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INDONESIA
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen
Published by STMIK Palangka Raya
ISSN : 20881770     EISSN : 25033247     DOI : 10.33020
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Jurnal Saintekom adalah singkatan dari Sains, Teknologi, Komputer dan Manajemen, merupakan jurnal ilmiah yang berfungsi sebagai media mengkomunikasikan ide, gagasan dan pemikiran seputar kajian aktual tentang sains, teknologi, komputer dan manajemen antarkademisi dan peneliti.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 164 Documents
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Integrasi Penyimpanan Data dan Keamanan Jaringan Kantor KEMENAG Menggunakan Metode PPDIOO Hadi, Abdul; Herkules, Herkules; Maryamah, Siti
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 15 No 2 (2025): September 2025
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33020/saintekom.v15i2.968

Abstract

Poor network design can lead to various issues, such as limited performance, increased operational costs, higher security risks, and difficulties in managing and monitoring network infrastructure. The KEMENAG XYZ office currently operates a local network with multiple wireless modem devices that are not interconnected, resulting in inefficiencies and security challenges in organizational data management.This study aims to implement centralized data storage and network device hardening to support the revitalization program for the use of information and communication technology as well as the optimization of public information transparency at the KEMENAG XYZ city office. The research adopts the Prepare, Plan, Design, Implement, Operate, and Optimize (PPDIOO) methodology by leveraging both hardware and software network technologies.The expected outcomes include a new network infrastructure topology design, more structured data management, and the implementation of enhanced security measures for network devices
Strategi Test-Driven Development dalam Arsitektur Microservices untuk Optimalisasi Pengembangan Aplikasi Payroll Agustin, Ai Dina; Hadi, Abdul; Suratno, Suratno
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 16 No 1 (2026): Maret 2026
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33020/saintekom.v16i1.925

Abstract

The development of payroll systems based on microservices demands highly reliable software, particularly in inter-service communication. This study aims to evaluate the effectiveness of the Test-Driven Development (TDD) approach in improving software quality within a microservice-based Payroll system. A mixed method was employed, combining qualitative analysis of TDD implementation and quantitative measurements of code coverage and defect rate. The system was implemented across three main services: Auth Service, Employee Service, and Payroll Service, all accessed through an API Gateway. Results show that the TDD approach increased statement coverage up to 96.81% and reduced the defect rate to 4.33 per 1000 lines of code. These findings confirm that TDD significantly contributes to the reliability and robustness of testing in microservice architectures. The outcomes of this study provide a solid foundation for the broader application of TDD in other modular system developments.
Perbandingan Decision Tree, KNN, dan Naive Bayes pada Klasifikasi Mood Musik Menggunakan Dataset Emotion Kaggle Rahman, Miftakhur; Lutfi, Muhammad Arham; Wakhidah, Nur
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 16 No 1 (2026): Maret 2026
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33020/saintekom.v16i1.1019

Abstract

The classification of music mood characteristics is a crucial instrument in Music Information Retrieval (MIR) systems to support recommendation technology and AI-based emotion analysis. This study aims to evaluate and compare the performance of three classification algorithms: Decision Tree, K-Nearest Neighbors (KNN), and Naive Bayes. The dataset utilized is sourced from the Kaggle Emotion Dataset, comprising 1,440 audio files. The feature extraction process was conducted using the Librosa library to capture acoustic parameters, including Mel-Frequency Cepstral Coefficients (MFCC), Delta-MFCC, Chroma, Spectral Contrast, Spectral Centroid, Spectral Bandwidth, and Tempo. All features were normalized using StandardScaler and distributed into training and testing sets with an 80:20 ratio. Based on the experimental results, the K-Nearest Neighbors algorithm demonstrated the most superior performance with an accuracy of 71.52%. Meanwhile, the Decision Tree algorithm achieved an accuracy of 54.16%, and Naive Bayes obtained 53.47%. The primary contribution of this research is the empirical evidence of the effectiveness of distance-based algorithms in identifying emotional patterns within multidimensional audio data. These findings provide a robust methodological reference for the future development of music emotion recognition systems
Aplikasi Deteksi Kesegaran Ikan Menggunakan Convolutional Neural Network dan Random Forest Sabardynata, Arjunaedy Restu; Prasetyo, Eko; Tias, Rahmawati Febrifyaning
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 16 No 1 (2026): Maret 2026
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33020/saintekom.v16i1.1027

Abstract

This study is motivated by the importance of selecting healthy food in Indonesia, especially fish as a high-protein source that is highly perishable. People often struggle to distinguish fresh fish from those unfit for consumption, posing health risks. To tackle this issue, this study developed an application for detecting fish freshness using a Convolutional Neural Network (CNN) as a feature generator and Random Forest as a classifier. The CNN models employed were Residual Network 50 (Resnet50) and Visual Geometry Group 16 (VGG16). Experiments were conducted on a dataset consisting of 1,663 images of three types of fish: milkfish, tilapia, and mujair. The freshness of the fish was classified into three categories: very fresh, fresh, and not fresh. Model training utilized 80% of the data, with the remaining 20% reserved for testing. Out of a total of 333 test images (20% of the dataset), Resnet50 achieved an accuracy of 64.23% (with 86.01% accuracy for the very fresh class, 43.16% for fresh, and 52.63% for not fresh). VGG16 performed slightly better, attaining an overall accuracy of 65.16% (89.36% for very fresh, 44.90% for fresh, and 53.41% for not fresh). In terms of average accuracy, precision, recall, and F1-score, VGG16 outperforms Resnet50, although both models still make incorrect predictions. Overall, VGG16 was more effective than Resnet50 for fish freshness classification in this study.

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