Article Per Year (5 Year)
p-Index From 2021 - 2026
1.463
P-Index
This Author published in this journals
All Journal Format : Jurnal Imiah Teknik Informatika Jurnal Teknik Komputer AMIK BSI Jurnal Khatulistiwa Informatika Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Mantik JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) INTI Nusa Mandiri Abditeknika - Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika Teknologi dan Sains (Jinteks) Paradigma Jurnal Aruna Mengabdi (ARMI)
Imam Nawawi
Universitas Bina Sarana Informatika
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 13 Documents
Search

 1   2 
SELEKSI ATRIBUT PADA DATA TIDAK SEIMBANG NASABAH KOPERASI DENGAN OPTIMASI SMOTE DAN ADABOOST Richky Faizal Amir; Andreyestha; Imam Nawawi; Andi Taufik; Eko Pramono; Fajar Akbar
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 5 No 3 (2023): EDISI 17
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v5i3.2757

Abstract

The credit procedure is the provision of credit on the basis of the bank's belief in the ability and ability of the customer to repay. In this study, the customer data tested was divided into 3 classes, namely 39 LOW customers, 84 MEDIUM customers, and 11 HIGH customers. Unbalanced datasets are pre-processed using the Synthetic Minority Over-sampling (SMOTE) technique. Classification methods such as Random Forest and Support Vector Machine will test cooperative customer data. The data is tested based on the attribute that has the highest matching value using the Particle Swarm Optimization method, this test is also optimized with the Adaboost method to increase its accuracy. The results of tests carried out using the Random Forest method obtained an accuracy of 89.05% and the Support Vector Machine algorithm obtained an accuracy of 81.75%. Meanwhile, testing with two methods optimized with Adaboost showed an increase in accuracy with Random Forest getting 91.24% accuracy and Support Vector Machine getting 82.48% accuracy. The highest accuracy in the cooperative customer data classification test was obtained from the Random Forest algorithm which was optimized with Adaboost at 91.24%
ANALISIS PEMBAYARAN DIGITAL DANA DENGAN APLIKASI ISO/IEC 9126 MODEL BERDASARKAN FAKTOR KEGUNAAN Oky Kurniawan; Richky Faizal Amir; Andreyestha Andreyestha; Andi Taufik; Fajar Akbar; Imam Nawawi
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 6 No 1 (2024): EDISI 19
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v6i1.3418

Abstract

Pembayaran digital sudah menjadi tren untuk saat ini. Kemudahan penggunaan aplikasi dan biaya transaksi yang relatif murah telah mengubah pola perilaku masyarakat dalam melakukan pembayaran dan transaksi yang serba digital. Dana merupakan aplikasi dompet digital yang juga berfungsi sebagai pembayaran digital. Banyaknya pesaing saat ini khususnya di bidang pembayaran digital memerlukan adanya evaluasi terhadap kualitas perangkat lunak. Salah satu metode yang digunakan untuk mengukur kualitas perangkat lunak adalah ISO/IEC 9126, sebuah organisasi standardisasi internasional yang digunakan sebagai pedoman model kualitas. Dalam proses evaluasi Usability pada ISO/IEC 9126 terdapat karakteristik kualitas Usability yang meliputi sub-karakteristik kualitas yaitu: keterpahaman, kemampuan belajar, pengoperasian, dan daya tarik. Pengendalian kualitas perangkat lunak yang akan dievaluasi difokuskan pada perspektif Usability dengan tujuan untuk memuaskan kebutuhan pengguna. Nilai dikelompokkan berdasarkan tiga kategori, dari 0-100% tidak memuaskan antara 0%-40%, marginal antara 40%-60%, dan memuaskan antara 60%-100%. Dari hasil perhitungan nilai Usability sebesar 0,84 maka dapat dikatakan kategori memuaskan (satisfactory).
Prediksi Keterlambatan Pembayaran Mahasiswa untuk Mitigasi Risiko Cuti Menggunakan SVM Optimasi PSO Hafis Nurdin; Imam Nawawi; Anus Wuryanto; Dewi Yuliandari; Hari Sugiarto
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 7 No. 1 (2025): Juni 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v7i1.15483

Abstract

Delayed tuition payments present challenges for higher education institutions, impacting both financial stability and students’ academic progress. This study proposes a predictive model using Support Vector Machine (SVM) optimized by Particle Swarm Optimization (PSO) to identify students at risk of payment delays. The dataset includes academic and social attributes. A dot kernel SVM was evaluated using 10-fold cross-validation. Results show that PSO optimization significantly improved model performance, particularly in recall, which increased from 36.10% to 65.51%, indicating better identification of delayed payment cases. The analysis also reveals that social factors, such as employment and academic status, strongly influence prediction outcomes. These findings highlight the potential of the SVM-PSO model as a decision-support tool for early intervention, enabling institutions to mitigate dropout risks and enhance financial planning. By leveraging this approach, universities can better support students while maintaining administrative efficiency and institutional sustainability.
Co-Authors Abdillah Ari Abdul Hamid Ahmad Fauzi Ahmad Hafidzul Kahfi Akrom, Akrom Andi Taufik Andreyestha Andreyestha Anus Wuryanto Ari Abdilah Ari Abdillah Dewi Yuliandari Eko Pramono Elva Mardiyani Fajar Akbar Ganda Wijaya Ginabila Ginabila Hafis Nurdin Hari Sugiarto - UBSI Hilda Rachmi Mochammad Abdul Azis Numan Musyaffa Nurajijah Nurajijah Nurajijah Nurajijah Oky Kurniawan Richky Faizal Amir Ricki Sastra Ridwansyah Ridwansyah Rino Ramadan Risa Wati - AMIK BSI Tasikmalaya Risa Wati - AMIK BSI Tasikmalaya Silvy Nur Azizah Siti Hani Nurlaela Toto Haryanto Verry Riyanto Veti Apriana Widi Intan Priyanti