Jurnal Informatika dan Teknologi Komputer ( J-ICOM)
Jurnal J-ICOM (Jurnal Informatika dan Teknologi Komputer) dimaksudkan sebagai media kajian ilmiah hasil penelitian, pemikiran dan kajian analisis-kritis mengenai penelitian Rekayasa Sistem, Teknik Informatika/Teknologi Informasi, Manajemen Informatika dan Sistem Informasi. Sebagai bagian dari semangat menyebarluaskan ilmu pengetahuan hasil dari penelitian dan pemikiran untuk pengabdian pada Masyarakat luas dan sebagai sumber referensi akademisi di bidang Teknologi dan Informasi. Jurnal J-ICOM (Jurnal Informatika dan Teknologi Komputer) menerima artikel ilmiah dengan lingkup penelitian pada: Rekayasa Sistem Sistem Pakar Sistem Penunjang Keputusan Data Mining Kecerdasan Buatan Jaringan Komputer Teknik Komputer Pengolahan Citra Algoritma Genetik Perancangan Sistem Informasi Business Intelligence and Knowledge Management Database System Big Data Enterprise Computing Cloud Computing Technology Management Topik kajian lainnya yang relevan Dengan artikel yang memiliki sitasi primer dan tidak pernah dipublikasikan secara online atau versi cetak sebelumnya.
Articles
105 Documents
<![CDATA[Analisis Sentimen Layanan Jasa Pengiriman Pada Ulasan Play Store: Systematic Literature Review ]]>
<![CDATA[Dellavianti Nishfi]]>
<![CDATA[ Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 4 No 2 (2023): Jurnal Informatika dan Teknologi Komputer ( JICOM) ]]>
Publisher : <![CDATA[Universitas Samudra ]]>
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DOI: 10.55377/j-icom.v4i2.7718
<![CDATA[Sentiment analysis is a tool that assists in mining opinions and capturing expressions of individuals when analyzing their emotions generated from decisions, whether unfair of fair, when accompanied by personal sentiment. The courier service company has broad access in Indonesia and caters to various types of deliveries, driving research based on classifying reviews based on positive, negative, or neutral sentiments using user review data from the Google play store. This research utilizes the Systematic Literature Review method and discusses the result of sentiment levels. Furthermore, the analysis reveals that factors such as delivery speed, timeliness, and service quality are the most common consumer complaints. The findigns of this paper compile published maaterials regarding courirer service companies and opinions from netizen reviews. Journals collected span from 2015 to 2023. It is hoped that this research will be beneficial and serve as a reference for consumers in choosing a courier service company that suits their needs.]]>
<![CDATA[Analisis Perbandingan Decision Tree Algoritma C4.5 dengan algoritma lainnya: Sistematic Literature Review ]]>
<![CDATA[Nazifah, Naurah]]>
<![CDATA[ Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 4 No 2 (2023): Jurnal Informatika dan Teknologi Komputer ( JICOM) ]]>
Publisher : <![CDATA[Universitas Samudra ]]>
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DOI: 10.55377/j-icom.v4i2.7719
<![CDATA[Decision tree is one of the popular methods in data analysis and machine learning. The C4.5 algorithm is one of the most widely used decision tree algorithms because of its ability to produce decision rules that can be understood easily. However, various variations and developments of other decision tree algorithms have emerged, offering improved performance and new features. This study aims to carry out a comparative analysis between the C4.5 decision tree algorithm and several other decision tree algorithms that have been developed. The method used in this research is a systematic literature review, in which the researcher conducts a structured search and evaluation of relevant scientific articles. Researchers will compare the performance of the C4.5 algorithm with other algorithms based on several criteria, including predictive accuracy, computational complexity, interpretability of decision rules, and ability to handle unbalanced data. The results of the analysis show that the selection of a decision tree algorithm must be based on the specific purpose of the analysis and the characteristics of the data used. If the interpretability of decision rules is a major factor, the C4.5 algorithm remains a good choice. However, if predictive accuracy and handling of unbalanced data is a priority, algorithms such as Random Forest, Naive Bayes, or KNN may be a better choice.]]>
<![CDATA[Analisis Sentimen dalam Memprediksi Hasil Pemilu Presiden dan Wakil Presiden : Systematic Literature Review ]]>
<![CDATA[Oktaviami Manullang]]>;
<![CDATA[Cahyo Prianto]]>
<![CDATA[ Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 4 No 2 (2023): Jurnal Informatika dan Teknologi Komputer ( JICOM) ]]>
Publisher : <![CDATA[Universitas Samudra ]]>
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DOI: 10.55377/j-icom.v4i2.7723
<![CDATA[In this research, an analysis of public sentiment towards the presidential and vice presidential candidates will be carried out through the Twitter social network. Sentiment analysis has become an interesting topic in predicting the results of this election, therefore the writing in this paper summarizes the use of sentiment analysis in the presidential election from 2014 to 2019. Looking at the public's response to presidential and vice presidential candidates on social media, especially Twitter, there is responding positively and negatively. The purpose of this journal is to compare methods for predicting public response to elections, and to conduct a systematic review of the literature to identify, review, and synthesize research related to the use of sentiment analysis in predicting the results of the Presidential and Vice-Presidential elections.]]>
<![CDATA[Penerapan Algoritma K-Means Clustering Pada Tingkat Penyelesaian Pendidikan Di Provinsi Indonesia ]]>
<![CDATA[Akhmad Basalamah]]>;
<![CDATA[Resad Setyadi]]>
<![CDATA[ Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 4 No 2 (2023): Jurnal Informatika dan Teknologi Komputer ( JICOM) ]]>
Publisher : <![CDATA[Universitas Samudra ]]>
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DOI: 10.55377/j-icom.v4i2.7893
<![CDATA[The level of educational attainment plays a crucial role in the progress of a country, as higher levels of education achieved have a positive impact on the development of the nation. The data used in this research is obtained from the Indonesian Central Bureau of Statistics (BPS), which is a reliable and authoritative source of statistical information in the country. The objective of this study is to conduct data mining analysis and examine the educational attainment levels across 34 provinces in Indonesia based on education levels and provinces from 2020 to 2022. The analysis process is performed using RapidMiner Data Mining software. The findings of this study include the mapping of education levels and provinces into specific clusters. The method applied is K-Means, a data mining technique used for classifying data with the aim of identifying patterns or groups within it. In this research, cluster mapping is divided into three groups: high cluster (K0), medium cluster (K1), and low cluster (K2). For elementary school (SD) level, there are 29 high clusters, 4 medium clusters, and 1 low cluster. For junior high school (SMP) level, there is 1 high cluster province, 20 medium clusters, and 13 low clusters. It is hoped that the information derived from the research findings has the potential to provide a deeper understanding and valuable insights as a comprehensive overview of the educational attainment levels in Indonesian provinces over the past few years, aiming to enhance the quality of education in Indonesia.]]>
<![CDATA[Pengenalan Kata Bahasa Isyarat Fingerspelling Menggunakan Metode Convolutional Neural Network ]]>
<![CDATA[Siti Abiola Fatika Abiola]]>;
<![CDATA[Munawir munawir]]>;
<![CDATA[Nurul Fadillah]]>
<![CDATA[ Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 3 No 2 (2022): Jurnal Informatika dan Teknologi Komputer ( JICOM) ]]>
Publisher : <![CDATA[Universitas Samudra ]]>
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DOI: 10.55377/j-icom.v3i2.7565
<![CDATA[Sign language is a way of visual communication used by people who have limitations in carrying out normal verbal communication. Alphabet sign language is a basic tool used by educators to teach the deaf and mute. However, many people have difficulty communicating with these circles due to a lack of public knowledge about hand sign language. Research on hand sign language has made great progress in processing static images but is still experiencing problems due to difficulties in processing dynamic images/videos considering that most of hand sign language is represented by body, hand and facial movements. This research uses the Convolutional Neural Network (CNN) method in real time. The research was conducted through the stages of acquisition. The results of this study were conducted in 3 experimental categories, namely trials of all sign language word recognition objects based on angles obtained an accuracy of 85% and trials of all sign language word recognition objects based on distance yielded an accuracy of 93.3% and sign language word recognition based on random positions received an accuracy of 85 %.]]>
<![CDATA[Implementasi Penggunaan Kubernetes Cluster Google Cloud Platform untuk Deployment Aplikasi Wiki.js ]]>
<![CDATA[Haryo, Ahmad Kusumo]]>;
<![CDATA[kusuma, chandra]]>
<![CDATA[ Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 5 No 1 (2024): Jurnal Informatika dan Teknologi Komputer ( JICOM) ]]>
Publisher : <![CDATA[Universitas Samudra ]]>
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DOI: 10.55377/j-icom.v5i1.7944
<![CDATA[Every company has its own digital product, with the rapid pace of digital technology and the needs of users, it requires companies to be able to deliver their digital services, so they are always available and can be accessed at any time. In order to present digital services that are available at any time and in large quantities, the developers in the company create applications according to the large number of requests and needs, this creates problems in several ways, for example the more applications, the more servers are needed, of course this adds costs to application development, besides that there are dependencies between one application and another are make the application experience problems when the developer releases the latest version of the application. Google cloud platform is one of the platforms cloud computing which has many services, such as Google Kubernetes Engine. Kubernetes is an orchestration technology container which allows developer for management of containers in large numbers, this can be the one-off benefit for the application, so that is not easy to experience downtime, because container itself has separate resources from other applications. In this research will be tested for implementation deployment wiki.js application via kubernetes. The trial results of this research are the wiki.js application which can be accessed by utilizing the Kubernetes cluster technology.]]>
<![CDATA[Studi Literatur: Optimasi Algoritma Machine Learning Untuk Prediksi Penerimaan Mahasiswa Pascasarjana ]]>
<![CDATA[Zuhri, Burhanudin]]>;
<![CDATA[Harani, Nisa Hanum]]>
<![CDATA[ Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 5 No 1 (2024): Jurnal Informatika dan Teknologi Komputer ( JICOM) ]]>
Publisher : <![CDATA[Universitas Samudra ]]>
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DOI: 10.55377/j-icom.v5i1.8074
<![CDATA[Machine learning algorithms are mathematical procedures used to find complex and hidden patterns in data with a high degree of accuracy and have brought major advances in various fields for fast and precise decision making. One of these fields is the field of education, which is to predict the admissions process for postgraduate students. The purpose of admitting postgraduate students is to select prospective students who are qualified and meet the academic requirements set by the institution concerned based on GRE (Graduate Record Examination) scores, TOEFL (Test of English as a Foreign Language) scores, university rankings, letters of recommendation, GPA bachelor degree, and research experience. Success in postgraduate admissions can open opportunities to earn advanced degrees and acquire more in-depth knowledge and skills in areas of interest. In this study, an analysis was carried out on various machine learning algorithm optimizations used to optimize topics or trends in previous studies. In this case, the researcher compares performance and selects the best algorithm optimization to be applied to the topic of graduate student admissions. The results of this review show that the hybrid algorithm has the best performance in optimizing predictions for most of the data in previous studies. The results of this study indicate that the CNN-LSTM (Convolutional Neural Network - Long Short-Term Memory) hybrid model is expected to be an appropriate alternative in optimizing predictions of postgraduate student admissions. Therefore, further research is needed to develop this algorithm and expand its application to the topic of graduate student admissions.]]>
<![CDATA[Algoritma K-Medoids Untuk Prediksi Hasil Produksi Buah Kelapa Sawit Berdasarkan Curah Hujan ]]>
<![CDATA[Nuralaeyda, Vivi]]>;
<![CDATA[bachtiar, lukman]]>
<![CDATA[ Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 5 No 1 (2024): Jurnal Informatika dan Teknologi Komputer ( JICOM) ]]>
Publisher : <![CDATA[Universitas Samudra ]]>
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DOI: 10.55377/j-icom.v5i1.8102
<![CDATA[Oil palm is a variety of plantation crops that have an important role in the agricultural sector. Even so, there are several problems that are still unknown, one of which is the effect of rainfall on oil palm fruit yields. To understand the relationship between rainfall and oil palm yields, the clustering method uses the K-Medoids algorithm. The clustering method with the K-Medoids algorithm is used to classify oil palm yield data based on the rainfall that occurs. The purpose of using this algorithm is to determine the highest level of yield or production at PT. Sarana Titian Permata 2. In this study, the clustering results showed that there were four clusters produced. Based on performance testing, the best cluster chosen is 4 clusters. The selection of this cluster is based on the lowest Davies Bouldin Index (DBI) value obtained, which is -0.773 and the cluster results are obtained from data that has been grouped into four clusters. cluster 0 consists of 39 data, cluster 1 consists of 27 data, cluster 2 consists of 17 and cluster 3 consists of 13 data. By selecting this cluster, it is possible to identify oil palm yield groups that have better performance in relation to rainfall. This research provides a better understanding of the relationship between rainfall and oil palm yields in 2022 at PT. Sarana Titian Permata 2. By knowing the best clusters, efforts can be made to increase the productivity and efficiency of palm oil production based on existing rainfall conditions.]]>
<![CDATA[Studi Literatur: Prediksi Kata Berikutnya dengan Metode Recurrent Neural Network ]]>
<![CDATA[Trigreisian, Alwizain Almas]]>;
<![CDATA[Harani, Nisa Hanum]]>
<![CDATA[ Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 5 No 1 (2024): Jurnal Informatika dan Teknologi Komputer ( JICOM) ]]>
Publisher : <![CDATA[Universitas Samudra ]]>
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DOI: 10.55377/j-icom.v5i1.8104
<![CDATA[Next-word prediction is one of the most frequently used tasks in natural language processing. The Recurrent Neural Network (RNN) method is one method that has been proven to be effective in predicting the next word in a sentence, as it is capable of processing text data with order and context. In this research, various algorithms used in the development of next word prediction using the RNN method were analyzed. Some of these algorithms include LSTM (Long Short-Term Memory) and bidirectional LSTM. The results of this research show that the use of the RNN method in predicting the next word is able to provide better results compared to other methods. However, there are still some challenges that need to be overcome in developing the RNN model to predict the next word. Therefore, further research needs to be done in overcoming these challenges so that the use of the RNN method can be further optimized in predicting the next word in a sentence.]]>
<![CDATA[Pemanfaatan Selenium WebDriver untuk Pengujian Regresi Aplikasi Berbasis Web ]]>
<![CDATA[Sofani, Ratu Fairuz Hasna]]>;
<![CDATA[idris, Moh]]>
<![CDATA[ Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 5 No 1 (2024): Jurnal Informatika dan Teknologi Komputer ( JICOM) ]]>
Publisher : <![CDATA[Universitas Samudra ]]>
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DOI: 10.55377/j-icom.v5i1.8109
<![CDATA[Software testing is an important stage in software development that aims to verify the performance and suitability of an application or system with user requirements. Software that is being developed will not avoid bugs because the software will continue to update and/or change the system, so regression testing is needed to ensure that the changes that made during the software development process is not affecting the functionality of the system that was running well before. In recent years, automated testing has become a popular and efficient method of performing software testing, especially on iterative regression testing. By using automation tools such as Selenium WebDriver, regression testing can be carried out thoroughly and does not take much time. Keywords: Software Testing, Regression Testing, Automation Testing, Selenium WebDriver]]>
