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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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Pengembangan Website Edukasi Kesehatan Balita dengan Menggunakan Metode Iterative Incremental Abidzar Zulfa Arifa Kusyono; Taufik Nur Adi; Elvira Lailatuth Thohiroh
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 5 No. 1 (2024): Agustus 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v5i1.1962

Abstract

This study aims to develop a health education website for toddlers in Indonesia that provides relevant and comprehensive information to the community, especially parents. The study identifies issues in providing toddler health information and designs a website that facilitates access to this information. It also explores the technologies and methods needed for developing the website. The research methodology includes the Information Systems (IS) environment, which is divided into actors, organizations, and technology, as well as theories encompassing UML concepts, Laravel, and PHP. An Iterative Incremental approach is used for website development, including stages such as initial planning, planning, requirement, analysis and design, implementation, testing, and deployment. Testing is conducted using the Black Box Testing method. Data collection is carried out through literature studies, online surveys, and interviews with parents and health science students. Literature studies help build the theoretical foundation, provide current data, and identify best practices. Online surveys measure user needs and community awareness regarding toddler health issues, while interviews delve into user preferences and behaviors. The results show that the development of the toddler health education website in Indonesia successfully provides complete and relevant information, facilitating easy access for parents. The implementation using the Laravel framework and PHP programming language, along with testing using the Black Box Testing method, proved effective. Iterative evaluation improved the website's functionality based on user feedback. This study significantly contributes to the development of toddler health education media that meets the needs of the Indonesian community.
Analisis Sentimen Ulasan Pengguna Aplikasi Netflix Pada Google Play Menggunakan Algoritma Naïve Bayes Ananda Bagas Pranata; Allif Rizki Abdillah; Faldy Irwiensyah
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.v4i6.1964

Abstract

The rapid development of information technology has advanced rapidly, including advancements in film technology. In this modern era, watching movies no longer requires going to the cinema, as there are applications available to watch movies anytime and anywhere. One popular application for watching movies is Netflix, a widely used streaming platform for films and series. Netflix also ranks 10th in terms of access in Indonesia. This study focuses on identifying user satisfaction levels with the Netflix application based on reviews on the Google Play Store. The research aims to analyze user review sentiment of an application available on Google Play, namely Netflix. These reviews will be used to gauge user satisfaction with the Netflix application. Researchers obtained these reviews using a Python web scraper with a total of 1000 unprocessed data points. After processing these 1000 data points by removing duplicates and symbols, researchers obtained 893 data points ready for sentiment analysis using RapidMiner. Out of the 893 data points, researchers manually labeled 635 data points, while 258 data points were labeled automatically using machine learning, namely Naive Bayes. Researchers also created a confusion matrix to determine the accuracy level of the algorithm used in this study. The accuracy result of the confusion matrix obtained by researchers in this study is 93.39%. The positive class precision value of 85.52% indicates that most positive reviews were identified accurately, while the negative class precision value of 100% demonstrates excellent capability in identifying negative reviews. In conclusion, the Netflix application receives diverse responses from users, and the algorithm used effectively identifies reviews accurately
Kombinasi Principal Component Analysis dengan Algoritma K-Means untuk Klasterisasi Data Stunting Gladys Fouriza Ibanez; Giri Wahyu Wiriasto; Rosmaliati
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 5 No. 1 (2024): Agustus 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v5i1.1977

Abstract

Dompu Regency in West Nusa Tenggara ranks as the fourth highest regency in terms of stunting prevalence among toddlers in the NTB province in 2022, with a rate of 34.5%. This study utilizes data from the Dompu Regency Health Office in 2023, covering 81 villages and six variables that influence the prevalence of stunting. The objective of this research is to determine and understand the characteristics of areas based on stunting factors in Dompu Regency using the K-Means Clustering method combined with Principal Component Analysis (PCA). The K-Means method produces clusters that represent areas with different characteristics, derived from the data reduction results of PCA, which form the principal components. The optimization of the number of clusters using the Elbow method indicates 3 clusters, consisting of Zone Type 1 with 42 villages, Zone Type 2 with 12 villages, and Zone Type 3 with 27 villages. Subsequently, an evaluation phase using the Silhouette method resulted in 2 clusters: Zone Type 1 with 54 villages and Zone Type 2 with 22 villages, with a Silhouette Score of 0.53, indicating a fairly good cluster structure. PCA produced two principal components with the highest eigenvalues, each explaining 58.7% and 14.28% of the variance, with a cumulative variance of 72.9%. This demonstrates that these two principal components can effectively represent the factors influencing stunting prevalence in Dompu Regency.
Analisis Sentimen Terhadap Rangka E-SAF Honda Pada Media Sosial X Dengan Algoritma Naïve Bayes Cleary Syafi'i, Akbar; Ade Davy Wiranata
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 5 No. 1 (2024): Agustus 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v5i1.1993

Abstract

Motorcycles are the best vehicles for traveling when traffic is heavy because motorcycles allow people to save time while going about their daily commute due to their small size and ability to move on narrow streets. An important component in a motorcycle is the motorcycle frame, the motorcycle frame is a useful part to support the weight of these components in the motorcycle vehicle system. However, it is rumored that a motorcycle frame with the E-SAF type has poor quality, so a sentiment analysis is needed. This research aims to collect the number of comments, both positive and negative, from social media users X about the E-SAF framework, and also to determine the accuracy of the application of the Naive Bayes method. The datasets collected from social media X amounted to 756 datasets. Then after going through the stages of data cleaning such as cleansing, tokenize, and stopword filters, the data that can be used for this research amounted to 696 datasets. The next stage is data labeling, namely by dividing the dataset with a ratio of 60:40, namely 60% of the training data totaling 417 datasets that have been manually labeled with the results of 224 negatively charged data, 193 positively charged data while the test data is 40% with a total of 279 datasets which will later be automatically labeled with the implementation of the Naive Bayes method. The next stage is that the test data goes through the data processing stage so that the test data is ready to be implemented into the Naive Bayes method. After implementing the Naive Bayes method, the accuracy obtained was 70.27% with a precision of 76% and also a recall of 79.17%. There was also a true Positive data of 57 and a true Negative data of 21. Data Visualization also displays words that appear frequently in the dataset. Here it shows that the Naive Bayes method is quite effective for the classification of sentiment analysis
Deteksi Jenis Penyakit Pada Tanaman Padi Menggunakan Yolo V5 Muhammad Deden Miftah Fauzi; Tohirin Al Mudzakir; Cici Emilia Sukmawati; Jamaludin Indra
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 5 No. 1 (2024): Agustus 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v5i1.2009

Abstract

The problem of disease in rice plants is an obstacle faced by farmers after planting. One of the rice diseases that really worries farmers is disease brown spot, Bacterial Blight, and blast, which causes the leaves to turn yellow prematurely, spots and rice stalks rot. One farmer in Parigi Village whose rice field was attacked by disease suffered a loss of Rp. 6,000,000 to Rp. 8,000,000 per hectare. From all the rice fields of Parigi Village residents. This research aims to detect types of disease in rice plants by applying methods deep learning using YOLO v5 (You only Look Once). The trained model is able to recognize Brown Spot, Bacterial Blight and Blast diseases with a high level of accuracy.  In this analysis, two epochs stand out as the best candidates, namely epoch 250 and epoch 200. At epoch 250, the model shows the highest precision (0.802) and a strong mAP@0.5 value (0.702), indicating excellent model performance without overfitting. Meanwhile, at epoch 200, although precision and recall were slightly lower, the highest mAP@0.5:0.95 value (0.393) indicated better generalization ability. Based on these metrics, epoch 150 is identified as the optimal epoch, although epoch 200 also shows strong performance, especially in generalization over a wide range of threshold IoU. The calculation results show the following performance metrics: Precision: 92.5%; Recall: 90.8%; F1-Score: 91.6%; Mean Average Precision (mAP): 93.2%.
Analisis Penerapan Logika Fuzzy Pada Sistem Diagnosis Infeksi Saluran Pernapasan Akut Berbasis Android Ifriandi Labolo; Citra Yustitya Gobel; Satriadi Ali; Muhammad Isla; Ridwan Y Kulu
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 5 No. 1 (2024): Agustus 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v5i1.2014

Abstract

Acute respiratory tract infection is a health problem that often occurs and requires a fast and accurate diagnosis to prevent further complications. The high incidence of ARI raises the need for a more accurate and faster diagnosis system. This research aims to analyze the application of fuzzy logic in an expert system so that it can diagnose acute respiratory infections (ARI) in Android-based applications. This expert system uses a fuzzy logic method to handle uncertainty and variability in the symptoms experienced by patients. The method used in this research involves collecting symptom data from patients, which is then processed using fuzzy rules to produce a diagnosis. This system is designed to provide easy access for users via Android devices by facilitating users in entering the symptoms experienced by the patient and providing an initial diagnosis along with the level of confidence in each diagnosis given so that the patient can carry out an initial examination independently before consulting with professional medical personnel. The research results show that the application of fuzzy logic to this expert system is able to provide fairly accurate diagnosis results, seen from the Deviation results. Patients suffering from mild acute respiratory infections with a final score of 4,804. Apart from that, this system also received a positive response from users because of its ease and speed in use. This research concludes that fuzzy logic is effectively applied to expert systems for the diagnosis of respiratory tract infections and has the potential to be further developed to improve health services in the community.
Analisis Klasifikasi Teks Pada Kata Slang di Media Sosial Menggunakan Pengolahan Bahasa Alami untuk Trending Topik Shabrina Rasyid Munthe; Sudi Suryadi; Fadhil Laksono
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 5 No. 1 (2024): Agustus 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v5i1.2018

Abstract

This study aims to analyze trending topics related to the use of slang words on social media by utilizing natural language processing (NLP) techniques. The main focus of this research is to understand the patterns and trends of slang use on social media platforms, which can uncover important social and linguistic dynamics. The dataset used consisted of  tweets in Indonesia and United Kingdom containing slang words, collected from Twitter over a six-month period. The analysis process begins with data cleansing to eliminate irrelevant elements, followed by tokenization and lemmatization to normalize the text. Furthermore, the Support Vector Machine (SVM) and Random Forest classification models are applied to detect and classify slang words in the dataset. The results show that the SVM model achieves a slang detection accuracy of 88% with an F1-score of 0.87, while the Random Forest model achieves an accuracy of 85% with an F1-score of 0.84. Further linguistic analysis showed that 60% of slang words are most commonly used in informal contexts such as everyday conversation, while the other 40% are related to popular culture trends, including music, movies, and fashion. In addition, these findings indicate that there is a variation in the use of slang between Indonesian and United Kingdom-speaking Twitter users, where slang in Indonesian tends to be more creative and contextual, while in United Kingdom it is more standardized and spread globally. This study confirms the effectiveness of both models in classifying slang words as well as identifying key trends in their use on social media. The contribution of this research is important for the study of digital linguistics because it expands the understanding of the dynamics of online slang use, and shows the great potential of NLP applications in linguistic analysis in the digital age. With the results obtained, this research can be a valuable guide for researchers and practitioners interested in understanding the evolution of language on social media, while providing a foundation for the development of more sophisticated and adaptive NLP technologies in handling language variations on digital platforms.
Analisa Performa Convolutional Neural Network dalam Klasifikasi Citra Apel dengan Data Augmentasi Dzalfa Tsalsabila Rhamadiyanti; Kusrini
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 5 No. 1 (2024): Agustus 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v5i1.2023

Abstract

Augmentation is creating new samples from an original dataset by applying small random transformations to the original dataset but retaining its labels. This research applies Data Augmentation to the Convolutional Neural Network model for apple image classification. The apple images used are Braeburn apples which have orange to red skin with a yellow background, Crimson Snow apples which have red skin, and Pink Lady apples with bright pink skin and yellow and green hues. There are 675 apple images used, divided into three classes, each with 225 photos. Four augmentation techniques are applied, namely flipping, cropping, rotation, and noise injection. This research carried out six scenarios, namely without augmentation, using each augmentation technique separately and combining two augmentation techniques, which produced the highest accuracy values. From the six scenarios, it was found that the augmentation technique that produced the best accuracy value was noise injection, namely 98.82%, followed by flipping with an accuracy of 72.78%, then rotation with an accuracy value of 68.64% and an augmentation technique that produced an accuracy value. The lowest is cropping, namely 67.46%. The two best augmentation techniques, noise injection, and flipping, were combined and produced an accuracy value of 84.02%. The accuracy value obtained by this combination could be more optimal due to the effect of noise injection, which can erase consistent changes in orientation from flipping. This needs to be improved so that the model can learn consistent features. It is hoped that future research can maximize the effectiveness of augmentation techniques by choosing augmentation techniques that complement each other and suit the characteristics of the data being processed
Implementasi Metode Certainty Factor Untuk Mendiagnosis Penyakit Malaria Linda Perdana Wanti; Ulfiyah, Waffa
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 5 No. 1 (2024): Agustus 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v5i1.2026

Abstract

Malaria is a disease caused by the plasmodium parasite transmitted through the bite of an infected female anopheles mosquito. This disease often occurs in tropical and subtropical areas, including Indonesia. Symptoms of malaria generally appear between 10-15 days after the bite of an infected mosquito and include high fever, chills, sweating, headache, nausea, vomiting, muscle aches, and fatigue. The first identification is carried out when the patient comes to a health facility and perhaps the disease is already in a severe phase. A system that can identify malaria quickly and precisely is needed for emergencies so that patients receive early treatment, and this is one of the preventive measures to reduce casualties due to malaria. The right system built to overcome this is expert. The certainty factor (CF) method was chosen for the malaria diagnosis expert system because it handles uncertainty in decision-making. CF is a numerical value used to represent confidence or confidence in a hypothesis or conclusion based on existing evidence. The result of this research is that the level of accuracy of expert system diagnosis using the certainty factor method is 98,35% when compared with expert diagnosis. This means that the certainty factor method has been proven to be able to diagnose malaria accurately according to the symptoms experienced by the patient.
Sistem Pakar Diagnosa Penyakit Pada Tanaman Sawi Menggunakan Metode Convolutional Neural Network Berbasis Android Muhammad Yusuf; Syamsudin Aliphadji Talaohu; Jumria Purnamasari
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 5 No. 1 (2024): Agustus 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v5i1.2031

Abstract

Mustard greens are one of the vegetable crops that are very easy to cultivate, because they are able to grow in the highlands and lowlands, mustard greens are also susceptible to diseases that can reduce yields and quality, identifying mustard diseases manually is difficult and requires in-depth knowledge of the symptoms. and causes of disease in mustard plants. The aim of this research is to build a system that can be used to identify mustard plant diseases. The diseases used consist of 3 types, namely, leaf miner, leaf rot, and armyworm. With current technological developments that can help farmers minimize errors in determining diseases in mustard plants, Deep Learning is a field of machine learning that utilizes artificial neural networks to solve problems with large datasets. One algorithm that is often used in deep learning systems is Convolutional Neural Network (CNN). The results obtained with the dataset used were with the highest accuracy of 100% train accuracy and 97.78% validation accuracy, and obtained the highest accuracy results using f1-score, namely 95.56%.

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