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All Journal Nuansa Informatika Telematika JUITA : Jurnal Informatika Indonesian Journal on Computing (Indo-JC) JETT (Jurnal Elektro dan Telekomunikasi Terapan) JOIV : International Journal on Informatics Visualization Jurnal Informatika Jurnal Pilar Nusa Mandiri Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Mantik Jurnal Teknik Informatika C.I.T. Medicom International Journal of Advances in Data and Information Systems EKONOMI, KEUANGAN, INVESTASI DAN SYARIAH (EKUITAS) Tematik : Jurnal Teknologi Informasi Komunikasi Innovation in Research of Informatics (INNOVATICS) Jurnal Pengabdian Masyarakat Nusantara Journal of Dinda : Data Science, Information Technology, and Data Analytics Naratif : Jurnal Nasional Riset, Aplikasi dan Teknik Informatika AJAD : Jurnal Pengabdian kepada Masyarakat Indonesian Journal of Business Analytics (IJBA) Formosa Journal of Applied Sciences (FJAS) Paradigma Journal Of Information System And Artificial Intelligence Jurnal Pengabdian Masyarakat Tapis Berseri SisInfo : Jurnal Sistem Informasi dan Informatika Nuansa Informatika International Journal of Computer Technology and Science
Nur Alamsyah
Fakultas Ilmu Komputer PASIM
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Social Sciences

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Pengaruh Electronic Word Of Mouth Dan Citra Merek Terhadap Keputusan Pembelian Aplikasi Tokopedia Wala Erpurini; Nur Alamsyah; Eli Nofita Sari
TEMATIK Vol. 11 No. 1 (2024): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2024
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v11i1.1864

Abstract

This study evaluates the influence of Electronic Word of Mouth (eWOM) and brand image on the purchasing decisions of students of the Faculty of Economics, Pasim Bandung National University on the Tokopedia application. eWOM includes reviews, testimonials, and recommendations through digital platforms, while brand image refers to students' perceptions of Tokopedia as a brand. This study uses a survey method with a questionnaire to collect data from students who have used the Tokopedia application. The analysis was conducted with multiple linear regression to determine the relationship between eWOM, brand image, and purchasing decisions. The results showed that eWOM had an influence of 4.7% on purchasing decisions, while brand image had an influence of 59.8%. Hypothesis testing shows that eWOM and brand image significantly influence purchasing decisions, with an R2 value of 0.646. Thus, about 64.6% of the variation in purchasing decisions on the Tokopedia application can be explained by the combined influence of eWOM and brand image. The remaining 35.4% is caused by other factors not examined in this study.
Optimalisasi Pemasaran dan Produksi Kopi Tangsi Wangi melalui Digital Marketing dan Edukasi Petani Erpurini, Wala; Leonandri, Dino Gustaf; Alamsyah, Nur
AJAD : Jurnal Pengabdian kepada Masyarakat Vol. 4 No. 3 (2024): DECEMBER 2024
Publisher : Divisi Riset, Lembaga Mitra Solusi Teknologi Informasi (L-MSTI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59431/ajad.v4i3.413

Abstract

The community engagement program aims to optimize the marketing and production of Tangsi Wangi Coffee through the implementation of digital marketing and farmer education in the region. Tangsi Wangi Coffee, as a prominent local product, holds significant potential for further development in the global market. However, limited knowledge of digital marketing strategies and efficient production techniques poses major challenges for local coffee farmers. Therefore, this program focuses on two key aspects: first, providing training for farmers on improved and sustainable coffee cultivation techniques; and second, enhancing marketing capacities by introducing them to digital marketing concepts, including the use of social media, e-commerce, and other online marketing platforms. This initiative is expected to boost the competitiveness of Tangsi Wangi Coffee in local and international markets while positively impacting farmers' welfare by increasing income and expanding market access. Beyond improving farmers' skills in production and marketing, the program aims to create greater added value for the local community and strengthen the global competitiveness of Tangsi Wangi Coffee. Additionally, it seeks to establish a mutually beneficial ecosystem among farmers, coffee entrepreneurs, and consumers.
CNN-LSTM for MFCC-based Speech Recognition on Smart Mirrors for Edge Computing Command Aji Gautama Putrada; Ikke Dian Oktaviani; Mohamad Nurkamal Fauzan; Nur Alamsyah
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 4 No 2 (2024): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v4i2.1504

Abstract

Smart mirrors are conventional mirrors that are augmented with embedded system capabilities to provide comfort and sophistication for users, including introducing the speech command function. However, existing research still applies the Google Speech API, which utilizes the cloud and provides sub-optimal processing time. Our research aim is to design speech recognition using Mel-frequency cepstral coefficients (MFCC) and convolutional neural network–long short-term memory (CNN-LSTM) to be applied to smart mirror edge devices for optimum processing time. Our first step was to download a synthetic speech recognition dataset consisting of waveform audio files (WAVs) from Kaggle, which included the utterances “left,” “right,” “yes,” “no,” “on,” and “off. ” We then designed speech recognition by involving Fourier transformation and low-pass filtering. We benchmark MFCC with linear predictive coding (LPC) because both are feature extraction methods on speech datasets. Then, we benchmarked CNN-LSTM with LSTM, simple recurrent neural network (RNN), and gated recurrent unit (GRU). Finally, we designed a smart mirror system complete with GUI and functions. The test results show that CNN-LSTM performs better than the three other methods with accuracy, precision, recall, and an f1-score of 0.92. The speech command with the best precision is "no," with a value of 0.940. Meanwhile, the command with the best recall is "off," with a value of 0.963. On the other hand, the speech command with the worst precision and recall is "other," with a value of 0.839. The contribution of this research is a smart mirror whose speech commands are carried out on the edge device with CNN-LSTM.
Enhancing Data Management Efficiency in Higher Education: A Case Study on the Development of P2M Applications Dirham Triyadi; Rijwan Rijwan; Budiman Budiman; Nur Alamsyah; Reni Nursyanti; Elia Setiana
International Journal of Computer Technology and Science Vol. 2 No. 1 (2025): International Journal of Computer Technology and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijcts.v2i1.221

Abstract

Developing research and community service (P2M) applications is crucial in enhancing efficiency and accuracy in managing related data at higher education institutions. This research aims to design a web-based application that simplifies the data management process for research, community service, and associated activities at Universitas Informatika dan Bisnis Indonesia (UNIBI). The research engaged the Rapid Application Development (RAD) methodology to actively incorporate stakeholders throughout the application development lifecycle, thereby guaranteeing alignment with their requirements. The results showed that the developed Application effectively resolved inaccurate data displays, manual data collection, and inefficient validation processes. Key features include a more accurate dashboard, an automated article validation tool integrated with Google Scholar, and streamlined submission community service activities. The activity submission process enhances operational efficiency and improves transparency and accountability in managing academic data. This research contributes to the broader adoption of digital solutions in educational administration, offering significant improvements in data accuracy and management at UNIBI.
Optimization of Human Development Index in Indonesia Using Decision Tree C4.5, Support Vector Machine Algorithm, K-Nearest Neighbors, Naïve Bayes, and Extreme Gradient Boosting Ramadhan, Ilham; Budiman, Budiman; Alamsyah, Nur
Jurnal Informatika Vol 12, No 1 (2025): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v12i1.21874

Abstract

The Human Development Index (HDI) is a measure of human development achievement based on quality of life indicators such as Life Expectancy (LE), Mean Years of Schooling (MYS), Expected Years of Schooling (EYS), and Adjusted Per Capita Expenditure (AECE). HDI describes how people access development outcomes through income, health, and education. The determination of development programs implemented by local governments must be based on district/city priorities based on their HDI categories and must be right on target. Therefore, a decision system is needed that can accurately determine the HDI category in each district/city in Indonesia, using machine learning models such as Decision Tree C4.5, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes, and Extreme Gradient Boosting (XGBoost). Machine learning models will be used to classify the HDI in Indonesia in 2022 and determine the performance of the most optimal model in classification. This research uses the CRISP-DM method with secondary data from the Central Statistics Agency (BPS) as much as 548 data. The analysis results show that the Decision Tree C4.5 models have an accuracy of 0.86, KNN of 0.95, Naïve Bayes of 0.90, XGBoost of 0.93, and SVM provides the most optimal results with an accuracy of 0.97. UHH, RLS, and HLS variables significantly influence changes in HDI values in Indonesian regions based on the Chi-square, Pearson Correlation, Spearman, and Kendal test results. 
OPTIMIZED FACEBOOK PROPHET FOR MPOX FORECASTING: ENHANCING PREDICTIVE ACCURACY WITH HYPERPARAMETER TUNING Alamsyah, Nur; Restreva Danestiara, Venia; Budiman, Budiman; Nursyanti, Reni; Setiana, Elia; Hendra, Acep
Jurnal Techno Nusa Mandiri Vol. 22 No. 1 (2025): Techno Nusa Mandiri : Journal of Computing and Information Technology Period o
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v22i1.6507

Abstract

MPOX (Monkeypox) has become a significant global health concern, requiring accurate forecasting for effective outbreak management. This study improves MPOX case prediction using Facebook Prophet with hyperparameter optimization. The dataset consists of global MPOX case records collected over time. Data preprocessing includes missing value imputation, normalization, and aggregation. Facebook Prophet is applied to forecast case trends, with model performance evaluated using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). A baseline Prophet model is first trained using default parameters. The model is then optimized by fine-tuning seasonality mode, changepoint prior scale, and growth model. The results show that hyperparameter tuning significantly enhances forecasting accuracy. The optimized model reduces MSE from 541,844.77 to 320,953.34 and RMSE from 736.10 to 566.53, demonstrating improved precision. The model also captures trend shifts and seasonal fluctuations more effectively. In conclusion, this study confirms that tuning Facebook Prophet improves epidemic forecasting, making it a reliable tool for MPOX monitoring. Future research should integrate external factors, such as vaccination rates and mobility data, to further refine predictions.
SENTIMENT ANALYSIS OF PLAYER FEEDBACK IN ALGORUN: A STUDY OF DEEP LEARNING MODELS FOR GAME-BASED LEARNING Rio Andriyat Krisdiawan; Nur Alamsyah; Tito Sugiharto
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6015

Abstract

AlgoRun: Coding Game is a game-based learning application aimed at teaching computational thinking (CT) concepts such as variables, conditions, loops, and functions. Evaluating user feedback in such educational games is challenging, as traditional sentiment analysis techniques often overlook nuanced responses. Despite its potential to inform content improvements, sentiment analysis in game-based learning remains underexplored. This study compares the performance of deep learning models—DNN, CNN, RNN with LSTM, and Bidirectional LSTM—for sentiment classification of AlgoRun user reviews, using TF-IDF and word embeddings as feature extraction methods. A total of 1,440 reviews were scraped from the Google Play Store, translated, and preprocessed using data preparation techniques (dropna, fillna), text preprocessing (case folding, cleaning, tokenization, stopword removal, stemming), and feature extraction (TF-IDF and word embeddings). The dataset was labeled into negative, neutral, and positive classes, and split 80% for training and 20% for testing. Among the tested models, the DNN with TF-IDF achieved the highest accuracy of 98.86%, followed by CNN with Word Embeddings (96.97%), Bidirectional LSTM (96.59%), and RNN with LSTM (92.42%). The DNN also showed stable performance and convergence at the 10th epoch, outperforming other models in precision, recall, and F1-score. These results suggest that DNN with TF-IDF is highly effective for sentiment classification in the context of game-based learning. The findings offer useful guidance for developers to adapt content and enhance game quality based on user feedback. This research also contributes to the growing body of literature on leveraging sentiment analysis to optimize educational applications.
THE ROLE OF L1 REGULARIZATION IN ENHANCING LOGISTIC REGRESSION FOR EGG PRODUCTION PREDICTION Nur Alamsyah; Budiman Budiman; Elia Setiana; Valencia Claudia Jennifer
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6409

Abstract

Poultry egg productivity is strongly influenced by various environmental factors, such as air and water quality. However, accurately predicting productivity remains a challenge due to the complex interplay of multiple environmental variables and the risk of overfitting in predictive models. This study improves egg productivity prediction using Logistic Regression with L1 regularization, which enhances model generalization by performing automatic feature selection. The research methodology includes data collection of key environmental indicators—Air Quality Index (AQI), Water Quality Index (WQI), and Humidex—followed by data preprocessing, exploratory data analysis (EDA), and model training using L1-regularized Logistic Regression. Model evaluation was performed using classification metrics and learning curve analysis to assess stability and effectiveness. Experimental results indicate that Logistic Regression without regularization achieved an accuracy of 90.7%, with misclassification occurring in the lower production categories. By applying L1 regularization, accuracy increased significantly to 97%, demonstrating its ability to reduce overfitting while improving classification performance. This study also compares its findings with previous research, such as De Col et al. (wheat epidemic prediction, 80–85% accuracy) and Jia Q1 et al. (heart disease prediction, 92.39% accuracy), confirming that our approach outperforms prior Logistic Regression models in similar predictive tasks. These findings suggest that L1 regularization is an effective solution for improving egg productivity prediction, particularly in scenarios with complex environmental influences. Future work will explore advanced ensemble learning techniques and real-time IoT-based monitoring to further enhance prediction accuracy and practical applicability.
Digital Marketing Strategy Optimization Using Support Vector Machine Algorithm AlFauzi, Ihsan; Budiman, Budiman; Alamsyah, Nur
Jurnal Informatika Vol 12, No 1 (2025): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v12i1.22459

Abstract

Information and communication technology (ICT) is essential in rapidly disseminating information. This research discusses the influence of ICT use in marketing promotions through TV, radio, and social media and compares the performance of several classification algorithms in processing the promotion data. The dataset is from Kaggle, with promotional attributes on TV, radio, and social media. The Cross-Industry Standard Process for Data Mining (CRISP-DM) is used. Algorithms tested include Naive Bayes, K-Nearest Neighbor, Support Vector Machine (SVM), Random Forest, and XGBoost. The results showed that SVM had the best performance with 80% accuracy, followed by KNN (79%), Naive Bayes (77%), XGBoost (77%), and Random Forest (76%). SVM provided the most accurate and consistent predictions in marketing promotion classification. This research concludes that the optimal utilisation of ICT and the application of appropriate classification algorithms can increase the effectiveness of marketing promotions in the digital era.
Approximate Bayesian Inference for Bayesian Confidence Quantification in DNA Sequence Classification Using Monte Carlo Dropout Approach Alamsyah, Nur; Budiman, Budiman; Nursyanti, Reni; Setiana, Elia; Danestiara, Venia Restreva
Innovation in Research of Informatics (Innovatics) Vol 7, No 1 (2025): March 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i1.14349

Abstract

Splice junction classification in DNA sequences is critical for understanding genetic structures and processes, particularly the differentiation between exon-intron (EI), intron-exon (IE), and neither boundaries. Traditional neural network models achieve high accuracy but often lack the ability to quantify uncertainty, which is essential for reliability in sensitive applications such as bioinformatics. This study addresses this limitation by incorporating Bayesian confidence quantification into DNA sequence classification using the Monte Carlo Dropout (MCD) approach. A baseline neural network was first implemented as a reference, achieving a test accuracy of 95.61%. Subsequently, MCD was applied, which not only improved the test accuracy to 96.03% but also provided uncertainty estimation for each prediction by sampling multiple inferences. The uncertainty values enabled the identification of low-confidence predictions, enhancing the interpretability and reliability of the model. Experiments were conducted on a binary-encoded DNA sequence dataset, representing nucleotides (A, C, G, T) and their splice junctions. The results demonstrated that MCD is a robust approach for DNA sequence classification, offering both high predictive performance and actionable insights through uncertainty quantification. This research highlights the potential of Bayesian confidence quantification in genomic studies, particularly for tasks requiring high reliability and interpretability. The proposed approach bridges the gap between accurate predictions and the need for robust uncertainty estimation, contributing to advancements in bioinformatics and machine learning applications in genetic research.
Co-Authors Acep Hendra Aji Gautama Putrada Akbar, Imannudin Alamsyah, R Yadi Rakhman AlFauzi, Ihsan Arnold Ropen Sinaga Bayu Wibisono, Gunthur Budi Sulistiyo Jati Budiman Danestiara, Venia R Della Octa Irmulvani Devi Silaen Dirham Triyadi Eli Nofita Sari Erpurini, Wala Fauzan, Mohamad Nurkamal Habibi, Chairul Hani Fitria Rahmani Hasan Nuraripin Hermawan, Arief Karditya Ikke Dian Oktaviani Indah Susilawati Ismi Nur Muhamad Januantara Prima, Alif Jennifer Kaunang, Valencia Claudia Kaunang, Valencia Kaunang, Valencia Claudia Jennifer Leonandri, Dino Gustaf Mohamad Nurkamal Fauzan Mohamad Nurkamal Fauzan Mohamad Nurkamal Fauzan Muhamad Andriansyah Nurcahya Muhamad Nurkamal Fauzan Muhammad Yusup Iskandar Mutaqin Akbar Nur Imam Febrianto PARAMA YOGA, TITAN Permata Hati, Tiara R. Yadi Rakhman Alamsyah Reni Nursyanti Reni Nursyanti Rijwan Rijwan Rimbun Kencana RIO ANDRIYAT KRISDIAWAN Setiana, Elia Sidik Prabowo Silvana Anggraeni, Zulmeida Syafrial Fachri Pane Tiara Nurhasanah Titan Parama Titan Parama Yoga Tito Sugiharto Tutik Ultsa Rahmatika Valencia Claudia Jennifer Venia Restreva Danestiara Wala Erpurini Wala Erpurini Wia Handayani Yeni