Article Per Year (5 Year)
p-Index From 2021 - 2026
5.07
P-Index
This Author published in this journals
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
Unknown Affiliation
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

Published : 38 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 38 Documents
Search

 1   2   3   4 
Convolutional Neural Network Implementation in BISINDO Alphabet Sign Language Recognition System Kinanti, Aning Aning; Maulana, Donny; Edora, Edora
IJNMT (International Journal of New Media Technology) Vol 11 No 1 (2024): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v11i1.3629

Abstract

This research develops a system for recognizing finger spelling gestures in Indonesian Sign Language (BISINDO) using Convolutional Neural Network (CNN). The objective of this research is to apply the Convolutional Neural Network (CNN) method to the BISINDO finger spelling gesture recognition system to improve its accuracy. The method employed is Convolutional Neural Network (CNN), an effective method for processing image data for pattern recognition. Based on the test results, the system demonstrates that the developed CNN model is capable of recognizing BISINDO finger spelling gestures with an accuracy of 97.5%. This indicates that the BISINDO finger spelling gesture recognition system performs well in pattern recognition. The implementation of the system for real-time prediction via a web interface using Flask also enhances its accessibility. However, there is still room for improvement, particularly in recognizing one of the 26 letters that has not been predicted accurately. For further development, it is recommended to consider collecting a larger dataset and incorporating more complex gesture variations to improve recognition accuracy.
PREDICTION OF 2024 PRESIDENTIAL ELECTION USING K-NN WITH METRIC APPROACHES CHEBYSHEV AND EUCLIDEAN BASED ON TWITTER DATA INVESTIGATION Darmawan, Steven Ryan; Fatchan, Muhamad; Maulana, Donny
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.2.1720

Abstract

The potential difference between the popularity of presidential candidates on social media and in the general public poses a serious challenge in predicting the outcome of the 2024 presidential election. Technical constraints in collecting, cleaning and analyzing dynamic and large-scale social media data can threaten the accuracy and validity of predictions. To overcome this problem, careful steps and in-depth understanding are needed. Therefore, this study aims to predict the winner of the 2024 presidential election from the popularity of presidential candidates Anies Baswedan, Ganjar Pranowo, and Prabowo Subianto on Twitter. The K-Nearest Neighbor (K-NN) method with the Both Metric approach (Euclidean and Chebyshev) was used to analyze 51,192 tweet data through the Knowledge Discovery in Database (KDD) stage using Orange software. The evaluation results show almost the same performance, with AUC values of 0.725 for Euclidean and 0.720 for Chebyshev. The CA result was 55.6% for Euclidean and 55.4% for Chebyshev. Although F1, precision, and recall were almost the same, overall, the Euclidean metric was better. The prediction shows Prabowo Subianto as the most popular candidate on Twitter. Nonetheless, these results need to be interpreted with caution and strengthened with further analysis and additional data to get a more comprehensive conclusion. This research shows that K-NN with both metrics can provide predictions above 50%, reliable enough to be able to predict the most popular candidates on Twitter.
Pengembangan Sistem Aplikasi E-Kaizen Berbasis Website Menggunakan Metode Agile (Studi Kasus PT Cataler Indonesia) Maulana, Donny; Surojudin, Nurhadi; Juluw, Sephia Maharani Niki
Journal of Practical Computer Science Vol. 4 No. 2 (2024): November 2024
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/jpcs.v4i2.5233

Abstract

Technological developments are increasingly developing with technological developments, the activities carried out benefit many parties, one of which is in the manufacturing industry. PT Cataler Indonesia is still experiencing difficulties in the keizen application process because it still uses manual, namely using Microsoft Excel, data collection is in the form of paper, this is prone to data loss or document damage. Therefore, it is necessary to carry out research to develop a website-based e-kaizen application system. The aim of this research is to make it easier for employees to submit kaizen applications and process kaizen data. The method used is the Agile Method, a software development that emphasizes flexibility and responsiveness to change. In implementing the e-kaizen system, it uses the Javascript programming language and Firebase as the database. Based on the research, it can be concluded that the development of a website-based e-kaizen application system that replaces manual processes really supports the fulfillment of needs quickly, accurately and with more updates.
Pelatihan Pengelolaan Sdm Untuk Meningkatkan Kualitas Umkm Dalam Produktivitas Penjualan Rismawati; Purwanti; Anshor, Abdul Halim; Maulana, Donny; Huda, Miftahul
SABAJAYA Jurnal Pengabdian Kepada Masyarakat Vol. 3 No. 01 (2025): SABAJAYA : Jurnal Pengabdian Kepada Masyarakat
Publisher : SABA JAYA PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59561/sabajaya.v3i01.541

Abstract

Pelatihan "Pengelolaan SDM untuk Meningkatkan Kualitas UMKM dalam Produktivitas Penjualan" berhasil menunjukkan bahwa peningkatan pengelolaan Sumber Daya Manusia (SDM) memiliki peran krusial dalam memperkuat kinerja dan produktivitas UMKM di Bandung Barat. Melalui pelatihan ini, peserta memperoleh pemahaman strategis dan keterampilan praktis dalam aspek rekrutmen, pengembangan karyawan, motivasi, dan penilaian kinerja. Hasil pelatihan menunjukkan peningkatan signifikan dalam pengelolaan SDM, yang mendukung pertumbuhan usaha dan meningkatkan daya saing UMKM. Pendampingan pasca-pelatihan menjadi elemen vital untuk penerapan keterampilan yang diperoleh secara efektif dan berkelanjutan. Diharapkan, peserta dapat menyebarluaskan pengetahuan yang didapat kepada UMKM lain, sehingga dampak positif dari pelatihan ini dapat meluas dan berkontribusi pada pengembangan ekonomi daerah
COMPARATIVE ANALYSIS OF CLASSIFICATION ALGORITHMS IN HANDLING IMBALANCED DATA WITH SMOTE OVERSAMPLING APPROACH Nugroho, Agung; Wiyanto; Maulana, Donny
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6956

Abstract

Most machine learning algorithms tend to yield optimal results when trained on datasets with balanced class proportions. However, their performance usually declines when applied to data with significant class imbalance. To address this issue, this study utilizes the Synthetic Minority Oversampling Technique (SMOTE) to improve class distribution before model training. Several classification algorithms were employed, including Decision Tree, K-Nearest Neighbors, Logistic Regression, Support Vector Machine, and Random Forest. Experimental results reveal that the Random Forest model produced the highest accuracy (95.70%) and the best F1-score, demonstrating a well-balanced trade-off between precision and recall. In contrast, the Logistic Regression algorithm achieved the highest recall (74.20%), indicating better sensitivity in identifying positive instances despite a lower F1-score. These outcomes highlight the importance of choosing appropriate classification methods based on the specific evaluation goals whether prioritizing accuracy, recall, or overall model balance.
Transformasi Digital dan Inovasi Pemasaran untuk Meningkatkan Daya Saing UMKM di Desa Sukabungah Kabupaten Bekasi Kartini, Tri Mulyani; Anshor, Abdul Halim; Maulana, Donny; Rismawati, Rismawati
Jurnal Pengabdian West Science Vol 4 No 12 (2025): Jurnal Pengabdian West Science
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/jpws.v4i12.3007

Abstract

Pengabdian masyarakat ini dilaksanakan dengan tujuan mendukung transformasi digital dan penguatan inovasi pemasaran bagi pelaku Usaha Mikro, Kecil, dan Menengah (UMKM) di Desa Sukabungah, Kabupaten Bekasi. Latar belakang kegiatan ini berangkat dari rendahnya pemanfaatan teknologi digital dan strategi pemasaran modern oleh UMKM, yang berdampak pada keterbatasan daya saing di tengah perkembangan ekonomi digital. Metode yang digunakan adalah pelatihan, pendampingan, dan praktik langsung terkait literasi digital, pengelolaan keuangan berbasis aplikasi, serta pemanfaatan media sosial dan e-marketplace sebagai sarana pemasaran. Hasil kegiatan menunjukkan peningkatan signifikan dalam kemampuan peserta, antara lain pada penggunaan WhatsApp Business (dari 20% menjadi 80%), pemanfaatan Instagram Business (dari 10% menjadi 65%), pencatatan keuangan digital (dari 10% menjadi 50%), serta pemanfaatan e-marketplace (dari 5% menjadi 30%). Temuan ini mengindikasikan bahwa intervensi berbasis transformasi digital dapat meningkatkan literasi digital, memperluas jangkauan pasar, dan memperkuat daya saing UMKM. Studi ini merekomendasikan perlunya pendampingan berkelanjutan, sinergi dengan pemerintah daerah, dan penguatan ekosistem digital lokal untuk memastikan keberlanjutan hasil yang dicapai.
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%.
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 Amali Amali, Amali Aminurdin, Majid Andri Firmansyah 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 Huda, Miftakul Ikhsan Romli Indah Wahyu Puji Utami, Indah Wahyu Indradewa, Rhian Ira Restu Kurnia Isarianto, Isarianto 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 Miharja, Muhammad Najamuddin Dwi Muhamad Fatchan Muhtajuddin Danny Nani Hartati Nawangsih, Ismasari Nisawati, Inna Nur Ilman Ilyas Nurhidayanti, Nisa Pupung Purnamasari Putra, Fibi Eko Putri, Isria Miharti Mahaerni Rachman, Nazwa Aulia Rahardjo, Sugeng Budi Retno Purwani Setyaningrum Rismawati Rismawati Rismawati sanjay, sanjay Sihombing, Veronika Rustiani Dame Simanjuntak, Antonius Siswahyudianto Suratman Suratman Surojudin, Nurhadi Tedi, Nanang Tri Ngudi Wiyatno Tri Sasongko, Ananto Turmudi Zy, Ahmad Umah, Rani Nur Wiyanto Wulandari , Anna Yansyah, Hamdi Yoga Religia