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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 63 Documents
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Analisis Klasifikasi Hewan Menggunakan Metode K-Nearest Neighbor, Decision Tree, dan Naïve Bayes Robbi, Mukhlishoh Syaukati
Jurnal Ilmu Komputer Vol 1 No 2 (2023): Jurnal Ilmu Komputer (Edisi Desember 2023)
Publisher : Universitas Pamulang

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Abstract

Animal classification is an important topic in biology and conservation science, appropriate data analysis methods can help in identifying, classifying, and understanding animal characteristics. This research aims to analyze and compare various animal classification methods using the K-Nearest Neighbor, Decision Tree, and Naïve Bayes methods by implementing them in the Orange Data Mining environment. This study uses a dataset that includes a wide range of biological, morphological, and behavioral attributes of animals. Through the implementation of the orange tools, three different classification methods were developed and evaluated based on their performance in classifying animals into groups according to their characteristics. The research results show that the performance of the K-Nearest Neighbor method is superior to the others. From 101 data tested using the K-Nearest Neighbor method, an accuracy value of 94.1% was obtained. Comparative analysis reveals differences in accuracy and predictive ability between K-Nearest Neighbor, Decision Tree, and Naïve Bayes. These results provide insight into the effectiveness of each method in the context of animal classification and can potentially serve as a basis for selecting appropriate methods in biological research, conservation, or other animal science studies. This research enriches understanding of orange tools in the context of biology and animal science.
Prediksi Inflow Daerah Aliran Sungai Larona Dengan Model Seasonal Autoregressive Integrated Moving Average Tukiyat, Tukiyat; Sutrisno, Sutrisno; Anggai, Sajarwo
Jurnal Ilmu Komputer Vol 1 No 2 (2023): Jurnal Ilmu Komputer (Edisi Desember 2023)
Publisher : Universitas Pamulang

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Abstract

Larona Watershed (DAS) Inflow Prediction entering the reservoir has a very important role in managing the reservoir's water resources. Various approaches using mathematical models have been carried out, the results of which can be used as management tools to understand estimates and predictions of future inflow values, especially in the context of managing and planning water utilization for company needs at PT Vale Indonesia Tbk. The research aims to find a prediction model for the water inflow of the Towuti, Matano and Mahalona reservoirs. The research method uses a statistical approach using the SARIMA (Seasonal Autoregressive Integrated Moving Average) model. Research data, time series data, monthly inflow of the Larona watershed for January 2006 – December 2019. The research results showed that the best model was SARIMA (2,0,1)(0,1,1)12. The mathematical model prediction formulated is 4.786 + 1.459t-1 – 0.648t-2 – 0.714 e_(t-1). The model accuracy level was tested using the RMSE (Root Mean Squared Error) criteria of 0.767, MAE (Mean Absolute Error) level of 0.592, MAPE (Mean Absolute Percentage Error) of 14.58. To validate the predicted values, the F test, Siegel-Turkey, Bartlett, Levene was carried out at the α=5% level. The test results for the difference between actual and predicted values were concluded to accept the null hypothesis, which means that there is no significant difference between the actual data values and the predicted data values.
Pengembangan Sistem Deteksi Digit pada Meteran Air PDAM Menggunakan Model Deep Learning YOLOv5 Wang, Albert Kingston; Thoyyibah, Thoyyibah
Jurnal Ilmu Komputer Vol 1 No 2 (2023): Jurnal Ilmu Komputer (Edisi Desember 2023)
Publisher : Universitas Pamulang

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Abstract

The use of PDAM (Regional Drinking Water Company) water meters has become the standard for measuring water consumption by customers in Indonesia. However, the process of reading water meters is still mostly done manually by officers, which can cause various problems. For example, reading errors can occur due to human factors or environmental conditions, such as poor lighting or a dirty water meter. Additionally, this process requires a lot of time and effort and has the potential to lead to fraud. To overcome this challenge, this research focuses on developing a digit detection system for PDAM water meters using the YOLOv5 deep learning model. Using a dataset covering various lighting conditions and viewing angles, the model is trained to recognize and classify the digits on water meters. Initial results show that this model can produce accurate predictions, with high levels of precision and recall. However, more testing and evaluation are needed to ensure that these systems can perform well in real-world conditions.
Analisis Sentimen Pelayanan Pelanggan Mini Market Alfamart Pada Media Sosial Twitter Dengan Naïve Bayes Classifier Aziz, Awaludin; Susanto, Agung Budi; Wiharjo, Sudarno
Jurnal Ilmu Komputer Vol 1 No 2 (2023): Jurnal Ilmu Komputer (Edisi Desember 2023)
Publisher : Universitas Pamulang

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Abstract

Twitter is one of the social media that is currently popular, here the public is free to have opinions, write, and comment on anything. PT Sumber Alfaria Trijaya with its trademark Alfamart is a company engaged in the retail sector. Not infrequently consumers submit complaints, criticisms, and suggestions through this social media. Community opinion can be used as evaluation material in improving services. In this study, sentiment analysis for Alfamart minimarket customer service was carried out based on data obtained from Twitter. This sentiment analysis aims to classify Alfamart's customer service tweets into positive, negative, and neutral sentiments using the naive Bayes classifier algorithm. The data used is 2000 tweet data and then preprocessing is carried out so that 1691 tweets are clean data. Of the 1691 data analyzed, 1017 positive tweets, 297 negative tweets, and 377 neutral tweets were obtained. Then the data will be divided into 80% training data and 20% test data. The results of the accuracy value are 70% with a Precision value of 70%, a Recall value of 70%, and an F1-Score value of 66%.
Analisis Vulnerabilitas Situs Web Universitas Pamulang Menggunakan Nessus Nursalam, Asep Herman; Subekti, R.P. Fiki Wisnu; Safitri, Astried Nirmala; Prasmono, Yossy Veifbrian Fitri; Otafiyani, Adila Indriyani
Jurnal Ilmu Komputer Vol 2 No 1 (2024): Jurnal Ilmu Komputer (Edisi Juli 2024)
Publisher : Universitas Pamulang

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Abstract

The Pamulang University (UNPAM) website is an official website that is used for various purposes. Therefore, website security needs to be maintained so that it is not exploited by irresponsible parties. Vulnerability analysis is one way to find out the vulnerabilities that exist in a system. This research aims to conduct vulnerability analysis on the UNPAM website using Nessus. The research results show that the UNPAM website has a high level of vulnerability. This is indicated by the existence of high and medium levels of vulnerability. These vulnerabilities can be exploited by irresponsible parties to attack the UNPAM website. To mitigate these vulnerabilities, UNPAM website managers can take preventative steps by upgrading to a cipher suite with a key length of 128 bits or more, verifying the authenticity of the SSL certificate, enabling DNSSEC and implementing a DNSSEC-enabled resolver, using a DNS firewall, and disabling TLS 1.0 and enabling TLS 1.2 or higher version.
Prediksi Risiko Serangan Jantung dengan Pendekatan Data Mining dan Algoritma Naïve Bayes Nurdiyanto, Didik
Jurnal Ilmu Komputer Vol 1 No 2 (2023): Jurnal Ilmu Komputer (Edisi Desember 2023)
Publisher : Universitas Pamulang

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Abstract

Heart attack is one of the deadliest cardiovascular diseases worldwide. Heart attack risk prediction plays an important role in prevention and early treatment. In this study, we propose an approach to optimize heart attack risk prediction using data mining and Naïve Bayes algorithm. This method utilizes data mining techniques to analyze complex health datasets and extract hidden patterns that can identify heart attack risk factors. Naïve Bayes algorithm is used to predict the risk of heart attack based on the discovered patterns. We conducted experiments using patient datasets with relevant health parameters and optimized the performance of the prediction model. The experimental results show that this approach produces accurate and reliable heart attack risk prediction. This research makes an important contribution to the field of cardiovascular disease prevention and provides a basis for the development of more efficient heart attack prediction systems.
Pengembangan Sistem Kontrol Pemilah Kematangan Buah Pisang Pada Konveyor Menggunakan Metode Klasifikasi K-Nearest Neighbors Berbasis OpenCV Andrean, Kelvin; Tukiyat, Tukiyat; Susanto, Agung Budi
Jurnal Ilmu Komputer Vol 1 No 2 (2023): Jurnal Ilmu Komputer (Edisi Desember 2023)
Publisher : Universitas Pamulang

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Abstract

This research focuses on developing a micro-controller-based banana ripeness sorting tool with the implementation of the K-Nearest Neighbors (KNN) algorithm for the classification of ripeness levels based on RGB color image processing using the OpenCV library. Banana is an important fruit in society because of their high nutritional content, but manual sorting of banana fruit is a challenge for farmers and officers. The tool built uses Arduino UNO as a controller, a conveyor belt with a dynamo motor, and a servo motor for sorting. The KNN method is used for classification based on banana skin color. The results showed that the success rate of sorting reached 100% at the neighboring value of K = 3, 93.33% at K = 5, and 86.66% at K = 1. This tool can be an efficient solution for automatically sorting bananas based on ripeness level with high accuracy.
Pendeteksian Senjata Api pada Manusia dalam Situasi Real-Time Menggunakan Model YOLOv4-Tiny Sulthoni, Dimas Alifta; Thoyyibah, Thoyyibah
Jurnal Ilmu Komputer Vol 1 No 2 (2023): Jurnal Ilmu Komputer (Edisi Desember 2023)
Publisher : Universitas Pamulang

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Abstract

This research aims to develop a real-time human firearm detection system using the YOLOv4-tiny method. The system is implemented and tested on public security CCTV cameras to enhance responses to potential security threats. The research results indicate that the developed detection system achieves an accuracy level of approximately 95%. Real-time testing successfully detects various types of firearms, including rifles, shotguns, and handguns. This success demonstrates the potential of YOLOv4-tiny as an effective solution for improving public safety with fast and accurate firearm detection. The research makes a significant contribution to security technology development, offering an efficient means to prevent violent incidents and protect communities effectively.
Rancang Bangun Private Server Menggunakan Platform Proxmox dan Penerapan Zero Trust Model dengan Cloudflare Yulianto, Bimo Tri; Quraisy, Muhamad; Daulay, Anggriyana; Daulay, Anggriyani; Sari, Ayu Puspita
Jurnal Ilmu Komputer Vol 1 No 2 (2023): Jurnal Ilmu Komputer (Edisi Desember 2023)
Publisher : Universitas Pamulang

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Abstract

This implementation emphasizes using Proxmox as the primary virtualization platform combined with the Zero Trust security concept, where each access request is rigorously assessed before being permitted. Integration with Cloudflare provides an additional layer of security through features such as web application firewall (WAF), DDoS protection, and strict access control. By adopting the Zero Trust model and leveraging Cloudflare services, the server infrastructure becomes more resilient against current cyber threats. The meticulous integration between Proxmox and Cloudflare offers a high level of security at every server access point, creating a reliable and safeguarded environment for IT services.
Analisis Sentimen Ulasan Aplikasi MyUnpam di Google Play Store Menggunakan Metode Naive Bayes Quraisy, Muhamad; Tanjung, Thoyyibah
Jurnal Ilmu Komputer Vol 1 No 2 (2023): Jurnal Ilmu Komputer (Edisi Desember 2023)
Publisher : Universitas Pamulang

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Abstract

Sentiment analysis is the process of automatically extracting, understanding and processing unstructured text data to obtain sentiment information contained in opinions or opinion statements that are positive, negative, or neutral. The data is classified using Naive Bayes. The analysis is divided into 10 stages: crawling, labeling, data cleaning, pre-processing, case folding, stopwords removal, tokenizing, stemming, word weighting, and sentiment classification. Word weighting employs the TF-IDF method (Term Frequency - Inverse Document Frequency). The data is classified into 3 classes: positive, negative, and neutral. Subsequently, the data is evaluated using confusion matrix testing with parameters such as precision, recall, f1-score, and support. The test results indicate that for the 3-class test (positive, negative, and neutral), the best result was achieved with an accuracy of 71.33%.

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