Article Per Year (5 Year)
p-Index From 2021 - 2026
3.078
P-Index
This Author published in this journals
All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Information Management For Educators And Professionals (IMBI) JURSIMA (Jurnal Sistem Informasi dan Manajemen) E-Link: Jurnal Teknik Elektro dan Informatika Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Journal of Artificial Intelligence and Engineering Applications (JAIEA) Journal of Student Research Jurnal Riset Manajemen, Bisnis, Akuntansi dan Ekonomi (JAMBAK) JURSIMA
Irfan Ali
Unknown Affiliation
Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Library & Information Science Mechanical Engineering Public Health Social Sciences Other

Published : 20 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 20 Documents
Search

 1   2 
ANALISIS DATA PENJUALAN MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING PADA TOKO KECANTIKAN PUTRI Agung Prayogo Bagustio; Ade Irma Purnamasari; Irfan Ali
PROSISKO: Jurnal Pengembangan Riset dan Observasi Sistem Komputer Vol. 11 No. 2 (2024): Prosisko Vol. 11 No. 2 September 2024
Publisher : Pogram Studi Sistem Komputer Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/prosisko.v11i2.7928

Abstract

Abstrak - Industri kecantikan mengalami pertumbuhan pesat dalam beberapa tahun terakhir, mendorong tingginya permintaan produk kecantikan. Toko Kecantikan Putri merupakan salah satu toko kecantikan yang berkembang pesat di wilayah Cirebon. Untuk meningkatkan strategi penjualan dan memahami pola pembelian pelanggan, Toko Kecantikan Putri perlu menganalisis data penjualan secara efektif. Analisis data penjualan tradisional tidak dapat memberikan insights yang mendalam mengenai pola pembelian pelanggan. Hal ini dapat menghambat Toko Kecantikan Putri dalam mengembangkan strategi penjualan yang tepat dan efektif. Penelitian ini menerapkan algoritma K-Means clustering untuk menganalisis data penjualan Toko Kecantikan Putri selama bulan Januari 2024. Data yang dianalisis terdiri dari 122 baris dengan 22 atribut. Data penjualan meliputi informasi produk, kategori produk, dan jumlah penjualan. Algoritma K-Means clustering digunakan untuk mengelompokkan data penjualan berdasarkan karakteristik produk. Teknik analisis menggunakan Knowledge Discovery in Database (KDD) merupakan proses untuk menemukan pengetahuan baru dari data yang dikumpulkan. Hasil analisis menunjukkan bahwa data penjualan Toko Kecantikan Putri dapat dilakukan menjadi menjadi 9 cluster, dari cluster k=2 sampai k=9. Masing-masing cluster memiliki karakteristik pola pembelian yang berbeda. Cluster terbaik terdapat pada cluster K=8 dengan Nilai DBI sebesar 0,021 yang berasal dari measure Types Mixed Measures nilai davies bouldin index 0.077, Numerical Measures nilai davies bouldin index 0.114, Bregman Divergences nilai davies bouldin index 0.114. Analisis data penjualan Toko Kecantikan Putri dengan Algoritma K-Means Clustering menghasilkan insights berharga mengenai pola pembelian pelanggan. Evaluasi menyeluruh terhadap berbagai parameter, jumlah cluster, dan nilai Davies Bouldin Index optimal, menunjukkan bahwa Algoritma K-Means Clustering menghasilkan pengelompokan data yang paling optimal dan informatif.Kata Kunci: Algoritma K-Means clustering, Analisis Data Penjualan, Toko Kecantikan Putri, Pola Pembelian Pelanggan, Strategi Penjualan
Enhancing Election Staff Selection through Decision Tree-Based Classification Rizal Rayyan Firdaus; Nana Suarna; Irfan Ali; Ahmad Rifai
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.768

Abstract

The selection of competent election committee members is a critical aspect in ensuring the success of a fair and transparent election process. However, the subjective nature of this selection process necessitates a data-driven approach to optimize the selection of officials who meet the required competency criteria. This research aims to classify the competencies of prospective election committee members using the Decision Tree algorithm based on demographic data and technological attributes of the population. The study employs the Knowledge Discovery in Databases (KDD) methodology, which includes the stages of data selection, preprocessing, transformation, data mining, and evaluation. In this process, data collected through various attributes are processed to build a classification model. The Decision Tree algorithm is applied to extract patterns from the data, resulting in a decision tree that can classify individuals into different competency classes based on existing features. The research findings indicate that the Decision Tree algorithm effectively classifies respondents into several competency classes that represent varying levels of skills and interest in the election process. The model shows that Class 4 is the dominant class, indicating that most respondents have moderate competency in technological skills and interest in elections. Class 3 represents individuals with higher technological skills but moderate interest, while Classes 2 and 1 represent individuals with varying combinations of interest and skills. This study demonstrates that using the Decision Tree algorithm in the KDD process is highly effective in objectively classifying the competencies of prospective election committee members. By analyzing the interactions among relevant attributes, the model provides insights that can improve the accuracy of election official selection. This data-driven approach can be adapted to other contexts requiring competency classification, offering broader benefits for various criteria-based selection systems.
K-Means Clustering Method to Make Credit Payment Groupinhg Efficient Siti Nur Illah; Nana Suarna; Irfan Ali; Dodi Solihudin
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.815

Abstract

Credit payment management is one of the main challenges in the financial sector, especially in grouping customers based on risk and payment patterns. This study aims to evaluate the K-Means Clustering method in improving the efficiency of credit payment data clustering. The dataset used includes information on payment history, loan amount, tenor, and credit status from financial institutions. The research approach involves data processing stages, application of the K-Means algorithm, and evaluation of results using the Davies-Bouldin Index and Silhouette Score metrics. The results of the analysis show that the K-Means method is effective in identifying customer payment patterns and dividing them into three main clusters: high, medium, and low risk. In addition, this study found that determining the optimal number of clusters using the Elbow Method can improve the accuracy of the clustering results. The resulting model makes a significant contribution to credit risk management, helping financial institutions make strategic decisions related to credit policies and risk mitigation. This study offers practical implications, including increased operational efficiency and predictive ability against potential bad debts. Further studies are recommended to integrate this method with other algorithms to improve the performance of large-scale data analysis.
Convolutional Neural Networks for Classification Motives and the Effect of Image Dimensions Siti Aisyah; Rini Astuti; Fadhil M Basysyar; Odi Nurdiawan; Irfan Ali
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5623

Abstract

Although Indonesian batik patterns vary by location, they usually depict local customs and cultures. Each batik has a unique quality and, to correctly identify the batik designs, you need to understand the design patterns. However, many people struggle to identify and categorize these kinds of motivation because they don't have the requisite knowledge, understanding, or access to sufficient information. This study used photo data to classify batik patterns into 15 different groups. Batik Kawung, Megamendung, Lasem, Pole, Machete, Gills, Nutmeg, Karaswasih, Cendrawasih, Geblek Renteng, Bali, Betawi, and Dayak are all included in this category. 1,350 images were used in the research. Google supports the collection of data. To provide the highest level of precision and to evaluate how image dimensions affect the classification of batik designs, this study employs convolutional neural networks (CNNs). The results of this study show that Multi-Layer Perceptron (MLP) is a well-liked deep learning method for data classification, especially in domains where picture classification is involved. The size of the images utilized affects the accuracy of computational neural network (CNN) algorithms. The results showed that the test using training data comparisons of 60%, 30% and 10% resulted in a 01.89% loss of 1.18% and a 100% improvement in accuracy.
Optimizing the Social Assistance Recipient Model in CangkringVillage Using the Naïve Bayes Algorithm Rotika; Nana Suarna; Irfan Ali; Dendy Indriya Efendi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.849

Abstract

Social assistance is one of the methods used by the government to help the underprivileged. Cangkring Village is a village in Cirebon Regency that has inaccurate data on recipients of social assistance or underprivileged people. The Naive Bayes algorithm is one of the most effective techniques in machine learning for classifying data, in determining the eligibility of recipients of social assistance. The method works with a probabilistic approach to analyze data efficiently and accurately, can group data based on attributes and produce high accuracy. The problem in Cangkring Village, namely the accuracy of data on recipients of social assistance, is still a problem that requires special attention. This inaccuracy not only reduces the effectiveness of social assistance programs but also creates injustice for people in need. Invalid and inappropriate data causes the distribution of social assistance to be suboptimal. The purpose of this study is to optimize the accuracy model of social security recipients using the Naive Bayes algorithm, which can help improve the accuracy in determining eligible recipients.The method used in the study is secondary data processing taken from social assistance recipient data in Cangkring Village. This process includes data preprocessing stages, training and testing data distribution, and implementation or application of the Naive Bayes algorithm to perform classification. The results of the study show that the Naive Bayes algorithm is able to increase the accuracy of the classification of social assistance recipients with an accuracy rate of 90%, compared to the conventional method used previously. This study contributes to providing a more efficient and targeted method in selecting social assistance recipients, so that it can improve the social assistance distribution system in the future. Thus, the Naive Bayes algorithm can be an effective method for data-based decision making in the context of social policy.
Improving the Voter List Clustering Model Fixed(DPT) using the K-Means Algorithm in Girinata Village Rizki Aldi; Nana Suarna2; Irfan Ali; Dendy Indriya Efendi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.876

Abstract

Elections are one of the pillars of democracy that require accurate voter data to ensure transparency and fairness. The Permanent Voter List (DPT) is a crucial element in supporting the smooth running of elections, but there are often data validity problems such as duplicate data, voter location errors, or voter data that does not meet the requirements. This research focuses on the application of the K-Means algorithm to increase the accuracy and validity of the DPT at TPS 05, Girinata Village. The problem formulation in this research includes the accuracy level of the DPT, the effectiveness of the K-Means algorithm in identifying inaccuracies, as well as factors that influence the accuracy of voter data. This research aims to analyze the accuracy level of the DPT, evaluate the effectiveness of the K-Means algorithm in grouping data, and identify factors contributing to the validity of the DPT. The analysis results show that the K-Means algorithm succeeded in grouping voter data with good quality, with a Davies-Bouldin Index (DBI) value of 0.389, which indicates clearly defined clusters. The main factors that influence clustering are age, distance to TPS, and location (RT and TPS). This research shows that the K-Means algorithm can be used to detect inaccuracies in voter data, such as data that does not match the TPS location or age that does not meet the requirements as a voter. With these results, the K-Means algorithm makes a significant contribution to validating voter data, thereby supporting a more transparent and accountable election process.
Identify Rattan Sales Patterns Using the FP-Growth Algorithm on CV. Busaeri Rattan Robi; Nana Suarna; Irfan Ali; Dendy Indriya Efendi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.877

Abstract

This research was conducted to recognize the pattern of purchasing rattan products at CV. Busaeri Rattan by utilizing the FP-Growth algorithm. The rattan industry is faced with the challenge of understanding consumer habits in order to improve marketing strategies. The FP-Growth algorithm was chosen for its ability to efficiently identify frequent itemset patterns without requiring a lot of memory. This research includes collecting rattan sales transaction data for one year, data preprocessing, FP-Tree structure formation, and frequent itemset analysis. The analysis was conducted using RapidMiner software with a minimum support setting of 0.005 and confidence of 0.1. The processed data was then used to find combinations of products that are often purchased together. The results revealed some significant patterns, such as the products “Mandola 3/4” and “Jawit 8/11,” which are often purchased together with a confidence level of 100%. These findings provide important insights for CV. Busaeri Rattan in increasing sales through promotional strategies such as bundling or discount offers. In addition, the FP-Growth algorithm proved to be faster and more resource-efficient than traditional methods such as Apriori. The discussion shows that the discovered purchasing patterns can help CV. Busaeri Rattan better manage stock, minimize the risk of running out of goods, and design data-driven marketing strategies. The combination of products that are often purchased together can be utilized to improve customer satisfaction as well as operational efficiency. The conclusion of this research is that the FP-Growth algorithm is an effective tool for analyzing large-scale transaction data. Further research is recommended to explore the application of this algorithm to other types of products or compare it with other data mining algorithms.
Analisis Peramalan Tingkat Pengangguran Terbuka di Jawa Barat: Pendekatan Time Series menggunakan Metode ARIMA Adi Pangestu; Ade Irma Purnamasari; Irfan Ali
Jurnal IT UHB Vol 5 No 1 (2024): Jurnal Ilmu Komputer dan Teknologi
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/ikomti.v5i1.1298

Abstract

Tingkat pengangguran menjadi indikator penting dalam mengukur stabilitas ekonomi suatu daerah. Penelitian ini membahas tentang analisis model dan peramalan tingkat pengangguran terbuka di Wilayah Jawa Barat menggunakan metode ARIMA (Autoregressive Integrated Moving Average). Penelitian ini menggunakan data deret waktu (time-series) 6 bulanan tingkat pengangguran terbuka dari tahun 2007 hingga 2023 yang bersumber dari Badan Pusat Statistik, Opendatajabar dan Dinas Ketenagakerjaan Provinsi Jawa Barat. Analisis dimulai dengan identifikasi model ARIMA, estimasi parameter, cek diagnostik, evaluasi model dan peramalan. Peramalan dilakukan untuk 3 tahun ke depan (2024 - 2026). Model ARIMA(1,0,0) dipilih berdasarkan evaluasi parameter seperti Root Mean Squared Error(RMSE), Mean Absolute Error(MAE) dan Mean Abs Percent Error(MAPE). Hasil peramalan menunjukkan peningkatan tingkat pengangguran pada Februari 2024 7,92% menjadi 8,40% pada Agustus 2026. Hasil menunjukkan pola tren naik yang berkelanjutan. Peningkatan tingkat pengangguran di Wilayah Jawa Barat dipengaruhi oleh faktor kebijakan ekonomi, struktur industri, dinamika teknologi, dan pendidikan. Peran pemerintah dalam menciptakan lapangan kerja, reformasi pendidikan, dan kebijakan investasi menjadi krusial dalam menanggulangi masalah ini. Penelitian ini memberikan kontribusi dalam pemahaman dan peramalan tingkat pengangguran terbuka di Wilayah Jawa Barat. Model ARIMA(1,0,0) dapat digunakan sebagai alat yang efektif untuk meramalkan perubahan tingkat pengangguran di masa mendatang. Oleh karena itu, perumusan kebijakan yang mendukung pertumbuhan ekonomi dan penciptaan lapangan kerja diperlukan untuk mengatasi tantangan ini.
Penerapan Algoritma FP-Growth Untuk Menentukan Pola Penjualan Toko Ellia Umami Lintang Mugi Lestari; Irfan Ali
Journal of Student Research Vol. 1 No. 3 (2023): Mei: Journal of Student Research
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jsr.v1i3.1267

Abstract

Toko ellia umami berupaya meningkatkan produksi produknya agar lebih banyak orang mengetahuinya, mirip dengan berapa banyak orang yang membeli dan menjual produk yang sama secara bersamaan. Toko Ellia Umami menjual berbagai macam barang, yang menimbulkan kemungkinan data tidak konsisten dan tidak dapat disamakan. Beberapa produk mungkin lebih populer dari yang lain, oleh karena itu toko ellia umami harus mengetahui aturan yang mengatur penjualan dan transaksi serta produk itu sendiri. Algoritma FP-Growth adalah satu-satunya algoritma penambangan data yang digunakan untuk memeriksa persyaratan transaksi data. Di toko ellia umami, FP-Growth dapat digunakan untuk menganalisis data transaksi pembelian produk dan mengidentifikasi transaksi yang tertunda.Tujuan dari tugas akhir ini adalah mengimplementasikan algoritma FP-Growth untuk menginformasikan kepada toko ellia umami tentang rekomendasi produk dan layanan berdasarkan data dari transaksi yang melibatkan penjualan toko ellia umami. Berdasarkan hasil akhir, pola hubungan data dari toko ellia umami dengan tingkat minimum (support 0,1) dan (confiden 0,1) menunjukkan bahwa jika pelanggan membeli Libby M, pelanggan juga akan membeli Celpen dan anak.
Penerapan Algoritma Fp-Growth Untuk Menentukan Pola Penjualan Pada Toko Sheryl Cosmetic Dan Titanium Collection Ayunda Fitriyana; Irfan Ali
Journal of Student Research Vol. 1 No. 4 (2023): Juli: Journal of Student Research
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jsr.v1i4.1483

Abstract

Skincare saat ini adalah barang yang sangat digemari oleh para remaja tidak hanya perempuan adapun laki-laki juga memakainya, dengan berbagai macam merk yang banyak sekali manfaat dan kandungan dari berbagai macam skincare. Toko Sheryl Cosmetic dan Titanium Collection sebuah toko online dan offline yang bergerak dibidang penjualan skincare dll. Dalam Bisnisnya ini aturan transaksi penjualan nya sangat tidak stabil dan tidak efektif. Agar aturan asosiasi menjadi lebih efektif dan efisien maka dilakukanlah analisis suatu transaksi penjualan karena analisis transaksi ini bertujuan untuk merancang strategi yang efektif untuk dengan cara memanfaatkann data transaksi penjualan produk skincare mana saja yang dibeli oleh konsumen. Toko Sheryl Cosmetic dan Titanium Collection dapat mengidentifikasi pembelian pola-pola yang paling sering terjadi di setiap toko dengan menggunakan algoritma FP-Growth. Informasi seperti ini dapat digunakan untuk meningkatkan efektivitas manajemen persediaan dan strategi pemasaran, serta memungkinkan toko untuk memberikan rekomendasi pembelian yang lebih disesuaikan dan efektif kepada pelanggan.
Co-Authors Ade Irma Purnamasari Adi Pangestu Agung Prayogo Bagustio Ahmad Rifai Aji Santoso Aji Santoso Arif Rinaldi Dikananda Ayunda Fitriyana Bagustio, Agung Prayogo Dendy Indriya Efendi Dian Ade Kurnia Dodi Solihudin Fadhil M Basysyar Farid Ali Ma'ruf Gian Fitralisma Gifthera Dwilestari INAYATUL AFRIYANI Irma Purnamasari, Ade Jayawarsa, A.A. Ketut Lintang Mugi Lestari Martanto Nana Suarna Nana Suarna2 Nining R Nur Alia Sumanti Odi Nurdiawan Purnamasari, Ade Irma Rini Astuti Rizal Rayyan Firdaus Rizki Aldi Robi Rotika Rusbandi Rusbandi Saeful Anwar Saefullah Fasa Siti Aisyah Siti Nur Illah Yudhistira Arie Wijaya