Article Per Year (5 Year)
p-Index From 2021 - 2026
4.405
P-Index
This Author published in this journals
All Journal Jurnal Informatika dan Rekayasa Elektronik Jurnal Informatika dan Rekayasa Perangkat Lunak JURSIMA (Jurnal Sistem Informasi dan Manajemen) ICIT (Innovative Creative and Information Technology) Journal E-Link: Jurnal Teknik Elektro dan Informatika Jurnal Dinamika Informatika (JDI) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Journal of Student Research Jurnal Kendali Akuntansi JURSIMA Jurnal Manajemen Dan Bisnis Ekonomi Akuntansi: Jurnal Riset Ilmu Akuntansi Sintek Kuwera
agus bahtiar
Unknown Affiliation
Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Library & Information Science Public Health Social Sciences

Published : 25 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 25 Documents
Search

 1   2   3 
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

Abstract

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

Abstract

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

Abstract

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.
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

Abstract

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

Abstract

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

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

Abstract

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.
Analisis Kinerja Algoritma Machine Learning untuk Klasifikasi Prestasi Mahasiswa pada Mata Kuliah Bahasa Inggris Riri Narasati; Dadang Sudrajat; Ahmad Faqih; Indra Wiguna Marthanu; Agus Bahtiar
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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

Abstract

This study analyzes the performance of several machine learning algorithms in classifying student achievement in English language courses. The research focuses on comparing the performance of K-Nearest Neighbors (KNN), Naïve Bayes, Random Forest, and Support Vector Machine (SVM) using the K-Fold Cross Validation approach to evaluate accuracy, F1-score, and fairness. The dataset, consisting of students’ final grades, was processed through data pre-processing and feature scaling. Results show that the KNN model with K=5 achieved the highest accuracy of 100%, followed by Naïve Bayes with 95.59%. Statistical tests indicated a significant performance difference between Random Forest and SVM, while fairness evaluation revealed that Random Forest provided the most balanced error distribution. These findings confirm that KNN and Random Forest algorithms are highly effective for academic performance classification based on numerical data. The study highlights the potential of machine learning to enhance adaptive, objective, and equitable educational evaluation systems.
Klasifikasi Telur Fertil dan Infertil Berbasis Hybrid MobileNetV3 dengan Mekanisme Attention dan Texture Fusion Bani Nurhakim; Dadang Sudrajat; Tati Suprapti; Ade Rizki Rinaldi; Agus Bahtiar
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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

Abstract

Accurate fertile-infertile egg classification is crucial to improve hatching productivity and sorting efficiency. This study proposes MobileFusionV3, a MobileNetV3 architecture enriched with CBAM (Convolutional Block Attention Module) and Hybrid Texture Fusion (LBP and GLCM) to combine deep and texture features to be more robust to candling illumination variations. A dataset of 1,275 candling images (675 fertile, 600 infertile) was subjected to preprocessing (resizing, normalization, background enhancement) and realistic data augmentation (rotation, brightness/contrast changes, Gaussian noise, illumination variations). The model was trained using transfer learning, early stopping, and an evaluation scheme based on accuracy, precision, recall, F1-score, and AUC. The test results showed an accuracy of 97.2%, precision of 96.8%, recall of 97.5%, F1 of 97.1%, and AUC of 0.99, surpassing previous designs that did not use attention mechanisms and texture fusion. Grad-CAM++ analysis confirms the model's focus on physiologically relevant regions (embryonic shadow and air-cell), thus improving the reliability of interpretation. These findings indicate that lightweight, efficient designs based on attention and texture fusion have the potential to be implemented in smart hatchery systems and edge/mobile devices while maintaining high accuracy.
Model Machine Learning Untuk Prediksi Risiko Penyakit Liver Dengan Random Forest Teroptimasi Rizky Andrea Arifa; Nana Suarna; Agus Bahtiar; Nining Rahaningsih; Willy Prihartono
Jurnal Sistem Informasi dan Teknologi Vol 6 No 1 (2026): Jurnal Sistem Informasi dan Teknologi (SINTEK)
Publisher : LPPM STMIK KUWERA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56995/sintek.v6i1.204

Abstract

Penyakit liver merupakan salah satu kondisi kronis dengan tingkat mortalitas tinggi, sehingga diperlukan pendekatan prediksi yang akurat untuk mendukung deteksi dini. Penelitian ini bertujuan mengembangkan model machine learning untuk memprediksi risiko penyakit liver menggunakan algoritma Random Forest yang dioptimalkan dengan RandomizedSearchCV. Dataset yang digunakan terdiri dari 1.700 entri yang mencakup variabel klinis dan gaya hidup, termasuk usia, jenis kelamin, BMI, konsumsi alkohol, kebiasaan merokok, riwayat genetik, aktivitas fisik, diabetes, hipertensi, serta hasil Liver Function Test. Proses penelitian meliputi preprocessing, normalisasi skala, pembagian data menggunakan train-test split 80:20, pembangunan model baseline, dan optimasi hiperparameter. Hasil eksperimen menunjukkan bahwa optimasi menghasilkan peningkatan performa model, dengan akurasi 0.91, peningkatan recall sebesar 3.20%, dan AUC-ROC mencapai 0.96. Analisis feature importance menunjukkan bahwa LiverFunctionTest, BMI, dan AlcoholConsumption merupakan fitur paling berpengaruh terhadap prediksi risiko penyakit liver. Dengan demikian, Random Forest teroptimasi terbukti efektif dalam menghasilkan model prediksi yang akurat dan dapat digunakan sebagai alat pendukung keputusan dalam deteksi dini penyakit liver.
Analisis Dan Prediksi Risiko Kelahiran Bayi Menggunakan K-Means Dan Deep Neural Network (DNN) Mukhlisin Ilahudin; Nana Suarna; Agus Bahtiar; Mulyawan; Irfan Ali
Jurnal Sistem Informasi dan Teknologi Vol 6 No 1 (2026): Jurnal Sistem Informasi dan Teknologi (SINTEK)
Publisher : LPPM STMIK KUWERA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56995/sintek.v6i1.206

Abstract

Risiko kelahiran bayi merupakan indikator penting dalam evaluasi kesehatan ibu dan anak sehingga diperlukan pendekatan analitis yang mampu mengidentifikasi pola risiko secara akurat. Penelitian ini bertujuan menganalisis dan memprediksi risiko kelahiran bayi dengan mengintegrasikan metode K-Means dan Deep Neural Network (DNN). Dataset yang digunakan terdiri dari 983 data rekam medis ibu hamil yang telah melalui tahap pengumpulan data, pembersihan, dan preprocessing meliputi normalisasi, encoding variabel kategorikal, penanganan outlier, serta seleksi fitur. Metode K-Means digunakan untuk mengelompokkan data berdasarkan kemiripan karakteristik klinis guna membentuk representasi pola risiko awal, yang selanjutnya digunakan sebagai fitur tambahan pada model DNN. Model DNN dirancang menggunakan beberapa hidden layer dengan fungsi aktivasi ReLU dan regularisasi dropout. Hasil pengujian menunjukkan bahwa model menghasilkan akurasi sebesar 61,93% dan nilai ROC AUC sebesar 0,6402, yang mengindikasikan performa moderat dalam memprediksi risiko kelahiran bayi. Stabilitas kurva loss dan akurasi menunjukkan proses pelatihan yang berjalan dengan baik tanpa overfitting signifikan. Secara praktis, model ini berpotensi digunakan sebagai alat bantu awal bagi tenaga kesehatan dalam mengidentifikasi ibu hamil dengan risiko kelahiran lebih tinggi sehingga dapat dilakukan pemantauan dan intervensi lebih dini.
Co-Authors Ade Irma Purnama Sari Ade Irma Purnamasari Ade Rizki Rinaldi Ahmad Faqih Arya Gunawan Asep Saepul Anwar Bagas Al Haddad Bani Nurhakim Dadang Sudrajat Diding Herudin Diding Herudin Dila Nurhafidilah Edi Tohidi Eneng Okta Srirahmawati Fadlan Ilyas Lesmana Fahmi Ahmad Tohir Fatihanursari Dikananda Feri Irawan Irawan Fitri Adha Hariyati Airi Gifthera Dwilestari Iman Nurrohmat Indra Wiguna Marthanu Indriyani Indriyani Irfan Ali Kaslani Mukhlisin Ilahudin Mulyawan Nafisa Maysa Salma Nana Suarna Nining Rahaningsih Nur Amelia Odi Nurdiawan Rini Rahmawati Riri Narasati Rizky Andrea Arifa Rudi Kurniawan Sigit Rusmayana SRI WULANDARI Tati Suprapti Tengku Riza Zarzani N Umi Hayati Wafiq Azizah Willy Prihartono