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Journal : International Journal of Information Technology and Computer Science Applications (IJITCSA)

Exclusive Clustering Technique for Customer Segmentation in National Telecommunications Companies Kristian Vieri, Jhon; Munandar, Tb Ai; Srisulistiowati, Dwi Budi
International Journal of Information Technology and Computer Science Applications Vol. 1 No. 1 (2023): January - April 2023
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (917.495 KB) | DOI: 10.58776/ijitcsa.v1i1.19

Abstract

This study aims to empirically examine consumer behavior based on customer transaction history. Analyzing consumer behavior can provide very useful information for businesses in making decisions, particularly business decisions toward customers, in order to survive in such intense competition.Companies are becoming faster and more precise in reading environmental conditions and predicting what conditions may occur as a result of machine learning technology.This technology can also assist companies in making decisions that are more targeted according to actual secondary data provided for research. One of the machine learning methods, unsupervised learning, can help explicitly identify hidden structures or patterns in data and determine correlations. This method uses the Exclusive Clustering method, using two algorithms, namely, K-Means and K-Medoids, to use the comparison method to get optimal segmentation results. The results obtained are expected to be a reference for making a change in the company's marketing policy in order to retain and gain customers who are constantly decreasing.
K-Means Cluster Algorithm for Grouping Inequality in Regional Development Munandar, Tb Ai; Handayani, Dwipa
International Journal of Information Technology and Computer Science Applications Vol. 1 No. 1 (2023): January - April 2023
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (495.534 KB) | DOI: 10.58776/ijitcsa.v1i1.20

Abstract

Unsupervised learning is a subset of machine learning. Many unsupervised learning algorithms are used to solve various problems, especially the extraction of hidden data patterns. One of the problems that concerns unsupervised tasks is clustering. Clustering techniques are widely used for data grouping needs, one of which is development inequality clustering. The classification of development inequality is an important consideration in a country's regional development strategy. However, development groupings often do not pay attention to the hidden information aspects of the data, so they do not get the appropriate results. This research was conducted to provide an additional alternative in the realm of development inequality clustering, namely by classifying development data using the k-means algorithm. The data used is GRDP data for 41 regions in the western part of Java Island for the 2010–2021 range. The results show that the forty-one regions are grouped into four clusters. The first cluster (C1) contains 35 regions, the second cluster (C2) contains three regions, the third cluster (C3) contains four regions, and the fourth cluster (C4) contains three regions. Based on the cluster results, it can be seen that all members of cluster C4 are areas with the best level of development, while cluster C1 is the area with the lowest level of development. As for clusters C2 and C3, these are areas with development levels between clusters C1 and C4. The grouping results can be used by policymakers or local governments to determine the direction of future development priorities based on the cluster with the lowest level of development.
Comparative Study of Classification Algorithms for Customer Decisions on Telecommunication Products Using Supervised Learning Kristian Vieri, Jhon; Munandar, Tb Ai; Srisulistiowati, Dwi Budi; Handayani, Dwipa; No’eman, Achmad; Sri Lestari, Tyastuti
International Journal of Information Technology and Computer Science Applications Vol. 1 No. 2 (2023): May - August 2023
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (801.899 KB) | DOI: 10.58776/ijitcsa.v1i2.34

Abstract

Customers are the main goal of all business fields, without customers the company will not be able to continue or compete in the business field it is in, even though the company has brilliant products, if it does not have an increase in the number of customers the business will not be able to develop or even go bankrupt. Therefore, it is necessary to make observations and make applications that are able to predict customers who will subscribe so that companies can predict customers who will subscribe correctly without having to wait for confirmation from customers whose possibilities are still unknown. This can be very useful for any company because companies no longer need to look for random customers where it only takes time to find customers. PT. Telekomunikasi Indonesia with its product (Indihome) which is struggling to compete in the business world in the telecommunications and internet sector. Therefore research and development of this application are carried out so that PT. Indonesian telecommunications can get its customers quickly without having to spend a lot of money and effort. Making this application uses a classification method from machine learning technology based on customer historical data. The classification method has many strong algorithms for predicting variables that have more than 1 label. Some of the algorithms used are Logistic Regression, Random Forest Classifier, Support Vector Machine and Decision Tree which are provided by modules in the python programming language, namely SkLearn. The four algorithms will be tested with data balanced using the Oversampling method from the Smote algorithm to get optimal results in automatically predicting customers.
Multinomial Naive Bayes Algorithm for Indonesian language Sentiment Classification Related to Jakarta International Stadium (JIS) Rizki Surya Pratama, Daffa; Munandar, Tb Ai; Fadhilla Ramdhania, Khairunnisa
International Journal of Information Technology and Computer Science Applications Vol. 2 No. 1 (2024): January - April 2024
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v2i1.118

Abstract

The research focuses on analysing public evaluations, particularly those on Google Maps, about the Jakarta International Stadium (JIS). The study aims to employ the multinomial Naive Bayes algorithm to ascertain the sentiment expressed in these reviews. The objective of this study was to employ the multinomial Naive Bayes method to analyse the reviews on Google Maps pertaining to the Jakarta International Stadium (JIS). The utilised data consists of 2971 public reviews on Google Maps specifically pertaining to Jakarta International Stadium (JIS). These reviews were acquired through web scraping using a data miner. The acquired data is next processed in the text preparation phase to generate a prepared dataset suitable for analysis. This preprocessing stage includes operations such as casefolding, stopword removal, tokenizing, and stemming. The study yielded an accuracy of 0.83, or 83%, when tested on 733 data points. Out of these, 292 positive data points were correctly anticipated, while 59 positive data points were incorrectly forecast. Additionally, 317 negative data points were correctly predicted, while 65 negative data points were incorrectly predicted. The conducted modelling is subsequently categorised using a novel dataset of 161 review data points, with the objective of discerning the sentiment expressed within the dataset. The analysis of the new dataset yielded 101 reviews with positive sentiment and 50 reviews with negative sentiment.
Bayadome Geotours (BATOUR) Prototype for Geosite Management at Bayah Dome Geopark, Banten Munandar, Tb Ai; Sri Lestari, Tyastuti; Handayani, Dwipa; Noe’man,, Achmad; Fathurrazi, Ahmad; Priatna, Wowon; Karyaningsih, Dentik; Kapriadi, Engkap
International Journal of Information Technology and Computer Science Applications Vol. 2 No. 1 (2024): January - April 2024
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v2i1.119

Abstract

The objective of this study is to create a technology-driven application prototype, named "Bayadome Geotours," as a cutting-edge solution to enhance geotourism governance and environmental conservation in the Bayah Dome Geopark, Banten. This research advances the utilisation of information and geospatial technology to improve visitor experiences and bolster local community involvement. It achieves this through an emphasis on needs analysis, prototype design, implementation, and testing. The Bayadome Geotours prototype is specifically engineered to offer a dynamic and engaging tourism encounter. Geospatial navigation capabilities enable users to digitally explore geosites, while an intuitive user interface assures accessibility for visitors with different levels of knowledge. This programme offers precise and comprehensive geological information, providing a novel method to enhance comprehension of the geological resources found in the Bayah Dome Geopark. Bayadome Geotours is a good example of the value of local community involvement in geotourism administration. This application serves as both a travel guide and a venue for the exchange of knowledge, local narratives, and cultural heritage. Engaging the public in sharing information fosters a stronger connection between tourists and the environment, resulting in a beneficial influence on the preservation of geosites and the overall management of destinations. Prototype testing conducted using a unit testing methodology demonstrates the successful execution of all system functionalities. The JEST tool's test results confirm that the Bayadome Geotours application is prepared for distribution to the general user base. Nevertheless, there are obstacles in the way of effectively managing and modernising the application, as well as achieving general acceptance, that must be addressed in order to guarantee the ongoing triumph of this prototype. However, Bayadome Geotours has created significant opportunities for advancing sustainable geotourism governance.
Enhancing Film Genre Classification Using FastText Embeddings, Bidirectional GRU (BiGRU), and Attention Mechanisms Muhammad Fairuzabadi; Munandar, Tb Ai
International Journal of Information Technology and Computer Science Applications Vol. 2 No. 3 (2024): September - December 2024
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v2i3.169

Abstract

This research aims to enhance the classification of film genres using advanced natural language processing techniques. By integrating FastText embeddings with Bidirectional Gated Recurrent Units (Bi-GRU) and attention mechanisms, the proposed model addresses the limitations of existing methods that struggle with capturing both local and global dependencies within textual data. The model's performance is evaluated on a dataset from IMDb, demonstrating its capability to predict film genres from textual descriptions accurately. Key contributions include the development of a robust model architecture that effectively handles out-of-vocabulary words and contextual nuances, implementing regularization techniques such as DropConnect to improve generalization, and using advanced embeddings to enhance semantic representation. The results indicate significant improvements in genre classification accuracy, particularly for frequent genres, showcasing the model's potential for practical applications in media content analysis. Future work will address data imbalance and explore more sophisticated architectures to enhance performanc.
Agglomerative Spatial Clustering Analysis for Mapping Crime Risk Zone Clusters Munandar, Tb Ai; Ramdhania, Khairunnisa Fadhilla
International Journal of Information Technology and Computer Science Applications Vol. 3 No. 2 (2025): May - August 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v3i2.197

Abstract

Public safety and order are crucial aspects of social and economic life, especially in densely populated urban areas. High crime rates can undermine the sense of security and quality of life within society. Therefore, a deep understanding of crime distribution patterns is essential for designing effective prevention strategies. This study aims to map crime risk zones in Indonesia using the Agglomerative Clustering method, by integrating socio-economic and demographic variables. This method was chosen for its ability to group data based on similarity of characteristics, making it easier to identify areas with high-risk levels. The results show the formation of four main clusters that reflect crime risk distribution in Indonesia. The first cluster includes several provinces with similar crime patterns, while the other clusters reflect significant differences in crime patterns, particularly in Jakarta, which has very distinct criminal characteristics. This mapping provides valuable insights for the planning of more efficient, data-driven crime prevention policies. The research is expected to provide a strong foundation for policymakers and law enforcement agencies to formulate more targeted strategies to combat crime in Indonesia.
NeoCare: Telehealth System with Intelligent Notification for Neonatal Care Munandar, Tb Ai; Sri Lestari, Tyastuti; Noeman, Achmad
International Journal of Information Technology and Computer Science Applications Vol. 3 No. 3 (2025): September - December 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v3i3.239

Abstract

Neonatal mortality in low- and middle-income countries remains high, partly because early physiological deterioration is detected late and continuous monitoring is limited outside specialized units. To address this gap, this study presents NeoCare: Telehealth System with Intelligent Notification for Neonatal Care, a multi-actor platform that integrates neonatal data management, vital-sign monitoring, and machine-learning–based alerts. The research followed a software engineering approach comprising stakeholder and context analysis, requirements engineering, clinical data acquisition, system and database design, intelligent notification model design, and prototype implementation. Retrospective neonatal records from two Indonesian referral hospitals were used to characterize heterogeneous and homogeneous clinical populations and to inform the design of classification features for vital-sign–based risk assessment. NeoCare is realized as a layered architecture with sensor, device, communication, processing-intelligence, and application layers. The prototype includes web and mobile interfaces tailored to four actor groups: hospital administrators, doctors, midwives, and parents. Administrators manage users, hospitals, vital-sign data, and machine-learning models while supervising alert output. Doctors and midwives access dashboards that display neonatal lists, detailed histories, trend graphs, and consultation management, supporting triage and longitudinal follow-up. Parents use a simplified mobile interface to view their baby’s status, monitor vital-sign trends, receive alerts, and schedule consultations. The system embeds an intelligent notification mechanism that flags abnormal patterns and presents them through color-coded indicators and concise messages. The results demonstrate the technical feasibility and coherence of a role-based, data-driven telehealth platform for neonatal care, providing a solid foundation for future work on clinical validation, device integration, and large-scale deployment.