Article Per Year (5 Year)
p-Index From 2020 - 2025
8.148
P-Index
This Author published in this journals
All Journal Nuansa Informatika Sistemasi: Jurnal Sistem Informasi Jurnal Informatika Jurnal Pilar Nusa Mandiri JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology Technologia: Jurnal Ilmiah Journal of Empowerment Community Tematik : Jurnal Teknologi Informasi Komunikasi Innovation in Research of Informatics (INNOVATICS) Jurnal Pengabdian dan Edukasi Sekolah (Jubaedah) International Journal of Quantitative Research and Modeling Joutica : Journal of Informatic Unisla Formosa Journal of Applied Sciences (FJAS) Paradigma International Journal of Ethno-Sciences and Education Research Jurnal Pengabdian Masyarakat Tapis Berseri SisInfo : Jurnal Sistem Informasi dan Informatika Nuansa Informatika Jurnal Bhakti Karya dan Inovatif INTERNAL (Information System Journal) In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism) International Journal of Computer Technology and Science International Journal of Information Engineering and Science
Budiman
Fakultas Teknologi Dan Informatika Universitas Informatika Dan Bisnis Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Physics Social Sciences Transportation Other

Published : 66 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 66 Documents
Search

 3   4   5   6   7 
Analisis Sentimen Publik pada Media Sosial Twitter Terhadap Tiket.com Menggunakan Algoritma Klasifikasi Budiman, Budiman; Silvana Anggraeni, Zulmeida; Habibi, Chairul; Alamsyah, Nur
Jurnal Informatika Vol 11, No 1 (2024): April 2024
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v11i1.17988

Abstract

Analisis sentimen merupakan proses identifikasi emosional seseorang terhadap suatu objek yang akan menghasilkan sentimen positif, negatif dan netral. Kemajuan teknologi ini tentu memberikan pengaruh terhadap berbagai pelaku bisnis untuk saling mengintegrasikan sistem bisnisnya satu sama lain, salah satunya Tiket.com. Hal tersebut tentu menghasilkan sentimen dari masyarakat Indonesia yang diunggah pada platform media sosial Twitter, sehingga membantu individu maupun organisasi dalam mengambil keputusan. Penelitian ini dilakukan untuk mengetahui klasifikasi sentimen masyarakat Indonesia terhadap Tiket.com menggunakan algoritma Naïve Bayes Classifier (NBC), K-Nearest Neighbor (KNN), Support Vector Machine (SVM) dan Random Forest (RF). Berdasarkan perhitungan data sentimen terhadap Tiket.com terdapat 90.3% sentimen positif dan 9.7% sentimen negatif. Persentase tersebut menunjukkan bahwa Tiket.com cukup berpengaruh positif terhadap penggunanya. Berdasarkan hasil pengujian algoritma klasifikasi, diketahui NBC memperoleh tingkat akurasi sebesar 88%, KNN dengan nilai k = 11 mendapatkan akurasi sebesar 91%, SVM menghasilkan tingkat akurasi sebesar 92%, dan tingkat akurasi RF mencapai 93% dengan n_estimators = 100. Kesimpulan pada penelitian ini, Random Forest merupakan algoritma yang memiliki tingkat akurasi paling tinggi dibanding dengan algoritma klasifikasi lain.
XGBOOST HYPERPARAMETER OPTIMIZATION USING RANDOMIZEDSEARCHCV FOR ACCURATE FOREST FIRE DROUGHT CONDITION PREDICTION Alamsyah, Nur; Budiman, Budiman; Yoga, Titan Parama; Alamsyah, R Yadi Rakhman
Jurnal Pilar Nusa Mandiri Vol. 20 No. 2 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i2.5569

Abstract

Climate change and increasing global temperatures have increased the frequency and intensity of forest fires, making fire risk evaluation increasingly important. This study aims to improve the accuracy of predicting forest fuel drought conditions (Drought Code) by using the XGBoost algorithm optimized with RandomizedSearchCV. The research methods include collecting data related to forest fires, preprocessing data to ensure quality and consistency, and using RandomizedSearchCV for XGBoost hyperparameter optimization. The results showed that the optimized XGBoost model resulted in a decrease in Mean Squared Error (MSE) and an increase in R-squared value compared to the default model. The optimized model achieved an MSE of 0.0210 and R2 of 0.9820 on the test data, indicating significantly improved prediction accuracy for forest fuel drought conditions. These findings emphasize the importance of hyperparameter optimization in improving the accuracy of predictive models for forest fire risk assessment.
Pemanfaatan Augmented Reality Untuk Eksplorasi Gunung Berapi Di Jawa Barat Nursyanti, Reni; Budiman; Anto Widianto
Joutica Vol 9 No 2 (2024): SEPTEMBER
Publisher : Universitas Islam Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30736/informatika.v9i2.1289

Abstract

Gunung berapi adalah salah satu destinasi yang populer untuk menambah pengalaman baru, dengan banyaknya objek wisata yang dapat dieksplorasi. Gunung Tangkuban Parahu, Gunung Papandayan, dan Gunung Ciremai adalah beberapa gunung di Jawa Barat yang menawarkan beragam pilihan destinasi wisata. Namun, banyak wisatawan yang masih belum mengetahui tempat wisata apa saja yang ada di sekitar gunung-gunung tersebut. Augmented Reality (AR) merupakan teknologi yang dapat digunakan untuk memberikan visualisasi kepada masyarakat, khususnya wisatawan, tentang berbagai tempat wisata dan daerah di sekitar gunung berapi tersebut. Penelitian ini menggunakan metode Luther-Sutopo, yang meliputi pengumpulan data melalui wawancara, observasi, studi literatur, dan studi pustaka. Selain itu, pengujian dilakukan dengan menggunakan kuesioner kepada 20 responden melalui Alpha dan Beta Testing. Hasil pengujian Alpha menunjukkan bahwa penggunaan multimedia untuk eksplorasi gunung berapi di Jawa Barat ini sudah sesuai, dan Beta Testing menunjukkan bahwa 83% responden merasa puas dengan multimedia eksplorasi tersebut. Hasil penelitian ini menunjukkan bahwa multimedia eksplorasi gunung berapi berbasis Augmented Reality di Jawa Barat dapat membantu masyarakat dalam mengeksplorasi gunung berapi di wilayah tersebut.
COMPARISON LINEAR REGRESSION AND RANDOM FOREST MODELS FOR PREDICTION OF UNDERGROUND DROUGHT LEVELS IN FOREST FIRES Alamsyah, Nur; Budiman, Budiman; Yoga, Titan Parama; Alamsyah, R Yadi Rakhman
Jurnal Techno Nusa Mandiri Vol. 21 No. 2 (2024): Techno Nusa Mandiri : Journal of Computing and Information Technology Period o
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v21i2.5237

Abstract

The increase in forest fires poses a significant risk due to its impact on underground dryness, which can cause long-term environmental damage and challenge fire suppression efforts. This research aims to develop a prediction model for underground drought levels in the context of forest fires using machine learning techniques. The methodology used in this research follows the CRISP-DM (Cross-Industry Standard Process for Data Mining) framework, which includes the stages of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. This study analyzes a forest fire dataset, applies encoder labels to transform categorical variables, and uses linear regression and random forest models to predict underground drought levels. The goal is to create a predictive model that can help inform wildfire risk management strategies by anticipating underground drought levels. The results showed that the random forest model achieved higher prediction accuracy than the linear regression, with an R-squared value of 0.97. This suggests that the random forest model is a more robust tool for predicting underground drought levels, providing valuable insights for forest fire management. This research contributes to the understanding of underground drought levels, aiding the development of effective wildfire risk management strategies.
ENHANCING BASIC LITERACY THROUGH INTERACTIVE LEARNING MEDIA AND ENJOYABLE AT KB AN NUR MUTTAQIN Karlina, Nichi Hana; Sarifiyono, Aggi Panigoro; Budiman, Budiman; Rd. Zidni Rizan Al-Zhahir Yanuar; Hernawan, Kartika Nursyabanita
Jubaedah : Jurnal Pengabdian dan Edukasi Sekolah (Indonesian Journal of Community Services and School Education) Vol. 4 No. 3 (2024): Jurnal Pengabdian dan Edukasi Sekolah (Jubaedah)
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/jub.v4i3.273

Abstract

This Community Service Program addresses the low literacy skills among early childhood students at KB A Nur Muttaqin, where about 60% of children struggle with reading and writing. Contributing factors include the lack of interactive teaching methods and limited learning materials. Teacher and parent involvement in the literacy process is also minimal. The program aims to improve literacy through interactive learning media, physical and digital libraries, and training for teachers and parents. The methods used are Participatory Action Research (PAR) and Asset-Based Community Development (ABCD), focusing on participation and local resources. Key activities include media development, library setup, and training. The program seeks a 20% increase in children's literacy skills, 80% improvement in teachers' competence using media, and enhanced parental literacy awareness. Early results show increased reading interest and greater parental engagement.
Perancangan Aplikasi Sistem Tindak Lanjut Pelanggan Pada Pt. Xyz Budiman Budiman
SisInfo Vol 2 No 1 (2020): SisInfo
Publisher : Universitas Informatika dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (408.988 KB)

Abstract

Pemanfaatan teknologi informasi semakin berkembang, perusahaan atau organisasi sudah bertransformasi menggunakan teknologi informasi dalam menunjang setiap pelayanan yang diberikan kepada pelanggan. PT. XYZ merupakan perusahaan yang bergerak di bidang penyedia produk dan jasa. Semakin tinggi penggunaan produk dan jasa maka semakin rentan sebuah perusahaan mengalami masalah dalam memberikan layanan kepada pelanggan. Jika terjadi masalah keluhan dari pelanggan, perusahaan dapat menindaklanjuti melalui bagian atau unit terkait. Tindak Lanjut Pelanggan merupakan layanan prima yang diberikan perusahaan atas keluhan pelanggan. Untuk merancang kebutuhan sistem dalam aplikasi disajikan dalam bentuk diagram Unified Modelling Language. Sehingga perancangan aplikasi sistem tindak lanjut pelanggan dapat mengelola data keluhan pelanggan serta dapat dilanjutkan pada tahap pengembangan sistem sehingga pada saat implementasi perusahaan dapat meningkatkan layanan dan kinerja dalam melayani pelanggan.
Pengembangan Aplikasi Sistem Informasi Persediaan Barang Berbasis Website Chairul Habibi; Budiman .
SisInfo Vol 2 No 1 (2020): SisInfo
Publisher : Universitas Informatika dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (320.376 KB)

Abstract

Kebutuhan informasi menjadi sangat penting dalam semua aspek kehidupan. Kebutuhan akan informasiyang cepat, relevan dan terpercaya menjadi hal yang utama bagi para pengguna informasi tersebut. Olehkarena itu dukungan berupa Teknologi Informasi sangat dibutuhkan di era serba otomatis saat ini terutamadalam mengembangkan sistem informasi. Semua komponen yang terlibat dalam pembangunan sebuahsistem informasi saat ini tanpa terkecuali terus melakukan perbaikan dan pembaruan terhadap teknologinyademi memenuhi kebutuhan dalam memperoleh informasi yang cepat dan akurat sebagai saranapengambilan keputusan. Toko Radita merupakan sebuah toko yang menyediakan penjualan barang secaragrosir maupun eceran. Dalam melayani pelanggan, toko tersebut mengalami kesulitan dalam pencatatantransaksi penjualan dan persediaan barang, sehingga waktu yang diperlukan dalam transaksi tidak efektifdan efisien karena transaksi masih dilakukan dalam buku besar. Untuk mengetahui kebutuhan fungsional,analisis dan perancangan sistem yang dikembangkan dalam penelitian ini menggunakan model prosesObject Oriented yang digambarkan dalam diagram Unified Modelling Language. Berdasarkan hasilimplementasi dan pengujian maka aplikasi sistem dapat melakukan pencatatan, pelaporan rekapitulasi datapenjualan dan manajemen persediaan barang yang tersusun secara periodik.
Analisis Sentimen Penggunaan Aplikasi Traveloka di Twitter Menggunakan Model Klasifikasi Tiara Sartina Jayanti; Budiman Budiman; Chairul Habibi; Elia Setiana
SisInfo Vol 6 No 1 (2024): SisInfo
Publisher : Universitas Informatika dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37278/sisinfo.v6i1.751

Abstract

Traveloka is an online travel platform that provides booking services for transportation tickets, accommodation, tourist attraction entrance tickets, and others. This research will conduct sentiment analysis using five methods and conduct a comparative analysis between these methods. The goal is to find out how to do sentiment analysis and do a comparison analysis and get the best results for Traveloka sentiment analysis on Twitter. This research uses Twitter to get data and only focuses on tweets about Traveloka. Sentiment analysis also provides benefits for Traveloka in monitoring and analyzing user responses to their products and services from reviews and feedback posted by users on social media such as Twitter, Traveloka can gain valuable insights into the strengths and weaknesses of their services. This dataset consists of 85.6% positive sentiments and 14.4% negative sentiments. In this analysis, the library used is Scikitlearn. Five classification methods were used, namely, Random Forest (RF), Support Vector Machine (SVM), Naive Bayes Classifier (NBC), K-Nearest Neighbor (KNN), and XGBOOST. The steps in this research are data crawling, data preprocessing, data weighting, classification, model testing, model evaluation, comparison analysis, and result analysis. The results show that SVM has better accuracy based on metric evaluation with a value of 90%. However, through model testing using AUC, XGBOOST obtained the highest value of 71%.
Analisis Sentimen Penggunaan Aplikasi Traveloka di Twitter Menggunakan Model Klasifikasi Tiara Sartina Jayanti; Budiman Budiman; Chairul Habibi; Elia Setiana
SisInfo Vol 6 No 1 (2024): SisInfo
Publisher : Universitas Informatika dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37278/sisinfo.v6i1.751

Abstract

Traveloka is an online travel platform that provides booking services for transportation tickets, accommodation, tourist attraction entrance tickets, and others. This research will conduct sentiment analysis using five methods and conduct a comparative analysis between these methods. The goal is to find out how to do sentiment analysis and do a comparison analysis and get the best results for Traveloka sentiment analysis on Twitter. This research uses Twitter to get data and only focuses on tweets about Traveloka. Sentiment analysis also provides benefits for Traveloka in monitoring and analyzing user responses to their products and services from reviews and feedback posted by users on social media such as Twitter, Traveloka can gain valuable insights into the strengths and weaknesses of their services. This dataset consists of 85.6% positive sentiments and 14.4% negative sentiments. In this analysis, the library used is Scikitlearn. Five classification methods were used, namely, Random Forest (RF), Support Vector Machine (SVM), Naive Bayes Classifier (NBC), K-Nearest Neighbor (KNN), and XGBOOST. The steps in this research are data crawling, data preprocessing, data weighting, classification, model testing, model evaluation, comparison analysis, and result analysis. The results show that SVM has better accuracy based on metric evaluation with a value of 90%. However, through model testing using AUC, XGBOOST obtained the highest value of 71%.
Peningkatan Kinerja Administrasi Melalui Aplikasi E-Office Ahmad Rizqy Hamdy; Budiman Budiman; Reni Nursyanti; Elia Setiana
SisInfo Vol 6 No 1 (2024): SisInfo
Publisher : Universitas Informatika dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37278/sisinfo.v6i1.752

Abstract

Agency administration plays an important role in various information services and data management, which often causes a waste of time and energy. Currently, many government agencies and organizations have implemented E-office, FTI (Faculty of Technology and Informatics) as one of the faculty at the University of Informatics and Business Indonesia (UNIBI) also faces challenges in carrying out administrative processes. The purpose of this research is to simplify the administrative process that runs in FTI by designing and building E-Office applications. The design stage uses the Waterfall method with 6 (six) UML (Unified Modeling Language), namely use case, class, package, component, sequence, and activity. The development stage uses the Laravel 10 framework and MySQL. Testing this application using Black-Box Testing. The result of this research is the making of a web-based E-Office application that can simplify the administrative process that runs at FTI.
Co-Authors Acep Hendra Aggi Panigoro Sarifiyono Akbar, Imannudin Alamsyah, R Yadi Rakhman AlFauzi, Ihsan Amos Duan Nugroho Anto Widianto Ari Rizki Fauzi Bayu Wibisono, Gunthur Cahya Miftahul Falah Catherin Rumambo Mogot Pandin Chairul Habibi Chairul Habibi Danestiara, Venia R Dani Rizky Zaelani Darsiti . Dirham Triyadi Dirham Triyadi Erpurini, Wala Fauzi Ramadhan, Ahmad Fikri Rizqillah Hasani Gelar, Trisna Habibi, Chairul Hani Fitria Rahmani Hasan Nuraripin Hernawan, Kartika Nursyabanita Ismi Nur Muhamad Januantara Prima, Alif Karlina, Nichi Hana Kaunang, Valencia Claudia Jennifer M. Rizki Ramadhan Niqotaini, Zatin NUR ALAMSYAH Nur Alamsyah Nur Alamsyah, Nur Nursyanti, Reni PARAMA YOGA, TITAN Permata Hati, Tiara R. Yadi Rakhman A R. Yadi Rakhman A4 R. Yadi Rakhman Alamsyah R. Yadi Rakhman Alamsyah Rakhman Alamsyah, Rd. Yadi Ramadhan, Muhammad Rizki Rd. Zidni Rizan Al-Zhahir Yanuar Reni Nursyanti Reni Nursyanti Reni Nursyanti Reynaldy Gimnastiar Rijwan Rijwan S.W. Manurip, Atanasius Angga Setiana, Elia Silvana Anggraeni, Zulmeida Sophian Ramadhan Suci Fitriani Setiawan Titan Parama Titan Parama Yoga Tutik Ultsa Rahmatika Valencia Claudia Jennifer Valencia Claudia Jennifer Kaunang Vani Maharani Nasution Venia Restreva Danestiara Wulandari Wulandari Yoga Rizki Rahmawan Zein Suna Arfigan Said