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All Journal Jurnal Buana Informatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Format : Jurnal Imiah Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ComTech: Computer, Mathematics and Engineering Applications bit-Tech eCo-Buss Algor SATIN - Sains dan Teknologi Informasi
Junaedi
Universitas Buddhi Dharma
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Mathematics

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Unveiling the Synergy: How Entrepreneurial Marketing and Product Quality Drive Purchase Decisions through the Lens of Digital Marketing Mariana Purnamasari; Aditiya Hermawan; Junaedi
eCo-Buss Vol. 6 No. 3 (2024): eCo-Buss
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/eb.v6i3.1181

Abstract

  This study delves into the complex interactions between entrepreneurial marketing, product quality, and digital marketing within Indonesia's dynamic digital marketplace, aiming to decipher their combined impact on purchase decisions. Employing a quantitative causal approach alongside Structural Equation Modeling (SEM) and Smart PLS software, the research endeavors to uncover the underlying mechanisms driving consumer behavior in this context. Findings indicate that while Entrepreneurial Marketing may not exert a direct influence on purchase decisions, both Product Quality and Digital Marketing play significant roles in shaping consumer choices. Notably, Digital Marketing emerges as a crucial moderating factor, enhancing the effects of Entrepreneurial Marketing and Product Quality on purchase decisions. These results underscore the pivotal role of digital platforms in influencing consumer behavior and preferences. The study provides actionable insights for businesses, advocating for the integration of digital marketing strategies and the promotion of high-quality products to effectively engage online consumers and drive purchasing behavior. While acknowledging limitations such as sample size and generalizability, this research lays the groundwork for future investigations into the nuanced dynamics of digital marketing's impact on marketing strategies and product quality enhancement. Overall, this study contributes to advancing the understanding of digital consumer behavior, emphasizing the transformative potential of digital marketing in today's evolving digital marketplace.
Eksplorasi Algoritma Support Vector Machine untuk Analisis Sentimen Destinasi Wisata di Indonesia Junaedi; Alexius Hendra Gunawan; Verri Kuswanto; Jonathan
bit-Tech Vol. 7 No. 2 (2024): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i2.1810

Abstract

Penelitian ini mengeksplorasi penerapan algoritma Support Vector Machine (SVM) dalam Text Mining untuk analisis sentimen sektor pariwisata di Indonesia, menggunakan data dari platform Twitter. Data dikumpulkan melalui API Twitter dan diproses melalui tahapan prapemrosesan teks, termasuk tokenisasi, normalisasi, penghapusan stopword, dan stemming, untuk memastikan kesiapan data dalam analisis. Model SVM diuji dengan tiga kernel berbeda—linear, radial basis function (RBF), dan sigmoid—serta menggunakan rasio data latih-uji 7:3 dan 8:2. Hasil menunjukkan bahwa kernel linear dengan rasio 7:3 menghasilkan kinerja terbaik dengan akurasi 92,89%, precision 92%, recall 74%, dan F1-score 81%. Evaluasi berdasarkan kelas sentimen menunjukkan performa tinggi pada sentimen positif (F1-score 96%) tetapi moderat pada kelas netral (F1-score 67%), mencerminkan pengaruh ketidakseimbangan data. Penelitian ini memberikan kontribusi signifikan dalam mendukung pengambilan keputusan berbasis data untuk pengembangan sektor pariwisata. Temuan ini memungkinkan pengelola destinasi wisata untuk memahami opini wisatawan secara otomatis, menyusun strategi promosi yang lebih efektif, serta meningkatkan kualitas layanan. Dengan menerapkan analisis sentimen berbasis SVM, penelitian ini mendukung pengelolaan pariwisata berbasis data untuk meningkatkan daya saing destinasi wisata di Indonesia. Penelitian lanjutan disarankan untuk mengatasi ketidakseimbangan data melalui teknik resampling atau penerapan algoritma alternatif seperti deep learning, guna meningkatkan akurasi klasifikasi sentimen yang lebih kompleks. Dengan demikian, penelitian ini menjadi langkah strategis dalam memanfaatkan teknologi analitik untuk pengelolaan pariwisata yang lebih inovatif.
Enhancing Stock Price Forecasting: Optimizing Neural Networks with Moving Average Data Aditiya Hermawan; Stanley Ananda; Junaedi; Edy
bit-Tech Vol. 7 No. 3 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i3.2196

Abstract

This research focuses on optimizing a neural network model for stock price prediction using Particle Swarm Optimization (PSO), considering the inherent risks and potential high returns associated with stock investment. Given the challenges posed by stock price volatility, this study combines Moving Average (MA) a fundamental statistical technique in stock market analysis with advanced data mining approaches, specifically neural networks and PSO, to enhance prediction accuracy. The primary objective is to improve the efficiency of neural networks by minimizing error rates and equipping investors with more reliable tools for financial decision-making. The proposed methodology involves converting historical stock price data into a Simple Moving Average (SMA) over a 5-day period, followed by optimizing a neural network model using PSO. This optimization process fine-tunes key parameters, particularly the weight distributions of various stock market indicators, including Open SMA, High SMA, Low SMA, and Close SMA. Model performance is evaluated using Root Mean Square Error (RMSE) as a validation metric. The findings indicate a significant enhancement in the predictive accuracy of the neural network model after PSO optimization. The optimal configuration is identified in a two-layer neural network with a specific node arrangement. This optimized model not only improves stock price forecasting precision but also has practical implications for investors and financial analysts in risk management and profit maximization.
Optimizing Sentiment Classification of E-Commerce Product Reviews: A Comparative Study of Naïve Bayes and SVM with SMO Riki; Sonya Eliesse Dameria; Aditiya Hermawan; Junaedi; Yusuf Kurnia
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 3, November 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i3.26642

Abstract

The rapid growth of e-commerce has led to a surge in user-generated product reviews, making manual sentiment analysis impractical. This study explores automated sentiment classification using two machine learning algorithms: Naïve Bayes and Support Vector Machine (SVM) that is optimized with Sequential Minimal Optimization (SMO). The dataset comprises 2,000 Shopee product reviews that are labeled as positive, neutral, or negative. The study focuses on assessing the effectiveness of these algorithms in classifying product reviews, especially in the diverse and high-volume data that is typically on e-commerce environments. Empirical evaluation shows that Naïve Bayes achieves 68% accuracy, while SVM with SMO attains 79%. Additionally, the study evaluates other important performance metrics, such as precision, recall, and F1-score. This study show that SVM with SMO outperforms Naïve Bayes in accurately classifying product reviews. These findings highlight the superior capability of SVM with SMO in handling complex sentiment data, thereby offering a more robust foundation for automated review classification. This research provides insights into selecting suitable classifiers for improving customer experience and strategic decision-making in digital commerce.
Impact of Dataset Background on Deep Learning-Based Waste Classification Nazzua Azzahra; Aditiya Hermawan; Junaedi; Yusuf Kurnia; Edy
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): Juni 2026 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.6965

Abstract

Accurate waste classification plays a vital role in supporting effective waste management and promoting environmental sustainability, especially amid the continuing increase in global waste generation. This study investigates how the presence and removal of image backgrounds influence the performance of deep learning models in automated waste classification. Two Convolutional Neural Network architectures, namely MobileNetV2 and DenseNet169, were evaluated using a dataset comprising 5,054 images across six waste categories: cardboard, glass, metal, paper, plastic, and trash. Each architecture was trained and tested on two dataset variants: original images with backgrounds and images with the backgrounds removed. Model performance was assessed using accuracy, precision, recall, F1-score, and ROC AUC. The results indicate that DenseNet169 consistently outperformed MobileNetV2 across all evaluation metrics. The highest accuracy, reaching 88.33%, was achieved by DenseNet169 when trained on images retaining their original backgrounds. This suggests that background information may provide meaningful contextual features that enhance classification performance. Conversely, removing backgrounds can limit the visual information available to the model and potentially reduce predictive effectiveness. These findings emphasize the importance of carefully considering background characteristics during dataset preparation and model training. Moreover, the study demonstrates that selecting an appropriate model architecture in relation to dataset properties is essential for optimizing classification outcomes. Overall, this research offers practical insights for improving dataset design and model selection in future automated waste classification systems, while contributing to the advancement of scalable and intelligent deep learning-based waste management solutions.
Co-Authors Abidin Abidin Aditiya Hermawan Aditiya Hermawan Alexius Hendra Gunawan Alexius Hendra Gunawan Ardiane Rossi Kurniawan Maranto Benny Daniawan Deviastati Putri Sugiarta Karlim Edy Hok Ki Jelvin Putra Halawa Jonathan Leonardo Lianata Luis Alpianto Mariana Purnamasari Maysha Permata Putri Nazzua Azzahra Oscar Hasan Putra Riki Riki Riki Sonya Eliesse Dameria Stanley Ananda Suwitno Verri Kuswanto Verry Kuswanto Yusuf Kurnia Yusuf Kurnia