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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) Jurnal ULTIMA InfoSys ComTech: Computer, Mathematics and Engineering Applications Journal of Information Systems and Informatics bit-Tech eCo-Buss Algor Abdi-Dharma SATIN - Sains dan Teknologi Informasi
Junaedi
Universitas Buddhi Dharma
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Social Sciences

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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; Riki Riki
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): June 2026
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.
Sentiment Analysis of Indonesian Tourism Social Media Using Naïve Bayes for Decision Support Nathaniel Felix Fraderic; Aditiya Hermawan; Junaedi Junaedi
ULTIMA InfoSys Vol 17 No 1 (2026): Ultima InfoSys : Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/si.v17i1.4139

Abstract

The tourism sector remains one of the main contributors to Indonesia's economy, but its development is still challenged by inadequate communication and publication. Understanding visitors' opinions is essential for improving tourist destinations. This study aims to analyze public sentiment toward Indonesian tourist attractions by automatically processing visitor responses using sentiment analysis. The proposed approach applies text mining with the Naïve Bayes algorithm to classify sentiments efficiently. Data were collected through the X platform API using tourism-related keywords and hashtags, providing real-time public opinions on Indonesian destinations. A web-based application was developed using Python to visualize the sentiment analysis results as graphs showing the distribution of sentiment categories. The proposed model achieved an accuracy of 87%, demonstrating its effectiveness in classifying public responses. The findings provide useful insights for tourism stakeholders to evaluate visitor perceptions, identify areas for improvement, and support data-driven decision-making to enhance tourism services and visitor experiences. This study contributes to tourism research by applying the Naïve Bayes algorithm to Indonesian-language tweets related to tourist attractions, demonstrating the feasibility of text mining for automatically analyzing public opinion and supporting tourism development.
Residual-Based Hybrid SARIMA–LSTM for Bali Tourism Demand Forecasting Using Google Trends Junaedi; Aditiya Hermawan; Yusuf Kurnia; Ardiane Rossi Kurniawan Maranto
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1644

Abstract

Accurate tourism demand forecasting is essential for destinations characterized by strong seasonality, nonlinear fluctuations, and post-pandemic recovery uncertainty. This study develops a residual-based hybrid SARIMA–LSTM model for forecasting monthly international tourist arrivals to Bali, Indonesia, using historical arrival data and Google Trends search query data. The dataset covers January 2009 to December 2024, comprising 192 monthly observations. A chronological split was applied, with January 2009 to December 2022 used for training and January 2023 to December 2024 used for testing. SARIMA was employed to capture linear and seasonal structures, while LSTM was used to learn nonlinear residual patterns. The proposed model was compared with SARIMA, Random Forest, standalone LSTM, and SARIMA–RF using RMSE, MAPE, and R². The SARIMA–LSTM model achieved the best performance, with RMSE = 35,915.36, MAPE = 5.64%, and R² = 0.68, compared with SARIMA, which obtained RMSE = 37,052.68, MAPE = 5.70%, and R² = 0.65. These findings indicate that residual-based hybridisation provides incremental forecasting improvement. However, the independent contribution of Google Trends is not separately isolated in this study and should therefore be interpreted cautiously as a complementary behavioural signal within the proposed forecasting framework.
Peningkatan Literasi Digital Siswa melalui Pelatihan Pembuatan Website Menggunakan Google Sites di SMK Bangun Nusantara Junaedi; Ardiane Rossi Kurniawan Maranto; Harisa Mardiana
Abdi Dharma Vol. 6 No. 1 (2026): Jurnal Abdi Dharma (Jurnal Pengabdian Masyarakat)
Publisher : LP3kM Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/ad.v6i1.4245

Abstract

Pesatnya perkembangan teknologi informasi menuntut mahasiswa untuk memiliki kemampuan literasi digital yang kuat agar tetap kompetitif di era digital. Namun, banyak mahasiswa di SMK Bangun Nusantara yang masih mengalami keterbatasan dalam keterampilan penerbitan digital dan pengembangan portofolio online. Kegiatan Pengabdian Masyarakat (PkM) ini bertujuan untuk meningkatkan literasi digital mahasiswa melalui pelatihan pembuatan website portofolio sederhana menggunakan Google Sites sebagai media untuk mempublikasikan karya dan membangun identitas profesional awal. Program ini menerapkan pendekatan pembelajaran berbasis praktik partisipatif (belajar sambil melakukan) yang dilakukan selama periode satu bulan. Peserta terdiri dari 25 mahasiswa dari program Teknik Komputer dan Jaringan (TKJ), didukung oleh guru mata pelajaran yang produktif dan tim dosen dari Fakultas Sains dan Teknologi Universitas Buddhi Dharma. Tahapan kegiatan meliputi pengenalan publikasi digital yang etis, demonstrasi fitur Google Sites, praktik langsung terstruktur dalam mengembangkan halaman situs web seperti profil dan portofolio, dan pendampingan untuk meningkatkan struktur navigasi dan kualitas konten hingga publikasi. Hasilnya menunjukkan peningkatan kemampuan siswa untuk merancang dan mengelola situs web pribadi yang berisi profil diri dan portofolio digital. Siswa juga menunjukkan peningkatan kemandirian dalam mengatur informasi dan menerapkan prinsip-prinsip dasar keterbacaan dan konsistensi visual. Selain itu, program ini menghasilkan modul pelatihan berbasis cloud yang dapat dimanfaatkan oleh guru untuk mendukung pembelajaran berbasis proyek. Secara keseluruhan, pelatihan secara efektif memperkuat implementasi Kurikulum Merdeka dan menumbuhkan budaya publikasi digital di lingkungan sekolah vokasi.
Analisis Komparatif Algoritma Pembelajaran Mesin untuk Mengidentifikasi Keterlambatan Kelulusan Mahasiswa Benny Daniawan; Suwitno Suwitno; Andri Wijaya; Ardiane Rossi Kurniawan Maranto; Junaedi Junaedi
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.30186

Abstract

Delayed student graduation is a critical issue in higher education because it affects academic planning, student support, and institutional performance evaluation. This study develops a leakage controlled machine learning framework for early identification of students at risk of delayed graduation, using academic records available through the sixth semester. A dataset of 564 students was used, with graduation status defined as on-time for students graduating in the eighth semester or earlier and delayed for those graduating after the eighth semester. To prevent temporal data leakage, post-outcome variables were excluded from the predictor set. Five supervised learning algorithms were evaluated: Decision Tree, Support Vector Machine, Random Forest, Naïve Bayes, and K-Nearest Neighbor. Preprocessing was performed using one-hot encoding and standardization within a pipeline, and model performance was assessed using stratified five-fold cross-validation. The tuned Random Forest achieved the most balanced performance, with 0.956 accuracy, 0.861 delayed-class precision, 0.805 delayed-class recall, 0.832 delayed-class F1-score, and 0.977 ROC-AUC. The tuned SVM with a threshold of 0.30 achieved higher delayed-class recall (0.857) and ROC-AUC (0.980). Feature-importance analysis indicated that fourth and fifth semester GPAs were the strongest predictors. These findings show that machine learning can support early academic intervention and data driven decision making in higher education.
Kemampuan Strategis untuk Keberlanjutan UMKM: Peran Transformasi Digital, Kompetensi Sumber Daya Manusia, Dukungan Pemerintah, dan Sustainable Competitive Advantage Junaedi; Sihar Tambun; Toni Yoyo; Alexander Alexander; Eso Hernawan
eCo-Buss Vol. 9 No. 1 (2026): eCo-Buss
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/eb.v9i1.3969

Abstract

Penelitian ini mengkaji dampak transformasi digital, kompetensi sumber daya manusia, dan dukungan pemerintah terhadap keberlanjutan bisnis UMKM, dengan keunggulan kompetitif berkelanjutan sebagai variabel moderator. Dengan menggunakan desain eksplanasi kuantitatif, data dikumpulkan melalui survei cross-sectional terhadap 230 pelaku UMKM di Kota Tangerang, Indonesia, yang dipilih melalui purposive sampling. Data tersebut dianalisis menggunakan Partial Least Squares Structural Equation Modeling (PLS-SEM). Temuan tersebut mengungkapkan bahwa transformasi digital, kompetensi sumber daya manusia, dukungan pemerintah, dan keunggulan kompetitif yang berkelanjutan memiliki efek langsung yang positif dan signifikan terhadap keberlanjutan bisnis UMKM. Transformasi digital menunjukkan efek langsung terkuat, menunjukkan pentingnya adopsi teknologi digital dalam meningkatkan kelangsungan bisnis dan kemampuan beradaptasi. Analisis moderasi menunjukkan bahwa keunggulan kompetitif berkelanjutan secara signifikan hanya memperkuat hubungan antara kompetensi sumber daya manusia dan keberlanjutan bisnis UMKM, sementara efeknya yang memoderasi terhadap transformasi digital dan dukungan pemerintah tidak signifikan. Temuan ini menunjukkan bahwa keberlanjutan UMKM dibentuk oleh sumber daya teknologi, manusia, kelembagaan, dan kompetitif, tetapi keunggulan kompetitif berkelanjutan bekerja lebih kuat melalui kemampuan internal manusia.
Co-Authors Abidin Abidin Aditiya Hermawan Aditiya Hermawan Alexander Alexander Alexius Hendra Gunawan Alexius Hendra Gunawan Andri Wijaya Ardiane Rossi Kurniawan Maranto Ardiane Rossi Kurniawan Maranto Benny Daniawan Deviastati Putri Sugiarta Karlim Edy Eso Hernawan Harisa Mardiana Hok Ki Jelvin Putra Halawa Jonathan Leonardo Lianata Luis Alpianto Mariana Purnamasari Maysha Permata Putri Nathaniel Felix Fraderic Nazzua Azzahra Oscar Hasan Putra Riki Riki Riki Sihar Tambun Sonya Eliesse Dameria Stanley Ananda Suwitno Suwitno Suwitno Toni Yoyo Verri Kuswanto Verry Kuswanto Yusuf Kurnia Yusuf Kurnia