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INDONESIA
KLIK: Kajian Ilmiah Informatika dan Komputer
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Budi Darma
ISSN : -     EISSN : 27233898     DOI : -
Core Subject : Science,
Computer Science & IT
Topik utama yang diterbitkan mencakup: 1. Teknik Informatika 2. Sistem Informasi 3. Sistem Pendukung Keputusan 4. Sistem Pakar 5. Kecerdasan Buatan 6. Manajemen Informasi 7. Data Mining 8. Big Data 9. Jaringan Komputer 10. Dan lain-lain (topik lainnya yang berhubungan dengan Teknologi Informati dan komputer)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 561 Documents
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Deteksi Aktifitas Malware pada Internet of Things menggunakan Algoritma Decision Tree dan Random Forest Syamsul Arifin, M. Agus; Tri Susilo, Andri Anto; Susanto, Susanto; Martadinata, A. Taqwa; Santoso, Budi
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1903

Abstract

The Internet of Things (IoT) has become an integral part of modern life, connecting smart devices to enhance efficiency and convenience. However, with the increased adoption of IoT, cybersecurity threats, particularly malware, have also risen. This research focuses on detecting malware attacks in IoT networks using machine learning algorithms, specifically Decision Tree and Random Forest. The dataset used is CICIoT2023, which includes various types of IoT network traffic such as BenignTraffic, Mirai-greeth_flood, Mirai-greip_flood, and Backdoor_Malware. In this study, both algorithms demonstrated exceptionally high accuracy on the training data, reaching 100%, and on the test data, achieving 99.94% accuracy for the Random Forest algorithm and 99.90% for the Decision Tree algorithm. Although the performance of both algorithms on the training data was almost identical, Random Forest showed better performance in detecting the Backdoor_Malware class compared to Decision Tree when using test data. Random Forest achieved a precision of 99%, recall of 64%, and F1-Score of 78%, while Decision Tree achieved a precision of 71%, recall of 72%, and F1-Score of 72%. Results from 10-fold cross-validation indicate that the models did not experience overfitting, suggesting reliable and well-generalized models. This research provides insights that the Random Forest algorithm is more effective in detecting malware attacks in IoT networks compared to Decision Tree, particularly in identifying the Backdoor_Malware class. These findings are expected to contribute to the development of more efficient and reliable malware detection systems for IoT networks.
Analisis Sentimen Terhadap Sebuah Figur Publik di Twitter Menggunakan Metode K-Nearest Neighbor Yenggi Putra Dinata; Yusra; Fikry, Muhammad; Yanto, Febi; Cynthia, Eka Pandu
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1904

Abstract

The development of online media, particularly through social media platforms like Twitter, has created a vast stage for various activities, including political campaigns and public opinion on public figures. When information technology advances rapidly, public opinion can be conveyed without time constraints through social media. Twitter, with its character limitations and the use of hashtags by users, is considered easier to gather information about existing opinions and sentiments. Currently, social media is widely used for communication and making friends, but also for other activities. Advertising products, buying and selling anything, including advertising political parties and campaigning for members of Congress or presidential candidates. This research focuses on sentiment analysis towards Puan Maharani, the Speaker of the Indonesian House of Representatives (DPR RI), using data from the social media platform Twitter. Twitter, as a platform that allows users to express opinions in a concise format, is used as the main source of information in this research. The K-Nearest Neighbor algorithm for sentiment analysis technique is utilized to classify individual tweets into positive or negative categories regarding views on Puan Maharani. The methods used in this research include data crawling, labeling, and data preprocessing, which involve case folding, cleaning, tokenizing, negation handling, normalization, stopword removal, and stemming. For the classification process, the K-Nearest Neighbor method, feature weighting (TF-IDF), and feature selection (thresholding) are employed, with a threshold value of 0.001. The data used comprises 9,000 tweets in the Indonesian language. The results of the testing conducted in the K-Nearest Neighbor method, using confusion matrices, with 6 different values of K (3, 5, 7, 9, 11, 13), with comparison mechanisms of 90:10, 80:20, and 70:30 achieved the highest accuracy of 90.00% with K = 11 from the comparison using the 90:10 ratio
Analisis Performa Jaringan Local Area Network Dengan Menggunakan Metode Quality Of Service Arvansyah, M Afdhol; Iwan Iskandar; Teddie Darmizal; Novriyanto, Novriyanto; Pizaini, Pizaini
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1905

Abstract

The LAN network center of RSUD Arifin Achmad Pekanbaru is located in the EDP (Electrical Data Processing) building, from where the network is distributed to other buildings within RSUD Arifin Achmad Pekanbaru for data transfer. A common issue that arises is delays in sending and receiving data from computer users in other buildings, causing disruptions in the data reception process. To address this problem, a network performance analysis is necessary to assess the quality of both internet and intranet connections within the RSUD Arifin Achmad Pekanbaru LAN. Therefore, a research study was conducted to measure network performance using the Quality of Service (QoS) method. The objective of this study was to analyze and evaluate the quality of internet and intranet performance in the RSUD Arifin Achmad Pekanbaru LAN. The research findings for internet performance indicate that live streaming on YouTube (720p), downloading a 250MB file, uploading a 250MB file, and accessing national and international websites all fall into the “Excellent” category. Based on the TIPHON standard, the LAN internet network at RSUD Arifin Achmad Pekanbaru is considered very good. Regarding intranet performance, the average throughput is 27,835.666Kbps, with zero packet loss, placing it in the “Excellent” category. The average delay is 374.614ms, categorized as “Moderate,” and the average jitter is 11.46066937ms, categorized as “Good” according to the TIPHON standard
Machine Learning Klasifikasi Status Gizi Balita Menggunakan Algoritma Random Forest Handayani, Putri; Abd. Charis Fauzan; Harliana, Harliana
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1909

Abstract

The future growth and development of toddlers is greatly influenced by nutritional problems at the age of 0-59 months. To achieve optimal health, high nutritional status is required. Improper development, insufficient energy for exercise, decreased immunity, and long-term impaired brain function can all be caused by malnutrition. In this case, the Integrated Service Center Post(Posyandu) is tasked with monitoring children's nutritional health. Anthropometric data, or human body measurements, such as height and weight, are part of this monitoring procedure. Other variables include position measurements and complaints that havebeen submitted. The aim of this research is to use the Random Forest algorithm to classify the nutritional status of children in Nglegok District. This study uses a confusion matrix to evaluate random forest yields. Four scenarios, each with training and test data, are created from the data to perform testing. The test results show that dividing 90% training data and 10% testing is the optimal scenario, with accuracy of 88.6%, precision of 88.1%, recall of 88.6%, and F1-Score of 88.2%.
Implementasi Metode Design Thinking Pada Perancangan User Interface Aplikasi Rumah Baca Cerdas Library Mobile Dinda Puspa Aprilia; Aminudin, Aminudin
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1914

Abstract

The digitalization era has transformed many systems into digital, including the digitalization of libraries. The use of library applications aims to facilitate users in borrowing books and other processes. However, in the use of the library website owned by RBC Institute, there is a gap between user needs and user experience. Therefore, the aim of this study is to design a library application that meets user needs by applying the Design Thinking method. This method allows designers to understand user needs and desires, so that the resulting application can meet user expectations. In addition to applying the Design Thinking method, this study also uses the System Usability Scale method to test the design solutions that have been made. The results obtained are 9 display menus and an SUS score of 84.5. The results of the study are expected to meet user needs and ease of use, thus creating a good user experience and can be used as a design in developing the RBC Library application
Klasifikasi Penyakit Pada Daun Tanaman Padi Berbasis YoloV5 (You Only Look Once) Aditia Putra Pranjaya; Rizki, Fido; Kurniawan, Rudi; Khairani Daulay, Nelly
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1916

Abstract

The rise of disease attacks on plant leaves causes huge losses for farmers, especially rice farmers. Lack of knowledge in identifying the symptoms of disease in rice plants can cause farmers to have difficulty in dealing with diseases that attack their rice plants, causing errors in handling diseases in rice plants that result in crop failure. When looking at the facts that occur today, it is very necessary to have a technology that can be used to recognise diseases in rice plants, so that it can help rice farmers in recognising a symptom of a disease that attacks their rice plants. With the application of computer vision using the YOLOv5 algorithm, we can create an introduction system related to diseases in rice plants based on the type of disease. In the process of applying the YOLOv5 algorithm, we will collect as many as 1500 images of 2 types of diseases and 1 type of normal rice leaves and each class we collect 500 images, and divide the data into 3 parts, the percentage of which is 70% for train data, 20% for valid data and 10% for test data and this process we do in Roboflow for image labelling and dataset creation. We will process the dataset from roboflow using the YOLOv5 algorithm. Based on the model evaluation results, the highest value of mAP 95%, precision 88%, recall 100% is obtained. The last stage is testing the system in real-time with a webcam and producing a test accuracy value in the Narrow Brown Spot class of 93%, in the Leaf Blight class of 81%, and Normal Rice Leaves 91%.
Analysis of User Satisfaction of PELNI Website with WebQual 4.0 and TAM Methods Syam, Arniati; Dedi I. Inan; Ratna Juita; Merlinda Sanglise; Lorna Y. Baisa
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1919

Abstract

The success of an organization is heavily influenced by the quality of its website in the current digital era. PT Pelayaran Nasional Indonesia (PELNI), as one of the State-Owned Enterprises (BUMN) that plays a crucial role in sea transportation in Indonesia, understands the importance of having an effective website to support its mission and vision. Therefore, PT Pelayaran Nasional Indonesia (PELNI) has developed a website that aims to facilitate the public in accessing information and services related to sea transportation. The analysis of user satisfaction with the PT Pelayaran Nasional Indonesia (PELNI) website is the particular goal of this study. The methods used in this research are WebQual 4.0 and the Technology Acceptance Model (TAM). A total of 103 respondents using the PELNI website participated in this study, where data was collected through a Google Form-based questionnaire. The analysis's findings indicate that a number of factors, including usability, perceived usefulness, information quality, system quality, and informativeness, positively and significantly affect user satisfaction (T-Statistic> 1.96 at the 5% significance level and P-Value <0.05). However, variables such as Perceived Ease of Use and Service Quality do not show a significant effect on user satisfaction (T-Statistic < 1.96 and P-Value > 0.05). These findings can serve as a foundation for PELNI to make further improvements and development to their website, with the aim of improving service quality and better meeting user needs
Prediksi Jumlah Kedatangan Pasien Puskesmas Menggunakan Metode Backpropagation Artificial Neural Network Sandi Satria Alamsyah; Maimunah, Maimunah; Pristi Sukmasetya
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1922

Abstract

Patient visit prediction is a crucial aspect of community health center management to optimize the allocation of available resources. However, the erratic pattern of patient visits often complicates the planning and decision-making processes. This research aims to develop a patient visit prediction model for Grabag 1 Community Health Center using the backpropagation artificial neural network method. Backpropagation is a learning algorithm technique used in artificial neural network models with multiple hidden layers. In this research, there are several stages of data processing, including selecting the data attributes used, handling missing values, data normalization, sliding window, and dividing the data into training and testing sets. This prediction can be utilized by the health center to efficiently plan resource requirements, such as scheduling medical staff, managing medication supplies, and maintaining supporting facilities. The data used in this research is a time series spanning from 2019 to 2023. After conducting various experiments, the best results were obtained using a combination of 500 epochs, 30 input neurons, 1 hidden layer, 7 hidden neurons, and 1 output neuron. This artificial neural network architecture configuration achieved a Mean Squared Error (MSE) of 0.00229501 for the training data and 0.00782101 for the testing data.
Komparasi Performa Naive Bayes Gaussian dan K-NN Untuk Prediksi Kelulusan Mahasiswa dengan CRISP-DM Mubarak, Rosyid; Hanafi, Mukhtar; Sasongko, Dimas
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1924

Abstract

Predicting student graduation is a crucial aspect to assess the quality and credibility of higher education institutions. Naive Bayes and K-NN algorithms have been recognised for their effectiveness in predicting graduation. However, most of these studies are limited to academic data. Meanwhile, the variables of thesis completion duration and thesis start time are rarely studied. This study aims to compare the performance of Naive Bayes Gaussian and K-NN algorithms in predicting student graduation using the CRISP-DM method. The data used in this research is the data of students of informatics study programme of Magelang muhammadiyah university. Unlike previous studies that only rely on academic data such as ipk, gender, age, marital status, employment status, and stress level. This research includes the duration of thesis completion and thesis start time as key variables. To compare the performance of Naive Bayes Gaussian and K-NN algorithms, this study adopted three data sharing scenarios: scenario 1 60% training data 40% testing data, scenario 2 70% training data 30% testing data, and scenario 3 80% training data 20% testing data. The results showed that the K-NN algorithm in scenario 2 showed the highest accuracy reaching 91% with precision, recall, and f1-score values of 83.5%, 87.5%, and 85.5%, respectively. On the other hand, Naive Bayes Gaussian reached a maximum accuracy of 88% in Scenario 1 with precision, recall, and f1-score reaching 93%, 77.5%, and 82%, respectively. The research findings show that the K-NN algorithm is superior in predicting student graduation compared to Naive Bayes Gaussian.
Penerapan Normalisasi Data Metode Decimal Scaling Dan Metode K-Means Dalam Mengelompokkan Kasus Demam Berdarah Ila Yati Beti; Hengki Juliansa
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1925

Abstract

Dengue fever, caused by a virus transmitted through the bite of Aedes aegypti and Aedes albopictus mosquitoes, continues to show an alarming trend of increasing cases. Although prevention and control efforts have been widely implemented, this increase is raising serious concerns among public health experts and governments. The causes of the increase in dengue fever cases in 2024 may vary, including climate change which affects the distribution of mosquitoes that carry the virus, urbanization which increases mosquito habitat, and changes in human behavior that affect the level of environmental cleanliness. However, this data is often scattered and has different scales, making it difficult to directly analyze. Data analysis of dengue fever cases is important to understand the pattern of disease spread and take preventive steps using the Decimal Scaling method and grouping data using the K-Means method helps in understanding patterns of dengue fever cases. Where is the Decimal Scaling method to produce better and balanced data. After the data is normalized, the next process is to explore information on dengue fever data by applying data mining grouping using the K-Means method. Based on the results of the cendroit test results where cluster 0 has a greater value for each dengue fever grade value as seen in Figure 4, the test results were obtained with a total sample of 197 test data from 2019 to 2023 with 2 number of clusters where cluster 0 has 81 members and cluster 1 has 115 members. So it can be concluded that those who get priority for treatment are more important in the C0 cluster group.

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