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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) Jurnal Komputer, Informasi dan Teknologi
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

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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.
Klasifikasi Ketidakhadiran di Tempat Kerja Menggunakan Metode Support Vector Machine Natasya Sumeisey; Titik Misriati; Imam Nawawi
Jurnal Komputer, Informasi dan Teknologi Vol. 5 No. 1 (2025): Juni
Publisher : Penerbit Jurnal Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53697/jkomitek.v5i1.2618

Abstract

Penelitian ini bertujuan untuk mengklasifikasikan ketidakhadiran karyawan di tempat kerja menggunakan metode Support Vector Machine (SVM), dengan menggunakan dataset yang mencakup informasi demografi, pekerjaan, dan faktor-faktor lain yang terkait dengan ketidakhadiran. Dataset yang digunakan berisi catatan ketidakhadiran karyawan sebuah perusahaan kurir di Brasil dari UCI Machine Learning. Penelitian ini mengimplementasikan model SVM dengan kernel Radial Basis Function (RBF), yang dipilih karena kemampuannya dalam menangani data non-linier. Hasil evaluasi model menunjukkan kinerja yang sangat baik, dengan AUC sebesar 0,995, akurasi mencapai 98,1%, dan skor F1 sebesar 0,981, yang menunjukkan keseimbangan yang sangat baik antara presisi dan ingatan. Model tersebut berhasil memprediksi sebagian besar ketidakhadiran karyawan secara akurat, dengan kesalahan prediksi yang minimal. Namun, masih ada beberapa kesalahan kecil dalam memprediksi ketidakhadiran, yang dapat diperbaiki dengan menyetel hiperparameter dan menambahkan fitur tambahan yang terkait dengan faktor-faktor yang memengaruhi ketidakhadiran. Secara keseluruhan, penelitian ini menunjukkan bahwa model SVM merupakan alat yang efektif dan efisien untuk memprediksi ketidakhadiran karyawan, dengan hasil yang dapat diterapkan dalam mengelola ketidakhadiran di organisasi.
Optimization of the YOLOv7 Object Detection Algorithm for Estimating the Amount of Apple Harvest Verry Riyanto; Imam Nawawi; Ridwansyah Ridwansyah; Ganda Wijaya; Toto Haryanto
Paradigma - Jurnal Komputer dan Informatika Vol. 25 No. 1 (2023): March 2023 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v25i1.1809

Abstract

The increasing population consumed in high production and food needs for survival. Apples are one of the crop harvest products in Indonesia whose needs are increasing, because they are not only needed for human vitamins but can be used as hand fruit or a form of gratitude to those who receive the fruit. In the process of harvesting apples in agricultural land, harvesting is often found which is not feasible in the hands of consumers because it takes too long for apples to not be harvested when the condition of the fruit is feasible in maturity. Therefore, the authors approach this problem by processing the image results obtained to form a detection model, whether the apples are said to be feasible to be harvested immediately and from the image results it can also be calculated the number of fruits captured by the image model , feature enhancements Estimates on objects from this image model are expected to provide more timely harvest predictions in order to provide longer aging of apples and good fruit quality after reaching consumers
SISTEM MONITORING BARANG CETAK BERBASIS WEB MENGGUNAKAN METODE WATERFALL Imam Nawawi; Nurajijah Nurajijah; Abdillah Ari
INTI Nusa Mandiri Vol. 14 No. 1 (2019): INTI Periode Agustus 2019
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

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

Abstract

PT. Sinar Dewasa adalah sebuah perusahaan yang bergerak dibidang percetakan, yang didalam bisnisnya terlebih dahulu harus memenangkan tender. Sampai saat ini PT. Sinar Dewasa hanya menggunakan sistem sederhana dalam pemasaran informasi dan pencatatan, sehingga menimbulkan kelambatan dalam pemasaran informasi dan memonitoring barang cetak yang telah dikirim. Oleh karena itu dari Hasil Sistem penelitian ini dapat membantu perusahaan dalam pembuatan program aplikasi yang dapat mempercepat pengerjaan yang selama ini masih dilakukan secara manual terutama dalam masalah pencatatan barang dan distribusi barang yang ada. Dan dengan adanya penerapan system teknologi internet diharapkan dapat membatu mempercepat dari segi efisiensi waktu.
OPTIMISASI PEMILIHAN FITUR UNTUK PREDIKSI GAGAL JANTUNG: FUSION RANDOM FOREST DAN PARTICLE SWARM OPTIMIZATION Imam Nawawi
INTI Nusa Mandiri Vol. 18 No. 2 (2024): INTI Periode Februari 2024
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v18i2.5031

Abstract

Heart failure is a serious, life-threatening cardiovascular disease that increases with age and unhealthy lifestyles. Early prediction is essential to provide timely treatment and reduce mortality. The use of machine learning techniques, especially the Random forest (RF) method, for predicting heart failure has been previously researched, so the problem that occurs is that the RF method does not have maximum results because of irrelevant features. Selection of relevant features is a key step in building an accurate prediction model. Particle Swarm Optimization (PSO) is used to improve feature selection by searching for optimal combinations. The aim of the research is to reduce the mortality rate by improving the RF method with relevant features so as to increase the accuracy of predictions with Fusion RF and PSO. The results show an increase in accuracy of 02.78% to 87.33% with PSO, although the AUC decreased by 0.031%. The advantage of PSO is a significant increase in accuracy, but the disadvantage is a slight decrease in AUC. Future developments could explore how to address AUC degradation without compromising accuracy and transmitting additional relevant features.
Co-Authors Abdillah Ari Abdul Hamid Ahmad Fauzi Ahmad Hafidzul Kahfi Andi Taufik Andreyestha Andreyestha Anus Wuryanto Ari Abdilah Ari Abdillah Dewi Yuliandari Eko Pramono Elva Mardiyani Fajar Akbar Ganda Wijaya Ginabila Hafis Nurdin Hari Sugiarto - UBSI Hilda Rachmi Mochammad Abdul Azis Natasya Sumeisey 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 Titik Misriati Toto Haryanto Verry Riyanto Veti Apriana, Veti Widi Intan Priyanti