Garuda - Garba Rujukan Digital

p-Index From 2020 - 2025

1.8

P-Index

This Author published in this journals

All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Machine Learning and Soft Computing Jurnal Keuangan dan Akuntansi Terapan (KUAT) Jurnal Dharmabakti Nagri International Journal of Information Technology and Computer Science Applications (IJITCSA) Jurnal Informatika: Jurnal Pengembangan IT

Tb Ai Munandar, Tb Ai

Unknown Affiliation

Author-ID : 644211

Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Social Sciences Other

Published : 12 Documents Claim Missing Document

Claim Missing Document

Articles

1 2

Decision Supporter for Determining Priority in Supply of Shoe Raw Materials Using the Simple Additive Weighting Method Sani, Ardila; Munandar, Tb Ai; Suhendar, Akip
Journal of Machine Learning and Soft Computing Vol 1, No 1 (2019): Volume 1, Nomor 1, Maret 2019
Publisher : Universitas Serang Raya

More and more types of raw materials will be used to expedite the shoe production process, making it difficult for companies to determine the priority of raw materials. So that there are often advantages and disadvantages of raw materials during the production process, which causes changes in the budget of production costs that are not in accordance with the initial plan, resulting in decreased corporate profits. Decision Support System (SPK) is a system that can help someone in making decisions that are accurate and right on target. Many problems can be solved by using SPK, one of which is determining priorities in the supply of raw materials. There are several methods that can be used in building a DSS, including simple addtive weighting (SAW). The Simple Additive Weighting (SAW) method is often also known as the weighted sum method, by finding the weighted sum of performance ratings for each alternative of all attributes. This study discusses the making of a decision support system as a tool for determining priorities in the supply of raw materials using the simple additive weighting (SAW) method. This application was developed with the Power Builder 8.0 programming language and Sybase SQL Anyware 5.0 as its database. The results of this study are able to help system users determine the priority of the supply of raw materials in order to facilitate the production process.

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

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

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

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

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

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.

Workshop Membangun Perusahaan Perseorangan Untuk Usaha Bidang IT Untuk Mahasiswa Semester Akhir Universitas Bhayangkara Jakarta Raya Munandar, Tb Ai; Handayani, Dwipa; Wulandari, Retno
Jurnal Dharmabakti Nagri Vol 2 No 1 (2023): Desember 2023 - Maret 2024
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/jdn.v2i1.105

The community service activity entitled "Workshop on Building Individual Companies for Businesses in the IT Sector for Final Semester Students at Bhayangkara University, Jakarta Raya" is a real effort to increase the knowledge and skills of final semester students in planning and managing individual companies in the Information Technology (IT) sector. This workshop aims to provide practical guidance in starting an IT company, introduce the legal aspects of IT business, and invite students to apply their knowledge in real situations. This activity covers various relevant topics, such as business planning, IT product development, financial management, marketing strategy, and legal aspects related to IT business. In addition, the workshop also involved experienced IT practitioners and entrepreneurs as guest speakers, who provided invaluable practical insights. The results of this workshop reflect an increase in participants' understanding and skills in planning and starting an IT business. Participants have a better understanding of the process of registering a private company online via the AHU website and the importance of registering for a Kemenkumham permit. This workshop not only improves conceptual understanding, but also provides practical skills that can be applied in the real world. Thus, this workshop has a positive impact on the development of entrepreneurship among final semester students at Bhayangkara University, Jakarta Raya. Furthermore, sustainability evaluations and follow-up steps are needed to ensure that the knowledge and skills acquired remain sustainable and support students in their efforts to become entrepreneurs in the IT industry.

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

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.

PKM : Digitalisasi Aktivitas Geowisata Kawasan Geopark Bayah Dome untuk Mendukung Pariwisata Berkelanjutan Karyaningsih, Dentik; Munandar, Tb Ai
KUAT : Keuangan Umum dan Akuntansi Terapan Vol 6 No 2 (2024): Edisi November
Publisher : Politeknik Keuangan Negara STAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31092/kuat.v6i2.2444

Perubahan digital telah merubah berbagai aspek kehidupan manusia, termasuk dalam sektor pariwisata pada masa sekarang ini. Dalam hal ini, Digitalisasi dalam pariwisata merujuk pada pemanfaatan teknologi digital untuk memperkenalkan, meningkatkan, dan mengelola destinasi geowisata. Meskipun Kawasan Geopark Bayah Dome memiliki potensi alam yang luar biasa, kekurangan digitalisasi dalam kegiatan pariwisata telah menjadi hambatan bagi pengembangan pariwisata berkelanjutan di kawasan ini. Proyek pengabdian masyarakat ini bertujuan untuk memperkenalkan konsep digitalisasi dalam kegiatan pariwisata di Kawasan Geopark Bayah Dome sebagai langkah strategis untuk mendukung pariwisata berkelanjutan. Melalui kolaborasi antara peneliti dari akademisi, masyarakat lokal kawasan, dan otoritas terkait, kami berhasil mengembangkan solusi digital yang dapat meningkatkan pengalaman wisatawan, mempromosikan warisan budaya lokal, dan mendukung pelestarian lingkungan, Dalam pengabdian ini, kami membantu paguyuban UMKM melalui pengurus dan pengelola kawasan geowisata, pelaku kriya dan homestay di sekitar desa sawarna dengan mengadopsi teknologi digital untuk meningkatkan daya saing destinasi geowisata mereka diantaranya dengan mempromosikan bisnis dengan google bisnisku serta content promosi berupa editing foto dan video dengan canva. Tujuan dari pengabdian masyarakat ini adalah untuk mengedukasi masyarakat lokal kawasan geowisata tentang potensi dan manfaat digitalisasi geowisata dan membantu pengembangan dan promosi destinasi geowisata lokal melalui pemanfaatan platform digital.

A Supervised Learning Model for Sentiment Analysis Based on Regional Dialects in Tourism-Related Issues Munandar, Tb Ai
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 3 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i3.8627

Indonesia has an exceptionally rich diversity of regional languages, one of which is the Bekasi dialect, often used in social media communication. The uniqueness of this dialect presents specific challenges in extracting public opinion, especially in text-based sentiment analysis. This study aims to develop a sentiment analysis framework that incorporates regional dialects from social media data and evaluate the effectiveness of various supervised learning algorithms. Data were collected from the Facebook group “Explore Bekasi Tourism,” totaling 1,257 posts and comments, which were filtered down to 1,000 relevant instances. A manual validation process was conducted by linguistic experts to convert non-standard terms and regional dialects into standardized Indonesian, followed by translation into English for annotation purposes. The analysis method involved preprocessing steps (tokenizing, case folding, stemming), feature weighting using TF-IDF, and sentiment classification using four algorithms: Naive Bayes, K-Nearest Neighbor, Support Vector Machine, and Decision Tree. The evaluation results show that Naive Bayes achieved the best performance with an accuracy of 76%, followed by K-Nearest Neighbor (67.5%), SVM (65.5%), and Decision Tree (28%). These findings highlight the crucial role of expert judgment in processing dialect-based data to ensure accurate sentiment classification. The study recommends developing a broader annotated corpus of regional dialects and exploring deep learning methods in future research to enhance classification performance and generalizability.

Co-Authors Dwi Budi Srisulistiowati Dwipa Handayani Fathurrazi, Ahmad Kapriadi, Engkap Karyaningsih, Dentik Khairunnisa Fadhilla Ramdhania Kristian Vieri, Jhon Muhammad Fairuzabadi Noe’man,, Achmad No’eman, Achmad Priatna , Wowon Ramdhania, Khairunnisa Fadhilla Retno Wulandari Rizki Surya Pratama, Daffa Sani, Ardila Sri Lestari, Tyastuti Suhendar, Akip Yunizar Yusuf Pratama, Ajif

Title

Found 12 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 12 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 12 Documents
Search