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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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Analisis Sentimen Terhadap Pelayanan TransJakarta Berdasarkan Tweets Menggunakan Metode Naïve Bayes Classifier Muflih, Hilmy Zhafran; Hasan, Firman Noor
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.v4i6.1927

Abstract

The high use of private transportation in Indonesia, especially in the Jakarta area, causes several impacts, one of which is traffic jams. This congestion condition can be reduced by public transportation. It is hoped that public transportation can now reduce the level of congestion in Jakarta. One of the public transportation in Jakarta is TransJakarta. TransJakarta is a form of transportation that can carry a relatively large number of passengers and TransJakarta offers various facilities to users, such as the availability of priority seating, stops that are quite comfortable, comfortable conditions on the bus plus low prices so that it gets various responses from users who led researchers to conduct research on the views of TransJakarta users regarding TransJakarta services, whether TransJakarta users' responses were positive or negative. The purpose of this research is to understand whether users are satisfied or not with the services provided by TransJakarta. The method used in the research is the Naïve Bayes Classifier algorithm which is used to carry out the sentiment analysis process regarding TransJakarta services with the help of the RapidMiner application. The data collected by researchers was 773 tweet data obtained via social media X to be used as a dataset. The results of sentiment analysis from the Naïve Bayes Classifier algorithm obtained 80.6% or 623 negative sentiments and 19.4% or 150 positive sentiments from 773 datasets. The results of the confusion matrix evaluation obtained an accuracy value of 73.96%.
Perbandingan Metode Naïve Bayes dan Support Vector Machine Dalam Analisis Sentimen Terhadap Tokoh Publik Ardiyansah; Parjito
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.v4i6.1928

Abstract

The existence of Twitter, or now replaced with Name X, has been widely used by various levels of society in recent years. And social media X is one of the media that represents public responses to public figures. This study aims to perform sentiment analysis on the opinions of the Indonesian public regarding the public figure Luhut Binsar Pandjaitan on social media X. The data used is 4008 data related to the topic which was obtained through web scraping techniques. This study compares the performance of two popular classification algorithms in sentiment analysis, namely Naïve Bayes and Support Vector Machine (SVM). Before the comparison, SMOTE (Synthetic Minority Over-sampling Technique) optimization was carried out to balance the number of minority and majority data so that both algorithms could learn better from each sentiment class. The results of the comparison show that the Naïve Bayes algorithm produces an accuracy of 95%, while the SVM produces an accuracy of 99%, precision 99%, recall 100%, and F1-Score 99%. Performance evaluation was also carried out by analyzing the confusion matrix of each algorithm. It can be concluded that SVM has the best performance in classifying positive and negative sentiments more accurately than Naïve Bayes for the case of sentiment analysis towards the public figure Luhut Binsar Pandjaitan. Therefore, the SVM algorithm can be a better choice for sentiment analysis towards public figures. This research contributes to the understanding of public opinion about Luhut's performance while serving as the Coordinating Minister for Maritime Affairs and Investment of Indonesia
Optimasi Algoritma C4.5 Menggunakan Metode Forward Selection Dan Stratified Sampling Untuk Prediksi Kelayakan Mahasiswa Penerima Beasiswa Bentar Candra P; Kusrini, Kusrini; Tonny Hidayat
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.v4i6.1933

Abstract

Every prospective student has the opportunity to get a scholarship within an educational institution, but it is often not on target so a more accurate data mining approach is needed. However, the C4.5 algorithm has a weakness in its level of accuracy when managing large amounts of data so it needs to be optimized. This research aims to optimize the C4.5 algorithm using stratified sampling and forward selection methods in determining the eligibility of scholarship recipients. The data came from prospective students at Anwar Medika University with a sample size of 263 records which were then processed using the RapidMinner application for the C4.5 algorithm without optimization and the C4.5 algorithm with optimization of the stratified sampling + forward selection method. The research results show a higher level of accuracy in the C4.5 algorithm with optimization using the stratified sampling + forward selection method, namely 81.75% compared to the accuracy level in the C4.5 algorithm without optimization, namely 80.23%. Thus, the conclusion of this research is that the C4.5 algorithm with optimization using stratified sampling and forward selection methods is more effective and can overcome the shortcomings of the C4.5 algorithm without optimization
Analisis Sentimen Pengguna Terhadap Kinerja Sistem Transportasi Umum Jakarta Menggunakan Algoritma Naive Bayes Muhammad Imam Santoso; Ahmad Rizal Dzikrillah
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.v4i6.1936

Abstract

This study uses the Naive Bayes algorithm to analyze netizen sentiment regarding public transportation in Jakarta. In the past, Jakarta's public transportation system was dominated by private operators, including buses, angkot (minibuses), and taxis. However, various challenges arose, such as lack of coordination, inconsistent service quality, safety issues, and inadequate coverage. To improve the quality and availability of public transportation, local or national governments have intervened by taking over public transportation services or imposing stricter regulations on private operators. Significant investments have been made in developing public transportation modes such as TransJakarta (bus rapid transit), KRL Commuter Line (electric train), MRT (Mass Rapid Transit), Jaklingko, and other public transport services. This study aims to analyze the benefits of public transportation, which has largely been taken over by the government, to minimize existing shortcomings. The research focuses on analyzing the differing opinions spread across various social media platforms. Data was collected from social media platforms such as YouTube and X. A total of 987 data points were used in this study, with 612 positive and 375 negative data points. After conducting the research, an accuracy of 94.22% was achieved. The analysis revealed significant variations in sentiment among netizens regarding public transportation in Jakarta. Some groups of netizens have begun to feel positive effects from the current integration of public transportation, but there are still execution shortcomings. The analysis also identified key factors influencing differing opinions, such as user areas, the uneven distribution of drivers with good personal qualities, and the economic conditions of the community. Consequently, this study contributes to sentiment analysis and natural language processing by applying problem-solving procedures to classify netizen comments on various platforms. The results of this study indicate that the Naive Bayes algorithm can be used to classify netizen sentiment about public transportation in Jakarta with a high level of accuracy. These findings can be useful for the government and Jakarta residents in finding solutions to these issues. Thus, this study can serve as a basis for a more comprehensive understanding of the government's response to public transportation issues in Jakarta.
Classification of Palm Oil Ripeness Level using DenseNet201 and Rotational Data Augmentation Nabyl Alfahrez Ramadhan Amril; Yanto, Febi; Elvia Budianita; Suwanto Sanjaya; Fadhilah Syafria
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.v4i6.1937

Abstract

Indonesia is a country in Southeast Asia with the largest palm oil production in the world. Based on Indonesian Central Statistics Agency data, in 2022 Indonesia produced 46,8 million Tons of Crude Palm Oil (CPO). To produce a high-quality oil, palm oil fruit must be harvested in an optimal condition. But, even a experienced and trained person found it difficult to identify whether the fruit is ripe or raw. In this research theres two type of classification which is ripe and raw, this is because palm oil milling factory only accept pure ripe palm oil fruit and not half ripe or almost ripe. The data that is used in this reseacrh was collected from two sources, the first source is from https://www.kaggle.com/datasets/ahmadfathan/kematangansawit and the second source was collected manually by going to palm oil plantation. The total of data that is used for this research is 1000 data and 1000 augmented data. Dense Convolutional Network (DenseNet) that is used in this research is a CNN architecture that was first introduced in 2017. Compared to DenseNet121 and DenseNet169, DenseNet201 is proven to have a higher level of accuracy. The 90:10 data scheme succeeded in getting the highest accuracy with a total accuracy of 97.50% with a learning rate of 0.001 and a dropout of 0.01
Deep Learning Menggunakan Algoritma Xception dan Augmentasi Flip Pada Klasifikasi Kematangan Sawit Masaugi, Fathan Fanrita; Yanto, Febi; Budianita, Elvia; Sanjaya, Suwanto; Syafria, Fadhilah
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.v4i6.1938

Abstract

Palm oil is an important commodity in Indonesia, especially as Indonesia is the highest palm oil exporting country in the world. Ripe palm fruit is marked by a change in color of the fruit from black to reddish yellow. Apart from that, immature palm fruit has a negative and significant effect on CPO production. The data collection process was carried out by directly taking pictures of palm fruit on oil palm plantations and data obtained from Kaggle. The total amount of data is 1000 images and 1000 data resulting from flip augmentation. The Xception algorithm is an algorithm in deep learning which stands for Extreme version of Inception. This combination was then proven to provide better accuracy in classifying images from a dataset. The optimizer used is the optimizer in TensorFlow, namely Adam (Adaptive Moment Estimation) using learning rate and dropout values. Images of mature and immature palm oil were classified using the Xception algorithm with augmented and without augmented data. In addition, experiments were carried out by changing the parameter values ??of learning rate to 0.1, 0.01, 0.001 and dropout to 0.1, 0.01, 0.001. It was found that the data division was (90;10) with the best accuracy reaching 95%. Test parameters carried out by trialling were proven to increase accuracy when compared to without using parameters and flip augmentation. The best accuracy of the Xception model is 95% on augmented data with a learning rate of 0.001 and a dropout of 0.1.
Peningkatan Performa Klasifikasi Sentimen Tweet Kaesang Menggunakan Naïve Bayes dengan PSO pada Dataset Kecil Muhammad Ravil; Agustian, Surya; Fikry, Muhammad; Insani, Fitri
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.v4i6.1939

Abstract

After the news of Kaesang's appointment as the Chairman of the Indonesian Solidarity Party (PSI), various speculations emerged on social media, particularly on Twitter (X). This study aims to classify sentiments regarding Kaesang's appointment as PSI Chairman using the Naïve Bayes algorithm optimized with Particle Swarm Optimization (PSO). The data used in this study consists tweets about Kaesang and tweets related to COVID-19. The text preprocessing process includes cleaning, case folding, tokenizing, stemming, and stopword removal. TF-IDF is used to represent words in vector form. In the initial experiment, Naïve Bayes performed classification using Kaesang data combined with COVID-19 data, with 300 data points for each label. Particle Swarm Optimization was used to improve the performance of the Naïve Bayes algorithm. The experiment results showed that the model tested with test data achieved the highest f1-score of 50%.
Implementasi VGG 16 dan Augmentasi Zoom Untuk Klasifikasi Kematangan Sawit Mazdavilaya, T Kaisyarendika; Yanto, Febi; Budianita, Elvia; Sanjaya, Suwanto; Syafria, Fadhilah
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.v4i6.1940

Abstract

Indonesia is a country that has very abundant palm oil plantations and makes palm oil one of the largest export commodities in Indonesia. Fruit maturity on oil palms has a significant influence on palm oil and kernel production. The level of ripeness in palm oil fruit can affect several contents in it, such as tocopherol content, yield and FFA. The classification will be divided into 2 classes, namely between ripe and immature fruit with data on 500 images of ripe fruit and 500 images of immature fruit, data taken from the Kaggle site and private gardens taken using a cellphone camera. The data that has been obtained is augmented which is useful for enriching the data to make it more abundant. Data augmentation uses zoom augmentation and makes the original 1000 data increase to 2000 data. The model used is VGG 16 which is part of deep learning. The existing dataset is then preprocessed, resized and rescaled, then divides the data into 3, namely train, test and valid data. After dividing the data, then carry out the classification process with VGG 16 and set the hyperparameters after that the model will learn with 20 epochs. The model will learn with 57 schemes to compare and find highest accuracy. After the model has finished learning, it is evaluated using a confusion matrix. The results obtained were that the 90:10 data division using data augmentation with a learning rate of 0.01 and a dropout of 0.001 obtained the best accuracy, reaching 93.8%.
Sentiment and Toxicity Analysis in the Narratives of Wamena's Cultural Heritage: Understanding Community Perspectives and External Influences Yan Dirk Wabiser; Yerik Afrianto Singgalen
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.v5i1.1941

Abstract

This study analyzes digital narratives surrounding Wamena's cultural heritage using the Digital Content Reviews and Analysis Framework, focusing on sentiment, toxicity, and thematic content. The research explores the complex interplay between community perspectives, cultural preservation, modernization, and external influences such as tourism. Toxicity analysis revealed that while most online discourse is supportive, there are instances of harmful language that could disrupt social cohesion, with toxicity scores peaking at 0.50790. These findings highlight the need for continuous moderation to foster a positive digital environment. Sentiment analysis provided a deeper understanding of emotional tones, showing a predominance of positive sentiments and highlighting frustration and dissent related to cultural erosion. The study employed machine learning algorithms for sentiment and toxicity classification, with the Support Vector Machine (SVM) enhanced by Synthetic Minority Over-sampling Technique (SMOTE) demonstrating superior accuracy at 87.29%. Content analysis identified vital themes such as community dynamics, cultural resilience, and the dual impact of tourism as both an economic catalyst and a potential threat to cultural integrity. The findings underscore the importance of maintaining an inclusive digital environment that promotes constructive dialogue and cultural preservation. This framework provides valuable insights for policymakers and community leaders, emphasizing the need for culturally sensitive strategies to manage digital content and support sustainable cultural tourism. Future research should expand this framework to other contexts to enhance the understanding of digital communication dynamics in diverse cultural settings.
Implementasi Metode Rank Order Centroid Dan Multi Attributive Border Approximation Area Comparison Dalam Penerimaan Karyawan Muhamad Hidayatullah; Ali Ikhwan
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.v4i6.1943

Abstract

The presence of employees is an important aspect of a company. Therefore, the employee recruitment process must be carried out professionally and carefully to ensure that the selected employees can make a meaningful contribution to the company. If the recruitment process is carried out subjectively and conventionally, the company may face difficulties in making the right decisions regarding employee recruitment. If this continues over a long period of time, it can have a negative impact on company performance and hinder the achievement of goals. To obtain prospective employees who are competent and in accordance with the required classification, employee recruitment requires appropriate selection. The aim of this research is to build a decision support system in employee recruitment so that it can help companies in determining the right employee candidates. To ensure objectivity in the employee recruitment process, it is necessary to use appropriate and appropriate methods in employee recruitment. The methods used in this research are Rank Order Centroid (ROC) and Multi-Attributive Border Approximation Area Comparison (MABAC). In the employee recruitment process, four criteria are used, namely honesty, presence, attitude and skills. The results of the decision support system using the ROC and MABAC methods are getting a ranking of prospective employees so that they become the best recommendations in the employee recruitment process

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