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 14 Documents
 1   2 
Search results for , issue "Vol 2 No 2 (2024): Jurnal Ilmu Komputer (Edisi Desember 2024)" : 14 Documents clear
Analisis Kelayakan Pembiayaan Anggota Koperasi Dengan Metode Komparasi Algoritma K-Nearest Neighbors Dan Naive Bayes (Studi Kasus Pada KSP. XYZ) Lutfansyah, Rafi
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

Savings and Loan Cooperatives often face the challenge of defaulted loans, which pose risks to financial stability and member trust. This study aims to compare the performance of the K-Nearest Neighbors (KNN) and Naive Bayes algorithms in classifying loan eligibility using the CRISP-DM (Cross-Industry Standard Process for Data Mining) approach. A case study was conducted on member financing data to identify a more accurate classification model to minimize loan defaults. The CRISP-DM methodology encompasses business understanding, data analysis, data preparation, modeling, evaluation, and deployment. The results show that KNN achieved the highest accuracy rate of 92.86%, while Naive Bayes only reached 85.71%. Additionally, KNN outperformed Naive Bayes in terms of precision and recall. Thus, KNN was selected as the optimal model to assist cooperatives in predicting loan eligibility. The implementation of this model is expected to improve financing efficiency, reduce default risks, and strengthen data-driven decision-making in cooperatives.
Analisis Pengembangan Dalam Penerapan Recommender System Menggunakan Metode Algoritma Apriori Dan K-Means Clustering Pada Aplikasi E- Commerce. (Studi Kasus Di Big Sport Tangerang) Sukanda, Ahmad; Achmad Hindasyah; Taswanda Taryo
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

The sales and marketing system in Big Sport is still carried out conventionally, causing the problem of sales transactions which causes a decrease in turnover. The solution to this problem is an e-commerce application for Big Sport and implementing a strategy recommendation system. By implementing the a priori algorithm method used to find out product recommendations on Big Sport to look for products that frequently appear (frequent itemset) with a minimum support calculation of 3 and a minimum Confidence of 50% from sales transaction data in June 2023 from 18 product data to determine the Association Rule for a combination of itemsets that gets an average lift ratio test value of 1.67 with a maximum Confidence value of 100% which forms 22 Association Rule results to provide good and accurate product recommendations for e-commerce applications based on sales transaction history data . The K-Means Clustering method was implemented using tolls rapidminer using transaction data for 6 months from 18 products. From the rapidminer run, the results from cluster 0 contain 8 items, cluster 1 has 7 items, and cluster 2 has 3 items with an average value. within a centroid distance of 2381.332, where cluster 0 has a value of 1975.234, cluster 1 has a value of 2995.918 and cluster 2 has a value of 2030.222. It can be concluded that items in cluster 0 are products with low sales levels, items in cluster 2 with medium sales levels, and items in cluster 1 with high sales levels. And the Davies Bouldin Index value is 0.462 which shows the fact that the centroid distance assessment results are almost close to 0 which can be concluded to have satisfactory results because the lower the DBI value, the better the cluster value so that it can be used as a reference in product procurement.
Uji Performansi Algoritma K.Means Dalam Mencari Cluster Terbaik Pada Data Sales, Stock Produk Food Beverage (Studi Kasus: PT.Airmas International) Haris Fadhillah; Achmad Hindasyah; Joni Prasetyo
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

This research was conducted with the aim of knowing the products that are most in demand by customers, also knowing the products that sell less and knowing the accuracy of testing the performance of sales, stock data. The qualitative method used focuses on interpretation and understanding of interviews or observations to collect sales data, stock of 23 food beverage product items. The results of manual calculation of the k-means algorithm with sales data, stock is to find out the number of products that are in demand by customers with categories: very salable = 3 product items, salable = 5 product items, less salable = 15 product items, then sales data, stock is tested with RapidMiner and Python applications for clustering / grouping what products are in demand by customers the results are the same as the categories: very salable = 3 product items, salable = 5 product items, less salable = 15 product items and looking for the best number of clusters is 3.
Analisis Sentimen Pembangunan IKN (Ibu Kota Nusantara) Pada Twitter Menggunakan Metode K- Nearest Neighbor, Naive Bayes Dan Support Vector Machines Prasmono, Yossy Veiebrian Fitri; Arya Adhyaksa Waskita
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

This research investigates the Nusantara Capital City (IKN) relocation, which has generated diverse opinions, including concerns over the chosen location and the swift ratification of related laws. Recently, the Indonesian government has called on the public to support IKN's development. To assess public sentiment regarding this relocation, sentiment analysis was performed on a dataset of tweets. After data cleaning, 502 tweets were analyzed, yielding 337 positive and 163 negative comments. The analysis utilized Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest Neighbor (K-NN) algorithms, incorporating feature selection through Particle Swarm Optimization (PSO). This study compares the performance of Naive Bayes, SVM, and K-NN without feature selection against those methods with feature selection, specifically analyzing their Area Under Curve (AUC) values to identify the most effective algorithm. The results indicate that the PSO-based SVM algorithm achieved the highest performance, with an accuracy of 97.63% and an AUC of 0.997. This research successfully identifies an optimal algorithm for classifying positive and negative comments regarding the relocation of the Nusantara Capital City, contributing valuable insights to public sentiment analysis in this context.
Analisis Kinerja Sistem Deteksi Intrusi Jaringan Internet Of Things Berbasis Metode Ensemble Eko Kristianto; Arya Adhyaksa Waskita; Thoyyibah Tanjung
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

Network intrusion has rapidly evolved, posing significant risks to IT infrastructure. To address this, ensemble learning, known for its robust classification capabilities, is applied to IoT network traffic using the public RT_IOT2022 dataset. Models such as CatBoost, Extreme Gradient Boost (XGBoost), and LightGBM were developed and evaluated. The dataset was normalized using the Normalizer and MinMaxScaler functions from the scikit-learn framework. Model training was conducted with an 80:20 fixed data split for training and testing, along with 5-fold cross-validation. Testing revealed that XGBoost with MinMaxScaler and the 80:20 split achieved the highest accuracy of 99.89%. However, accuracy decreased to 94.04% when using 5-fold cross-validation. Nevertheless, XGBoost with MinMaxScaler consistently demonstrated the fastest computation time across all schemes. For instance, it required only 15 seconds for the fixed split scheme compared to 59 seconds for 5-fold cross-validation. These findings highlight the efficiency and accuracy of XGBoost when combined with MinMaxScaler under specific validation schemes.
Analis Perancangan Dan Penerapan Keamanan Jaringan Menggunakan Metode Intrusion Detection System (IDS), Intrusion Prevention System (IPS) Dan Demilitarized Zone (DMZ) Pada PT. Maha Digital Indonesia (Mahapay) Trijanitra, Evan; Arya Adhyaksa Waskita; Taswanda Taryo
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

Network security systems, in recent years have become the main focus in the world of securing other important data, this is due to the high number of suspicious threats (Suspicious Threats) and attacks from the Internet. Network security involves efforts to protect data and computer systems from detrimental threats, such as cyberattacks, malware, and data theft. The existence of increasingly complex and evolving threats has increased awareness of the need for strong network security. PT. Maha Digital Indonesia (Mahapay) is a company operating in the field of EDC Field Service where it is very important that client data is kept confidential. This requires good network security to maintain the confidentiality of the data. So the aim of this research is to implement network security using the Intrusion Detection System (IDS), Intrusion Prevention System (IPS) and Demilitarized Zone (DMZ) methods as network security at PT . Maha Digital Indonesia (Mahapay). The results of this research are the formation of connections between networks in the topology along with the successful functioning of the Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) detecting and preventing suspicious activities carried out by attackers and the operation of rules for the DMZ area. Success in the application is tested again by carrying out several attack methods that will be analyzed such as Syn Flood Attack, Ping Of Death and Port Scanning which will be handled by the configuration that has been applied to the network and server.
Analisis Data Produksi Biskuit Dengan Algoritma Naive Bayes Dan Random Forest Sabarrudin; Agung Budi Susanto; Sajarwo Anggai
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

In the manufacturing industry, production problems often occur, often production does not match market demand, production is not well planned, therefore this study aims to develop a classification model using machine learning based on the Naive Bayes and Random Forest algorithms to classify biscuit production data. The main focus of this study is to utilize variables such as dough, number of mixers, production time parameters, and other relevant production factors to improve accuracy in classification. The dataset used in this study includes information from several previous production periods, namely data in 2019-2023, which is then used to train and test the Naive Bayes and Random Forest algorithm models. The training and validation process is carried out using commonly used model performance evaluation techniques. The results of the study show that the Random Forest model is able to provide high accuracy, namely 97.54% while Naive Bayes is 96.45%. Further analysis was also carried out to identify the variables that most influence production results, providing additional insights for optimizing the production process. The results of this study can contribute to the development of classification models for the food and beverage industry, especially in biscuit products, but also offer a more specific view of the factors that influence biscuit production. The implementation of this study can be a basis for manufacturers to make more precise and effective decisions in managing their production.
Technical Comparison Between Classical and Quantum Architectures: Quantum Error Challenges and Qubit Stability Bonie Wijaya; Muhammad Fahrizal; Muhrodi; Dhamma Nagara; Yan Everhard
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

The age of evolving computational technologies, classical architecture (traditional digital computing) and quantum architecture have emerged as two prominent approaches, offering diverse computational solutions. Classical computing bases its operations on transistors and binary logic gates, while quantum computing leverages the principles of quantum mechanics to perform information processing. This article provides a technical comparison between the two architectures, encompassing essential characteristics, algorithms, processing models, problem-solving capabilities, and challenges faced. In particular, this article highlights the key challenges in quantum computing, namely quantum errors and qubit stability, which significantly impact its reliability and practical implementation. The method used in this research is a literature review study, analyzing various reference sources such as journals, articles, and research reports. With the growing influence of quantum computing in specific sectors, this study is expected to provide a clearer view of the potential and limitations of both architectures, as well as the steps needed to overcome these challenges. The main conclusion of this study is that quantum computing has the potential to revolutionize certain fields, but still faces challenges in terms of stability and error correction.
Analisis Sentimen Opini Masyarakat Terhadap Pemilu 2024 Melalui Media Sosial X Dengan Menggunakan Naive Bayes, K-Nearest Neighbor Dan Decision Tree Cut Shifa Khoirunnisa; Tukiyat; Sajarwo Anggai
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

This study aims to analyze public opinion sentiment towards the 2024 election using three machine learning classification algorithms: Naïve Bayes, K-Nearest Neighbors and Decision Tree. The data used in this study were taken from Social Media X, which is one of the social media platforms with a large and diverse data volume. The object of this study is public opinion expressed on Social Media X, with the subject of research in the form of tweets taken using the Twitter API, resulting in 5000 data with 2469 clean data. Data analysis involves text extraction and preprocessing processes that include data cleaning, tokenization, stopwords and stemming. The results of the study show the distribution of sentiment as follows: positive sentiment dominates with 96% of the total tweets, followed by neutral sentiment at 2% and negative sentiment at 1%. From the modeling results among the algorithms tested, K-Nearest Neighbors showed the best performance with an accuracy value reaching 97.50%, followed by Decision Tree having a performance with an accuracy value of 97.25% while Naïve Bayes had the lowest performance with an accuracy value of 96.14%. Although there is variation in performance among the algorithms used, none of them are completely consistent in classifying sentiment. This study makes a significant contribution in mapping public sentiment related to the 2024 election in Indonesia through data analysis from social media X, and provides insight into the effectiveness of various Data Mining Algorithms in sentiment analysis.
Analisis Dan Implementasi Sistem Manajemen Keamanan Informasi Menggunakan ISO/IEC 27001 (Studi Kasus Pada PT.XYZ) Wibowo, Rizki Septiyanto; Tukiyat; Sajarwo Anggai; Winarni
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

It has become a current necessity in every company regarding the implementation of information and communication technology governance in efforts to improve service quality. The implementation of information and communication technology governance is a critical factor in enhancing service quality across various companies. Therefore, the adoption of an Information Security Management System (ISMS) based on the ISO 27001:2013 standard becomes essential, in line with the conduct of regular audits to ensure its effectiveness. This research aims to develop and design an information security governance framework in accordance with ISO/IEC 27001 and to conduct audits on the system that has been implemented in PT. XYZ, to ensure its compliance with good and efficient standards. The methodology used is Plan-Do- Check-Act (PDCA), with data collection techniques through interviews and distribution of questionnaires for internal audits. The research findings indicate that the average ISO/IEC 27001 maturity level is at levels three and four. It is expected that this research can assist and provide recommendations related to security controlsused as guidelines and procedures for the implementation of information security, as well as ensuring the overall operation runs in accordance with ISO 27001 standards.

Page 1 of 2 | Total Record : 14

 1   2 

Filter by Year

2024 2024


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)