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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Information System JOIV : International Journal on Informatics Visualization International Journal Of Science, Technology & Management (IJSTM) The Indonesian Journal of Computer Science JP2N: Jurnal Pengembangan Dan Pengabdian Nusantara
Sanjaya, Samuel Ady
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A Systematic Literature Review: A Comparison Of Available Approaches In Chatbot And Dialogue Manager Development Jayavardhana, Arya; Sanjaya, Samuel Ady
International Journal of Science, Technology & Management Vol. 4 No. 6 (2023): November 2023
Publisher : Publisher Cv. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46729/ijstm.v4i6.983

Abstract

The present study reviewed a number of articles chosen from a screening and selecting process on the various different methods that can be used in the context of chatbot development and dialogue managers. Since chatbots have seen a significant rise in popularity and have played an important role in helping humans complete daily tasks, this systematic literature review (SLR) aims to act as a guidance for future research. During the process of analyzing and extracting data from the 13 articles chosen, it has been identified that Artificial Neural Network (ANN), Ensemble Learning, Recurrent Neural Network (RNN), and Long-Short Term Memory (LSTM) is among some of the most popular algorithms used for developing a chatbot. Where all of these algorithms are suitable for each unique use case where it offers different advantages when implemented. Other than that, dialogue managers lean more towards the field of Deep Reinforcement Learning (DRL), where Deep Q-Networks (DQN) and its variants such as Double Deep-Q Networks (DDQN) and DDQN with Personalized Experience Replay (DDQN-PER) is commonly used. All these variants have different averages on episodic reward and dialogue length, along with different training time needed which indicates the computational power needed. This SLR aims to identify the methods that can be used and identify the best proven method to be applied in future research.
A Web-based Point of Sales for Automotive Component Industry using Rapid Application Development model Melvin, Melvin; Wiratama, Jansen; Sutomo, Rudi; Sanjaya, Samuel Ady
JOINS (Journal of Information System) Vol. 8 No. 2 (2023): Edisi November 2023
Publisher : Fakultas Ilmu Komputer, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/joins.v8i2.9383

Abstract

This research aims to increase efficiency in Micro, Small, and Medium Enterprises (MSMEs) in the automotive components industry, focusing on CV Bengkel Megamakmur. This workshop is one of the MSMEs engaged in Indonesia's car spare parts industry. In the ongoing business process, these MSMEs need some help in inventory management, stock of goods, and less integrated sales transaction recording. The main problem identified was the discrepancy between the number of products sold and sales transaction reports. A web-based Point of Sale (POS) application was implemented, which allows the integration of inventory data and sales transactions. The method used in this research is Rapid Application Development (RAD), suitable for projects with limited resources and short work schedules. This research results in a Point of Sale application that can optimize business processes for managing inventory by monitoring stock and recording sales transactions in an integrated manner. This application has been tested using the System Usability Scale (SUS) method and obtained an average score of 86.6, included in the "very good" category. The test results show that this application has good functionality and can optimize MSME business processes in the automotive components industry.
Dealing imbalance dataset problem in sentiment analysis of recession in Indonesia Kristiyanti, Dinar Ajeng; Sanjaya, Samuel Ady; Tjokro, Vinsencius Christio; Suhali, Jason
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2060-2072

Abstract

Global recession news dominates social media, particularly in Indonesia, with social news platforms on Twitter generating public responses and re-tweetings on the issue. Mining these opinions from Twitter using a sentiment analysis approach yields invaluable insights. The research stages included data collection, pre-processing, data labeling using the lexical-based method like valence aware dictionary and sentiment reasoner (VADER) and TextBlob, sampling techniques using synthetic minority oversampling technique (SMOTE) and random over sampling (ROS) before and after splitting data, and modeling using machine learning such as support vector machines (SVM), k-nearest neighbour (KNN), naive Bayes, and model evaluation. The problem is that almost 300,000 data collected from NodeXL are unbalanced. The findings show that models with balanced datasets show better model evaluation results. The sampling technique was carried out before and after splitting the data. The model evaluation results show that the Bernoulli-naive Bayes algorithm, with the VADER labeling technique, and the SMOTE sampling technique after splitting data, obtains the best accuracy of 84%, and using the ROS technique obtains an accuracy of 81%. On the other hand, with the SMOTE and ROS technique before splitting data on the SVM algorithm, it gets the best accuracy of 93% from before if only using SVM only reached 84%.
BISINDO Sign Language Recognition: A Systematic Literature Review of Deep Learning Techniques for Image Processing Sanjaya, Samuel Ady; Faustine Ilone, Hadinata
The Indonesian Journal of Computer Science Vol. 12 No. 6 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i6.3539

Abstract

This study uses the Systematic Literature Review (SLR) method to examine the application of deep learning techniques focusing on BISINDO (Bahasa Isyarat Indonesia) image recognition. This is crucial for enhancing communication accessibility for the hearing-impaired community. The SLR process involves three stages: planning, conducting, and reporting. During the planning stage, research topics, questions, and search criteria are established, while the conducting stage involves comprehensive article retrieval and rigorous filtering. In the reporting stage, the study highlights the significance of various deep learning methodologies, including the implementation of several algorithms that ace in image recognition. For example, Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), and a combination of the two methods, each with unique advantages and limitations. Along with that, this paper aims to find gaps in previous research and act as a guide for future deep learning model development. Moreover, the research outlines the development of a high-performance model, emphasizing key phases such as image augmentation and data preprocessing, as well as model optimization. These efforts contribute to a better understanding of BISINDO image recognition, offering valuable insights for researchers and practitioners aiming to support easier accessibility and communication for the hearing-impaired community through advanced deep learning approaches.
Comparison of Salp Swarm Algorithm and Particle Swarm Optimization as Feature Selection Techniques for Recession Sentiment Analysis in Indonesia Kristiyanti, Dinar Ajeng; Sanjaya, Samuel Ady; Irmawati, Irmawati; Ekachandra, Kristian; Suhali, Jason; Hairul Umam, Akhmad
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3102

Abstract

Amidst global economic uncertainty, this study focuses on Twitter sentiment during the global recession issue on social media, especially in Indonesia. By utilizing sentiment analysis, this study uses machine learning algorithms such as Naïve Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) which are still less than optimal on high-dimensional Twitter data. The purpose of this study is to improve the accuracy of conventional machine learning using basic metaheuristic algorithms, namely the Salp Swarm Algorithm (SSA) and Particle Swarm Optimization (PSO) as feature selection. From January to May 2023, this study captures the evolving sentiment in response to economic conditions. Data preprocessing, including labeling through the TextBlob and VADER libraries, sets the stage for the analysis. Performance is compared based on labeling techniques, feature selection, and classification algorithms. Specifically, when applied to VADER labeled data without feature selection, the SVM model achieves an outstanding accuracy of 83% and an F1 score of 67%—notably, the application of SSA and PSO results in a reduction in model accuracy by 1%. However, the application of SSA and PSO slightly reduced the model accuracy performance by 1%. On the TextBlob labeled data, SVM showed an outstanding performance (80% accuracy, 77% F1 score). Interestingly, PSO on TextBlob data with SVM significantly decreased the model's performance. These findings contribute significantly to understanding the intricacies of sentiment dynamics during economic uncertainty on social media platforms, with SVM emerging as a strong choice for practical sentiment analysis.
PENGEMBANGAN SISTEM E-COMMERCE UNTUK UMAT PAROKI KARAWACI Desanti, Ririn Ikana; Suryasari; Wella; Wiratama, Jansen; Sanjaya, Samuel Ady
JP2N : Jurnal Pengembangan Dan Pengabdian Nusantara Vol. 2 No. 1 (2024): JP2N :September - Desember 2024
Publisher : Yayasan Pengembangan Dan Pemberdayaan Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62180/vxyvwz23

Abstract

Paroki Karawaci memiliki sebuah divisi yang bernama Pengembangan Usaha Sosial dan Modal (PUSM) yang bertujuan untuk membantu menyejahterakan kehidupan umat gereja melalui beberapa kegiatan yang salah satunya yaitu kegiatan menjual berbagai macam produk makanan yang berasal dari para umat. Sebelumnya proses pemasaran dan penjualan dilakukan secara door-to-door ke rumah para umat, namun sayangnya hal tersebut tidak dapat dilakukan lagi pada saat pandemi melanda Indonesia sehingga menyebabkan omset penjualan menurun drastis bahkan hampir tidak ada. Berdasarkan permasalahan tersebut, tim pengabdian kepada masyarakat (PKM) dari program studi sistem informasi UMN mengusulkan cara pemasaran dan penjualan secara online menggunakan situs web e-commerce yang dirancang dan dipergunakan khusus untuk umat gereja paroki karawaci. Situs web e-commerce dirancang menggunakan metode prototyping dan bahasa pemodelan yang digunakan adalah UML diagram. Situs web e-commerce tersebut memiliki fitur utama pengelolaan produk, pengelolaan penjual, transaksi penjualan dan pengiriman barang. Hal yang perlu menjadi perhatian khusus oleh tim pengembang adalah sistem harus user friendly dan seluruh fiturnya juga harus dibuat sederhana karena calon pengguna sistem merupakan pengguna pemula (novice user). Kegiatan PKM telah berhasil terlaksana dengan baik dan untuk tahap selanjutnya situs web e-commerce akan mulai diimplementasi oleh paroki karawaci dan sebelumnya akan diberikan pelatihan kepada calon pengguna.
A Comparative Analysis of Building Hidden Layer, Activation Function, and Optimizer on Neural Network Sentiment Analysis Sanjaya, Samuel Ady; Kristiyanti, Dinar Ajeng; Irmawati, Irmawati; Hadinata, Faustine Ilone; Karaeng, Cristin Natalia
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2906

Abstract

The increasing diversity of opinions on social media offers a rich source for sentiment analysis, especially on controversial issues like the potential recession in Indonesia. This study aims to examine social media sentiment by utilizing three Deep Learning methods: Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN). The main objective is to configure key hyperparameters, including the number of hidden layers, activation functions, and optimizers, to optimize performance. A dataset of 38,000 cleaned Twitter posts was used for this study. The preprocessing steps involve various techniques to prepare analysis, including case folding to standardize text, removal of punctuation to eliminate noise, stemming to reduce words to their root forms, and sentiment labeling using advanced tools like VADER and BERT to ensure accurate classification. Each deep learning model is trained using a diverse range of configurations for activation functions, such as Sigmoid and Swish, as well as optimizers like Adam and others to fine-tune performance. Among the models, the CNN, configured with 15 hidden layers, a Sigmoid activation function, and the Adam optimizer, outperformed the others, achieving the highest accuracy of 0.870 and a low loss of 0.316. The results highlight that while the number of hidden layers influences model performance, the choice of activation function and optimizer has a more significant impact on accuracy. Furthermore, the findings offer implications for future research, suggesting that activation functions and optimizers should be prioritized over hidden layers when aiming for improved sentiment analysis performance in various contexts.
Classifying classical music’s therapeutic effects using deep learning: a review Angelin, Angelin; Sanjaya, Samuel Ady; Kristiyanti, Dinar Ajeng
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4933-4942

Abstract

Mental health issues are the leading cause of global disability, increasing the need for treatment options. While there is much research on the emotional recognition of music in general, there is a gap in studies that directly connect musical features with their therapeutic effects using deep learning. This systematic literature review explores the use of deep learning in classifying the therapeutic effects of classical music for mental health. Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework, a total of 15 papers were reviewed. This review synthesized studies on the role of musical elements that affect mental states. Different feature extraction methods, including mel-frequency cepstral coefficients (MFCCs), spectral contrast, and chroma features, are discussed for their roles in classifying these therapeutic effects. This review also looks at deep learning algorithms like convolutional neural network (CNN), deep neural network (DNN), long short-term memory (LSTM) network, and combined models to assess their effectiveness. Common evaluation methods are also assessed to measure the performance of these models in audio classification. This review highlights the potential of combining deep learning with classical music for mental health support, and to future possibilities for applying these methods in the real world.
Co-Authors Angelin, Angelin Dinar Ajeng Kristiyanti, Dinar Ajeng Ekachandra, Kristian Faustine Ilone, Hadinata Hadinata, Faustine Ilone Hairul Umam, Akhmad Irmawati Irmawati Jayavardhana, Arya Karaeng, Cristin Natalia Melvin, Melvin Ririn Ikana Desanti Suhali, Jason Suryasari Sutomo, Rudi Tjokro, Vinsencius Christio Wella Wiratama, Jansen