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JUTIK : Jurnal Teknologi Informasi dan Komputer
Published by Universitas Dhyana Pura
ISSN : 2442241X     EISSN : 25285211     DOI : -
Core Subject : Economy, Science, Education, Social, Engineering,
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Engineering Social Sciences
Jurnal Teknologi Informasi dan Komputer berisi tulisan yang diangkat dari hasil penelitian di bidang teknologi informasi dan komputer. Jurnal ini merupakan sarana bagi peneliti di bidang ilmu teknologi informasi dan komputer untuk mempublikasikan karya-karya penelitiannya. Redaksi penyunting jurnal Teknologi Informasi dan Komputer terdiri dari dosen-dosen yang terkait bidang ilmu teknologi informasi dan komputer dalam konsentrasi antara lain : Rekayasa Perangkat Lunak, Sistem Informasi, Jaringan dan Keamanan Komputer, Pengolahan Citra, Multimedia dan Kecerdasan Buatan, dan konsentrasi lainnya terkait bidang ilmu teknologi informasi dan komputer.
Arjuna Subject : -
Articles 458 Documents
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ANALISIS KINERJA ARSITEKTUR CNN ALEXNET DAN VGG16 UNTUK KLASIFIKASI TUMOR OTAK Agustina Diah Kusuma Dewi; Efandra Eka Julita; Rizki Wahyu Yulianti
Jurnal Teknologi Informasi dan Komputer Vol. 11 No. 2 (2025): JUTIK : Jurnal Teknologi Informasi dan Komputer, Edisi Oktober 2025
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36002/jutik.v11i2.3900

Abstract

Early detection of brain tumors is essential for determining appropriate treatment strategies and increasing patient survival rates. This study analyzes and compares the performance of two Convolutional Neural Network (CNN) architectures Alexnet and VGG16 for classifying brain tumor MRI images into three categories: glioma, meningioma, and pituitary. The dataset, annotated by medical experts, was split into 80% for training and 20% for testing. Each image underwent preprocessing steps including resizing, normalization, and data augmentation. Both models were initialized with pre-trained weights from ImageNet and trained for 15 epochs using the Adam optimizer. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results show that Alexnet achieved a testing accuracy of 78.99% with a weighted F1-score of 0.79, while VGG16 obtained an accuracy of 78.01% and a weighted F1-score of 0.75. Although VGG16 has a deeper architecture capable of capturing more complex features, Alexnet demonstrated more stable and balanced performance across all tumor classes. These findings suggest that Alexnet is more effective for classifying brain tumor MRI images within the evaluated dataset and holds strong potential for integration into medical decision-support systems based on deep learning.
OPINI MASYARAKAT TERHADAP BONUS DEMOGRAFI PADA KANAL YOUTUBE DENGAN METODE TF-IDF, NAÏVE BAYES DAN SMOTE Samuel Effendi Pratama; Jolyn Lucretia; Hafiz Irsyad; Abdul Rahman
Jurnal Teknologi Informasi dan Komputer Vol. 11 No. 2 (2025): JUTIK : Jurnal Teknologi Informasi dan Komputer, Edisi Oktober 2025
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36002/jutik.v11i2.3902

Abstract

This study examines public opinion on the demographic bonus issue expressed through comments on YouTube channels using the TF-IDF, Naïve Bayes, and SMOTE methods. The data used consists of 870 comments that have been manually labeled into positive and negative sentiments. The research stages include data pre-processing in the form of case folding, removal of non-alphabetic characters, stopword removal, and stemming, then feature extraction using TF-IDF to convert text into numeric representations that can be processed by the algorithm. This study compares the performance of the Naïve Bayes sentiment classification model in two scenarios, namely without and with the application of SMOTE. The SMOTE technique is used to overcome data imbalance between sentiment classes so that the classification results are more balanced and unbiased. The evaluation results show that the model without SMOTE produces an accuracy of 70% but has a very low recall in the positive class. After applying SMOTE, the accuracy increased to 77%, with the highest precision of 0.89 in the negative class and the highest recall of 0.92 in the positive class. The word cloud visualization shows the dominant words that reflect the pattern of public opinion regarding the demographic bonus clearly and informatively. The results of this study can provide a quantitative picture of public perception and be a consideration for policy makers. In the future, this method can be further developed with other algorithms and data from various social media platforms to improve the accuracy and representativeness of sentiment analysis.
ANALISIS PENERIMAAN OPENAI CHATGPT OLEH MAHASISWA SURABAYA MENGGUNAKAN MODEL UTAUT 2 Farrel Ega Nur Royyan; Adriano Femaz Rivaldy; Wahyu Setiawan
Jurnal Teknologi Informasi dan Komputer Vol. 11 No. 2 (2025): JUTIK : Jurnal Teknologi Informasi dan Komputer, Edisi Oktober 2025
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36002/jutik.v11i2.3981

Abstract

This study uses the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) model to investigate and analyze the factors that may influence Surabaya university students in accepting and using ChatGPT. Data collection was conducted by conducting a questionnaire survey of 119 student respondents was used to collect data and Partial Least Squares - Structural Equation Modeling (PLS-SEM) was used to process the data. The findings show that the most important predictors of intention and behavior of using ChatGPT are habit and conducive circumstances. On the other hand, behavioral intentions were not significantly influenced by other factors including hedonic motivation, social influence, or performance expectations. These results suggest that ChatGPT adoption patterns have changed from initial impressions of ease of use or technological advantages to being based on routine and infrastructure support.
PENERAPAN SIMPLEQUEUE PADA BANDWIDTH MANAGEMENT MIKROTIK RB941 – 2ND UNTUK PEMBATASAN BANDWIDTH INTERNET Pritiy Singgam; Thania Dealva Arsyad; Afifah Naila Nasution; Dedy Kiswanto
Jurnal Teknologi Informasi dan Komputer Vol. 12 No. 1 (2026): JUTIK : Jurnal Teknologi Informasi dan Komputer, Edisi April 2026
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36002/jutik.v12i1.3792

Abstract

The rapid development of information technology demands a reliable and efficient internet network. One common issue in network usage is the uneven distribution of bandwidth, which results in some users enjoying smooth internet access while others face disruptions. This study aims to analyze and explain the implementation of the Simple Queue feature on the Mikrotik RB941–2nD device as a solution for bandwidth management. The method used is an applied experiment by configuring data rate limitations and setting packet priorities on a WiFi network. The purpose of this test is to evaluate the effectiveness of the Simple Queue feature on the MikroTik router in limiting internet bandwidth for each device on the network. The configuration sets a bandwidth limit of 64 kbps, tested through the Rate graph on the MikroTik interface and a speed test from the client side. The test results show that the client device receives a download speed of only 0.04 Mbps and an upload speed of 0.18 Mbps when the restriction is active, which is close to the predefined limit. This proves that the Simple Queue feature successfully performs its function in controlling bandwidth usage according to the specified schedule, namely Monday to Friday from 08:00 to 12:10. This limitation also effectively prevents internet access domination by a single device, allowing for more equitable connection distribution. However, the 64 kbps speed limit is considered too low for current internet activity needs and should be adjusted to continue supporting an optimal user experience.
PERANCANGAN SISTEM INFORMASI AKUNTANSI PENCATATAN KAS PADA KLINIK ANDINA KARAWANG Muhammad Edi Iswanto; Arif Maulana Yusuf; Joko Irawan; Vera Wati
Jurnal Teknologi Informasi dan Komputer Vol. 12 No. 1 (2026): JUTIK : Jurnal Teknologi Informasi dan Komputer, Edisi April 2026
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36002/jutik.v12i1.3843

Abstract

The purpose of this research is to design and develop a web-based accounting information system for Klinik Andina Karawang to improve the process of recording cash inflows and outflows. The system is intended to enable a more structured and efficient financial documentation process. It was developed to replace the previously utilized manual recording methods, which were prone to errors, data loss, delays, and inconsistencies in financial reporting. This research adopts the Design Science Research Methodology (DSRM), which comprises six stages: problem identification, objective definition, system design and development, demonstration, evaluation, and communication. The outcome of this research is a fully implemented system, as verified through functional testing and user validation. These evaluations indicate that the system’s features functioned as expected and contributed positively to improving operational efficiency.
EVALUASI KUALITAS WEBSITE E-COMMERCE ZI-SHOP DENGAN MODEL SQO-OSS MENGGUNAKAN METODE PROFILE MATCHING Mohammad Ferdian Saputro; Harsya Mahardika Pratama; Adhek Satrya Alfiansyah; Muhammad Naufal Dzaky Robbaniyyin; Rani Purbaningtyas
Jurnal Teknologi Informasi dan Komputer Vol. 12 No. 1 (2026): JUTIK : Jurnal Teknologi Informasi dan Komputer, Edisi April 2026
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36002/jutik.v12i1.3928

Abstract

The development of digital technology has encouraged businesses to shift from conventional methods to digital platforms, such as e-commerce. However, challenges such as high commission fees in the marketplace encourage MSMEs to develop independent e-commerce websites. This study aims to evaluate the software quality of ZI-Shop, an independent e-commerce website developed using Laravel and Livewire at Politeknik Negeri Jember. The evaluation was conducted based on three software quality indicators according to the SQO-OSS model, namely maintainability, reliability, and security. The research used the profile matching method to compare the actual profile of application quality with a predetermined ideal profile. The data was collected through a questionnaire using a 1-5 Likert scale distributed to 20 respondents who use the ZI-Shop application. The weight of maintainability and reliability indicators was 40%, and security was 20%, while core factors and secondary factors were given a weight of 60% and 40% respectively. The final results of the profile matching method are used to calculate the level of conformity of application quality to the expected ideal value. This research is expected to provide an objective picture of the quality of the ZI-Shop application and become a reference in the development of other independent e-commerce websites in order to increase the efficiency and profitability of online businesses.
OPINI PUBLIK TERHADAP ULASAN VIDEO RUU TNI MENGGUNAKAN TF-IDF, NAÏVE BAYES DAN SMOTE Patrisius Satria Hendrawan; Michael Gunawan; Hafiz Irsyad; Abdul Rahman
Jurnal Teknologi Informasi dan Komputer Vol. 12 No. 1 (2026): JUTIK : Jurnal Teknologi Informasi dan Komputer, Edisi April 2026
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36002/jutik.v12i1.3955

Abstract

The rapid development of digital technology has encouraged the public to actively express their opinions on public issues through social media platforms, including YouTube. The comment section on videos discussing the Draft Law on the Indonesian National Armed Forces (RUU TNI) has become a space for the public to convey support or rejection. This study aims to analyze public opinion regarding the RUU TNI by classifying YouTube comments into two sentiment categories: positive and negative. The methods employed include text preprocessing, feature extraction using TF-IDF, sentiment classification with the Naïve Bayes algorithm, and data balancing using the SMOTE technique to address class imbalance. The evaluation results show that the model achieved 80.7% accuracy before SMOTE; however, the recall and f1-score for the positive class were very low due to the imbalanced dataset. After applying SMOTE, the accuracy slightly decreased to 80.38%, but there was a significant improvement in the evaluation metrics for the positive class, with recall reaching 86.21% and f1-score 81.3%. WordCloud visualization also revealed dominant keywords that represent each sentiment. These findings indicate that the Naïve Bayes algorithm, when combined with SMOTE, is more effective in producing a balanced sentiment classification and is recommended for use in analyzing imbalanced textual data related to public opinion.
IMPLEMENTASI HYBRID REKURSIF-ITERATIF UNTUK PENINGKATAN KINERJA ALGORITMA TRAVELLING SALESMAN PROBLEM (TSP) Puspa Dwi Setyorini; Lintang Tsaniatu Azzahro; Ramona Aprilia Yuniar; Imam Prayogo Pujiono4
Jurnal Teknologi Informasi dan Komputer Vol. 12 No. 1 (2026): JUTIK : Jurnal Teknologi Informasi dan Komputer, Edisi April 2026
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36002/jutik.v12i1.3957

Abstract

The advancement of optimization algorithms in computer science has encouraged various approaches to solving classical problems such as the Travelling Salesman Problem (TSP), which involves finding the shortest route from one point to all others without revisiting any point. While recursive and iterative approaches have been widely applied individually, each has its limitations—particularly in execution time and memory usage when applied to large-scale data. This study proposes and implements a hybrid recursive-iterative approach to enhance algorithmic performance in solving TSP. The experiment, conducted using the python programming language, used a randomly generated symmetric graph dataset with 10 sample with city description A-J. Three methods were compared: iterative, recursive, and hybrid. The results showed that all approaches produced identical total route distances (246 units), yet varied significantly in execution time and memory usage. The hybrid method recorded the fastest execution time of 11.3550 seconds—50.1% faster than the iterative approach and 73.3% faster than the recursive approach. In terms of memory, the hybrid used 1.14 KB, slightly higher than the iterative (0.86 KB) but lower than the recursive (1.12 KB). These findings indicate that the hybrid approach offers the best trade-off between speed and resource usage, making it an efficient solution for medium to large-scale TSP scenarios. This study contributes to the development of optimization algorithms based on multi-paradigm adaptation.
PENGEMBANGAN APLIKASI BERBASIS ARTIFICIAL INTELLIGENCE DALAM REKOMENDASI JALUR PENDIDIKAN BERDASARKAN MINAT DAN KEMAMPUAN SISWA M. Rizki Andrian Fitra; Neysa Talitha Jehian; Delvita Aulia Artika; Bunga Dwi Febrianti; Adidtya Perdana
Jurnal Teknologi Informasi dan Komputer Vol. 12 No. 1 (2026): JUTIK : Jurnal Teknologi Informasi dan Komputer, Edisi April 2026
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36002/jutik.v12i1.3959

Abstract

Many high school and vocational students in Indonesia experience confusion when choosing a college major due to a lack of understanding of their own potential and limited access to relevant information. This study aims to develop an Artificial Intelligence (AI)-based major recommendation system that is personal, adaptive, and transparent. The system is designed using a Hybrid Recommendation System approach, combining Content-Based Filtering, Rule-Based System, and a Weighted Scoring Algorithm, with weights based on hobbies, academic grades, favorite subjects, personality, and career aspirations. The technologies used include Laravel (backend), Vue.js (frontend), and Python API for the AI component. Trial results with 15 students showed that over 60% of respondents found the system very helpful, while over 30% found it moderately helpful and felt the recommendations aligned with their interests and goals, indicating the system’s effectiveness in supporting educational decision-making. The system is also flexible for further development in terms of both datasets and algorithms. Future enhancements include the integration of personality tests such as MBTI, implementation of feedback-based machine learning, and cross-school testing for broader validation. This system is expected to become a data-driven educational solution that supports digital transformation in the education sector.
DETEKSI DAN PENANGANAN PENYAKIT KULIT BERBASIS WEB DENGAN CNN-SVM DAN GEMINI Muhammad Nor Hanafi; Mohammad Minan Abdul Nafi; Rauhillah; Arif Setiawan
Jurnal Teknologi Informasi dan Komputer Vol. 12 No. 1 (2026): JUTIK : Jurnal Teknologi Informasi dan Komputer, Edisi April 2026
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36002/jutik.v12i1.3965

Abstract

This research develops an image-based classification system to detect eight skin diseases cellulitis, impetigo, athlete’s foot, nail fungus, ringworm, cutaneous larva migrans, chickenpox, and shingles through an interactive web application. The system uses transfer learning with MobileNetV2 pretrained on ImageNet to extract salient visual features such as texture and color patterns from skin images. These features are classified by a Support Vector Machine (SVM) with a linear kernel, generating accurate and efficient predictions. Unlike previous studies that focused solely on model development or provided an interface without supplementary guidance, this system integrates classification and follow-up information. Via a simple and user-friendly interface, users upload a photo of a skin lesion through a browser and immediately receive classification results along with confidence scores. The system also forwards its prediction to the AI Gemini model, which supplies additional details, including disease descriptions, primary symptoms, common treatments, safe self-care guidelines, and advice on when to seek professional care. Performance evaluation shows that the system achieves an accuracy of 0.97, with an average precision of 0.98, an average recall of 0.97, and an average F1-score of 0.97 confirming consistent classification across all disease categories. Overall, this system not only functions as an early diagnosis tool, but also as an educational medium that supports early treatment and decision-making by general medical personnel.

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