Article Per Year (5 Year)
p-Index From 2021 - 2026
0.817
P-Index
This Author published in this journals
All Journal Jurnal Informatika Progres Jurnal Indonesia : Manajemen Informatika dan Komunikasi The Indonesian Journal of Computer Science
Baharuddin, Suardi Hi
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
SISTEM PENCARIAN RUTE TERBAIK EKSPEDISI BARANG MENGGUNAKAN METODE ANT COLONY PADA PT. PELINDO TPKNM Faizal, Muhammad Rahmat; Sitorus, Kasirun; Tandililing, Mika; Baharuddin, Suardi Hi
PROGRESS Vol 16 No 1 (2024): April
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v16i1.388

Abstract

This research is focused on building and implementing a system for finding the best route in cargo expeditions for PT. Pelindo. PT. Pelindo faces a significant challenge in providing the best service to its customers to enhance their satisfaction. In response to this challenge, the developed system employs the Ant Colony Optimization (ACO) approach, inspired by the foraging behavior of ants when searching for the shortest path from their nest to a food source. Through ACO, the system aims to provide optimal and efficient route solutions. The research results encompass the development and implementation of ACO calculations within a system capable of generating the best alternative routes. The routes generated involve eight main points, namely A-B-C-D-E-F-G-H-A. It is expected that this system will be a highly valuable tool for users in finding the most efficient and cost-effective travel routes for cargo expeditions. Furthermore, the outcomes of this research are anticipated to contribute positively to improving the quality of service provided by PT. Pelindo to their customers, thus enhancing customer satisfaction and overall logistics operational efficiency.
IMPLEMENTASI ALGORITMA K-NEAREST NEIGHBOR TERHADAP PENENTUAN RISIKO KREDIT USAHA MIKRO KECIL DAN MENENGAH Ida; Baharuddin, Suardi Hi; Faisal, Muhammad; Ramadhan, Nur; Darniati
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 1 (2023): Januari
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i1.163

Abstract

This research was carried out in the context of implementing the K-NN algorithm so that a source of information can be produced as a basis for supporting decisions on initial credit applications by customers so that they can help cooperative managers more as knowledge of the progress of credit proposals that are carried out at the Micro, Small and Medium Enterprise Cooperative Service Office ( SMEs) South Sulawesi Province. The K-Nearest Neighbor algorithm is used to classify objects based on attributes and training samples. Among them, from k objects, the k-Nearest Neighbor algorithm uses neighbor classification as the predicted value. The results show that the algorithm produces a classification with a faster calculation time based on the prediction of customer data resulting from the calculation.
Klasifikasi Jenis Peralatan Gym Menggunakan Convolutional Neural Network Andika, Farid; Yunarti, Sry; Baharuddin, Suardi Hi
The Indonesian Journal of Computer Science Vol. 14 No. 3 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i3.4065

Abstract

The use of artificial intelligence, especially Convolutional Neural Networks (CNN), has shown significant progress in image classification and object recognition. This research aims to develop an effective CNN model for automatically classifying gym equipment types, with the potential to improve the operational efficiency of fitness centers. The CNN model was trained using TensorFlow and Keras with the Adam optimizer and categorical cross-entropy loss function for 10 epochs, with data augmentation using ImageDataGenerator. The model evaluation shows satisfactory accuracy with a precision value of 0.9760, recall of 0.9772, and F1-score of 0.9766. The model successfully identified image samples from test data with a high level of confidence. The results of this study show that the use of CNNs in gym equipment classification has great potential to improve the efficiency of equipment recognition and contribute to the development of more advanced fitness technologies.
IMPLEMENTASI SISTEM PENDAFTARAN SISWA BARU BERBASIS WEB MENGGUNAKAN ALGORITMA SAW DI SANGGAR KEGIATAN BELAJAR UJUNG PANDANG Sarumpaet, Calvin Bonar; Fajri, Hidayatul; Baharuddin, Suardi Hi
PROGRESS Vol 17 No 1 (2025): April
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v17i1.464

Abstract

The new student registration process at Sanggar Kegiatan Belajar (SKB) Ujung Pandang was previously conducted manually, resulting in several issues such as data processing delays, file accumulation, and lack of transparency in selection. This study aims to develop a web-based registration information system integrated with the Simple Additive Weighting (SAW) algorithm to enhance the efficiency and objectivity of the selection process. The system was developed using the Waterfall model and implemented using PHP and MySQL-based web technology. The implementation results show that the system can automate registration and selection in real-time. The SAW algorithm effectively produces objective participant rankings based on criteria such as exam scores, age, and domicile. Evaluation indicates that the system improves selection speed, result accuracy, and facilitates data management for users. It can be concluded that this system provides significant benefits for both SKB administrators and applicants and is relevant in supporting the digital transformation of non-formal education.
Co-Authors . Darniati Andika, Farid Faizal, Muhammad Rahmat Hidayatul Fajri Ida Mulyadi, Ida Muhammad Faisal Nur Ramadhan, Nur Sarumpaet, Calvin Bonar Sitorus, Kasirun Tandililing, Mika Yunarti, Sry