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All Journal JUPIIS: Jurnal Pendidikan Ilmu-ilmu Sosial JAIS (Journal of Applied Intelligent System) JITK (Jurnal Ilmu Pengetahuan dan Komputer) International Journal of New Media Technology JATI (Jurnal Mahasiswa Teknik Informatika) JISA (Jurnal Informatika dan Sains) Idealis : Indonesia Journal Information System Jurnal Teknik Informatika (JUTIF) Brilliance: Research of Artificial Intelligence International Journal Software Engineering and Computer Science (IJSECS) Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Journal of Practical Computer Science (JPCS) Jurnal Pengabdian West Science SABAJAYA Jurnal Pengabdian Kepada Masyarakat Jurnal Lentera Pengabdian Proceeding International Pelita Bangsa Jurnal Pelita Pengabdian SAINTEK JPMAS : Jurnal Pengabdian Masyarakat Inaba of Community Services Journal Jurnal Pengabdian Nasional (JPN) Indonesia Cahaya Pengabdian
Maulana, Donny
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Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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Genetic Algorithm Optimization on Nave Bayes for Airline Customer Satisfaction Classification Religia, Yoga; Maulana, Donny
JISA(Jurnal Informatika dan Sains) Vol 4, No 2 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i2.925

Abstract

Airline companies need to provide satisfactory service quality so that people do not switch to using other airlines. The way that can be used to determine customer satisfaction is to use data mining techniques. Currently, the website www.kaggle.com has provided Airline Passenger Satisfaction data consisting of 22 attributes, 1 label and 25976 instances which are included in the supervised learning data category. Based on several previous studies, the Naïve Bayes algorithm can provide better classification performance than other classification algorithms. Several studies also state that the use of Naive Bayes can be optimized using Genetic Algorithm (GA) to obtain better performance. The use of Genetic Algorithm for Nave Bayes optimization in classifying Airline Passenger Satisfaction data requires further research to ensure the performance of the given classification. This study aims to compare the use of the Naive Bayes algorithm for the classification of Airline Passenger Satisfaction with and without GA optimization. The data validation process used in this study is to use split validation to divide the dataset into 95% training data and 5% testing data. The test results show that the use of GA on Naive Bayes can improve the classification performance of Airline Passenger Satisfaction data in terms of accuracy and recall with an accuracy value of 85.99% and a recall of 87.91%.
Digitalisasi Profil dan Informasi Sekolah Global Mandiri Patrol Melalui Pembuatan Website Turmudi Zy, Ahmad; Nugroho, Agung; Dasman, Sunita; Maulana, Donny; Isarianto, Isarianto
Cahaya Pengabdian Vol. 2 No. 2 (2025): Desember 2025
Publisher : Apik Cahaya Ilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61971/cp.v2i2.265

Abstract

Global Mandiri Patrol School, a private educational institution located in Indramayu, West Java, faces challenges in disseminating information to students, parents, and the wider community due to the absence of official digital information media. This research aims to implement a Content Management System (CMS)-based school website to support the digital transformation of educational institutions. The community service method is designed participatively through five stages: (1) program socialization, (2) teacher and staff training, (3) technology implementation for website development, (4) intensive website management mentoring, and (5) evaluation and sustainability planning. The research results show that the school website has been successfully launched with all planned features, 2–3 staff members have been trained in content management, and sustainability commitment from school leaders and the foundation has been documented. Measurable social impacts include improved information transparency, enhanced school communication efficiency, increased institutional image, and support for school accreditation processes. Regarding partner capacity, this activity has increased digital literacy among school teachers and staff and opened opportunities for sustained innovation. The conclusion of this research is that school website development is an effective strategic step in supporting the digital transformation of educational institutions and improving service quality to the community
Efektivitas Algoritma Support Vector Machine Dan Naive Bayes Dalam Mengiden Tifikasi Sentimen Ulasan Pengguna Aplikasi Jobstreet : Sebuah Analisis Komparatif Maulana, Donny; Rachman, Nazwa Aulia
Jurnal Pelita Teknologi Vol 19 No 2 (2024): September 2024
Publisher : Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/pelitatekno.v19i2.7297

Abstract

This study develops an automated sentiment analysis system to classify Indonesian-language user reviews of the JobStreet application from the Google Play Store. It compares the performance of two machine learning algorithms, Support Vector Machine (SVM) and Naive Bayes. The review data were preprocessed through cleaning, case folding, tokenization, normalization, stopword removal, and stemming before model training and evaluation. Performance was measured using accuracy, precision, recall, and F1-score. The results show that SVM outperformed Naive Bayes, achieving 97% accuracy, 0.98 precision, 0.96 recall, and a 0.97 F1-score. In comparison, Naive Bayes achieved 89% accuracy, 0.93 precision, 0.83 recall, and a 0.86 F1-score. SVM demonstrated more balanced precision and recall across sentiment classes, indicating better classification performance. These findings suggest that SVM is more effective for Indonesian-language sentiment analysis and has strong potential for implementation in automated systems to support intelligent recommendations and improve service quality on digital recruitment platforms
Co-Authors Abdul Halim Anshor Abdurrafi, Alfin ADI FITRA Aditiya, Febri Aditya Nugroho Widiadi Afriantoro, Irfan Agung Nugroho Agus Suwarno, Agus Amali Amali, Amali Aminurdin, Majid Andri Firmansyah Anggi Muhammad Rifa’i Aning Risky Montana Anwar, M. Syaibani ARIF SUSILO Asep Muhidin Auliyah, Risma Barokah, Awalina Budiarto, Eko Darmawan, Steven Ryan Daspar Daspar Edora Edora, Edora Edy Widodo Ferawati, Eva Fitria Hermiati, Novi Gatot Tri Pranoto Halim Anshor, Abdul Hardianti, Fazrin Putri Hariyono Hartati, Nani Huda, Miftakul Ikhsan Romli Indah Wahyu Puji Utami, Indah Wahyu Indradewa, Rhian Ira Restu Kurnia Isarianto, Isarianto Ismamudi Ismamudi Ismamudi, Ismamudi Juluw, Sephia Maharani Niki Karsito Kartini, Tri Mulyani Kinanti, Aning Aning Kurniadi, Nanang Tedi Lisa Kustina Majid, Annisa Maulana Maulana Majid, Annisa Mega Fatimah Rosana Miftahul Huda Miftakul Huda Miharja, Muhammad Najamuddin Dwi Muhamad Fatchan Muhtajuddin Danny Nawangsih, Ismasari Nisawati, Inna Nugroho, Agung Nur Ilman Ilyas Nurhidayanti, Nisa Pupung Purnamasari Putra, Fibi Eko Putra, Maha Putri, Isria Miharti Mahaerni Rachman, Nazwa Aulia Rahardjo, Sugeng Budi Retno Purwani Setyaningrum Rismawati Rismawati Rismawati sanjay, sanjay Sellina, Sesri Sihombing, Veronika Rustiani Dame Simanjuntak, Antonius Siswahyudianto Sunita Dasman Suratman Suratman Suriyanti Surojudin, Nurhadi Tedi, Nanang Tri Ngudi Wiyatno Tri Sasongko, Ananto Turmudi Zy, Ahmad Umah, Rani Nur Wiyanto Wulandari , Anna Yansyah, Hamdi Yoga Religia