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JITK (Jurnal Ilmu Pengetahuan dan Komputer)
Published by STMIK Nusa Mandiri
ISSN : -     EISSN : 25274864     DOI : -
Core Subject : Science,
Computer Science & IT
Kegiatan menonton film merupakan salah satu cara sederhana untuk menghibur diri dari rasa gundah gulana ataupun melepas rasa lelah setelah melakukan aktivitas sehari-hari. Akan tetapi, karena berbagai alasan terkadang seseorang tidak ada waktu untuk menonton film di bioskop. Dengan bantuan media internet, berbagai macam aplikasi nonton film android sangat mudah dicari. Hanya bermodalkan smartphone saja para penonton film dapat streaming berbagai macam jenis film di mana saja dan kapan saja mereka inginkan. Akan tetapi, karena banyaknya pilihan aplikasi nonton film android yang bisa digunakan, terkadang seseorang bingung memilihnya. Untuk itu, diperlukan suatu sistem pendukung keputusan yang dapat digunakan para pengguna sebagai alat bantu pengambilan keputusan untuk memilih dengan berbagai macam kriteria yang ada. Salah satu metode yang digunakan adalah metode Analytical Hierarchy Process (AHP). AHP melakukan perankingan dengan melalui penjumlahan antara vector bobot dengan matrik keputusan dengan tujuan agar hasil yang diberikan lebih baik dalam menentukan alternatif yang akan dipilih. Berdasarkan hasil penelitian yang dilakukan oleh 36 sampel responden didapatkan kriteria konten menjadi prioritas pertama pengguna untuk memilih aplikasi nonton film android dengan nilai bobot sebesar 0,224. Sedangkan Netflix menjadi alternatif dengan prioritas pertama keputusan pengguna dalam memilih aplikasi nonton film android dengan nilai bobot sebesar 0,352.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 394 Documents
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MODEL OF CYBERBULLYING DETECTION ON SOCIAL MEDIA USING MULTI-LABEL DEEP LEARNING: A COMPARATIVE STUDY Lemi Iryani; Junaidi Junaidi; Paisal Paisal; Mariana Purba; Nia Umilizah; Bakhtiar Bakhtiar; Nur Ani
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6004

Abstract

Cyberbullying is the deliberate act of using technology to harm others. This study aims to analyze 400 Instagram comments obtained via API from previous research. The data were labeled into three classes: negative (containing cyberbullying), positive (non-bullying, supportive), and neutral (neither positive nor negative). The data for experiment was divided into 70% for training and 30% for testing. The research methodology consists of three main stages. The first stage is text preprocessing, which includes tokenization (splitting comments into tokens), filtering (removing unimportant words or stop-words), and stemming (converting words with affixes into their root forms). The second stage is classification analysis using BiLSTM, LSTM, RNN, and CNN-1D methods. The third stage is evaluation by comparing the model's classification results with manually labeled data using accuracy as the evaluation metric. The results show that the BiLSTM model performed the best, achieving an accuracy of 98.51% on the training data and 81.82% on the testing data. The BiLSTM method used in this study can be further adapted to enhance the effectiveness of cyberbullying detection in various applications.
SENTIMENT ANALYSIS OF PLAYER FEEDBACK IN ALGORUN: A STUDY OF DEEP LEARNING MODELS FOR GAME-BASED LEARNING Rio Andriyat Krisdiawan; Nur Alamsyah; Tito Sugiharto
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6015

Abstract

AlgoRun: Coding Game is a game-based learning application aimed at teaching computational thinking (CT) concepts such as variables, conditions, loops, and functions. Evaluating user feedback in such educational games is challenging, as traditional sentiment analysis techniques often overlook nuanced responses. Despite its potential to inform content improvements, sentiment analysis in game-based learning remains underexplored. This study compares the performance of deep learning models—DNN, CNN, RNN with LSTM, and Bidirectional LSTM—for sentiment classification of AlgoRun user reviews, using TF-IDF and word embeddings as feature extraction methods. A total of 1,440 reviews were scraped from the Google Play Store, translated, and preprocessed using data preparation techniques (dropna, fillna), text preprocessing (case folding, cleaning, tokenization, stopword removal, stemming), and feature extraction (TF-IDF and word embeddings). The dataset was labeled into negative, neutral, and positive classes, and split 80% for training and 20% for testing. Among the tested models, the DNN with TF-IDF achieved the highest accuracy of 98.86%, followed by CNN with Word Embeddings (96.97%), Bidirectional LSTM (96.59%), and RNN with LSTM (92.42%). The DNN also showed stable performance and convergence at the 10th epoch, outperforming other models in precision, recall, and F1-score. These results suggest that DNN with TF-IDF is highly effective for sentiment classification in the context of game-based learning. The findings offer useful guidance for developers to adapt content and enhance game quality based on user feedback. This research also contributes to the growing body of literature on leveraging sentiment analysis to optimize educational applications.
CLASSIFICATION OF NATURAL DISASTERS IN WEST SEMARANG BASED ON WEATHER DATA USING DEEP LEARNING Nicholas Martin; Jason Permana; Tony Tony
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6147

Abstract

Natural disasters like floods, landslides, and fires pose serious threats to both life and mental well-being, especially in vulnerable areas like West Semarang, which frequently experiences extreme weather. To mitigate these risks, an accurate classification system is essential for timely prevention and response. This study compares the performance of three neural network models—Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)—in classifying natural disasters using weather data. LSTM and GRU are particularly effective for handling long-term dependencies and addressing vanishing gradient problems common in time series data. Data for the study comes from the Semarang City Regional Disaster Management Agency (BPBD) and the Meteorology, Climatology, and Geophysics Agency (BMKG), spanning 2019 to 2022. The models achieved a high accuracy of 95.8%, but this is due to an imbalanced dataset—70 records of natural disasters versus 1377 without—resulting in classification favoring "no disaster." Among the models, LSTM performed the best, reaching optimal accuracy in just 20.0671 seconds per epoch. This suggests LSTM is the most effective model for this classification task.
FOREST FIRE LOCATION AND TIME RECOGNITION IN SOCIAL MEDIA TEXT USING XLM-ROBERTA Hafidz Sanjaya; Kusrini Kusrini; Kumara Ari Yuana; Arief Setyanto; I Made Artha Agastya; Simone Martin Marotta; José Ramón Martínez Salio
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6194

Abstract

Forest fires have become a serious global threat, significantly impacting ecosystems, communities, and economies. Although remote sensing technology shows potential, limitations such as time delays, limited sensor coverage, and low resolution reduce its effectiveness for real-time forest fire detection. Additionally, social media can serve as a multimodal sensor, presenting multilingual text data with rapid and global coverage. However, it may encounter challenges in obtaining location and time information on forest fires due to limitations in datasets and model generalization. This study aims to develop a multilingual named entity recognition (NER) model to identify location and time entities of forest fires in social media texts such as tweets. Utilizing a transfer learning approach with the XLM-RoBERTa architecture, fine-tuning was performed using the general-purpose Nergrit corpus dataset containing 19 entities, which were relabeled into 3 main entities to detect location, date, and time entities from tweets. This approach significantly improves the model's ability to generalize to disaster domains across multiple languages and noisy social media texts. With a fine-tuning accuracy of 98.58% and a maximum validation accuracy of 96.50%, the model offers a novel capability for disaster management agencies to detect forest fires in a scalable, globally inclusive manner, enhancing disaster response and mitigation efforts.
IMPLEMENTATION OF CNN FOR CLASSIFYING PATCHOULI LEAF IMAGES BASED ON ACCURACY AND EVALUATION Arif Rahman Hakim; Dewi Marini Umi Atmaja
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6207

Abstract

Patchouli (Nilam leaves) holds significant potential as a high-value natural material, especially in the perfume and essential oil industries. However, the classification and quality analysis of patchouli leaves remain a challenge that requires an automated solution based on technology. This study aims to develop a Convolutional Neural Network (CNN) model capable of automatically classifying the condition of patchouli leaves. The image data of patchouli leaves were processed through several preprocessing stages and divided into training and testing data. The designed CNN model utilizes several convolutional layers, pooling, dropout, and dense layers for the training process. The evaluation results using the confusion matrix showed that the model had a very low error rate, with only 1 misprediction in the training data. For the testing data, the model achieved an accuracy of 85% with a loss value of 0.6191496. The model also demonstrated an accuracy of 98.75% with a loss of 0.443462 on the training data. However, improvements in model generalization are still needed to achieve more consistent performance on new data
COMPARATIVE STUDY OF YOLO VERSIONS FOR DETECTING VACANT CAR PARKING SPACES Muhammad Fathurrahman; Anan Nugroho; Ahmad Zein Al Wafi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6236

Abstract

The increasing vehicle density in urban areas has made parking space availability a significant challenge. With technological advancements, efficient smart parking systems based on object detection have become essential. This study evaluates the performance of YOLO versions 3 to 11 in detecting vacant parking spaces in urban environments, focusing on real-time processing, high accuracy with limited datasets, and adaptability to varying conditions. Using 4,215 annotated images and two test videos, YOLOv7 achieved the highest overall accuracy of 99.57% with an average FPS of 30.79, making it the most effective model for smart parking applications. YOLOv8 and YOLOv11 followed closely, with accuracies of  98.51% and 98.72%, respectively, and average FPS rates of 32.31 and 31.99, balancing precision and speed, which are ideal for real-time applications. Meanwhile, YOLOv5 stood out for its exceptional processing speed of 33.92 FPS. These results highlight YOLO's potential to revolutionize smart parking systems by significantly enhancing both detection precision and operational efficiency.   
PWA AND NON-PWA PERFORMANCE ANALYSIS: CHROME EXTENSION TESTING ON E-COMMERCE PLATFRORM Panji Revolusioner Saputro; Rifda Faticha Alfa Aziza
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6238

Abstract

This study compares Progressive Web Apps (PWA) and traditional web applications performance using a custom Chrome extension and Google Lighthouse, focusing on Tokopedia's e-commerce platform. The research employs a quantitative approach with controlled testing environments across three viewports for the custom extension (desktop, tablet, mobile) and two viewports for Google Lighthouse (desktop, mobile). The custom extension measures eleven metrics, including Core Web Vitals, PWA features, and resource usage, while Google Lighthouse provides five core metrics. Results show PWA implementation improves performance with 9.9% better First Contentful Paint on desktop and significant memory efficiency (29-33MB vs 59-62MB). The comparison between testing tools reveals methodology differences, with custom extension showing optimistic results in real-world conditions and Lighthouse providing more conservative measurements under throttled conditions. This research contributes to PWA performance measurement methodology by combining real-world and standardized testing approaches.
INTEGRATING AUGMENTED REALITY WITH C4.5 ALGORITHM TO ENHANCE TOURISM EXPERIENCE IN PEKALONGAN Muhamad Rizaludin; Nur Hadian; Nur Hayati
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6244

Abstract

The tourism industry demands interactive and personalized solutions to enhance the traveler experience. However, providing relevant and customized travel recommendations based on individual preferences remains a challenge. This study integrates Augmented Reality (AR) technology with the C4.5 algorithm to address this issue and improve the tourism experience in Pekalongan Regency. The research method involved collecting data from 500 respondents through an online questionnaire. The collected data underwent preprocessing, including handling missing data, data transformation, and class balancing. The C4.5 algorithm was applied to build a tourism recommendation model, while AR technology presented 3D visualizations of tourist destinations through an interactive application. The research results show that the recommendation model achieved an accuracy rate of 76.92%. The integration of AR provided an interactive experience that enhanced tourist engagement and satisfaction, although limitations were found in AR visualization quality and the completeness of destination information. Further improvements are needed to enhance AR realism, provide more detailed content, and optimize user satisfaction. This study contributes to the development of AR-based tourism technology integrated with the C4.5 algorithm. The findings encourage local tourism innovation and have the potential to enhance the traveler experience in Pekalongan Regency. This model can also be applied to other tourist destinations across Indonesia.
OPTIMIZING TRANSPORTATION SURVEILLANCE WITH YOLOV7: DETECTION AND CLASSIFICATION OF VEHICLE LICENSE PLATE COLORS Ridho Sholehurrohman; Kurnia Muludi; Joko Triloka
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6260

Abstract

Optimizing transportation surveillance requires accurate vehicle license plate color detection and classification; however, existing systems face significant challenges in achieving real-time accuracy and robustness, particularly in crowded traffic scenarios with varying lighting and plate conditions. In Indonesia, vehicle license plates are color-coded based on their usage, including white and black for private vehicles, yellow for public vehicles, red for government vehicles, and green for free-trade areas. Each plate color plays a crucial role in transportation management, enabling proper vehicle identification and regulation. Existing surveillance systems struggle with real-time detection accuracy, especially in distinguishing plate colors in crowded traffic. Traditional methods may not efficiently classify plate colors due to limitations in feature extraction and processing. To address this, this study implements the YOLOv7 model to improve vehicle license plate color detection (black, white, yellow, and red) while distinguishing non-plate vehicles in diverse scenarios. The model's effectiveness is evaluated using precision, recall, and F1-score to ensure robustness for surveillance applications. Results show an average precision of 95.27%, recall of 94.60%, and F1-score of 94.93%, demonstrating strong detection capabilities. Optimizing the Non-Plate category further improves system accuracy, efficiency, and scalability, enhancing transportation monitoring reliability.
THE ROLE OF THE INTERNET OF THINGS (IOT) IN ELECTRIC VEHICLE MANAGEMENT AND MAINTENANCE Callista Fabiola Candraningtyas; Fikri Arkan Maulana; Sapta Suhardono
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6298

Abstract

The growing adoption of electric vehicles (EVs) as an eco-friendly alternative to fossil fuel-based vehicles necessitates more advanced management and maintenance systems. The Internet of Things (IoT) presents significant potential to enhance EV management by enabling real-time monitoring and data analysis through interconnected sensors and technologies. This research investigates the integration of IoT in electric vehicle systems, focusing on real-time battery health monitoring, early detection of technical issues, and route optimization for improved energy efficiency. The study employs a system design and testing approach, supported by descriptive-analytical analysis using data from case studies, literature reviews, surveys, and interviews. Findings indicate that IoT implementation in EVs yields notable advantages. Real-time battery health tracking provides accurate performance insights, achieving a 92% accuracy rate in predicting battery degradation. Technical problem detection through sensor analysis enables timely maintenance, leading to a 30% reduction in vehicle downtime. Furthermore, IoT-based route optimization improves energy efficiency, reducing energy consumption by 15% and extending battery lifespan by 20% compared to traditional systems. These results underscore the practical benefits of IoT in enhancing EV performance and operational efficiency. The system enables users and service providers to make informed decisions regarding vehicle maintenance and usage, promoting better understanding of battery conditions. Ultimately, the application of IoT technology contributes to extending battery life, minimizing vehicle downtime, and supporting broader efforts in energy efficiency and carbon emission reduction

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