cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Ade Oktarino
Contact Email
adeoktarino@unaja.ac.id
Phone
+6281274461047
Journal Mail Official
adeoktarino@unaja.ac.id
Editorial Address
Jl. Ir H Juanda Lrg Hasanah III No 73
Location
Unknown,
Unknown
INDONESIA
JUTEKOM - Jurnal Teknologi dan Ilmu Komputer
Published by CV. Nolsatu
ISSN : -     EISSN : 30898838     DOI : https://doi.org/10.65258/jutekom
Core Subject : Science,
Computer Science & IT
Jurnal Teknologi Informasi dan Ilmu Komputer (JUTEKOM) adalah jurnal ilmiah yang berfokus pada pengembangan dan penerapan teknologi informasi serta ilmu komputer dalam berbagai bidang dengan memiliki e-ISSN 3089-8838. Jurnal ini hadir sebagai wadah terpercaya bagi peneliti, akademisi, dan praktisi untuk berbagi temuan terbaru, inovasi, serta kajian kritis di bidang teknologi dan komputasi. JUTEKOM diterbitkan sebanyak 4 kali pertahun dengan edisi Januari, April, Juli, Oktober. Jurnal ini menyediakan akses terbuka (open access) kepada pembaca secara gratis, memungkinkan siapa saja untuk mengakses, membaca, dan mengunduh artikel yang diterbitkan. Dengan frekuensi yang konsisten ini, JUTEKOM terus berkomitmen untuk berkontribusi terhadap perkembangan literatur akademik dan aplikasi praktis yang relevan dengan kebutuhan masa kini. JUTEKOM menyediakan akses terbuka (open access) secara gratis kepada pembaca. Kebijakan ini memungkinkan siapa saja untuk mengakses, membaca, dan mengunduh artikel-artikel yang telah diterbitkan tanpa biaya. Melalui pendekatan ini, jurnal mendukung penyebaran ilmu pengetahuan secara inklusif, transparan, dan mudah diakses oleh komunitas global. Jurnal ini secara khusus ditujukan kepada Dosen, Guru,Mahasiswa dan Praktisi sebagai salah satu sarana pengembangan keilmuan, referensi dalam penelitian, serta sumber pembelajaran yang dapat memperluas wawasan di bidang teknologi informasi dan ilmu komputer. Dengan cakupan yang luas, JUTEKOM diharapkan mampu memenuhi kebutuhan komunitas akademik dan pendidikan dalam mengeksplorasi inovasi dan aplikasi teknologi terkini.Dengan reputasinya yang terus berkembang, JUTEKOM menjadi salah satu jurnal yang diharapkan dapat menjadi referensi utama dan sumber inspirasi bagi penelitian dan pengajaran berbasis teknologi.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 35 Documents
 1   2   3   4 
Transformasi Sistem Informasi Menjadi Sistem Cerdas Untuk Meningkatkan Pengambilan Keputusan Dan Efisiensi Ikke Yamalia; Eka Martyani
Jurnal Teknologi informasi dan Ilmu Komputer Vol. 2 No. 1 (2026): Januari 2026
Publisher : Nolsatu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65258/jutekom.v2.i1.53

Abstract

The industrial world and information systems are undergoing a major shift in management practices. Growing and diverse user demands have driven information systems to evolve from passive data management into intelligent systems that assist organizational management in decision-making processes. This research aims to analyze the evolution of information systems, from conventional models to the implementation of Artificial Intelligence (AI) based systems within organizations. The findings indicate that the transformation of information systems is not merely a software update, but a paradigm shift: from passive systems reliant on manual input (Conventional Phase), to systems capable of integrated workflows (Automated Phase), and finally to systems that can learn and provide independent recommendations (Intelligent System Phase). The primary finding of this study is that the transition to intelligent systems significantly enhances operational efficiency. However, this must be balanced with high data quality as a reliable information source and supported by the readiness of human resources. In conclusion, information systems in the digital era have transformed from simple administrative tools into essential strategic partners that help organizations navigate data complexity and improve decision-making.
Dampak Tantangan dan Potensi Vibe Coding Berbasis AI dalam Lanskap Pendidikan Pemrograman: Tinjauan Literatur Sistematis Akhmad Rezki Purnajaya
Jurnal Teknologi informasi dan Ilmu Komputer Vol. 2 No. 2 (2026): April 2026
Publisher : Nolsatu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65258/jutekom.v2.i2.52

Abstract

Vibe coding transforms natural language commands into functional code through Artificial Intelligence (AI). This Systematic Literature Review (SLR) evaluates the impacts, challenges, and potential of this approach within education. Adhering to the PRISMA protocol, 17 studies were selected based on inclusion criteria. The distribution encompasses higher education (n=9) and K-12 (n=8), focusing on tools like GitHub Copilot and ChatGPT. Synthesis results from controlled experimental studies among higher education students indicate a significant increase in task completion efficiency of up to 35%. However, a distinction is observed where increased technical productivity does not equate to improved learning achievement; instead, risks concerning diminished conceptual understanding and academic integrity challenges remain. This review is limited by the geographic dominance of studies from North America and Europe and the high heterogeneity of study designs, which necessitated a narrative synthesis. This study recommends a pedagogical shift towards critical human-AI collaboration.
Evaluasi Usability Aplikasi MyASN Menggunakan Metode System Usability Scale (SUS) Firdaus, Aqfi Nur; Harun, Nia Rahma Faudila; Zulfaidil
Jurnal Teknologi informasi dan Ilmu Komputer Vol. 2 No. 2 (2026): April 2026
Publisher : Nolsatu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65258/jutekom.v2.i2.55

Abstract

The rapid digital transformation in the public sector necessitates the development of service applications that are not only functionally robust but also highly usable. MyASN, as a digital personnel service platform for Indonesian civil servants (Aparatur Sipil Negara/ASN), is designed to facilitate access to employment-related information and administrative services. However, its implementation has revealed several usability issues, including login difficulties, slow system responsiveness, and feature instability, which negatively affect the overall user experience. This study aims to evaluate the usability level of the MyASN application using the System Usability Scale (SUS) method. Data were collected through the distribution of SUS questionnaires to application users. The results indicate an average SUS score of 54.08, which falls below the standard benchmark, placing the application in the Not Acceptable – Marginal category, with an adjective rating of Poor–OK and a grade of D. Furthermore, the Net Promoter Score (NPS) falls into the Detractor category, indicating low user satisfaction and a limited likelihood of recommendation. These findings suggest that the MyASN application exhibits significant shortcomings in terms of interface design, navigation, feature consistency, and system performance. Therefore, improvements are required, particularly in simplifying the user interface, enhancing system stability, and optimizing overall performance to improve user experience and the quality of digital personnel services.
Evaluating Resampling Methods for Imbalanced Necrosis Classification on CT Scans Purnajaya, Akhmad Rezki; Masparudin
Jurnal Teknologi informasi dan Ilmu Komputer Vol. 2 No. 2 (2026): April 2026
Publisher : Nolsatu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65258/jutekom.v2.i2.56

Abstract

Necrosis, or body tissue death, occurs when there is insufficient blood flow to the tissue, which can be caused by injury, radiation, or chemicals. One of the main challenges in the automated diagnosis of necrosis is data imbalance in medical datasets, where the number of pathological cases is far less than normal cases. To address this issue, this study implements and evaluates various data sampling techniques, including Random Undersampling (RUS), Random Oversampling (ROS), Combination of Over-Undersampling (COUS), Synthetic Minority Over-sampling Technique (SMOTE), and Tomek Link, then using a Support Vector Machine (SVM) as the classifier. The test results show that the best sampling technique is the Synthetic Minority Over-sampling Technique (SMOTE), which successfully achieved an accuracy of 100% and an Area Under Curve (AUC) of 100%, indicating its significant potential in improving the accuracy of necrosis diagnosis from CT scans.
Evaluasi dan Perbandingan Model CNN Dan Transfer Learning Dalam Klasifikasi Kematangan Buah Kelapa Sawit Naufal Budiman; Ringga Chandra Perdana
Jurnal Teknologi informasi dan Ilmu Komputer Vol. 2 No. 2 (2026): April 2026
Publisher : Nolsatu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65258/jutekom.v2.i2.59

Abstract

The identification of oil palm fruit ripeness is an important factor in maintaining harvest quality and improving palm oil productivity. Manual identification processes still have limitations, including subjectivity and inconsistency in assessment results. This study aims to evaluate and compare the performance of a Baseline Convolutional Neural Network (CNN) model with several transfer learning architectures, namely EfficientNetB3, ResNet50, and DenseNet121, for oil palm fruit ripeness classification. The dataset consisted of 302 original images of oil palm fruits categorized into three classes: ripe, unripe, and rotten. To prevent data leakage, the dataset was first divided using a stratified split into training, validation, and testing sets before data augmentation was applied exclusively to the training set. The augmentation techniques included rotation, translation, zooming, brightness adjustment, and horizontal flipping to increase data variability and reduce overfitting. All models were trained using an input size of 224 × 224 pixels, the Adam optimizer, and categorical cross-entropy as the loss function. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics to assess the classification capability of each model. In addition, confusion matrix analysis was conducted to identify classification error patterns across the ripeness categories. The results indicate that transfer learning models outperformed the Baseline CNN model. DenseNet121 achieved the best overall performance, followed by EfficientNetB3 and ResNet50. These findings demonstrate that transfer learning is an effective approach for oil palm fruit ripeness classification, particularly when working with limited datasets. Nevertheless, further studies using larger and more diverse datasets are recommended to improve model generalization capabilities.

Page 4 of 4 | Total Record : 35

 1   2   3   4 

Filter by Year

2025 2026


Reset
Filter By Issues
All Issue Vol. 2 No. 2 (2026): April 2026 Vol. 2 No. 1 (2026): Januari 2026 Vol. 1 No. 4 (2025): Oktober 2025 Vol. 1 No. 3 (2025): Juli 2025 Vol. 1 No. 2 (2025): April 2025 Vol. 1 No. 1 (2025): Januari 2025