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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 161 Documents
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Evaluasi Model Deep Learning pada Pola Dataset Biomedis Gunawan, Gunawan; Wibowo, Septian Ari; Andriani, Wresti
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 14 No 2 (2024): September 2024
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33020/saintekom.v14i2.738

Abstract

This study aims to evaluate the effectiveness and efficiency of various deep learning models in recognizing patterns within diverse biomedical datasets. The methods involved the collection of biomedical data from various public and synthetic sources, including chest radiographs, MRI, CT scans, as well as electrocardiogram (ECG) and electromyography (EMG) signals. The data underwent preprocessing steps such as normalization, noise removal, and data augmentation to improve quality and variability. The deep learning models evaluated included Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), which were trained to identify patterns within the data. The performance evaluation was conducted using metrics like accuracy, sensitivity, specificity, and AUC to ensure the models' generalization capabilities on test datasets. The results revealed that CNNs excelled in medical image analysis, particularly in terms of accuracy and interpretability, while RNNs were more effective in handling sequential data such as medical signals. The primary conclusion of this study is that the selection of deep learning models should be tailored to the type of data and specific application requirements, emphasizing the importance of improving model interpretability and generalization for broader applications in clinical settings.
Analisis Kualitas Sistem Informasi Layanan pada PT. Inti Jasa Kreatif Menggunakan Metode Webqual 4.0 Putri, Nuryani Mawar; Achyani, Yuni Eka
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 14 No 2 (2024): September 2024
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33020/saintekom.v14i2.739

Abstract

PT Inti Jasa Kreatif is an Event Organizer company that relies heavily on the quality of information systems to maintain smooth operations, efficiency, and client satisfaction. This company uses websites to convey business information. To improve the quality of design and user interaction, PT Inti Jasa Kreatif uses the WebQual 4.0 method in analyzing website quality. This study will analyze the quality of the service information system www.intijasakreatif.co.id with a focus on the aspects of usability, informativeness, and trustworthiness, measuring client needs and satisfaction with services using the Webqual 4.0 method. The Webqual 4.0 method is a method developed to measure the quality of web-based services with the data used being the respondents of the questionnaire that has been distributed. The data collection method uses primary data in the form of questionnaires distributed to 102 respondents. Based on data processing, it can be seen that in the validity test of variable X1 (Usability Quality) the results show that variable X1 correlates with the potential quality of website usability that respondents find it easy to use the website application. From the results above, the author concludes that variable X1 has more influence than variables X2 and X3 on user satisfaction.
Model ITPOSMO untuk Evaluasi Keberhasilan Aplikasi Bravo Awaludin, Rizky; Ramanda, Kresna; Puspitasari, Diah; Sikumbang, Erma Delima
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 15 No 1 (2025): Maret 2025
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33020/saintekom.v15i1.754

Abstract

Bravo-PUPR application problems are caused by many factors, inaccurate data, inadequate integration, technical infrastructure and high process complexity. In addition, inconsistencies between organizational values and digital innovation, lack of training, weak coordination, and external support are also obstacles to the success of such e-government. To find the success rate of the Bravo-PUPR application, we used the ITPOSMO model to assess the gaps between the designs realized in the application. Of the 39,308 employees in the population used for the study, only 100 met the criteria to be selected using the Purpose Sampling technique. Quantitative data analysis is used, primarily to test for gaps. As a result, the Other Resource dimension received the lowest GAP score, which was 0.09. In addition, Staffing and Skills dimension received a score of 0.76, Objective and value obtained a score of 1.17, Technology obtained a score of 1.61, Information obtained a score of 1.69, and Management and Structure obtained a score of 1.82. The highest GAP score in the Process dimension is 2.44. This score is obtained based on the calculation of the overall assessment table. The project may have been successful due to its overall rating of 9.58, which ranges from 0 to 14.
Penerapan Naïve Bayes dalam Analisis Sentimen untuk Evaluasi Kinerja Pengajaran Dosen Fatmawati, Samsinah; Prasetya, M. Riko Anshori; Prastya, Septyan Eka; Cipta, Subhan Panji
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 15 No 1 (2025): Maret 2025
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33020/saintekom.v15i1.765

Abstract

Lecturer performance in the learning process directly affects student competence. Evaluating lecturers is essential to ensure optimal teaching and produce graduates ready for the job market. One way to assess teaching effectiveness is through sentiment analysis of student opinions. However, due to the large amount of data still processed manually, a more efficient approach is needed, namely AI-based sentiment analysis. This study implements the Naïve Bayes method to classify student sentiments as positive or negative and evaluate lecturer performance based on classification results. The process includes preprocessing and labeling. The Naïve Bayes algorithm is then applied for sentiment classification and evaluated using a confusion matrix. The results show that Naïve Bayes is highly effective, achieving 94% accuracy, 94% precision, 96% recall, and a 95% F1-score. Of the total data, 231 comments were positive, while 174 were negative. These findings confirm that sentiment analysis can be an efficient tool for assessing lecturers and improving teaching quality at universities.
Optimalisasi Kinerja Karyawan Berbasis HR Analytics dengan K-Means Clustering dan Analisis Faktor Demografi Ramadhani, Anandita Nabilla; Athalina, Ghita
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 15 No 1 (2025): Maret 2025
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33020/saintekom.v15i1.779

Abstract

Data-driven Human Resources (HR) management is an important aspect in improving organizational productivity and efficiency in the digital era. This research aims to cluster employees based on performance using the K-Means Clustering algorithm and evaluate the influence of demographic factors on job performance. The dataset used is a public dataset from Kaggle, including employee performance information such as Key Performance Indicators (KPIs), training scores, multiple trainings, performance appraisals, awards, as well as demographic attributes such as gender, age, education level, and recruitment channels. Using the six-stage CRISP-DM framework, the data was processed using StandardScaler, and the optimal number of clusters was determined through the Elbow Method, Davies-Bouldin Index, and Silhouette Score, resulting in two main clusters. Cluster 0 includes high-performing employees with KPIs above 80%, good performance ratings, and good training scores, while Cluster 1 consists of low-performing employees, with lower KPIs, poor performance ratings, and training scores. Analysis showed demographic factors did not significantly affect employee performance. This research recommends focused training for low-performing employees and rewards for high-performing employees, so that each employee can reach their full potential and support organizational productivity.
Analisis Metode Decision Tree dan Regresi Logistik Sebagai Sistem Rekomendasi Kenaikan Golongan Berdasarkan Kinerja Pegawai pada Universitas Lamappapoleonro Aksa, Andi Nurul; Achmad, Andani; Arda, Abdul Latief
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 15 No 1 (2025): Maret 2025
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33020/saintekom.v15i1.782

Abstract

This research focuses on the importance of employee performance in supporting organizational success, especially in the promotion process at Lamappapoleonro University which is still done manually. Therefore, this research aims to develop a recommendation system for promotion using the Decision Tree and Logistic Regression methods, which is expected to speed up and simplify the decision-making process regarding employee promotions. The Decision Tree algorithm is used to classify employee performance in the form of sufficient, good, and excellent variables, while the Logistic Regression algorithm is used to predict the feasibility of employee promotion with the variable feasible or inappropriate. The data used in this study includes 12 independent variables, such as attendance, discipline, responsibility, and innovative ability. The analysis results show that the Decision Tree and Logistic Regression methods are able to produce accurate predictions, with an accuracy rate of 91.67% and 100% respectively. The main factors that influence promotion are honesty, discipline, and innovation ability. With this recommendation system, the employee promotion process becomes more efficient and accurate, providing significant benefits for human resource management at Lamappapoleonro University.
Analisis Sentimen Sunscreen Azarine dengan Naïve Bayes di Toko Aneka Kosmetik Kupang pada Marketplace Shopee Sain, Adriana Yohana; Mola, Sebastianus Adi Santoso; Huan, Arni Yusfin; Nomleni, Inggi Rosina
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 15 No 1 (2025): Maret 2025
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33020/saintekom.v15i1.783

Abstract

Advancements in information and communication technology have changed the way customers shop and share experiences through reviews. Marketplaces like Shopee allow customers to rate products through reviews, making sentiment analysis crucial for understanding consumer perceptions. The Naïve Bayes algorithm is used in this study to analyze 3,504 reviews of the Azarine sunscreen product from Aneka Kosmetik Kupang on Shopee, followed by a text preprocessing process. The dataset is then split into 80% for training and 20% for testing, with reviews categorized into three sentiment classes: positive, negative, and neutral. Evaluation with a Confusion Matrix resulted in an accuracy of 84%, demonstrating the reliability of this algorithm in analyzing customer reviews. The findings of this study offer fresh perspectives for brand owners and potential buyers regarding public perception of the Azarine sunscreen product at Aneka Kosmetik Kupang.
Penilaian Kualitas Udara dan Polusi Menggunakan Algoritma Support Vector Machine Sholehurrohman, Ridho; Valentine, Ivani
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 15 No 1 (2025): Maret 2025
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33020/saintekom.v15i1.810

Abstract

Good air quality is essential for healthy and sustainable living, but increasing air pollution caused by industrialization, urbanization, and motor vehicles has become a serious global issue. Air pollution negatively affects health, the environment, and the quality of life, making air quality monitoring and assessment a priority. The complexity of air quality data renders conventional analytical approaches less effective; therefore, machine learning methods such as Random Forest and Neural Networks have been applied to address these challenges. However, these methods have limitations in handling non-linear patterns or computational efficiency. This study employs the Support Vector Machine (SVM) algorithm with various kernels to classify air quality based on pollution and environmental parameters into categories of Good, Moderate, Poor, and Hazardous. The results indicate that the Polynomial Kernel performs best for the Good category, while the RBF Kernel is also competitive but less optimal for the Hazardous and Poor categories. With parameter optimization using GridSearchCV, the combination of C=10 and Gamma=0.1 or scale achieved an average accuracy of 90.75%. CO concentration and proximity to industrial areas proved to be significant features in classification. This study aims to support air pollution management and mitigate its impacts on society.
Perbandingan Algoritma Machine Learning Menggunakan Pemilihan Fitur Chi-square dalam Pengklasifikasian Penyakit Jantung Hirmayanti, Hirmayanti; Utami, Ema
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 15 No 1 (2025): Maret 2025
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33020/saintekom.v15i1.815

Abstract

Heart disease is one of the deadliest diseases worldwide. This condition often presents symptoms that do not immediately cause severe effects on the sufferer, making early anticipation crucial. To reduce fatalities caused by heart disease or cardiovascular disorders, a system is required to identify its primary causes so that these factors can be minimized. Therefore, this study applies the Chi-square feature selection method to determine the key features influencing the accuracy of Machine Learning models. A comparison is conducted between K-Nearest Neighbor, Naïve Bayes, Logistic Regression, Support Vector Machine, and Random Forest algorithms. This comparison aims to obtain the most accurate results, as a higher algorithm accuracy leads to a more precise classification system for heart disease. The study’s findings indicate that eight key features selected using the Chi-square method yield the highest accuracy, specifically 93.51% with the KNN algorithm. These results demonstrate that using relevant features improves classification accuracy and system efficiency compared to utilizing all available features. Consequently, this research contributes to the selection of essential features in Machine Learning algorithms through the Chi-square technique, ensuring a more effective and optimized heart disease classification system.
Analisis Simulasi Routing AODV Adaptif dengan Learning Automata untuk Komunikasi V2V Effendi, Muhamad Denhas; Bintoro, Ketut Bayu; Widyaningsih, Maura
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 15 No 1 (2025): Maret 2025
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33020/saintekom.v15i1.820

Abstract

The study addresses the limitations of the Ad Hoc On-Demand Distance Vector (AODV) protocol in vehicle-to-vehicle (V2V) communication, explicitly targeting issues such as low data transfer rates, increased delay times, reduced throughput, and data congestion due to dynamic network topologies. The research introduces a novel protocol called Learning Automata Ad Hoc On-Demand (LAAODV) to enhance these areas. Utilizing NS3 and SUMO for dynamic traffic simulations, LAAODV demonstrated superior performance compared to AODV. Key findings include a higher packet delivery success rate with a Packet Loss Ratio (PLR) of 95%, lower than AODV's 96%, and a Packet Delivery Ratio (PDR) of 4.5% compared to AODV's 3.25%, indicating its effectiveness in reducing packet loss. The study also highlights significant improvements in PDR and Average Throughput, showcasing LAAODV's enhanced performance in dynamic traffic conditions. LAAODV provides an effective solution to the shortcomings of existing routing protocols, significantly enhancing V2V network performance. This research underscores the importance of developing robust and adaptive routing solutions to meet the evolving demands of dynamic vehicular environments, contributing to more efficient and reliable V2V communication protocols.

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