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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 168 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 Ai Dina Agustin; Abdul Hadi; 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 Miftakhur Rahman; Muhammad Arham Lutfi; Nur Wakhidah
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 Arjunaedy Restu Sabardynata; Eko Prasetyo; Rahmawati Febrifyaning Tias
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.
Determinan Perilaku Penggunaan SIAKAD: Integrasi Model UTAUT 2 pada Lingkungan Perguruan Tinggi Swasta Regional Setio Ardy Nuswantoro; Ika Safitri Windiarti; Wiwinti Wiwinti; Najwa Aulia Suwandini
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.v16i1.1014

Abstract

This study aims to analyze the factors influencing the behavioral intention and use behavior of the Academic Information System (SIAKAD) at Universitas Muhammadiyah Palangkaraya. Using the UTAUT 2 model adapted for a mandatory system context, the price value and hedonic motivation variables were excluded. This research involved 383 student respondents selected through purposive sampling. Data were analyzed using the Structural Equation Modeling (SEM) approach based on Partial Least Squares (PLS) via SmartPLS 4 software.The results indicate that all variables performance expectancy, effort expectancy, social influence, facilitating conditions, and habit have a positive and significant effect on behavioral intention (R2=0.502), which subsequently acts as a strong predictor of actual use behavior (R2=0.476). The novelty of this research lies in the modification of the UTAUT 2 model, aligned with the characteristics of mandatory information systems in regional higher education institutions. The findings show that while habits have been formed, the institution needs to strengthen facilitating conditions to minimize semi-manual processes. These results provide a theoretical contribution to the technology adoption literature as well as practical recommendations for university management in optimizing academic digital transformation.
Analisis Bukti Digital pada Private Chat Synology Menggunakan Metode Live Forensik Ryan Achmad Antama; Abdul Hadi; Maura Widyaningsih
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.v16i1.1016

Abstract

The adoption of Network Attached Storage (NAS) based private communication platforms such as Synology Chat is increasing in organizations and may store valuable digital evidence. However, encryption and closed system architecture can limit evidence acquisition using conventional forensic approaches. This study applies network live forensics by proactively capturing network traffic and analyzing capture files to recover deleted chat messages and transferred files. The investigation follows the National Institute of Standards and Technology NIST framework consisting of collection, examination, analysis, and reporting using digital evidence including packet capture PCAP or packet capture next generation PCAPNG files, communication artifacts, and reconstructed file objects. An experimental method is conducted by comparing server-side capturing via Secure Shell SSH on the NAS server and external capturing from a device within the same Local Area Network LAN. Results show that server-side capturing is more effective for file reconstruction and message recovery under certain conditions, while external capturing provides limited artifacts and cannot reveal plain text messages.
Pengembangan Model Prediksi Speech Recognition dengan Algoritma Deep Learning Convolutional Neural Network Abdul Halim Anshor; Aswan Supriyadi Sunge
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.v16i1.1020

Abstract

This study examines the development of an automatic speech recognition (ASR) system in Sundanese, which still faces data limitations. Dialect variations and the lack of labeled data are the main challenges in the speech recognition process. The approach used is a Convolutional Neural Network (CNN) with Mel-Frequency Cepstral Coefficients (MFCC) feature extraction. The data used were 100 voice recordings consisting of West Sundanese and South Sundanese dialects. The processing process was carried out through the stages of pre-emphasis, framing, windowing, Fourier transform, Mel filter bank, and Discrete Cosine Transform to obtain voice features. The data was divided into 80% training data and 20% test data. The CNN model was then trained to recognize the voice patterns of each dialect. Based on the test results, the model achieved an accuracy of 70% with a loss value of 0.60. These results indicate that the approach used can be applied to limited data, although its performance can still be improved in further research.
Kombinasi AHP-TOPSIS dalam Pemilihan Tim Satuan Reaksi Cepat (SRC) Badan Penanggulangan Bencana Daerah Tapteng Dariana Tanjung; Muhammad Dedi Irawan
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.v16i1.1023

Abstract

Indonesia is a country with a high level of disaster vulnerability, thereby requiring an objective, measurable, and operationally standardized selection mechanism for members of the Rapid Response Team (SRC). However, the selection process for SRC members at the BPBD of Central Tapanuli is still carried out without a structured assessment system, potentially resulting in personnel who do not fully meet operational field requirements. This study develops a Decision Support System (DSS) by applying a combination of the Analytical Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Five criteria serve as the basis for assessment, namely physical condition, special skills, age, distance, and communication ability. AHP is used to determine the weight of each criterion with a valid consistency level (CR = 0.044), while TOPSIS is employed to generate alternative rankings based on their proximity to the ideal solution. The results indicate that candidates A2, A4, and A8 achieved the highest preference scores and are therefore recommended as the best candidates. The AHP–TOPSIS combination has proven effective in providing a systematic selection process.

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