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Pengelompokan Remaja Berdasarkan Segmentasi Usia Menggunakan Metode K-Means Clustering (Studi Kasus : Desa Sindangsari) Rini Rahmawati; Agus Bahtiar
Akuntansi Vol. 2 No. 2 (2023): Juni : Jurnal Riset Ilmu Akuntansi
Publisher : Asosiasi Riset Ekonomi dan Akuntansi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/akuntansi.v2i2.236

Data mining is processing information from a database that can be used for various needs. One of the methods in data mining, namely Clustering which aims to find groupings from a series of patterns, points, objects and documents. The K-Means clustering algorithm is an algorithm that plays an important role in the field of data mining and is simple to implement and run. The K- Means Clustering method attempts to group existing data into several groups, where the data in one group have the same characteristics. By conducting clustering research youth based on age segmentation using the k-means clustering method is expected to be able to contribute especially to PIK R colleagues in dividing the segmentation of PIK R members easily and systematically without using manual methods. This age segmentation can be used to determine the level of development, needs, and preferences of adolescents in various aspects of life. This study aims to process the number of adolescents for members of the PIK-R organization, it is hoped that it will make it easier for secretaries in the Pik-R organization to manage new membership recruitment data based on age and knowing which hamlet has the most teenage population. In each cluster it is classified based on which criteria are prioritized. System testing was carried out 4 times with data consisting of 24 attributes 1789 records of new PIK-R members to get precision implementation results K-Means Clustering method.

Sistem Penerimaan Dan Pengelolaan Administrasi Keuangan Pendidikan Di Kober TK Islamic Centre Berbasis WEB Nur Amelia; Agus Bahtiar
Jurnal Kendali Akuntansi Vol. 1 No. 2 (2023): April : Jurnal Kendali Akuntansi
Publisher : Universitas Katolik Widya Karya Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59581/jka-widyakarya.v1i2.145

Administrative financial education is an important component in the field of education to obtain educational facilities, including in Kober TK Islamic Center. Recording of acceptance and management of school financial administration at the Kober TK Islamic Center using handwriting or has not been computerized, which can result in a lot of bookkeeping, writing errors, and delays in financial reporting. In addition to the long recording process, data search also takes a long time, which can make it difficult for administrative staff to process acceptance and manage financial administration. Based on the problems in the Kober TK Islamic Center, it is necessary to build an information system that can assist in recording and managing financial administration. This information system creates using the MySQL database, the PHP (Hypertext Preprocessor) programming language, and Sublime Text for the text editor. the test server uses XAMPP, and the data used is obtained from Kober TK Islamic Center. The expected results of this final project are to assist employees in recording acceptance and managing financial administration efficiently. Then, for this project, it is hoped that it can also assist in the acceptance and management of financial administration in the Kober TK Islamic Center.

Bibliometric Analysis: Machine Learning untuk Blended Learning Agus Bahtiar; Mulyawan
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Blended learning, which combines face-to-face learning methods with digital technology, has grown rapidly thanks to advances in information technology. Along with that, machine learning technology offers great potential to improve personalization and adaptation in blended learning. This research aims to explore the application of machine learning in blended learning systems through bibliometric analysis. By analyzing SCOPUS indexed publications from 2019 to 2024, this study identifies trends, challenges and opportunities in the integration of machine learning with blended learning. The methods used include search keyword definition, initial data collection, refinement of search results, statistical compilation, and data analysis. The main findings show that there is a significant increase in the number of publications on this topic, with the highest peak in 2022. The wide distribution of publications indicates significant international collaboration. Citation analysis indicates that the quality and impact of research is also increasing, with recent publications gaining more citations. This research highlights the importance of applying machine learning in blended learning to improve educational effectiveness and support the development of more adaptive learning methods. The findings provide valuable insights for academics and practitioners to encourage further innovation and improve the quality of education in the digital era.

Sentiment analysis to classify TikTok Shop Users on Twitter with Naïve Bayes Classifier Algorithm Lestari, Ayu; Ade Irma Purnamasari; Agus Bahtiar; Edi Tohidi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.748

Advances in information technology have facilitated the use of social media as an e-commerce platform, with TikTok Shop enabling in-person transactions. This research addresses the gap in understanding user perceptions of TikTok Shop through sentiment analysis on Twitter. Sentiment classification is performed using the Naïve Bayes Classifier algorithm. The dataset consists of 1,907 Indonesian tweets, collected from January 2023 to July 2024, and processed using RapidMiner in the Knowledge Discovery in Database (KDD) framework. The preprocessing stages include data cleaning, normalization, tokenization, stopword removal, and stemming. To overcome data imbalance, Synthetic Minority Oversampling Technique (SMOTE) was applied. The model achieved 93.98% accuracy, with balanced precision and recall for positive, neutral, and negative sentiments. The sentiment distribution among TikTok Shop users on Twitter was 35.5% positive, 35.5% negative, and 29.0% neutral. This research provides insights into consumer behavior on social media and emphasizes the importance of sentiment analysis to increase user engagement and understand market perception. This research is expected to provide information to platform developers and businesses looking to improve TikTok

K-Means Algorithm for Grouping Models of Dengue Fever Prone Areas in Cirebon City Aida Safitri; Ade Irma Purnamasari; Agus Bahtiar; Edi Tohidi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.834

Dengue hemorrhagic fever (DHF) is an infectious disease transmitted through the Aedes aegypti mosquito. DHF cases in Cirebon City show a significant increase every year. This study aims to classify dengue prone areas based on case data per health center in 2020-2024 obtained from the Cirebon City Health Office. The method used is the K-Means algorithm with the Knowledge Discovery in Database (KDD) approach, which includes data selection, preprocessing, data transformation, data mining, evaluation, and knowledge. Evaluation using Davies-Bouldin Index (DBI) showed optimal results at k = 6 with a DBI value of -0.445. The clustering results produced six clusters: cluster 5 (437 dengue cases in 34 health centers) showed high risk; cluster 0 (244 cases), cluster 2 (129 cases), and cluster 3 (279 cases) showed medium risk; while cluster 1 (69 cases) and cluster 4 (86 cases) showed low risk. This study shows that the K-Means algorithm is effective in identifying DHF risk distribution patterns and provides a strategic basis for the Cirebon City Health Office to prioritize interventions and develop more effective prevention strategies.

PENINGKATAN MODEL KLASIFIKASI SENTIMEN PENGGUNA APLIKASI TOMORO COFFEE MENGGUNAKAN ALGORITMA NAÏVE BAYES Dina Audina; Ade Irma Purnamasari; Agus Bahtiar; Edi Tohidi
Jurnal Informatika dan Rekayasa Elektronik Vol. 8 No. 1 (2025): JIRE APRIL 2025
Publisher : LPPM STMIK Lombok

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Kemajuan teknologi informasi telah merevolusi cara bisnis berinteraksi dengan pelanggan melalui aplikasi mobile, termasuk dalam sektor makanan dan minuman. Aplikasi Tomoro Coffee menghadapi tantangan dalam mempertahankan kepuasan pengguna akibat keterbatasan fitur dan masalah teknis. Penelitian ini bertujuan untuk menerapkan algoritma Naïve Bayes guna meningkatkan model klasifikasi sentimen ulasan pengguna, menganalisis distribusi sentimen positif dan negatif beserta faktor utama yang memengaruhinya, serta mengevaluasi performa model berdasarkan akurasi, presisi, recall, dan F1-score. Data ulasan dikumpulkan dari Google Play Store dan diolah menggunakan metode Knowledge Discovery in Database (KDD), yang mencakup pembersihan data, tokenisasi, penghapusan stopword, stemming, serta ekstraksi fitur menggunakan Term Frequency-Inverse Document Frequency (TF-IDF). Hasil penelitian menunjukkan bahwa algoritma Naïve Bayes mencapai akurasi sebesar 90%, dengan presisi 91,3%, recall 87,3%, dan F1-score 88,7%. Temuan ini memberikan wawasan strategis bagi pengembang aplikasi dalam meningkatkan layanan dan fitur berdasarkan analisis sentimen pengguna. Dari hasil analisis, 64,4% ulasan tergolong positif, didominasi oleh komentar seperti "kopinya enak", sementara 35,6% ulasan negatif umumnya berisi keluhan teknis, seperti "tidak tersedia".

Pengelompokan Remaja Berdasarkan Segmentasi Usia Menggunakan Metode K-Means Clustering (Studi Kasus : Desa Sindangsari) Rini Rahmawati; Agus Bahtiar
Akuntansi Vol. 2 No. 2 (2023): Juni : Jurnal Riset Ilmu Akuntansi
Publisher : Asosiasi Riset Ekonomi dan Akuntansi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/akuntansi.v2i2.236

Data mining is processing information from a database that can be used for various needs. One of the methods in data mining, namely Clustering which aims to find groupings from a series of patterns, points, objects and documents. The K-Means clustering algorithm is an algorithm that plays an important role in the field of data mining and is simple to implement and run. The K- Means Clustering method attempts to group existing data into several groups, where the data in one group have the same characteristics. By conducting clustering research youth based on age segmentation using the k-means clustering method is expected to be able to contribute especially to PIK R colleagues in dividing the segmentation of PIK R members easily and systematically without using manual methods. This age segmentation can be used to determine the level of development, needs, and preferences of adolescents in various aspects of life. This study aims to process the number of adolescents for members of the PIK-R organization, it is hoped that it will make it easier for secretaries in the Pik-R organization to manage new membership recruitment data based on age and knowing which hamlet has the most teenage population. In each cluster it is classified based on which criteria are prioritized. System testing was carried out 4 times with data consisting of 24 attributes 1789 records of new PIK-R members to get precision implementation results K-Means Clustering method.

Clustering Analysis of Administrative Service Types Using K-Means (Study Case: Village bojongsalam) Wafiq Azizah; Ade Irma Purnamasari; Agus Bahtiar; Kaslani
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.867

Advances in information technology present significant opportunities for the improvement of public services, especially in relation to the administrative functions of Bojongsalam Village. Reliance on traditional methods often leads to inefficiencies and inaccuracies in administrative processes. This research uses the K-Means algorithm to categorize administrative service data based on service type, document number, printing date, and accompanying remarks. Utilizing the Knowledge Discovery in Databases (KDD) framework, the analysis includes data selection, pre-processing, transformation, and clustering analysis conducted through RapidMiner software. The dataset consisted of 718 administrative records that had undergone a rigorous cleaning process, including attribute normalization. The analysis resulted in an optimal Davies-Bouldin Index (DBI) value of -0.498 at K = 4, with each cluster representing a different service utilization pattern. The issuance of Family Cards (KK) and Birth Certificates showed higher demand compared to other available services. This classification promotes workload optimization, fair resource allocation, and formulation of effective operational strategies. The application of the K-Means algorithm demonstrated its effectiveness in data clustering and made a significant contribution to technology-based administrative management. The findings lay a basic framework for addressing the needs of the community in a timely manner.

Perancangan Aplikasi Perhitungan Harga Pokok Produksi Berbasis Web pada Percetakan Daduh Ciledug Sri Wulandari; Agus Bahtiar
Journal of Student Research Vol. 1 No. 3 (2023): Mei: Journal of Student Research
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jsr.v1i3.1154

Percetakan Daduh adalah sebuah usaha percetakan yang memproduksi stempel, undangan, kartu nama, buku yasin, dan lainnya. Penggunaan teknologi yang dapat diterapkan pada salah satu faktor penentu pendapatan usaha yaitu harga jual, mengharuskan perusahaan percetakan untuk beradaptasi dengan kemajuan teknologi. Akan tetapi, saat ini mekanisme perhitungan harga pada Percetakan Daduh masih dilakukan secara manual dan tanpa memperhitungkan ketiga komponen biaya produksi. Hal ini dapat menyebabkan proses penentuan harga menghasilkan jumlah yang tidak tepat. Dengan adanya permasalahan tersebut, pemilik usaha membutuhkan sebuah program yang mempermudah perhitungan harga pokok produksi. User Interface sistem ini dikembangkan dengan menggunakan React JS sebagai framework JavaScript dan Firebase sebagai platform penyimpanan database. Pada sistem berbasis web ini, harga pokok produksi dihitung menggunakan metode harga pokok pesanan. Dengan menggunakan metode harga pokok pesanan, biaya produksi dihitung atas dasar jumlah pesanan. Sistem ini terdiri dari data biaya sebagai masukan dan harga pokok produksi sebagai keluaran. Data biaya yang diinputkan meliputi informasi yang berkaitan dengan komponen harga pokok produksi, yaitu biaya bahan baku, tenaga kerja, dan biaya overhead pabrik. Aplikasi ini mampu membantu percetakan Daduh dalam menetapkan harga pokok produksi dengan cepat dan efisien.

Analisis Data Hasil Laporan Skripsi Berbasis Aspect Based Sentiment Analysis Menggunakan Algoritma K-Means Clustering Nana Suarna; Dadang Sudrajat; Umi Hayati; Ade Rizki Rinaldi; Agus Bahtiar
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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This study discusses the application of Aspect-Based Sentiment Analysis (ABSA) combined with the K-Means Clustering algorithm to analyze student thesis report data. The research scope includes text data processing from VAK (Visual, Auditory, Kinesthetic) learning style questionnaires to identify research aspects and automatically group thesis themes. The objective is to obtain a structured and representative mapping of students’ research themes based on their fields of study. The methodology involves several stages, including text preprocessing, TF-IDF weighting, aspect extraction using ABSA, and clustering with K-Means, validated through the Davies-Bouldin Index (DBI). The dataset consists of 976 textual entries derived from student questionnaire responses. The results indicate that the optimal cluster is achieved at k = 3 with a DBI value of 3.276, forming three main groups: (1) data mining, (2) statistical analysis, and (3) learning technology. The study concludes that the combination of ABSA and K-Means is effective in accurately classifying research themes and provides an analytical foundation for academic decision-making regarding student research trends.

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