Jurnal Informatika dan Teknologi Komputer ( J-ICOM)

Studi Literatur: Optimasi Algoritma Machine Learning Untuk Prediksi Penerimaan Mahasiswa Pascasarjana

Burhanudin Zuhri, Nisa Hanum Harani — Mon, 01 Apr 2024 11:03:24 +0700

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.

Implementasi Penggunaan Kubernetes Cluster Google Cloud Platform untuk Deployment Aplikasi Wiki.js

Ahmad Kusumo Haryo, chandra kusuma — Mon, 01 Apr 2024 11:43:02 +0700

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.

Studi Literatur: Prediksi Kata Berikutnya dengan Metode Recurrent Neural Network

Alwizain Almas Trigreisian, Nisa Hanum Harani — Mon, 01 Apr 2024 11:50:39 +0700

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.

Algoritma K-Medoids Untuk Prediksi Hasil Produksi Buah Kelapa Sawit Berdasarkan Curah Hujan

Vivi Nuralaeyda, lukman bachtiar — Mon, 01 Apr 2024 00:00:00 +0700

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.

Pemanfaatan Selenium WebDriver untuk Pengujian Regresi Aplikasi Berbasis Web

Ratu Fairuz Hasna Sofani, Moh idris — Mon, 01 Apr 2024 12:18:48 +0700

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

Prediksi Banjir Di Dki Jakarta Dengan Menggunakan Algoritma K-Means Dan Random Forest

Mon, 01 Apr 2024 00:00:00 +0700

This research aims to develop a flood prediction method that can be used to implement effective prevention and mitigation measures in dealing with frequent natural disasters in DKI Jakarta. The approach used in this study involves the utilization of Machine Learning techniques with a combination of K-Means and Random Forest algorithms. Historical data on water gates, water levels, and other relevant factors are used as inputs for the development of the flood prediction model. The K-Means method is employed to cluster the water level data, and the results of the K-Means clustering process are then used as parameters in the Random Forest method. A total of 20 experiments were conducted, varying the value of k from 1 to 20 in the K-Means algorithm. The experimental results show that the best accuracy and f-1 score were achieved at k=14, with an accuracy rate of 95% and an f-1 score of 90%. This indicates that the developed flood prediction model is capable of providing accurate and reliable predictions in identifying flood potential. This research holds significant implications for flood management in vulnerable cities. With an effective flood prediction method, prevention and mitigation measures can be implemented more efficiently, thereby reducing the negative impacts caused by floods.

Peningkatan Kinerja Chatbot NLP Asisten: Tinjauan Literatur tentang Metode dan Akurasi dalam Aplikasi Berbasis Percakapan

M. Ilyas Tri Khaqiqi, Nisa Hanum Harani — Mon, 01 Apr 2024 12:50:40 +0700

Chatbots have been widely used in various industries such as e-commerce, banking, healthcare, and education to improve efficiency and provide 24/7 services to users. In the field of education, NLP chatbot brings the potential to improve soft skills and hard skills through online learning. This research aims to find suitable methods from previous research to be used in the creation of a conversational chatbot for supporting services of an application system. The research method used is Systematic Literature Review, with comprehensive journal search steps using appropriate keyword search strategies. The research results include 20 articles relevant to the topic of chatbot NLP assistants. The various methods identified in the research include machine learning, deep learning, rule-based approaches, and the use of third-party applications such as Dialogflow and IBM Watson. The analysis results show that the Dynamic Memory Network (DMN) method has the best performance with 91% accuracy. DMN combines the advantages of LSTM and Memory Network with a dynamic attention mechanism, allowing the model to focus on the most relevant information in sequential data. Although this study provides interesting findings, further research is needed to deal with the different characteristics and availability of data in various real-world scenarios. Thus, this article highlights the importance of continuously developing NLP chatbot technology for better applications and improved service quality for users. It is hoped that this article can contribute to the development of research related to NLP chatbot assistants in better and more efficient application systems.

Game Edukasi Berbasis Android Pengenalan Serangga Pada Anak Tunagrahita SLB Negeri Sukoharjo

Rama Elian Zuldi Rama, Fatah Yasin Al Irsyadi — Mon, 01 Apr 2024 15:46:03 +0700

Mental Retardation refers to individuals with below-average intellectual or mental limitations caused by abnormalities in the brain's structure or function. They have deficits in adaptive behaviors such as daily living skills, social skills, language, and communication. Special care and support, including special education, are needed to enhance their development and quality of life. SLB Negeri Sukoharjo in Central Java provides special education for individuals with Mental Retardation, teaching basic skills, social skills, and life skills for future independence. Observations and interviews reveal that SLB still uses conventional learning media, leading to issues like students folding, tearing, and discarding paper-based materials. To address this, the author develops educational games as interactive learning media to promote active student participation and make the learning process more engaging for Mental Retardation students. The game, Jelajah Serangga, is designed using Construct2 software and the Game Development Life Cycle (GDLC) research method. It achieves an excellent rating with an SUS score of 85, indicating high quality. This game effectively supports learning at Sukoharjo State Special School.

Penilaian Kematangan Manajemen Data Master, Studi Kasus Rumah Terapi XYZ

Asymala Permata Sari Asymala, Achmad Nizar Hidayanto — Tue, 23 Apr 2024 12:27:49 +0700

Master Data Management (MDM) provides a way for an organization to ensure its data consolidation and integration across the whole system using a single master data set, thus benefiting with seamless operational and analytical processes. Not all organizations have good directions nor know the strategies of master data management. Master Data Maturity Management Model (MD3M) provides a blueprint for organizations to assess their currently implemented master data maturity management. The case study in this paper assessed MDM implementation in Therapy House XYZ by using MD3M by Spruitz and Pietzka. The referred MD3M has 5 key topics and 13 focus areas regarding master data management implementations. By assessing each of these points, organizations could know in which directions or area they could improve regarding MDM. The data were collected by using questionnaires and interviewing a Subject Matter Expert (SME) that handled the data management and system in the organization. This research found that 57% or 36 out of 63 of MD3M capabilities have been implemented. The missing capabilities are spread thorough the 5 key topics. The organization can achieve a higher maturity level by implementing the missing capabilities.

Keywords: Master Data, Master Data Management, Master Data Maturity Assessment, MD3M

Arsitektur Enterprise Aplikasi SIP Menggunakan Kerangka Kerja Zachman

Yanto Sugiyanto, Shofa Shofia Hilabi, Baenil Huda — Thu, 02 May 2024 14:28:10 +0700

The Pension Information System (SIP) is a digital platform specifically designed for comprehensive and transparent management of pension information. In modern administration, administrative efficiency and transparency are very important to improve the quality of public services. Therefore, the development of an appropriate pension information system (SIP) has a strategic role in increasing the productivity and work efficiency of civil servants. Enterprise application architecture has become an important element in the development of complex information systems. In this research, we investigate the application of the Zachman framework to the architectural design of enterprise pension information system (LIS) applications. Research methods include documentary analysis and interviews with experts in the field of software architecture.

Our research results show that the application of the Zachman framework provides an organized and clear structure to the architectural design of enterprise pension information system (LIS) applications. By considering the various aspects provided by the Zachman framework, the architectural design process can be carried out systematically and efficiently.

It is hoped that this research can contribute to the development of a company application architecture design methodology, especially those related to the implementation of pension information systems (SIP). The findings of this research also encourage further research regarding the application of new technology to support information system integration at the agency level.