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All Journal Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Algoritma
Amril Mutoi Siregar
Informatic Departement, Faculty Of Computer Science, Universitas Buana Perjuangan Karawang, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering

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Penerapan Algoritma K-means Clustering dan Hierarchical Clustering dalam Mengelompokkan Data Pengangguran di Karawang Mulyana, Assyifa Alif Rahayu; Juwita, Ayu Ratna; Siregar, Amril Mutoi; Fauzi, Ahmad
Jurnal Algoritma Vol 21 No 2 (2024): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The existence of various new industrial areas in Karawang can trigger residents from outside the area to migrate so that this will increase the number of residents in Karawang. The increase in population can affect the unemployment rate in an area. To group data, you can use data mining techniques. The K-Means Clustering and Hierarchical Clustering algorithms have not been used to group unemployment data, so this research aims to group unemployment data with these two algorithms. The results are that the K-Means Clustering and Hierarchical Clustering algorithms can group data based on similar characteristics with the same number of clusters but have differences in data distribution within the clusters. The evaluation method with Silhouette Score shows that the two algorithms have the same performance in the analysis in this study.
Classification of Dog Emotions Using Convolutional Neural Network Method Hermawan, Slamet; Siregar, Amril Mutoi; Faisal, Sutan; Mudzakir, Tohirin Al
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 2 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i2.74340

Abstract

The utilization of neural networks in dog emotion classification has great potential to improve the understanding of pet emotions. The goal is to develop a dog emotion classification system. This is important due to the lack of public ability to recognize and understand dog emotions. Neural networks able to create learning models can be used for decision-making, thus helping to reduce the risk of dangerous dog attacks. CNN itself is part of neural networks, where the CNN model has a higher accuracy rate of 74.75% compared to ResNet 65.10% and VGG 68.67%. Modeling using ROC-AUC shows the model's ability to distinguish emotion classes well. Angry has the highest AUC of 0.97, happy 0.93 and sad 0.96. While relaxed has the lowest AUC of 0.92. Classification report results show model has the highest precision and F1-Score values in angry class, while the highest recall value is in sad class.
Model Klasifikasi Nominal Mata Uang Kertas Republik Indonesia Menggunakan Convolutional Neural Network Saputra, Arbi Niandi; Handayani, Hanny Hikmayanti; Sukmawati, Cici Emilia; Siregar, Amril Mutoi
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.5927

Abstract

Uang kertas adalah alat pembayaran umum di seluruh dunia saat ini karena digunakan dalam transaksi jual beli barang dan jasa. Nilai uang kertas Rupiah di Indonesia memiliki variasi yang mencakup ukuran, warna, dan pola yang berbeda. Identifikasi manual dapat menyebabkan kesalahan, sehingga diperlukan sistem pengenalan uang kertas yang efisien dan akurat. Permasalahan dalam mata uang terbaru menekankan pentingnya sistem pendeteksi yang selalu memperbarui data referensinya agar tetap akurat. Mata uang baru dengan desain atau fitur keamanan yang berbeda dapat menantang kemampuan sistem dalam mengenali keasliannya. Sistem harus mampu dengan cepat mengidentifikasi elemen baru dan memperbarui database referensi untuk menghindari risiko kesalahan atau penipuan. Oleh karena itu, penelitian perlu difokuskan pada pengembangan mekanisme pembaruan data secara real-time untuk menjaga responsivitas sistem terhadap perubahan mata uang. Maka dari itu, dilakukan klasifikasi nominal mata uang kertas Republik Indonesia Tahun Emisi 2022 menggunakan Convolutional Neural Network. Tahapan yang dilakukan yaitu proses akuisisi citra, preprocessing, pelatihan model, dan evaluasi. Dengan teknik pengenalan berdasarkan pola bunga yang terdapat pada uang kertas Republik Indonesia. Hasil yang peroleh yaitu akurasi sebesar 99% dengan 694 data berhasil diklasifikasi dari 700 data pengujian.
Co-Authors AA Sudharmawan, AA Faisal, Sutan Hanny Hikmayanti Handayani Hermawan, Slamet Juwita, Ayu Ratna Mudzakir, Tohirin Al Mulyana, Assyifa Alif Rahayu Saputra, Arbi Niandi Sukmawati, Cici Emilia