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All Journal EDUMATIC: Jurnal Pendidikan Informatika Jurnal Informatika Ekonomi Bisnis Bulletin of Computer Science Research Brilliance: Research of Artificial Intelligence Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB Ranah Research : Journal of Multidisciplinary Research and Development VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Jurnal Pelita Pengabdian SAINTEK
Naya, Candra
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Sistem Informasi Komisi Kurir Berbasis Web Pada Pt Lazada Indonesia Dengan Menggunakan Metode Rad Bogy Wijaya Sulaiman; Suherman; Naya, Candra; Imelda, Karina; Josef Anis, Billy
Jurnal SIGMA Vol 15 No 2 (2024): September 2024
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/sigma.v15i2.6035

Abstract

Dengan kemajuan teknologi komputer, banyak perusahaan kini memanfaatkan teknologi ini, termasuk yang bergerak di bidang ekspedisi. Baik perusahaan maupun individu sering menggunakan layanan kurir untuk mengirim barang dalam jumlah kecil maupun besar. Penerapan metode Rapid Application Development (RAD) dalam pengembangan aplikasi yang sudah ada diharapkan dapat mempercepat proses pembuatan aplikasi. RAD, yang menggunakan pendekatan berorientasi objek, dirancang untuk mempercepat tahap perencanaan, perancangan, dan implementasi sistem dibandingkan metode konvensional. Kepercayaan yang semakin tinggi terhadap layanan kurir dalam pengiriman barang telah mendorong perkembangan pesat di sektor ekspedisi. Digitalisasi seluruh sistem informasi perusahaan diharapkan dapat mempermudah dan meningkatkan operasional sistem secara keseluruhan. Salah satu contohnya adalah Lazada Logistik, perusahaan ekspedisi yang bergerak di bidang pengiriman barang untuk platform e-commerce Lazada Indonesia.
Model Hybird Fuzzy Logic dan Deep Learning untuk Prediksi Harga Saham Muhidin, Asep; Rilvani, Elkin; Naya, Candra
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 2 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i2.30890

Abstract

Stock price prediction is a major challenge in the financial sector due to nonlinear factors and data uncertainty. This study aims to develop a predictive model by integrating fuzzy logic into deep learning algorithms to improve accuracy and robustness against noise. This is a quantitative experimental study using 1,000 daily historical stock price data of BBCA (Bank Central Asia), collected via web scraping from public sources. The data were analyzed using three types of neural networks: Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), both before and after fuzzy integration. Fuzzification was applied to the price data to generate linguistic features, which were added as input to the neural network models. The models were evaluated using Train Cost, Test Cost, and the number of epochs, and a t-test was conducted to assess the statistical significance of performance differences. Our findings show that the LSTM model with fuzzy input achieved the best performance, with a Train Cost of 0.0002 and a Test Cost of 0.0052, and demonstrated superior capability in handling long-term dependencies. In contrast, RNN and GRU models showed decreased accuracy after fuzzy integration. The combining fuzzy and LSTM model shows promise for broader applications in time-series forecasting under uncertainty.
Pendampingan Penerapan Sistem Manajemen Bengkel Sepeda Motor untuk Meningkatkan Layanan Naya, Candra; Andriani; Arwan Sulaeman, Asep
VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Vol. 3 No. 1 (2025): Juni 2025
Publisher : VINICHO MEDIA PUBLISINDO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61946/vidheas.v3i1.123

Abstract

Kegiatan pendampingan ini bertujuan untuk meningkatkan kualitas layanan bengkel sepeda motor di SMK Al Amin Cibarusah melalui penerapan sistem manajemen bengkel yang lebih terstruktur dan profesional. Latar belakang kegiatan ini didasarkan pada kebutuhan akan pengelolaan bengkel yang efisien, sebagai bagian dari pembelajaran praktik siswa jurusan Teknik dan Bisnis Sepeda Motor (TBSM). Metode yang digunakan meliputi observasi awal, analisis kebutuhan, penyusunan standar operasional prosedur (SOP), pelatihan bagi guru dan siswa, serta pendampingan implementasi sistem manajemen bengkel. Hasil dari kegiatan ini menunjukkan adanya peningkatan dalam tata kelola bengkel, kedisiplinan kerja siswa, dan kualitas pelayanan kepada pelanggan. Selain itu, kegiatan ini juga mendorong terwujudnya budaya kerja industri di lingkungan sekolah. Dengan demikian, penerapan sistem manajemen bengkel secara konsisten dapat menjadi solusi strategis dalam mendukung program pendidikan vokasi yang berorientasi pada kebutuhan dunia kerja dan industri.
Pelatihan Pengenalan Teknologi Augmented Reality dalam Pembelajaran Multimedia Naya, Candra; Andriani; Arwan Sulaeman, Asep
VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Vol. 2 No. 2 (2024): Desember 2024
Publisher : VINICHO MEDIA PUBLISINDO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61946/vidheas.v2i2.124

Abstract

Kegiatan pelatihan ini bertujuan untuk memperkenalkan teknologi Augmented Reality (AR) kepada siswa-siswi SMK Al Amin Cibarusah sebagai salah satu inovasi dalam pembelajaran multimedia. Perkembangan teknologi digital yang pesat mendorong perlunya integrasi teknologi interaktif dalam dunia pendidikan guna meningkatkan efektivitas dan daya tarik proses belajar mengajar. Melalui pelatihan ini, peserta diperkenalkan pada konsep dasar AR, perangkat lunak pendukung, serta praktik pembuatan konten sederhana berbasis AR. Metode pelatihan yang digunakan meliputi ceramah interaktif, demonstrasi, dan praktik langsung menggunakan aplikasi AR yang mudah diakses. Hasil kegiatan menunjukkan bahwa peserta memiliki antusiasme tinggi dan mampu memahami serta mempraktikkan pembuatan media pembelajaran berbasis AR dengan baik. Pelatihan ini diharapkan dapat menjadi langkah awal dalam meningkatkan literasi teknologi dan kreativitas siswa dalam bidang multimedia, sekaligus mendorong pemanfaatan teknologi AR secara lebih luas di lingkungan pendidikan.
Sentiment Analysis Of Indosat's Mobile Operator Services On Twitter Using The Naïve Bayes Algorithm Butsianto, Sufajar; Fauziah, Sifa; Naya, Candra; Maulana, Futuh
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4084

Abstract

Twitter is a social media that allows users to share information with others in real time. Information that is shared on Twitter is usually referred to as a tweet. Sentiment analysis is a branch of research in the text mining domain where the process of identifying and extracting sentiment data will usually be categorized based on its polarity, whether it is positive, negative or neutral. We can process data from opinions on Twitter using data mining techniques, namely classification. The algorithm that will be used in this research is the Naïve Bayes Algorithm. This research will also use the RStudio application. It is a computer programming language that allows users to program algorithms and use tools that have been developed through R by other users. R is a high-level programming language and is also an environment for data and graph analysis. Based on the experimental results, using a comparison of training data and test data of 20%: 80%, 40%: 60%, 60%: 40%, 80%: 20% and 90%:10%, the results of sentiment classification using the Naïve Bayes method are obtained. and using 10-fold cross validation obtained an average value of 85.00% accuracy and The decrease in machine learning performance occurs in the ratio of 80:20 or 1440 training data: 360 data testing, while the ratio of 20%:80% and 90%:10% has the same accuracy value, namely 85.41%.
Prediction of Employee Assessments for Contract Extensions at PT Sagateknindo Sejati Using the Naïve Bayes Algorithm Naya, Candra; Siswandi, Arif; Butsianto, Sufajar; Febriyanti, Febriyanti
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4170

Abstract

Companies must be selective in conducting employee assessments in order to retain employees with the best performance. When assessing employee performance, it is seen from their perseverance and discipline. However, in reality, good employee performance sometimes gets bad reviews and even gets reprimanded by their superiors. This is caused by the employee assessment monitoring system used, namely only personal assessment without using an assessment system and the data collected is less than optimal. This research uses the Naive Bayes method to process data using a data mining algorithm to obtain predictions that can be used as additional references in making employee performance assessment decisions. Aims to predict employee assessments of contract extensions at PT Sagateknindo Sejati. This research is important because it helps in making more accurate decisions regarding employee contract extensions based on existing historical data. Naive Bayes is a data processing algorithm that is classified as a calculation that is easy to understand but its accuracy results are reliable. It is used because it is efficient in managing data with various attributes and is able to produce predictions based on the probability of each existing attribute. The data used in this research includes various variables, using the Rapidminer supporting application to test the accuracy of the system created. Testing was carried out by preparing 320 data and testing 50 randomly selected data. Test data will be analyzed using the Rapidminer supporting application. The test results produced an accuracy of 83.96%.
Prediksi Penjualan Brand di HGVR Store Menggunakan Algoritma C4.5 dan Naïve Bayes Naya, Candra; Rilvani, Elkin
Jurnal Informatika Ekonomi Bisnis Vol. 7, No. 3 (September 2025)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v7i3.1242

Abstract

HGVR Brand is a creative industry engaged in the production and distribution of ready-to-wear clothing established in 2015, which has a reseller network in several major cities in Java. This study aims to analyze the prediction of HGVR Store product sales levels using data mining methods, specifically the C4.5 and Naïve Bayes algorithms, so that it can assist the company in determining marketing strategies and inventory management. The data used in this study consists of 500 sales data collected in June 2019 through observation, interviews, and internal company documentation. The input variables used include the number of orders (PO), quantity, price, and sales status, while the target variable is the classification of sales into "high" and "low" categories. The analysis process is carried out through the stages of data cleaning, transformation, and validation using the split validation technique (70% training data and 30% testing data). The C4.5 algorithm is used to build a decision tree model, while the Naïve Bayes algorithm is used to calculate the classification probability. The test results show that the C4.5 algorithm has a 100% accuracy rate with an excellent classification category based on the ROC curve (AUC = 1.00). Meanwhile, the Naïve Bayes algorithm also produced good classification results, although its accuracy was lower than that of C4.5. The conclusion of this study is that the C4.5 algorithm is more optimal than Naïve Bayes in predicting sales levels at the HGVR Store. These findings are expected to inform decision-making for the HGVR Brand in formulating business strategies.
Analisis Tingkat Sentimen Opini Publik Terhadap Kebijakan TV Digital di Platform X Menggunakan Multinomial Naïve Bayes Sulaeman, Asep Arwan; Naya, Candra; Danny, Muhtajuddin; Effendi, M. Makmun
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.951

Abstract

The migration from analog to digital television broadcasting is part of the transformation of the broadcasting system aimed at improving broadcast quality and spectrum efficiency. However, the implementation of the digital television policy has generated diverse public responses, ranging from support to criticism. This study aims to analyze public opinion on the digital television policy in Indonesia using social media data from platform X. A quantitative approach was employed using text mining and supervised machine learning techniques. Data were collected through a crawling process using the keyword “tv digital”, resulting in 1,855 tweets. After data selection and cleaning, 789 tweets were obtained as the final dataset. The analysis stages included text preprocessing, feature extraction using Term Frequency–Inverse Document Frequency (TF–IDF), and sentiment classification using the Multinomial Naïve Bayes algorithm. The results indicate that positive sentiment dominates public opinion, with 478 tweets (60.58%), while negative sentiment accounts for 311 tweets (39.42%). Model performance evaluation shows an accuracy of 79.21%, precision of 82.45%, and recall of 85.06%, indicating that the model performs well and consistently in classifying sentiment. These findings demonstrate that social media–based sentiment analysis can serve as an empirical approach to understanding public perceptions of digital television policy.
Pendampingan Penerapan Sistem Absensi Digital Berbasis Web untuk Guru SMK Al Amin Naya, Candra; Triwibowo, Edi; Surojudin, Nurhadi
VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Vol. 3 No. 2 (2025): Desember 2025
Publisher : VINICHO MEDIA PUBLISINDO

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

Abstract

This community service activity aimed to assist in the implementation of a web-based digital attendance system for teachers at SMK Al Amin Cibarusah in order to enhance the effectiveness, accuracy, and transparency of attendance management. The challenges faced by the partner institution included a manual attendance process that was prone to recording errors, delays in report compilation, and limited real-time monitoring capabilities. The implementation method consisted of needs analysis, system design, web-based application development, user training, and evaluation of the implementation process. The developed system features time-based check-in and check-out with location validation, attendance history, monthly recaps, and digital submission of leave, sick, and official duty requests. The results indicate that the system accelerates the recording process, minimizes administrative errors, and provides structured and easily accessible reports. The mentoring process also improves teachers’ digital literacy in utilizing information technology to support more professional and accountable school governance. Therefore, the implementation of a web-based digital attendance system represents a strategic step toward supporting digital transformation in the educational environment. Keywords: Community Service, Digital Attendance, Web-Based System, Digital Transformation, Attendance
Co-Authors Afriantoro, Irfan Agus Suwarno, Agus Andriani ARIF SUSILO Arwan Sulaeman, Asep Aswan Supriyadi Sunge Baehaqi Bogy Wijaya Sulaiman Butsianto, Sufajar Effendi, M. Makmun Elkin Rilvani febriyanti febriyanti Fitriani Gatot Tri Pranoto Imelda, Karina Indra Permana, Indra Indradewa, Rhian Josef Anis, Billy Majid, Annisa Maulana Maringan Hutauruk, Basar Maulana, Futuh Muhamad Fatchan muhidin, asep Muhtajuddin Danny Nawangsih, Ismasari Pupung Purnamasari Sadikin, Mumin Sanudin Sarifah Faujiah, Rani Siswandi, Arif Suherman Sulaeman, Asep Arwan Surojudin, Nurhadi Tedi Kurniadi, Nanang Triwibowo, Edi Wachid Hasyim, Wachid Widodo , Edy Zahrotul Kamalia, Antika