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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 19 Documents
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Search results for , issue "Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)" : 19 Documents clear
Analisis Topik Penelitian Pendidikan Matematika Di Indonesia Dengan Menggunakan Metode Latent Dirichlet Allocation (LDA) junedi, Beni; Agung Budi Susanto; Sajarwo Anggai
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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Abstract

On the research topic of Mathematics Education readers or researchers still have difficulty identifying research topics in the field of Mathematics Education. This is because there is no system or model that can be seen or used in determining research topics. Besides that, there is no automation of the research direction of Mathematics Education in Indonesia using topic modeling, so it is necessary to conduct a study or research on this. In research, the most important thing is the trend of research that is currently developing so that it can determine the novelty of the studies that have been done before. While there is no system used to determine trends and state of the art from research in the field of Mathematics Education. The aim of the research is to find out an overview of the research topics in Mathematics Education in Indonesia in 2020-2023 and to find out the implementation of modeling research topics in Mathematics Education in Indonesia using the Latent Dirichlet Allocation (LDA) method for 2020-2023. The research design consisted of literature study, data collection, data pre-processing: tokenization, case folding, stopword removal, and stemming, topic analysis with LDA, evaluation of the LDA method, and conclusions. Analysis of Topic Modeling with Latent Dirichlet Allocation using packages used from python including the Gensim and pyLDAvis packages. Based on the coherence score, the best number of topics (K) = 18, with a coherence score = 0.426 (the highest), it can be concluded that the number of topics produced is 18 topics.
Analisis Eksperimental Kinerja Transformers, VADER, dan Naive Bayes dalam Analisis Sentimen Teks Bahasa Indonesia: Studi Kasus Komentar Terkait Judi Online Sugiyo; Agung Budi Susanto; Sajarwo Anggai
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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Abstract

Sentiment analysis is a subfield of Natural Language Processing (NLP) that focuses on detecting and classifying opinions expressed in textual data. In the digital social context, the increasing volume of public comments related to online gambling in Indonesia highlights the need to map public perception. This study aims to conduct an experimental analysis of the performance of three popular sentiment analysis approaches: VADER (Valence Aware Dictionary and sEntiment Reasoner), Naive Bayes, and Transformers-based models, specifically on Indonesian-language text. The dataset consists of public comments from social media and digital platforms containing keywords related to online gambling. The research process involves text preprocessing, data labeling, model training (for Naive Bayes and Transformers), and performance testing. Evaluation metrics include accuracy, precision, recall, and F1-score. The experimental results show that the Transformers model (using IndoBERT) achieves the highest performance in terms of accuracy and generalization ability, while VADER performs less optimally due to its limitations in understanding Indonesian linguistic context. Naive Bayes demonstrates moderate and consistent performance but lacks the capability to capture complex contextual meanings. These findings contribute to selecting appropriate sentiment analysis methods for non-English languages and support the development of more accurate public opinion detection systems in the future
Systematic Literature Review : Tren Perkembangan Model dan Algoritma Analisis Video Kerumunan-Padat Fristiyanto, Doni; Abu Khalid Rivai
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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Dense-crowd video analysis is a branch of computer vision that has various important applications in public safety, emergency management, urban planning, pedestrian traffic engineering, and crowd management at large events, such as religious activities, music concerts, and sports matches. This study presents a Systematic Literature Review (SLR) of 30 scientific publications published between 2010 and 2025. The main objective of this review is to identify the latest research trends, classification of algorithms used, application domains, and the main challenges still faced in crowd video analysis. The results of this SLR show that deep learning-based approaches, such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Transformer, still dominate various applications, especially in anomaly detection which aims to recognize suspicious behavior in dense crowds. This technology has significant potential for preventing  dangerous events such as riots, mass panic, or accidents. In addition, trends in the integration of new technologies are also found, such as the use of hybrid algorithms that combine several approaches, federated learning for distributed model training, and the use of multimodal data and drones to improve monitoring effectiveness. However, many challenges remain, such as limited representative datasets, decreased accuracy under extreme conditions, computational limitations for real-time applications, and issues of privacy and model interpretability. Therefore, the results of this SLR are expected to make a strategic contribution to the development of more sophisticated, adaptive, and relevant crowd analytics systems.
Analisis Prediksi Penerima Bantuan Bea Study Menggunakan Algoritma Id3, Naïve Bayes Dan K-Nearest Neighbor (Studi Kasus Pada Lembaga Amil Zakat Rydha) Muhamad Sibli; Taswanda Taryo; Murni Handayani
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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The RYDHA Amil Zakat Institution has not yet implemented a data-driven predictive system to objectively determine B-Best scholarship recipients, leaving the selection process manual and prone to bias. This study aims to compare the performance of ID3, Naïve Bayes, and K-Nearest Neighbors (KNN) algorithms in classifying scholarship eligibility. Primary data were obtained from the 2024 B-Best applicants’ records, including demographic, socio-economic, academic, and supporting documents, while secondary data consisted of selection guidelines and internal reports, collected through interviews, documentation, and observation. Data analysis employed the three algorithms with evaluation using the Confusion Matrix and ROC Curve. The results show that KNN achieved the best performance with 96.3% accuracy, 0.958 AUC, 0.944 F1-score, 0.944 precision, and 0.944 recall, thus recommended as the predictive model to support a more objective and accurate scholarship selection system.
Analisis Tipe Kecerdasan Majemuk Siswa Sekolah Dasar Berbasis Catatan Perilaku Menggunakan Algoritma Naive Bayes, K-Nearest Neighbors, dan Support Vector Machine Nursalam, Asep Herman; Agung Budi Susanto; Taswanda Taryo
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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This study aims to identify the types of multiple intelligences of elementary school students based on Howard Gardner's theory by utilizing machine learning algorithms, namely Naive Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The data used comes from student behavior records and intelligence type questionnaires obtained from students or parents. The SEMMA method (Sample, Explore, Modify, Model, Assess) is used, including text preprocessing and TF-IDF feature extraction. The classification process is carried out using Orange Data Mining software and evaluated using accuracy, precision, recall, F1-score, and AUC metrics. The evaluation results show that the SVM algorithm provides the best performance with an accuracy of 93.30% and AUC of 0.997. Naive Bayes follows with 90.50% accuracy and 0.994 AUC, while KNN reaches 89.50% accuracy and 0.941 AUC. The study also results in a web-based application prototype that classifies students' intelligence types and provides personalized learning recommendations. This confirms the effectiveness of machine learning in supporting personalized learning and student potential development.
Rancang Bangun Sistem Informasi Penjualan Kebutuhan Komputer Berbasis Web Pada Ruko Bantenbiz Komputer Menggunakan Metode Waterfall Ramdan, Fahru
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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Abstract

Ruko Bantenbiz Komputer is a business engaged in the sale of computer-related needs, including hardware, accessories, and other equipment. The sales process is still conducted manually, leading to several issues such as difficulties in managing transaction data, delayed services, and limited market reach. To address these problems, a web-based sales information system was designed and developed using the Waterfall method, which consists of requirement analysis, system design, implementation, testing, and maintenance stages. The system was built using PHP Native as the programming language, Dreamweaver as the Integrated Development Environment (IDE), and MySQL as the database. With this system, the business owner can manage sales and inventory more efficiently, accelerate transaction processes, and provide easier access for customers to view product information and make online purchases. This system is expected to improve operational effectiveness and expand the market reach of Ruko Bantenbiz Komputer.
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)
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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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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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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.

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