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All Journal Jiko (Jurnal Informatika dan komputer) JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) International Journal of Data Science, Engineering, and Analytics (IJDASEA) Jurnal Aplikasi Sains Data
Dwi Arman Prasetya
UPN "Veteran" Jawa Timur
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Mathematics

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Simple Sentiment Analysis Using LSTM and BERT Algoritmhs for Classifying Spam and Non-Spam Data Prismahardi Aji Riyantoko; Dwi Arman Prasetya; Tahta Dari Timur
Internasional Journal of Data Science, Engineering, and Anaylitics Vol. 2 No. 2 (2022): International Journal of Data Science, Engineering, and Analytics Vol 2, No 2,
Publisher : International Journal of Data Science, Engineering, and Analytics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijdasea.v2i2.40

Abstract

Sentiment analysis has become a useful tool for doing data analysis and classification based on words, phrases, or documents. Previously, researchers conducted extensive research on sentiment analysis using a variety of algorithms and models. Based on previous research, the results of the sentiment analysis have a negative impact on model performance and data type. At the moment, researchers are using the LSTM and BERT models to classify SMS data into spam and non-spam. The researcher using TD-IDF and GloVe algorithm to determine the weighting of the values represented in vectors in each word to optimize the results of value accuracy. Regardless of the results obtained, the methods BERT and LSTM have a value accuracy sensitivity of 99.35% and 98.22%, respectively. The results present that the completion of spam and non-spam dataset classification is very effective and efficient. Tests were also carried out using disaster twitter data, but the level of accuracy of the values decreased. Therefore, it can be supposed that the different types of datasets considerably affect the performance of the temptation model.
Penerapan Metode Decision Tree C4.5 untuk Klasifikasi Data Kandidat Tenaga Kerja pada Perusahaan Outsourcing Ahmad Ardhy Ansyah; Tresna Maulana Fahrudin; Dwi Arman Prasetya
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 6 No. 1 (2024): Juni 2024
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v6i1.12670

Abstract

Sebuah perusahaan biasanya melakukan screening menggunakan metode konvensional dalam mencari kandidat tenaga kerja. Hal tersebut berdampak pada proses pemilihan kandidat tenaga kerja yang menghabiskan waktu yang cukup lama dan ketidakkonsistenan dalam pengambilan keputusan. Oleh karena itu, dalam proses pemilihan kandidat tenaga kerja dapat menggunakan sebuah model machine learning yang dapat melakukan klasifikasi berdasarkan profil dan kompetensi kandidat tenaga kerja. Model machine learning yang digunakan salah satunya adalah Decision Tree C4.5 yang mampu menghasilkan sebuah keputusan pemilihan kandidat tenaga kerja secara otomatis berdasarkan data.  Hasil pengujian menunjukkan bahwa model ini memiliki akurasi dan precision yang tinggi, terutama pada validation model menggunakan holdout atau percentage split dengan proporsi data training dan testing sebesar 70:30 masing-masing yakni mencapai akurasi terbaik sebesar 0.99, dan precision sebesar 0.9. Dengan demikian, model ini dapat dimplementasikan dalam sistem seleksi kandidat untuk meningkatkan efisiensi proses seleksi dan pengelolaan data kandidat.
CLASSIFICATION OF JAVANESE NGLEGENA SCRIPT USING COMPLEXVALUED NEURAL NETWORK Adinda Aulia Rahmawati; Amri Muhaimin; Dwi Arman Prasetya
JIKO (Jurnal Informatika dan Komputer) Vol 7 No 1 (2024)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i1.7808

Abstract

Javanese script is one of the traditional scripts in Indonesia used by the Javanese people. The Javanese script used in Javanese spelling basically consists of 20 main characters (nglegena), namely from the Ha to Nga script. Javanese script has very high value, the uniqueness of the script is one thing that must be preserved. However, widespread use of Javanese script has declined as technology has developed. In this context, one of the problems that arises is the difficulty in automatically recognizing and classifying the Javanese Nglegena script. Therefore, the use of computational methods to automatically classify the Nglegena Javanese script is very important. This research compares 2 methods for classifying Javanese Nglegena script, namely Complex-Valued Neural Network (CVNN) and Convolutional Neural Network (CNN). This research aims to compare the best accuracy between CVNN and CNN. In this study, the Complex-Valued Neural Network method had a higher average accuracy, namely 96.332% and a loss of 0.1834. Meanwhile, the CNN method has an average accuracy of 93.72% and a loss of 0.4254. Artificial intelligence-based Javanese Nglegena script classification technology can help people to recognize the Javanese Nglegena script, especially in the fields of education and culture.
Dijkstra's Algorithm for Optimizing Humanitarian Aid Distribution Routes to Flood Victims in Cerme District, Gresik Datia Putri Nabila Br Tarigan; Desi Tristianti; Erika Fatimatul Hidayanti; Dwi Arman Prasetya; Tresna Maulana Fahrudin
Jurnal Aplikasi Sains Data Vol. 2 No. 1 (2026): Journal of Data Science Applications.
Publisher : Program Studi Sains Data UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/jasid.v2i1.32

Abstract

This study presents the development and analysis of a system designed to optimize the distribution routes of social aid during flood emergencies in the Cerme District, Gresik Regency. The primary objective is to ensure that logistical operations, particularly the delivery of aid to affected villages, are carried out in the most efficient and timely manner. To achieve this, Dijkstra’s Algorithm is employed due to its well-established reliability in computing the shortest path between nodes in a weighted graph. The graph used in this research is constructed based on real-world spatial data, with each node representing a village and the edges representing actual road distances obtained from mapping services. The system is implemented using an Object-Oriented Programming (OOP) paradigm in Python, which ensures modularity and scalability of the codebase. For graph modeling and shortest path computation, the NetworkX library is utilized, while the graphical user interface (GUI) is built using Tkinter to provide an interactive and user-friendly experience. The application enables users to select starting and destination points from dropdown menus, compute the shortest route dynamically, and visualize it on an interactive graph complete with route details and distances. Experimental trials were conducted by simulating various flood scenarios, and the results demonstrated that the system successfully identified optimal aid routes with minimized travel distances. These outcomes confirm the practicality and effectiveness of the proposed method. Moreover, the ability to update the graph dynamically allows the system to adapt to changes in road accessibility due to flooding. This makes the tool highly applicable in real-world disaster response scenarios. In conclusion, the developed application offers a valuable solution for both local government agencies and humanitarian volunteers, helping to improve coordination, reduce delivery time, and ensure that aid reaches flood-affected communities as efficiently as possible.
Comparative Analysis of Hierarchical Clustering and K-Medoids for Clustering Cases of Childhood Respiratory Diseases in Lamongan Regency Adelia Yuandhika; Nezalfa Sabrina; Cahya Eka Melati; Dwi Arman Prasetya; Prismahardi Aji Riyantoko
Jurnal Aplikasi Sains Data Vol. 2 No. 1 (2026): Journal of Data Science Applications.
Publisher : Program Studi Sains Data UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/jasid.v2i1.37

Abstract

Abstract— Respiratory diseases affecting children remain a significant health issue in Indonesia, including in Lamongan Regency. The region faces challenges related to pediatric respiratory illnesses, particularly Childhood Tuberculosis, Pneumonia in toddlers, and Cough in toddlers, which impact children's quality of life and development. Therefore, understanding the spatial distribution and correlation patterns among these diseases is essential to support more targeted health intervention planning. This study analyzes the distribution patterns of pediatric respiratory diseases in Lamongan Regency and clusters regions based on similarities in the number of cases using an unsupervised learning approach. The method employed is Hierarchical Clustering with four distance calculation techniques: single, complete, average, and ward linkage and K-Medoids with two distance calculation techniques: euclidean and manhattan distance. The data, sourced from the Lamongan District Health Office, include four numerical variables related to respiratory diseases, aggregated by sub-districts. Data normalization was carried out using standardization, and cluster quality was evaluated using three internal metrics: Silhouette Score, Davies-Bouldin Index (DBI), and Calinski-Harabasz Index (CHI). The analysis results indicate that the optimal number of clusters is three. Among all methods tested, the Hierarchical Clustering with ward linkage method yielded the best performance, with a Silhouette Score of 0.5447, a DBI of 0.5884, and a CHI of 20.3018. These results demonstrate that the ward linkage method is the most effective in clustering regions based on the characteristics of pediatric respiratory disease cases and can be used for mapping priority health intervention areas in Lamongan Regency.
Co-Authors Adelia Yuandhika Adinda Aulia Rahmawati Ahmad Ardhy Ansyah Amri Muhaimin Cahya Eka Melati Datia Putri Nabila Br Tarigan Desi Tristianti Erika Fatimatul Hidayanti Nezalfa Sabrina Prismahardi Aji Riyantoko Prismahardi Aji Riyantoko Tahta Dari Timur Tresna Maulana Fahrudin Tresna Maulana Fahrudin