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All Journal INTEGER: Journal of Information Technology Bulletin of Computer Science Research JAMAIKA: Jurnal Abdi Masyarakat LOGIC : Jurnal Ilmu Komputer dan Pendidikan
Astofa, Aniq
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Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Other

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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

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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%.
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.
PELATIHAN PENGENALAN LOGIKA PEMROGRAMAN MELALUI GAME SCRATCH BAGI ANAK - ANAK DIDIK PKBM SELUAS CITA NUSANTARA PARUNG PANJANG Astofa, Aniq; Akrom; Zainudin
JAMAIKA: JURNAL ABDI MASYARAKAT Vol 6 No 3 (2025): OKTOBER
Publisher : Universitas Pamulang

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Abstract

This training activity aims to introduce the basic concepts of programming logic to students at the Community Learning Center (PKBM) Seluas Cita Nusantara, Parung Panjang, through an interactive and enjoyable approach, namely using the visual programming platform Scratch. In the digital era, programming literacy has become a fundamental skill. However, its introduction is often considered difficult and abstract. This training addresses that challenge by utilizing Scratch, a tool that allows participants to create their own interactive stories, animations, and games by arranging blocks of code. The method used is hands-on learning, where participants are directly invited to design and develop simple projects. The main focus of the material is mastering basic concepts such as sequence, loop, conditional statements, and variables, which are important foundations in computational thinking. Keywords: PKBM Seluas Cita Nusantara, Scratch
Co-Authors Akrom, Akrom Apandi, Sopiyan Perani Rosyani Rahmawati, Rahmawati Sutono, Eko Zainudin