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All Journal International Journal of Advances in Intelligent Informatics ILKOM Jurnal Ilmiah Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Teknologi Sistem Informasi
Muttaqin, Muhammad Rafi
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Neuroscience Transportation

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Algoritma K-Means untuk Pengelompokan Topik Skripsi Mahasiswa Muttaqin, Muhammad Rafi; Defriani, Meriska
ILKOM Jurnal Ilmiah Vol 12, No 2 (2020)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v12i2.542.121-129

Abstract

In helping to develop technology in the field of education as well as bringing about a major change in competitiveness between individuals and groups, to be able to do so requires sufficient information and data to be analyzed further. In this case STT Wastukancana Purwakarta is under the auspices of Bunga Bangsa Foundation, seeing that STT Wastukancana Purwakarta students have several obstacles in their final project, one of which is difficult in determining the topic of the thesis title to be made so that sometimes the topic of the thesis title taken is not in accordance with their abilities each student. This problem can be overcome by applying the clustering method. The analytical method used is Knowledge Discovery in Database (KDD). The method of grouping students uses the clustering method and the K-Means algorithm as a clustering calculation where the Clustering aims to divide students into clusters based on grades obtained from semester 1 to 7, so as to produce student recommendations in taking thesis topics. The tool used to implement the algorithm is Rapidminer. The results of this study are grouping students according to their expertise, which is obtained based on the cluster that has the highest score and is dominated by the most subjects according to the subjects that have been grouped by each expertise. So, the results of this cluster are used as a reference for students to take the thesis title topic.
PENENTUAN STRATEGI MARKETING MENGGUNAKAN ALGORITMA K MEANS (studi kasus : PT. BPR Pondasi Niaga Perdana cabang Bekasi) Muchtar, Fira Rizkya; Muhyidin, Yusuf; Muttaqin, Muhammad Rafi
Jurnal Teknologi Sistem Informasi Vol 3 No 2 (2022): Jurnal Teknologi Sistem Informasi
Publisher : Program Studi Sistem Informasi, Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jtsi.v3i2.3071

Abstract

Bank Perkreditan Rakyat (BPR) adalah salah satu Lembaga keuangan yang ada di Indonesia yang memiliki peranan penting bagi kelangsungan perekonomian Indonesia. Persaingan antar bank dan dampak pandemic covid 19 membuat masyarakat Bekasi lebih memilih meminjam koperasi yang memiliki bunga besar. Untuk dapat memberikan jasa dan minat kepada nasabah, bank juga perlu mengumpulkan dan mengolah informasi mengenai nasabah, informasi nasabah adalah kunci yang penting dalam menjalankan stategi bisnis atau bisnis perbankan. Data mining merupakan dalah satu proses pengumpulan, pengolahan, dan penyajian informasi nasabah yang dapat digunakan oleh pihak marketing sebagai strategi yang akan dipakai untuk bersaing dengan pihak lain yang bergerak dalam bidang yang sejenis. Metode yang digunakan adalah Knowledge Discovery in Database (KDD). Metode pengelompokan data debitur kredit dengan mengimplementasikan algoritma K-Means menggunakan alat Rapidminer. Atribut yang digunakan jenis penggunaan, sumber dana pelunasan, tenor, kualitas, jumlah hari tunggakan, nominal tunggakan, plafon. Kata kunci: Data Mining, Clustering, Bank, K-Means
Market Basket Analysis Menggunakan Algoritma Hash Based pada Transaksi Penjualan Toko Bangunan Maju Bersama Purwakarta Pujadi, Octavia Saramitha; Muttaqin, Muhammad Rafi; Sunandar, Muhammad Agus; Bayu, Bayu
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 7, No 4 (2024): Agustus 2024
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v7i4.7897

Abstract

Abstrak - Dengan persaingan bisnis yang semakin ketat, pemahaman yang mendalam terhadap pola pembelian konsumen menjadi krusial untuk meningkatkan efisiensi strategi pemasaran dan pengelolaan persediaan. Analisis terhadap transaksi memungkinkan pengelola toko untuk mengenali kebutuhan pelanggan dengan lebih baik dan merumuskan strategi bisnis yang lebih tepat sasaran. Metode yang digunakan dalam penelitian ini adalah algoritma hash based dalam data mining, yang efektif dalam menemukan frequent itemset dari data transaksi bulan Januari dan Februari 2024. Algoritma ini memungkinkan pemrosesan data dalam jumlah besar dengan lebih efisien, mengidentifikasi pola pembelian yang sering terjadi di antara berbagai item produk. Hasil analisis memungkinkan untuk mengidentifikasi kombinasi produk yang sering dibeli bersama, dengan association rules yang ditemukan jika membeli triplek 3mm dan triplek 4mm, maka akan membeli triplek 8mm dan papan cor juga dengan nilai support 1.29% nilai confidence 100% dan nilai lift 77.5%. Penelitian ini memberikan wawasan yang bermanfaat bagi pengelola dalam menentukan strategi penempatan produk dan rekomendasi kepada pelanggan. Dengan demikian, penelitian ini tidak hanya memberikan manfaat praktis bagi pengelola toko tetapi juga berpotensi untuk pengembangan keilmuan dalam penerapan kecerdasan buatan dalam analisis bisnis.Kata kunci : Algoritma Hash Based, Data Mining, Market Basket Analysis Abstract - With increasingly fierce business competition, a deep understanding of consumer purchasing patterns is crucial to improve the efficiency of marketing strategies and inventory management. Analysis of transactions allows store managers to better recognize customer needs and formulate more targeted business strategies. The method used in this study is the hash-based algorithm in data mining, which is effective in finding frequent itemsets from transaction data for January and February 2024. This algorithm allows processing large amounts of data more efficiently, identifying frequent purchasing patterns among various product items. The results of the analysis make it possible to identify combinations of products that are often purchased together, with association rules found if you buy 3mm plywood and 4mm plywood, you will buy 8mm plywood and cor boards too with a support value of 1.29%, a confidence value of 100% and a lift value of 77.5%. This study provides useful insights for managers in determining product placement strategies and recommendations to customers. Thus, this study not only provides practical benefits for store managers but also has the potential for scientific development in the application of artificial intelligence in business analysis.Keywords: Data Mining, Hash Based Algorithm, Market Basket Analysis
Biological constraint in digital data encoding: A DNA based approach for image representation Muttaqin, Muhammad Rafi; Herdiyeni, Yeni; Buono, Agus; Priandana, Karlisa; Siregar, Iskandar Zulkarnaen; Kusuma, Wisnu Ananta
International Journal of Advances in Intelligent Informatics Vol 11, No 3 (2025): August 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i3.1747

Abstract

Digital data encoding is crucial for communication and data storage, but conventional techniques, such as ASCII and binary coding, have drawbacks in terms of processing speed and storage capacity. A potential substitute with parallel processing and high-capacity storage is DNA-based data encoding. The goal of this research is to develop a digital data encoding technique based on DNA, while considering biological constraints such as homopolymer and GC-content. The process involves converting image pixel values into binary format, followed by encoding into DNA sequences, ensuring they meet biological constraints. The validity of the resulting DNA sequences is assessed through transcription and translation processes. Additionally, Multiple Sequence Alignment analysis is conducted to compare the similarities between the encoded DNA sequences. The results indicate that the DNA sequences from MNIST images share similar characteristics, reflected in the phylogenetic tree's close clustering. Multiple Sequence Alignment analysis shows that biological constraints successfully preserved the core visual features, allowing accurate clustering. However, this method also faces drawbacks, particularly in the reduction of visual information and sensitivity to changes in image intensity. Despite these challenges, DNA-based encoding shows potential for digital image representation. Further development, particularly the integration of deep learning, could lead to more efficient, secure, and sustainable data storage systems, especially for image data.
Co-Authors Agus Buono Bayu, Bayu Defriani, Meriska ISKANDAR ZULKARNAEN SIREGAR Karlisa Priandana Muchtar, Fira Rizkya Muhyidin, Yusuf Pujadi, Octavia Saramitha Sunandar, Muhammad Agus Wisnu Ananta Kusuma