Article Per Year (5 Year)
p-Index From 2021 - 2026
3.673
P-Index
This Author published in this journals
All Journal Jurnal Informatika dan Teknik Elektro Terapan 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 Sintek Kuwera
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 : 23 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 23 Documents
Search

 1   2   3 
Perancangan Sistem Informasi Manajemen Gudang Spare Part Berbasis Website Qosdu Sabil; Dian Ade Kurnia; Irfan Ali
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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

Abstract

The rapid growth of information technology has significantly influenced industrial operations, including the petrochemical sector, where efficient spare part management is crucial to ensure continuous production. This study aims to design a web-based spare part warehouse management information system capable of managing inventory data in real-time and reducing operational inefficiencies. The system was developed using the waterfall model through several stages: requirement analysis, system design, implementation, testing, and maintenance. The application was built using PHP programming language with the Laravel framework and MySQL database. The developed system features user authentication, role-based access control, equipment and part management, stock monitoring, issue and receipt transactions, and report generation in PDF format. Testing using the black box method indicates that all functionalities perform as expected. The system enhances efficiency in spare part tracking, minimizes delays in equipment maintenance, and supports accurate stock recording. Therefore, the proposed system can be an effective solution for improving warehouse management performance within the petrochemical industry.
Analisis Dan Prediksi Risiko Kelahiran Bayi Menggunakan K-Means Dan Deep Neural Network (DNN) Mukhlisin Ilahudin; Nana Suarna; Agus Bahtiar; Mulyawan; Irfan Ali
Jurnal Sistem Informasi dan Teknologi Vol 6 No 1 (2026): Jurnal Sistem Informasi dan Teknologi (SINTEK)
Publisher : LPPM STMIK KUWERA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56995/sintek.v6i1.206

Abstract

Risiko kelahiran bayi merupakan indikator penting dalam evaluasi kesehatan ibu dan anak sehingga diperlukan pendekatan analitis yang mampu mengidentifikasi pola risiko secara akurat. Penelitian ini bertujuan menganalisis dan memprediksi risiko kelahiran bayi dengan mengintegrasikan metode K-Means dan Deep Neural Network (DNN). Dataset yang digunakan terdiri dari 983 data rekam medis ibu hamil yang telah melalui tahap pengumpulan data, pembersihan, dan preprocessing meliputi normalisasi, encoding variabel kategorikal, penanganan outlier, serta seleksi fitur. Metode K-Means digunakan untuk mengelompokkan data berdasarkan kemiripan karakteristik klinis guna membentuk representasi pola risiko awal, yang selanjutnya digunakan sebagai fitur tambahan pada model DNN. Model DNN dirancang menggunakan beberapa hidden layer dengan fungsi aktivasi ReLU dan regularisasi dropout. Hasil pengujian menunjukkan bahwa model menghasilkan akurasi sebesar 61,93% dan nilai ROC AUC sebesar 0,6402, yang mengindikasikan performa moderat dalam memprediksi risiko kelahiran bayi. Stabilitas kurva loss dan akurasi menunjukkan proses pelatihan yang berjalan dengan baik tanpa overfitting signifikan. Secara praktis, model ini berpotensi digunakan sebagai alat bantu awal bagi tenaga kesehatan dalam mengidentifikasi ibu hamil dengan risiko kelahiran lebih tinggi sehingga dapat dilakukan pemantauan dan intervensi lebih dini.
Segmentation of Coffee Purchasing Behavior Based on Transaction Time Using the K-Means Algorithm Yuslia Devitri; Nining Rahaningsih; Irfan Ali; Willy Prihartono
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1863

Abstract

This studyaims to identify customer behavior patterns based on the time of purchaseof beverages at a coffee shop using the K-Means method.Transaction data includes purchase time, payment type, product name,time category, day, and month. The research stages include data cleaning, time attribute transformation, and numerical feature normalization. The optimal number of clustersis determined through testing k = 2–10 with four evaluation metrics,namely Inertia, Silhouette Score, Davies–Bouldin Index, and Calinski–HarabaszIndex. Based on the validation results, k = 3 was selected because it provided the best balancebetween compactness and cluster separation. The clustering results showedthree main customer groups based on transaction time trends:nighttime buyers with a peak around 10:27 p.m., afternoon to early evening buyerswith a centroid of 7:01 p.m., and morning to noon buyers with a centroid11:13. The frequency distribution indicates that the morning–afternoon buyer groupis the largest, while the early evening–night group is thesmallest. Visualization of scatter plots, boxplots, and time category graphsemphasizes the differences in characteristics between clusters. Overall,this study proves that K-Means is effective in mapping the temporal patternsof customer behavior. These findings can be used to develop time-based marketing strategies, operational arrangements, and product stock management,as well as form the basis for further analysis in the industry.
Co-Authors Ade Irma Purnamasari Adi Pangestu Affan, Taryono Ibnu Agung Prayogo Bagustio agus bahtiar Aji Santoso Aji Santoso Arif Rinaldi Dikananda Ayunda Fitriyana Bagustio, Agung Prayogo Dadang Sudrajat Dendy Indriya Efendi Dian Ade Kurnia Fadhil M Basysyar Farid Ali Ma'ruf Gian Fitralisma Gifthera Dwilestari INAYATUL AFRIYANI Irma Purnamasari, Ade Jayawarsa, A.A. Ketut Lintang Mugi Lestari Martanto Mukhlisin Ilahudin Mulyawan Nana Suarna Nana Suarna2 Nining R Nining Rahaningsih Nur Alia Sumanti Odi Nurdiawan Purnamasari, Ade Irma Qosdu Sabil Rini Astuti Rizki Aldi Robi Rotika Rudi Kurniawan Rusbandi Rusbandi Saeful Anwar Saefullah Fasa Siti Aisyah Willy Prihartono Yudhistira Arie Wijaya Yuslia Devitri