Article Per Year (5 Year)
p-Index From 2021 - 2026
1.376
P-Index
This Author published in this journals
All Journal JAIS (Journal of Applied Intelligent System) Jurnal Teknik Informatika (JUTIF) Tedc Journal of Informatics and Electronics Engineering
Apnena, Riri Damayanti
Unknown Affiliation
Religion Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Veterinary

Published : 7 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 7 Documents
Search

 1 
Film Review Sentiment Analysis: Comparison of Logistic Regression and Support Vector Classification Performance Based on TF-IDF Ramdan, Dadan Saepul; Apnena, Riri Damayanti; Sugianto, Castaka Agus
(JAIS) Journal of Applied Intelligent System Vol. 8 No. 3 (2023): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i3.9090

Abstract

Film sentiment analysis is a process for evaluating a sentiment value that exists in film reviews, so that positive or negative responses from films can be identified. In this study, a sentiment analysis will be carried out on film reviews on IMBD. The analysis was carried out to find out which reviews were positive and negative from film critics. The method used to carry out sentiment analysis in this study is review analysis and processing with TF-IDF and a positive or negative prediction process based on reviews that have been processed using a logistic regression algorithm and support vector classification. The data to be used is film reviews on IMBD, which consists of 2000 data, which is divided into 1000 positive data and 1000 negative data. Which is where the data will be preprocessed first and split with a percentage of 70% training data and 30% testing data. In the prediction process using the logistic regression algorithm, obtaining a test accuracy of 80.61%. While the prediction process using the support vector classification algorithm obtains a test accuracy of 82.42%.
Smart Waste Management and Recycling Based on IoT using Machine Learning Algorithm Ginting, Gerinata; Apnena, Riri Damayanti
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 2 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i2.10766

Abstract

Smart waste management and recycling have become critical issues in urban planning and environmental sustainability due to the increasing volume of waste generated by modern societies. In this study, we investigated the performance of Support Vector Machine (SVM) and Neural Network (NN) methods in an Arduino-based waste sorting system. Our testing phase revealed exceptional performance, with SVM achieving an accuracy of 92% and NN achieving 96%, alongside perfect precision, recall, and F1-score metrics. The consistent True Positive (TP) results across all waste categories underscored the system's capability to accurately direct waste into correspondingcolored bins. These findings highlight the significance of automated waste management systems in promoting effective waste sorting practices and facilitating sustainable recycling efforts. Moreover, advancements in technology and machine learning algorithms offer promising prospects for further enhancing the efficiency and scalability of such systems, thereby contributing to a cleaner and healthier environment for future generations. Future research in smart waste management could focus on exploring additional machine learning algorithms, advanced sensor technologies, and Internet of Things integration. Investigating alternative algorithms beyond SVM and NN, adopting advanced sensors like hyperspectral imaging or lidar, and incorporating IoT devices for real-time monitoring could enhance waste sorting accuracy and scalability.
Multi-Class Mangrove Classification Using Transfer Learning with MobileNet-V3 on Multi-Organ Images Sudrajat, Ari; Apnena, Riri Damayanti; Rahayu, Ayu Hendrati; Iqtait, Musab
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4683

Abstract

Mangrove ecosystems are important for coastal protection, biodiversity conservation, and climate change mitigation. However, the accurate identification of mangrove species is very challenging due to the morphological similarities between different species, especially when the species are analyzed based on limited plant organs like leaves or stems. Manual identification methods have traditionally been time-consuming, error-prone, and require expert knowledge. Addressing these issues, this research suggests an automatic classification system based on Deep Learning techniques by leveraging the MobileNet-V3 architecture. The system is based on images of three different plant organs—leaves, stems, and seeds—of five mangrove species: Avicennia marina, Avicennia officinalis, Avicennia rumphiana, Rhizophora mucronata, and Sonneratia alba. Data augmentation techniques such as rotation, shifting, and flipping, as well as sharpness enhancement, were applied in the preprocessing step to enhance data variability and ease model generalization. The model was trained with a carefully selected set of hyperparameters and extensively validated through training and testing steps. The experiment results demonstrated outstanding performance with a training accuracy of 99.88% and perfect precision, recall, and F1-score values of 100%. Furthermore, testing with unseen data confirmed the robustness of the model since all test samples were correctly identified. This research concludes that the MobileNet-V3 architecture offers an effective approach to mangrove species classification and suggests that future work should involve larger and more varied datasets, real-world field environments, and the investigation of ensemble models to further extend the adaptability and scalability of mangrove monitoring systems.
Comparative Analysis of Supervised Learning Algorithms for Delivery Status Prediction in Big Data Supply Chain Management Apnena, Riri Damayanti; Ginting, Gerinata; Sudrajat, Ari; Islam, Hussain Md Mehedul
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4689

Abstract

This study addresses the problem of predicting delivery status in supply chain data, a critical task for optimizing logistics and operations. The dataset, which includes multiple features like order details, product specifications, and customer information, was pre-processed using oversampling to address class imbalance, ensuring that the model could handle rare cases of late or canceled deliveries. The data cleaning process involved handling missing values, removing irrelevant columns, and transforming categorical variables into numerical formats. After pre-processing and cleaning, five machine learning models were applied: Logistic Regression, Random Forest, SVM, K-Nearest Neighbors (KNN), and XGBoost. Each model was evaluated using metrics such as accuracy, precision, recall, and F1-score. The results showed that XGBoost outperformed the other models, achieving the highest accuracy and providing the most reliable predictions for the delivery status. This makes XGBoost the best choice for supply chain data analysis in this context. This study contributes to the growing application of machine learning in supply chain optimization by identifying XGBoost as a robust model for delivery status prediction in large datasets. For future research, exploring hybrid models and advanced feature engineering techniques could further improve prediction accuracy and address additional challenges in supply chain optimization, especially in the context of real-time data processing and dynamic supply chain environments.  
Aplikasi Pengajuan Dosen Pengampu Mata Kuliah Berbasis Web Studi Kasus : Politeknik TEDC Bandung Sugianto, Castaka Agus; Apnena, Riri Damayanti; ., Zulkipli
Journal of Informatics and Electronics Engineering Vol 1 No 2 (2021): Desember 2021
Publisher : Unit Penelitian dan Pengabdian kepada Masyarakat Politeknik TEDC Bandung Jl. Pesantren Km 2 Cibabat Cimahi Utara – Cimahi 40513 Jawa Barat – Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1011.064 KB)

Abstract

Penentuan dosen pengampu mata kuliah merupakan proses penentuan/memilah dosen pengampu mata kuliah setiap semester di Politeknik TEDC Bandung. Pengajuan dosen pengampu dilakukan oleh setiap program studi dan diserahkan ke bagian akademik. Yang selanjutnya bagian akademik mengumpulkan semua data pengajuan dosen pengampu dari setiap program studi dan mengadakan rapat sebaran bersama Wakil Direktur 1 dan pihak dari setiap program studi. Proses pengajuan dosen pengampu mata kuliah dirasa kurang efektif karena pengelolaan data masih menggunakan proses manual, namun sudah terkomputerisasi dengan menggunakan Microsoft Excel. Karena masih menggunakan proses ini. Pernah terjadi duplikasi mata kuliah disemester sebelumnya diajukan kembali disemester berikutnya dan ada dosen yang mendapatkan jumlah jam yang melebih kuantitas jam yang telah ditentukan. Maka untuk menangani hal tersebut dibutuhkan suatu wadah yang dapat mengefektifkan proses pengelolaan data pengajuan dosen pengampu mata kuliah untuk mengurangi faktor human error seperti duplikasi mata kuliah dan dapat merekap otomatis semua data pengajuan dosen pengampu yaitu dengan suatu aplikasi penentuan dosen pengampu mata kuliah berbasis web yang mudah diakses oleh banyak orang. Metode yang digunakan dalam pengembangan aplikasi penentuan dosen pengampu mata kuliah adalah metode waterfall. Berdasarkan hasil pengujian blackbox, semua fungsi yang ada dalam aplikasi penentuan dosen pengampu mata kuliah dapat berjalan sesuai yang diharapkan. Sedangkan berdasarkan hasil pengujian UAT aplikasi penentuan dosen pengampu mata kuliah terbukti dapat diterima dengan baik oleh pengguna dengan nilai persentase 97.1%.
Aplikasi Pembayaran SPP Sekolah Terintegrasi Whatsapp Berbasis Web Sudrajat, Ari; Wardhani, Shaffira Kusuma; Apnena, Riri Damayanti
Journal of Informatics and Electronics Engineering Vol 3 No 1 (2023): Juni 2023
Publisher : Unit Penelitian dan Pengabdian kepada Masyarakat Politeknik TEDC Bandung Jl. Pesantren Km 2 Cibabat Cimahi Utara – Cimahi 40513 Jawa Barat – Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70428/jiee.v3i1.717

Abstract

Proses pencatatan pembayaran yang kurang optimal dapat menyebabkan hilangnya data yang berakibat tidak akuntabel pada pembuatan laporan, seperti permasalahan yang ditemukan di SMK Mohamad Toha Cimahi sistem pembayaran SPP masih menggunakan sistem manual dimana Petugas Tata Usaha masih melakukan pencatatan menggunakan buku catatan pembayaran SPP. Hal ini menyebabkan banyak kesalahan yang terjadi dan memakan banyak waktu dalam proses pencatatan pembayaran SPP. Metode pendekatan yang penulis gunakan, menggunakan Metode Agile Develoment dengan model Extreme Programming. Pembangunan sistem akan di buat berbasis Web dengan terintegrasi pada aplikasi Whatsapp. Penyelesaian permasalahan pada saat implementasi sistem, penulis menggunakan bahasa pemrograman PHP, CSS, JavaScript, Node JS dengan framework laravel serta basis data MySQL. Metode pengujian sistem menggunakan Black Box dan User Acceptance Test (UAT), pengujian yang telah dilakukan menunjukan hasil yang sangat baik sebesar 88,43% yang diperoleh dari 14 orang responden dengan pengukuran terhadap 3 variabel yaitu desain 89,98%, fitur 86,76% dan kepuasan pengguna 88,56%. Kata Kunci— Aplikasi, SPP, Whatsapp, Web Abstrack— The process of recording payments that are less than optimal can cause loss of data which results in no accountability in preparing reports, such as the problems found at SMK Mohamad Toha Cimahi. This causes many errors to occur and takes a lot of time in the process of recording tuition fee payments. The approach method that the author uses, uses the Agile Development Method with the Extreme Programming model. The development of the system will be made web-based integrated into the WhatsApp application. Solving problems during system implementation, the author uses the programming language PHP, CSS, JavaScript, JS Node with the Laravel framework and MySQL database. The system testing method uses a Black Box and User Acceptance Test (UAT), the tests that have been carried out show very good results of 88.43% obtained from 14 respondents by measuring 3 variables, namely design 89.98%, features 86.76% and user satisfaction 88.56%. Keyword— Application, Tuition Fee, Whatsapp, Web
RANCANG BANGUN RANGKA MESIN PENGADUK DODOL KAPASITAS 10 KG Ramdani, Rizki; Prayoga, Kiki; Saleh, Agus; Ginting, Edison; Apnena, Riri Damayanti; -, Wachidin
Jurnal TEDC Vol 18 No 3 (2024): JURNAL TEDC
Publisher : UPPM Politeknik TEDC Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70428/tedc.v18i3.896

Abstract

Dodol is a traditional Indonesian food made from glutinous rice, palm sugar, coconut milk, salt, and water. All the ingredients are mixed and stirred until reaching a certain thickness, usually requiring 4-8 hours of cooking time. During the mixing process, dodol must be continuously stirred to prevent it from burning or forming a crust. This design aims to create a machine that can assist in the dodol production process, particularly in the stirring process, to make it more effective and efficient. The method involves designing a dodol stirrer machine frame. The design of this dodol stirrer machine frame uses Autodesk Inventor Professional 2023, with a material of 4x4 cm angle iron with a thickness of 3 mm, capable of supporting a load of 10 kg. The stress analysis results for this dodol stirrer frame are sufficiently safe, with a value of 29.5 MPa, compared to the maximum limit of 147.3 MPa. The results from the frame manufacturing process, using welding with an SMAW machine and E6013 electrodes, show that the frame can support each component and does not experience excessive vibration when the dodol stirrer machine is operational.
Co-Authors -, Wachidin Ari Sudrajat Ayu Hendrati Rahayu Castaka Agus Sugianto Gerinata Ginting Ginting, Edison Iqtait, Musab Islam, Hussain Md Mehedul Prayoga, Kiki Ramdan, Dadan Saepul Ramdani, Rizki Saleh, Agus Wardhani, Shaffira Kusuma Zulkipli ., Zulkipli