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All Journal SMARTICS Journal Journal of Artificial Intelligence and Innovative Applications (JOAIIA) Journal of Informatics and Communication Technology (JICT) KOMMAS: Jurnal Pengabdian Kepada Masyarakat LOGIC : Jurnal Ilmu Komputer dan Pendidikan Biner : Jurnal Ilmu Komputer, Teknik dan Multimedia OKTAL : Jurnal Ilmu Komputer dan Sains Journal of Informatics and Communication Technology (JICT) Journal of Engineering and Science Application
Agung Perdananto
Universitas Pamulang
Aerospace Engineering Humanities Astronomy Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Social Sciences Transportation Other

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SISTEM PENDUKUNG KEPUTUSAN PENENTUAN SISWA BERPRESTASI PADA SEKOLAH MENENGAH KEJURUAN NEGRI 02 KOTA TANGERANG SELATAN BERBASIS WEB MENGGUNAKAN METODE SIMPLE ADDITIVE Muhammad Arif Prasetyo; Agung Perdananto
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 2 No 03 (2023): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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Abstract

Determination of outstanding students plays an important role in the quality of schools. At SMK Negri 02 South Tangerang, the determination of outstanding students has not been maximized, because there is no special application that handles a decision support system for outstanding students. From the number of students can produce many spreadsheets and large capacity as well. With these problems, researchers will design a decision support system to determine outstanding students with a web-based system that uses the Simple Additive Weighting (SAW) method by taking from the criteria determined by the school, namely the average value of report cards, attendance and personality attitudes. With this system, it can make it easier for the school to determine outstanding students and make it easier to process the processing of high achieving students.
Penerapan deep learning pada Aplikasi prediksi penyakit Pneumonia berbasis Convolutional Neural networks Agung Perdananto
Journal of Informatics and Communication Technology (JICT) Vol. 1 No. 2 (2019)
Publisher : PPM Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52661/j_ict.v1i2.34

Abstract

Pneumonia merupakan salah satu penyebab kematian anak-anak tertinggi di dunia. Badan Kesehatan Dunia (WHO) memperkirakan bahwa penyakit ini memicu 15% dari seluruh kematian anak-anak di bawah usia 5 tahun (WHO, 2015). Dalam mengenali foto rontgen terdapat banyak metode yang bisa diimplementasikan. Salah satu dari implementasinya adalah dengan menggunakan Convolutional Neural Network (CNN). Convolutional Neural Network atau yang biasa disebut CNN merupakan bagian dari Deep Learning yang melakukan proses pembelajaran untuk mencari representasi terbaik. Dengan Menggunakan model tersebut yang diimplementasikan dengan aplikasi web didapatkan hasil akurasi sebesar 75% dari uji acak yang dilakukan dengan menggunakan data yang berbeda dari data training
Sistem Informasi Iuran Pengelolaan Lingkungan Untuk Prediksi Tunggakan Pembayaran Warga Alfy Rachman Sanad; Agung Perdananto
Journal of Engineering and Science Application Vol. 3 No. 2 (2026): Mei-Oktober
Publisher : Institute Of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/jesa.v3i2.93

Abstract

The management of environmental service fees (Iuran Pengelolaan Lingkungan/IPL) at Deparis Residence Kemuning still requires an integrated information system to support payment management and monitoring of residents’ payment arrears. This research aims to develop a web-based IPL Information System and implement the Decision Tree C4.5 algorithm to classify the potential for residents’ payment arrears. The system was developed using the Rapid Application Development (RAD) method with PHP, Laravel 11, and MySQL/MariaDB. The dataset consisted of 138 historical IPL payment records with six classification attributes, namely arrears amount, arrears duration, late payment frequency, payment history, average payment delay, and payment time pattern. The C4.5 algorithm was applied through entropy, information gain, split information, and gain ratio calculations to generate decision rules for the Tunggak and Tidak Tunggak classes. The calculation results showed that the arrears amount attribute obtained the highest gain ratio of 0.143778 and was selected as the root node. The resulting decision tree produced 25 classification rules. System testing using Black Box Testing achieved a 100% success rate from 31 test scenarios, while White Box Testing produced a cyclomatic complexity value of 24. User response testing obtained 96.00% from administrators and 98.80% from residents, both categorized as Very Good. The results indicate that the developed system can support IPL management and provide information on potential payment arrears.
Co-Authors Achmad Udin Zailani Alfy Rachman Sanad Alvino Octaviano Dahlan Supriatna Dede Amelia Luthfia Putri Nabilla Maulana Ardhiansyah Muhammad Arif Prasetyo Nurjaya Nurjaya Nyomo Hartanto Riki Muhammad Nurhidayat Samsoni Samsoni Sholihin Sholihin Yudi Kurniawan Zainap Arianti