, monitoring, reporting, administration and distribution of educational content. This research aims to develop an LMS that is in accordance with the five main features in the LMS. with software development methods using The Concurrent Development Model. In this model, work activities are carried out simultaneously, each work process has several work triggers for the activity. Triggers can come from the beginning of the work process or from other triggers because each trigger will be interconnected. In system design, the concept of Object-Oriented Analysis Design (OOAD) is used with use case diagrams, activity diagrams and class diagrams.
Application of Recurrent Neural Network Bi-Long Short-Term Memory, Gated Recurrent Unit and Bi-Gated Recurrent Unit for Forecasting Rupiah Against Dollar (USD) Exchange Rate Fayyad, Muhammad Fauzi; Kurniawan, Viki; Anugrah, Muhammad Ridho; Estanto, Baihaqi Hilmi; Bilal, Tasnim
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 2 No. 1: PREDATECS July 2024
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v2i1.1094

Abstract

Foreign exchange rates have a crucial role in a country's economic development, influencing long-term investment decisions. This research aims to forecast the exchange rate of Rupiah to the United States Dollar (USD) by using deep learning models of Recurrent Neural Network (RNN) architecture, especially Bi-Long Short-Term Memory (Bi-LSTM), Gated Recurrent Unit (GRU), and Bi-Gated Recurrent Unit (Bi-GRU). Historical daily exchange rate data from January 1, 2013 to November 3, 2023, obtained from Yahoo Finance, was used as the dataset. The model training and evaluation process was performed based on various parameters such as optimizer, batch size, and time step. The best model was identified by minimizing the Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Among the models tested, the GRU model with Nadam optimizer, batch size 16, and timestep 30 showed the best performance, with MSE 3741.6999, RMSE 61.1694, MAE 45.6246, and MAPE 0.3054%. The forecast results indicate a strengthening trend of the Rupiah exchange rate against the USD in the next 30 days, which has the potential to be taken into consideration in making investment decisions and shows promising economic growth prospects for Indonesia.
Sentiment Analysis of Towards Electric Cars using Naive Bayes Classifier and Support Vector Machine Algorithm Suryani, Suryani; Fayyad, Muhammad Fauzi; Savra, Daffa Takratama; Kurniawan, Viki; Estanto, Baihaqi Hilmi
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 1 No. 1: PREDATECS July 2023
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v1i1.814

Abstract

The use of non-renewable energy sources causes a reduction in fossil fuel resources, and greenhouse gas emissions. Based on the 2020 Climate Transparency Report, G20 member countries are trying to minimize gas emissions according to the target of the Nationally Determined Contribution (NDC), that the transportation sector contributes 27% of air pollution. The solution to reduce greenhouse gas emissions is to start using electric cars. The change from conventional transportation to electric transportation is expected to reduce carbon emissions and dependency on fossil fuels. However, the transition from conventional transportation to electric transportation raises pros and cons for the people of Indonesia. Social media Twitter is a forum for sharing opinions. Twitter users can express opinions on a matter. This study uses the sentiment analysis method to determine public opinion on the use of electric cars in Indonesia. Sentiment classification was performed using the NBC and SVM Algorithms. The results of this study indicate the use of two different algorithms, namely the Naive Bayes Classifier and SVM with the highest accuracy in Naive Bayes with k = 2 and k = 9 is 88%, while the highest accuracy in SVM with k = 9 and k = 10 is 90%. Thus, SVM has better capabilities than Naive Bayes in this study.
Co-Authors Andre Setiawan, Andre Anugrah, Muhammad Ridho Bilal, Tasnim Estanto, Baihaqi Hilmi Fayyad, Muhammad Fauzi Munzir, Medyantiwi Rahmawita Rice Novita Rita Novita Savra, Daffa Takratama Suryani Suryani