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Jurnal Ilmu Komputer
Published by Universitas Pamulang
ISSN : -     EISSN : 3031125X     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
Jurnal Ilmu Komputer merupakan jurnal ilmiah dalam bidang Ilmu Komputer, Informatika, IoT, Network Security dan Digital Forensics yang diterbitkan secara konsisten oleh Program Studi Teknik Informatika S-2, Program Pascasarjana, Universitas Pamulang, Indonesia. Tujuan penerbitannya adalah untuk memberikan informasi terkini dan berkualitas kepada para pembaca yang memiliki ketertarikan terhadap perkembangan ilmu pengetahuan dan teknologi di bidang-bidang tersebut. Setiap artikel yang dimuat dalam Jurnal Ilmu Kompute merupakan hasil kegiatan penelitian, tinjauan pustaka, dan best-practice. Jurnal Ilmu Komputer terbit dua kali dalam setahun, tepatnya pada bulan Juni dan Desember. Jumlah artikel untuk setiap terbitan adalah 10 artikel.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 77 Documents
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Analisis Sentimen Publik Terhadap Peluang Timnas Indonesia Lolos ke Piala Dunia 2026 Dengan Algoritma Naïve Bayes dan Random Forest Faizura Zadri
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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Abstract

Football is a sport that is highly anticipated by the Indonesian people in 2025. This enthusiasm increases with the opportunity for the Indonesian national team to compete in the world's biggest football event, the 2026 World Cup which will be held in Canada, Mexico and the United States. There are various public opinions regarding Indonesia's chances of qualifying for the event, ranging from optimistic to pessimistic. This study was conducted to analyze public sentiment towards the chances of the Indonesian national team qualifying for the 2026 World Cup using the Naïve Bayes and Random Forest algorithms. The test results show that Naïve Bayes produces an accuracy of 79.6%, while Random Forest has the highest accuracy, which is 87.3%. Sentiment analysis using Random Forest shows that the majority of public sentiment is Neutral, which is 66.34%. This finding indicates that in general, the public is still doubtful or unsure about the chances of the Indonesian national team to qualify for the 2026 World.
Penerapan Linear Programming dengan tools POM-QM dalam Analisis Biaya Purchase Order pada PT Mitsuba Indonesia, Cikande, Serang. Alfin Naufalzain
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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Abstract

In the manufacturing industry, cost efficiency is a key factor in enhancing a company's competitiveness. PT Mitsuba Indonesia, a company engaged in the production of automotive components, faces challenges in managing the increasing costs of purchase orders. This study aims to analyze and optimize purchase order costs using the Linear Programming method, implemented through the POM-QM for Windows software. This research is based on optimization theory, particularly the Simplex method in Linear Programming, and supported by literature on quantitative decision-making in operations management. The research method used is a case study with a quantitative approach. Data collected includes demand volume, vendor capacity, unit price, and constraints related to production and logistics. The analysis was conducted by modeling the problem into an objective function to minimize total cost, while considering the existing operational constraints. Calculations were performed using POM-QM tools, which facilitated computation and result interpretation. The results show that the application of Linear Programming can significantly reduce the total purchase order cost compared to the conventional methods previously used by the company. The model also provides optimal order quantities from each vendor that satisfy the given constraints. In conclusion, the Linear Programming method supported by POM-QM is proven effective in optimizing purchase order costs. It is recommended that the company adopt this model regularly as a decision-making tool in procurement processes.
Pengembangan Aplikasi Pembelajaran Berbasis Audio Matakuliah Pengantar Teknologi Informasi Untuk Mahasiswa Tuna Netra: Pengembangan Aplikasi Pembelajaran Berbasis Audio Matakuliah Pengantar Teknologi Informasi Untuk Mahasiswa Tuna Netra Septa; Syndhe Qumaruw Syty; Diki Rasapta
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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Abstract

This study aims to develop an audio-based learning application for visually impaired students in the Introduction to Information Technology course at Pamulang University. Visually impaired students often struggle to access materials that rely on visual elements. This application replaces these visual elements with audio delivery, allowing students to access and understand the material independently. The ADDIE model (Analysis, Design, Development, Implementation, Evaluation) was used as the development framework, encompassing a systematic process from needs analysis and design to development, pilot implementation, and evaluation. Functionality and user experience tests were conducted to assess the application’s effectiveness. The results, validated through black box testing and user questionnaires, show a high level of acceptance, with 95% of users finding the application effective and 85% rating it as easy to use. The primary challenge during development was ensuring the clear translation of complex visual concepts into audio. This research contributes to inclusive learning technologies and recommends future development to include more interactive features.
Penerapan Model T5-Small untuk Abstractive Text Summarization pada Berita Olahraga Prasetyo Ramadhan, Dandy; Waskita , Arya Adyhaksa
Jurnal Ilmu Komputer Vol 4 No 1 (2026): Jurnal Ilmu Komputer (Edisi Januari 2026)
Publisher : Universitas Pamulang

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Abstract

Sports news is characterized by its length and dense information, so readers often have difficulty quickly obtaining the main information. Manual summary creation is inefficient, while research on automatic summary systems in Indonesian, especially in the sports domain, is still very limited. This study develops an abstractive text summarization model based on the Transformer architecture (T5-Small) to generate summaries of Indonesian sports news. The dataset was obtained from Kaggle and then went through a pre-processing stage including data cleaning, text normalization, tokenization using T5Tokenizer, and the application of padding and truncation to match the model's input format. The model was trained using a data split of 80% for training, 10% for validation, and 10% for testing. Performance evaluation was conducted using the ROUGE-1, ROUGE-2, and ROUGE-L metrics by comparing the model summary against the reference summary (gold standard). The evaluation results using the ROUGE metric indicate that the model has quite good performance in producing relevant summaries. The ROUGE-1 value of 0.6011 indicates that more than half of the unigrams in the model summary match the reference summary. The ROUGE-2 value of 0.3940 indicates the model's ability to capture relationships between words, or bigrams, with a near 40% agreement rate. Meanwhile, the ROUGE-L value of 0.5411 confirms that the model's sentence sequence structure aligns with the original summary. Overall, these three values ​​confirm the model's ability to produce informative and consistent summaries.
Analisa Backup Dan Replikasi Virtual Server Dengan Menggunakan Penjadwalan Koneksi Data Pada Disaster Recovery Center (DRC) Pratama, Bayu Putra; Taryo, Taswanda; Hindansyah, Achmad
Jurnal Ilmu Komputer Vol 4 No 1 (2026): Jurnal Ilmu Komputer (Edisi Januari 2026)
Publisher : Universitas Pamulang

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Abstract

Server backup and replication are critical components in ensuring operational continuity and disaster recovery within modern information systems. One of the main challenges faced is the unscheduled transfer of data between the Data Center (DC) and the Disaster Recovery Center (DRC), which can lead to the spread of viruses, malware, and ransomware into the DRC environment, thereby disrupting the recovery process. This study aims to analyze the effectiveness of data connection scheduling in the backup and replication of virtual servers at PT XYZ Finance. The research adopts a quantitative approach by measuring system performance before and after the implementation of scheduled replication. Quantitative parameters include replication time, volume of successfully transferred data, backup success rate, and recorded security incidents. Data were collected through direct system testing and analyzed using descriptive and comparative statistical methods. The results show that implementing a structured data connection schedule in the replication system significantly supports faster operational recovery and reduces the number of security incidents impacting the DRC. Based on these findings, scheduled data connections in server replication have proven to be quantitatively effective in improving system efficiency and security. Therefore, this approach is recommended as part of a data-driven disaster recovery strategy in enterprise IT environments.
Analisis Sentimen Komentar Netizen Terhadap Isu Ijazah Presiden Joko Widodo Menggunakan Naive Bayes Dengan Pelabelan Fuzzy Logic Berbasis Leksikon Pratama, Wahyu
Jurnal Ilmu Komputer Vol 4 No 1 (2026): Jurnal Ilmu Komputer (Edisi Januari 2026)
Publisher : Universitas Pamulang

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Abstract

The controversy surrounding the legitimacy of President Joko Widodo's diploma has sparked widespread discussion on social media, generating diverse public comments with positive, negative, and neutral sentiments. This study aims to analyze Indonesian-language sentiment on the issue using a sequential approach that combines Fuzzy Logic-based labeling with Naive Bayes classification. The methodology encompasses several stages: comprehensive text preprocessing (case folding, tokenizing, filtering, and stemming), term weighting with TF-IDF (Term Frequency-Inverse Document Frequency), automated sentiment labeling using lexicon-based Fuzzy Logic with a conservative threshold of ±2, and supervised classification using the Naive Bayes algorithm. A total of 10,027 comments were collected from three major social media platforms Twitter (X), YouTube, and TikTok spanning the period from December 2024 to May 2025. The dataset was divided into 80% training data (8,021 comments) and 20% test data (2,006 comments). The Fuzzy Logic labeling process, utilizing 28 positive keywords and 36 negative keywords, identified a sentiment distribution of 72.38% neutral, 22.98% positive, and 4.64% negative comments. The Naive Bayes model achieved an overall accuracy of 80.76%, demonstrating excellent performance in detecting neutral sentiment (precision 0.82, recall 0.98) but exhibited lower performance for minority classes: positive sentiment (precision 0.70, recall 0.41) and negative sentiment (precision 0.80, recall 0.04). The class imbalance significantly influenced model predictions, with 85.48% of predictions classified as neutral.
Analisis Komparatif K-Nearest Neighbor, XGBoost, dan Support Vector Machine Menggunakan Orange Data Mining dalam Prediksi Kerusakan Aset BMN (Barang Milik Negara) Studi Kasus : Kejaksaan Negeri Kabupaten Tangerang Anggoro Seto, Cahyo
Jurnal Ilmu Komputer Vol 4 No 1 (2026): Jurnal Ilmu Komputer (Edisi Januari 2026)
Publisher : Universitas Pamulang

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Abstract

Optimal management of State-Owned Assets (BMN) is a crucial factor in ensuring the operational effectiveness of government institutions, particularly at the District Attorney's Office of Tangerang Regency. However, unexpected asset damage often disrupts workflows and leads to inefficiencies in maintenance budgets. This study addresses this issue using a data mining approach, with the primary objective of evaluating and comparing the performance of three machine learning algorithms: K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost) in predicting asset damage. The methodology employs a classification experimental study on historical asset maintenance data from the 2021–2025 period, analyzed using the Orange Data Mining platform. The research process includes data preprocessing stages (imputation and normalization), data partitioning into 70% training and 30% testing sets, and performance evaluation based on Area Under Curve (AUC), Accuracy, F1-Score, Precision, and Recall metrics. Comparative analysis results indicate that the XGBoost algorithm delivers superior performance, achieving the highest AUC of 0.987 and an F1-Score of 0.905, along with dominant prediction accuracy compared to other models. The K-NN algorithm demonstrates good and stable performance in the second position, whereas SVM exhibits lower accuracy levels than the other two models. Based on these findings, XGBoost is recommended as the optimal model for implementation within the District Attorney's Office asset management system. The adoption of this model is expected to support strategic decision-making, enable a transition to predictive maintenance, and enhance the efficiency of state asset management.
Analisis Klasifikasi Tumor Otak dengan Algoritma Berbasis Convolutional Neural Network dan Transformers Zhafar Usamah; Waskita , Arya Adyhaksa; Taswanda Taryo
Jurnal Ilmu Komputer Vol 4 No 1 (2026): Jurnal Ilmu Komputer (Edisi Januari 2026)
Publisher : Universitas Pamulang

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Abstract

This study focuses on comparing the performance of Convolutional Neural Networks (CNN) and Vision Transformers (ViT) in classifying brain tumors using Magnetic Resonance Imaging (MRI) data. The MRI images are grouped into four categories: normal, glioma, meningioma, and pituitary, which represent healthy brain conditions and several common types of brain tumors. Before the classification process, data preprocessing was carried out to improve image quality and consistency. This included resizing images and normalizing intensity values. The dataset was then divided into training and testing sets using three different ratios: 70:30, 80:20, and 90:10, allowing the models to be evaluated under varying data conditions.The CNN and ViT models were designed to extract important features from medical images using different approaches. CNN uses convolutional and pooling layers to capture local spatial features, making it well suited for identifying texture and structural patterns in MRI images. In contrast, ViT applies a self-attention mechanism that enables the model to learn global relationships across the entire image. To make the system more user-friendly, a graphical user interface (GUI) based on Tkinter was developed. This interface allows users to select datasets, train the models, and view evaluation results such as graphs and confusion matrices interactively. Model performance was assessed using accuracy, precision, recall, and F1-score metrics. The results show that CNN consistently achieved more stable and higher performance across all data splits. At the 90:10 ratio, CNN reached an accuracy of 95%, while ViT achieved 88%. Similar trends were observed at the 80:20 and 70:30 ratios
Komparasi Kinerja Sistem Rekomendasi Destinasi Wisata Menggunakan Content Based Filtering Dan Retrieval Augmented Generation (RAG) Awaludin, Rachmat Aziz; Waskita , Arya Adyhaksa; Mardiyanto
Jurnal Ilmu Komputer Vol 4 No 1 (2026): Jurnal Ilmu Komputer (Edisi Januari 2026)
Publisher : Universitas Pamulang

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Abstract

Advances in artificial intelligence have driven the development of recommendation systems in the tourism sector, which is characterized by diverse destinations. This condition often makes it difficult for tourists to select destinations that match their preferences. Based on literature studies, Content-Based Filtering (CBF) is widely used due to its efficiency; however, it has limitations in understanding contextual information. In contrast, the Retrieval Augmented Generation (RAG) approach has been developed to improve recommendation quality through semantic understanding. This study aims to compare the performance of CBF and RAG in tourism destination recommendation systems. CBF employs TF-IDF and cosine similarity to measure content similarity, while RAG integrates retrieval and generation processes using the LLaMA 3.2 model and the FAISS vector database. The research methodology includes data collection, text preprocessing, system implementation, and evaluation using context recall, faithfulness, answer relevancy, and similarity metrics. The results indicate that CBF achieved a context recall of 0.317, faithfulness of 1.000, answer relevancy of 0.190, and similarity of 0.293, demonstrating high accuracy with respect to source data but limited contextual understanding. Meanwhile, RAG achieved a context recall of 1.000, faithfulness of 0.783, answer relevancy of 0.617, and similarity of 0.715, indicating superior performance in generating relevant recommendations. In conclusion, RAG outperforms CBF in contextual and semantic aspects, while CBF remains more efficient in processing explicit data. This study is expected to serve as a reference for developing more adaptive and personalized tourism recommendation systems
Implementasi Yolov5 Deteksi Mata Lelah Berbasis Android Suri, Ajeng Permata; Waskita , Arya Adyhaksa; Mardiyanto
Jurnal Ilmu Komputer Vol 4 No 1 (2026): Jurnal Ilmu Komputer (Edisi Januari 2026)
Publisher : Universitas Pamulang

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Abstract

Excessive screen exposure can trigger digital eye strain, reducing visual comfort, attention, and overall productivity. Prior studies in computer vision indicate that deep learning–based object detection, particularly the YOLO family, can recognize facial and eye-related visual patterns efficiently, making it suitable for early-warning systems on mobile devices. This study aims to implement YOLOv5 to detect signs of eye fatigue in real time using the front camera of an Android smartphone. The novelty of this work lies in deploying a lightweight object-detection model on-device through TensorFlow Lite and integrating an automatic notification mechanism as a preventive intervention. The proposed methodology includes collecting and labeling an eye-image dataset into two classes (awake and drowsy), training a YOLOv5 model in Google Colab, optimizing and converting the trained model to TensorFlow Lite, and integrating it into an Android application for live-camera inference. System performance is evaluated using accuracy, precision, recall, and inference speed (FPS). Experimental results show that the system achieves 95.6% accuracy, 94.3% precision, 96.1% recall, and an Average speed of 22 FPS, enabling responsive detection and timely notifications. In conclusion, the Android-based YOLOv5 implementation is feasible as a preventive solution to help users monitor eye-fatigue symptoms and encourage healthier screen-use habits.

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