Article Per Year (5 Year)
p-Index From 2021 - 2026
0.983
P-Index
This Author published in this journals
All Journal INTEGER: Journal of Information Technology Bulletin of Computer Science Research LOGIC : Jurnal Ilmu Komputer dan Pendidikan AMMA : Jurnal Pengabdian Masyarakat
Astofa, Aniq
Unknown Affiliation
Humanities Chemistry Computer Science & IT Control & Systems Engineering Education Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing Other

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Komparasi Algoritma Support Vector Machine Dengan Naïve Bayes Untuk Analisis Kelayakan Pemberian Kredit Usaha Mikro Astofa, Aniq
INTEGER: Journal of Information Technology Vol 6, No 2 (2021): September
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.integer.2021.v6i2.2357

Abstract

Credit has a high risk of credit congestion, this is due to the accidental factor due to the disaster experienced by the debtor so that the credit provided does not increase the income of the debtor, in addition to the existence of bad faith of the debtor by not fulfilling the obligation as it should, the data technique mining using Particle Swarm Optimization-based vector-based support method with Naive bayes. Support Vector Machine method has an accuracy of 50.70%. The second experiment conducted using Particle Swarm Optimization's Support Vector Machine method has an accuracy value of 85.92% and Compared with al-goritma or naive bayes method the accuracy value of 91.16% .with Rapidminer software
Implementasi Algoritma Nave Bayes Untuk Memprediksi Kelayakan Kredit Nasabah Astofa, Aniq; Sutono, Eko
LOGIC : Jurnal Ilmu Komputer dan Pendidikan Vol. 2 No. 5 (2024): Logic : Jurnal Ilmu Komputer dan Pendidikan
Publisher : Shofanah Media Berkah

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

Abstract

Dalam memberikan kredit ada resiko yang dihadapi oleh, yaitu terlambatnya pembayaran pengembalian bahkan kegagalan pembayaran kredit. Masalah seperti ini terjadi karena kurang akuratnya pihak pemberi kredit dalam penilaian terhadap kemampuan nasabah, sehingga mengakibatkan kesalahan dalam keputusan pemberian kredit yang berujung pada kemacetan kredit. Oleh karena itu untuk mengatasi hal tersebut, pihak dalam memberikan pinjaman perlu memprediksi kelayakan pengajuan kredit terlebih dahulu supaya resiko yang timbul dari pemberian kredit kepada calon debitur tidak terlalu besar. Cara untuk menentukan prediksi kelayakan pengajuan kredit yaitu dengan menggunakan algoritma data mining. Salah satu fungsi dalam data mining yang dapat digunakan untuk menyelesaikan masalah yang dihadapi pihak untuk memprediksi kelayakan pengajuan kredit adalah klasifikasi. Metode yang diusulkan adalah naïve bayes. Algoritma naïve bayes bertujuan untuk melakukan klasifikasi data pada kelas tertentu. Tujuan penelitian ini adalah untuk melihat pola prediksi dari setiap atribut-atribut yang terdapat pada data set dengan menggunakan algoritma naïve bayes dan melakukan pengujian data training terhadap data testing untuk melihat pemodelan data sudah baik atau belum. Dalam membangun aplikasi ini penulis menggunakan bahasa pemrograman visual basic 2008 serta mysql sebagai basis data. Dari hasil implementasi pengujian model data training terhadap data testing sebesar 75%.
Pelatihan Penggunaan Microsoft Office Dalam Guna Meningkatkan Ketrampilan Dan Keahlian Kepada Para Siswa/i PKBM Penerus Bangsa Tangerang Selatan Akrom; Astofa, Aniq; Zainudin
AMMA : Jurnal Pengabdian Masyarakat Vol. 4 No. 7 : Agustus (2025): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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

Abstract

Education is an  important activity role in the development and changes that will occur in society. In today's era, competition for jobs is becoming increasingly fierce. To face this fierce competition in the world of work, students are expected to have skills, one of which is the use of Microsoft Office. PKBM Penerus Bangsa Tangerang Selatan aims to enhance students' skills in using the widely used software Microsoft Office Excel. Microsoft Office Excel is one of the most renowned software programs globally. To use Microsoft Office Excel effectively, students must master its techniques. Therefore, to assist our students, the PKM Dosen Universitas Pamulang team will conduct training and workshops titled “Training on the Use of Microsoft Office to Enhance Skills and Competencies for Students of PKBM Penerus Bangsa Tangerang Selatan”.
Pentingnya Edukasi Cyber Security Untuk Menjaga Keamanan Data Pribadi dari Serangan Cyber Phishing Bagi Siswa/Siswi PKBM INTAN Tangerang Selatan Akrom; Marwati, Fingki; Astofa, Aniq
AMMA : Jurnal Pengabdian Masyarakat Vol. 2 No. 12 : Januari (2024): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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

Abstract

The rise of Cyber ​​attacks on the Internet is increasingly worrying and causing more and more victims, because the public needs to be educated about the importance of Cyber ​​Security and the importance of maintaining the security of personal data, especially for PKBM INTAN students and the general public. It cannot be denied that today's increasingly rapid technological progress can open up various new opportunities in good things, but also bad things, the ease of accessing the internet for everyone is one of the positive things, meaning that the effects of technological progress can be felt by everyone. almost all people, but this can become a problem and develop as a bad thing if people do not understand the threat of cyber attacks or criminal attacks via the internet, one of the cyber attacks that is increasingly worrying at the moment is cyber attacks via Phishing. One of the impacts or effects of a Phishing attack is that it can cause leakage of our personal data. Seeing the problems faced by the general public and INTAN PKBM Students in particular, we of time Lecturer PKM Universitas would like to provide outreach to INTAN PKBM Students with the title: "The Importance of Cyber ​​Security Education to Protect the Security of Personal Data from Cyber ​​Phishing Attacks "For Students PKBM INTAN South Tangerang Selatan" to Support Making Final Project Proposals to Students PKBM Intan Tangerang Selatan”.
Evaluasi Komparatif Algoritma Machine Learning untuk Prediksi Dini Diabetes Astofa, Aniq; Rosyani, Perani; Rahmawati, Rahmawati; Apandi, Sopiyan
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.859

Abstract

Diabetes is one of the non-communicable diseases that is often detected at an advanced stage, thereby increasing the risk of serious complications. The application of machine learning has the potential to support early diabetes detection; however, most previous studies have focused on large-scale datasets and high predictive accuracy, while methodological evaluations on small-sized clinical data remain limited. This study aims to evaluate and compare the performance of several machine learning algorithms for early diabetes prediction using a limited clinical dataset, with particular emphasis on analyzing the impact of data characteristics on model performance. The dataset used in this study consists of 22 samples with eight clinical features and one target variable, which were divided into 17 training samples and 5 testing samples. The research stages include data preprocessing, training–testing data splitting, model training, and performance evaluation using accuracy, precision, recall, F1-score, and ROC-AUC metrics. The algorithms evaluated include Logistic Regression, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and XGBoost. The experimental results indicate that none of the evaluated models were able to effectively detect the diabetes class, as reflected by precision, recall, and F1-score values of zero across all models. Although Random Forest and XGBoost achieved an accuracy of 0.6, this value was largely influenced by the dominance of the non-diabetes class in the very limited test set. Correlation analysis further reveals that Glucose, BMI, and Diabetes Pedigree Function are the most influential features associated with diabetes status. The main contribution of this study lies in providing a realistic methodological evaluation of machine learning models applied to small-sized clinical datasets, highlighting that limited sample size and training–testing data partitioning have a substantial impact on model performance and the interpretation of evaluation metrics. These findings provide an important methodological reference for future studies aiming to develop more reliable early diabetes prediction models under constrained clinical data conditions.
Co-Authors Akrom, Akrom Apandi, Sopiyan Marwati, Fingki Perani Rosyani Rahmawati, Rahmawati Sutono, Eko Zainudin