cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
magisterkomputer@unpam.ac.id
Phone
+6281316281847
Journal Mail Official
dosen02680@unpam.ac.id
Editorial Address
Universitas Pamulang Viktor, Lt. 3, Jl. Raya Puspitek, Buaran, Kec. Pamulang, Tangerang Selatan, Provinsi Banten
Location
Kota tangerang selatan,
Banten
INDONESIA
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 63 Documents
 3   4   5   6   7 
Analis Data Penjualan Tiket Pesawat Ke Jepang Menggunakan Classic Machine Learning Pada PT. TTD Syarifuddin
Jurnal Ilmu Komputer Vol 2 No 2 (2024): Jurnal Ilmu Komputer (Edisi Desember 2024)
Publisher : Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Airline ticket sales to Japan is an important topic in the ever-evolving travel industry. The unpredictable demand for airline tickets is a common challenge in the aviation industry due to many complex and variable factors. This research analyzes the data on airline ticket sales to Japan using classic machine learning approaches. The data used for the research are the airline ticket sales to Japan in 2022 and 2023 for Japan Airlines and All Nippon Airways. Classical methods such as classification were applied to identify factors influencing the ticket sales patterns. The collected data was cleaned and organized to obtain a dataset for use in Machine Learning with the K-Nearest Neighbors, Naïve Bayes, and Decision Tree algorithms. After evaluating the models created using these three algorithms, the evaluation results with prediction models, model test & score, and confusion matrix, showed that the K-Nearest Neighbor algorithm achieved the highest values compared to the Naïve Bayes and Decision Tree algorithms with an accuracy of 99.5% (model predictions evaluation) & 98.9% (model test & score evaluation). The majority of ticket sales to Japan were for JAL flights, economy class tickets, and spring season being the most popular choices. The conclusion from this data is that Japan Airlines holds a strong market share in ticket sales to Japan
Analisis Kuantitatif Dampak Endorsement Politik Terhadap Tingkat Elektabilitas Pada Pilkada Serentak 2024 Fristiyanto, Doni; Makhsun
Jurnal Ilmu Komputer Vol 2 No 2 (2024): Jurnal Ilmu Komputer (Edisi Desember 2024)
Publisher : Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

 Simultaneous Regional Head Elections (Pilkada) in Indonesia took place on November 27 2024, covering 545 regions, including 37 provinces, 415 districts and 93 cities. Voter turnout reached an average of 71% nationally, reflecting public enthusiasm for the political process. This research also highlights the phenomenon of political endorsements from national figures which have proven effective in increasing candidate electability. To explore the phenomenon of political endorsement, this research uses Google News as a tool to collect and analyze relevant online news. The results of the analysis show that there is a significant correlation between the candidate's level of popularity and electability level, with a correlation value of 0.757. Apart from that, the level of positive sentiment towards candidate pairs also shows a strong correlation (0.74) with electability, indicating that candidates with high popularity and positive sentiment tend to have better electability. However, this research found that the number of political endorsements had a stronger influence on candidate electability, with a correlation value of 0.758. This shows that political endorsement can be a more significant determining factor in increasing electability compared to just relying on popularity or positive sentiment. This research provides important insights into the role of political endorsements as an effective strategy in increasing voter support for certain candidates.
Tinjauan Literatur Sistematis Pemodelan ER Untuk Sistem Informasi Bonie Wijaya; Muhammad Fahrizal; Amaliza Afifah Labib
Jurnal Ilmu Komputer Vol 2 No 2 (2024): Jurnal Ilmu Komputer (Edisi Desember 2024)
Publisher : Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Entity-Relationship (ER) Modeling is a fundamental approach in the design and development of databases for information systems. ER Modeling has long been recognized as an effective conceptual tool for database design. However, with advancements in technology and changing modern data processing needs, several limitations or gaps have emerged in its application, particularly when it comes to handling complex and heterogeneous data. This research presents a systematic literature review using the PRISMA framework to evaluate the development methodologies of ER Modeling in a modern context, including its challenges and opportunities. The main focus includes the adaptation of ER Modeling to technologies such as big data, NoSQL, and cloud computing. Key research issues related to ER Modeling include limitations in handling big data, challenges in representing semi-structured and unstructured data, a lack of support for dynamic data and schema evolution, limitations in integration with modern technologies, and deficiencies in representing complex relationships. The main findings reveal the traditional ER Modeling's limitations in managing complex, semi-structured, and distributed data, as well as the need for integration with modern technologies such as IoT and machine learning. This research contributes by offering insights into the development of more flexible and adaptive ER Modeling for current data needs.
Analisis Sentimen Pengguna Twitter Terhadap Universitas Pamulang Periode Penerimaan Mahasiswa Gelombang I Tahun Ajaran 2024/2025 Rohmani, Muhammad Faqih; Makhsun
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The development of information technology has had a significant impact on various aspects of life, including education. One of the universities that has gained public attention is Universitas Pamulang. As one of the largest private higher education institutions in Indonesia, Universitas Pamulang needs to continuously improve. One of the key references for these improvements is public opinion. To understand public opinion regarding Universitas Pamulang, an analysis was conducted on the social media platform Twitter. Therefore, this study examines public sentiment toward Universitas Pamulang using Twitter data and the Naïve Bayes method. The Naïve Bayes method was chosen due to its advantages in text classification, particularly in sentiment analysis. The research data was collected from Twitter during the first wave of new student admissions for the 2024/2025 academic year. The analysis process involved identifying the dominant sentiment (positive, negative, or neutral) in public opinion, exploring the institution's strengths and weaknesses, and providing recommendations for improving the quality of academic services, administration, and the reputation of Universitas Pamulang. The results of this study indicate that the Naïve Bayes algorithm can be effectively used for sentiment analysis, achieving a high level of accuracy. This research is expected to contribute academically to sentiment analysis studies in the higher education sector in Indonesia.
Analisis Resiko Stunting Di Kota Tangerang Menggunakan Metode Regresi Linier dan Support Vector Machine Muhamad Farid Hasan Khadafi; Achmad Hindasyah; Tukiyat
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Stunting remains a significant public health issue in Indonesia, particularly in Tangerang City, affecting the physical and cognitive development of children. This problem requires serious attention due to its long-term impacts on children's quality of life and their potential in the future.This study aims to analyze the risk factors contributing to the occurrence of stunting in Tangerang City using Linear Regression and Support Vector Machine (SVM) methods. The research question focuses on identifying and predicting the main risk factors influencing the prevalence of stunting. The research method employs Linear Regression Algorithm and Support Vector Machine Algorithm. The study population consists of children under five years old registered at community health centers in Tangerang City. Data samples were collected from 5,376 children, with 80% (4,300 children) used for training and 20% (1,076 children) for model testing. Several socio-economic and health variables were considered as potential risk factors, including household income, maternal education level, access to clean water and sanitation, dietary diversity, and the presence of antenatal care. Data analysis revealed performance differences between the two models used. The SVM model achieved a significantly higher accuracy of 89% with a standard error of 0.4, demonstrating strong predictive capability. In contrast, the Linear Regression model yielded a lower accuracy of 74% with a standard error of 1.5. This difference highlights the potential advantages of SVM in capturing complex and non-linear relationships within the dataset. These findings can inform targeted interventions and policy recommendations to address the causes of stunting in Tangerang City. Further research could explore a broader range of risk factors.
Evaluasi Efektivitas Tata Kelola Teknologi Informasi Di Rumah Sakit Umum Daerah Provinsi Nusa Tenggara Barat Dengan Menggunakan Kerangka Kerja Cobit 2019 Muh. Yusril Hidayat; Agung Budi Susanto
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Hospitals have a strategic responsibility to improve the quality of public health services. However, information technology (IT) management in hospitals often faces challenges such as lack of long-term planning, which causes information to be inefficient and ineffective. This study aims to evaluate the effectiveness of information technology governance at the West Nusa Tenggara Provincial Hospital using the COBIT 2019 framework. The focus of the study includes risk management (APO 12), change management (BAI 06), and security service management (DSS 05). The method used is a case study with a qualitative approach, involving interviews and questionnaires for data collection. The evaluation results show that the current average capability level is 2.5 with a target of 3. Key findings include the need for improvement in risk management and security services. Recommendations for improvement include the development of new risk policies, staff training, and adoption of the latest security technology. Implementation of COBIT 2019-based suggestions is expected to improve the quality of services and performance of information technology at the NTB Provincial Hospital.
Sentimen Analisis Kesehatan Mental Anxiety dengan Metode Decision Tree Menggunakan Software Orange Eva Fauziah; Makhsun
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Mental health, particularly anxiety disorders, has become a global concern due to the rising prevalence of mental health issues worldwide. Anxiety significantly affects individuals' quality of life and productivity, making it essential to accurately analyze and detect its symptoms. This study aims to apply the decision tree method for sentiment analysis of anxiety in texts collected from various sources such as mental health forums and social media. The decision tree method was chosen for its simplicity and effectiveness in classifying data based on identified patterns. Orange software was utilized to build the classification model due to its user-friendly interface and visualization capabilities. The results indicate that the decision tree model was able to effectively identify anxiety patterns in the texts, contributing to a better understanding of sentiment analysis in the mental health context. This study also introduces a more accessible approach for practitioners and researchers in this field.
Sistem Informasi Install Driver Dan Troubleshooting Produk Berbasis Web Di PT. Axis Media Nilovar Asyiah
Jurnal Ilmu Komputer Vol 2 No 2 (2024): Jurnal Ilmu Komputer (Edisi Desember 2024)
Publisher : Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Every product, especially new hardware, before use must require special procedures in advance in order to be operated.To be able to connect the computer with the hardware in need of a software so that later the hardware is operated easily. Within a certain period of hardware there is usually a problem and product damage that often arises. In order to overcome these obstacles or damage in need of knowledge or correct procedures for hardware that can be repaired and reusable. because of the large number of products it requires a lot of software drivers, and stock software is on the Compact Disk or CD that takes time to look for it and for troubleshooting product problems, often the support team in response to complaints from customers difficulties finding solutions spontaneously because of problems product improvements are usually handled by technicians, so sometimes the support team find out on the internet first or ask with a technician directly. The purpose of this study is the search for drivers, how to install drivers and troubleshooting articles can easily be in, because there is already a container so that drivers live to download without having to search first. As for the procedure of installing the driver or how to solve the troubleshooting of the product just do the title problem and then the article will appear, so in answering questions from customers quickly and assist technicians in completing the repair hardware.
Klasifikasi Berita Bahasa Indonesia Dengan Menggunakan Metode K-Nearest Neighbor Dan Naive Bayes Komariah Kukum Manieh Nuryasin; Taswanda Taryo; Sudarno
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In the era of rapid development of information technology, the need for a news classification system is crucial to manage the increasing volume of information. This study aims to develop a news classification system in Indonesian into five main categories: Politics, Economy, Health, Security, and Poverty. The methods used include the K-Nearest Neighbor (KNN) algorithm and Naïve Bayes. The dataset consists of 2,000 news items obtained from Kaggle, with preprocessing stages including cleaning, tokenizing, normalization, and TF-IDF weighting. The evaluation was carried out through three data sharing scenarios: 70%-30%, 80%-20%, and 90%-10%. The results showed that the KNN algorithm achieved the highest accuracy of 89% in the 80%-20% and 90%-10% scenarios, while Naïve Bayes produced the best accuracy of 78.66% in the 70%-30% scenario. KNN proved to be more reliable for data with balanced category distribution, while Naïve Bayes required further adjustment, especially for underrepresented data categories. This research provides significant contributions to the development of an automatic news classification system, which can be implemented to improve user experience in accessing information.
KLASIFIKASI PHISHING URL PADA WEBSITE BERBASIS METODE ENSEMBLE Bahrul Ulum; Taswanda Taryo; Sudarno
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study analyzes the performance of ensemble learning algorithms in detecting phishing URLs using the PhiUSIIL Phishing URL dataset. The three algorithms compared are CatBoost, XGBoost, and LightGBM. The research stages include data preprocessing, data division into an 80:20 train-test split, and performance evaluation based on accuracy, precision, recall, and F1-score metrics. The results show that XGBoost has the best performance with an accuracy of 97.54% and an ROC AUC of 93.05%, followed by CatBoost with an accuracy of 97.46% and an ROC AUC of 92.94%. LightGBM, although it has lower performance, still shows good results with an accuracy of 96.99% and an ROC AUC of 91.85%. The data cleaning process successfully improves efficiency by eliminating irrelevant attribute analysis. This study confirms that ensemble algorithms can be implemented for the development of more effective and accurate phishing detection systems. XGBoost is recommended as the primary algorithm in detecting phishing threats in cybersecurity applications, thanks to its ability to handle large and complex data.

Page 5 of 7 | Total Record : 63

 3   4   5   6   7 

Filter by Year

2023 2025


Reset
Filter By Issues
All Issue Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025) Vol 2 No 2 (2024): Jurnal Ilmu Komputer (Edisi Desember 2024) Vol 2 No 1 (2024): Jurnal Ilmu Komputer (Edisi Juli 2024) Vol 1 No 2 (2023): Jurnal Ilmu Komputer (Edisi Desember 2023) Vol 1 No 1 (2023): Jurnal Ilmu Komputer (Edisi Juni 2023)