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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Teknik Elektro AGRIVITA, Journal of Agricultural Science Edu Komputika Journal Jurnal Teknologi dan Sistem Komputer Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer International Journal of Artificial Intelligence Research JMM (Jurnal Masyarakat Mandiri) SAP (Susunan Artikel Pendidikan) Jurnal Riset Rekayasa Elektro Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Journal of Applied Data Sciences Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Informatika Polinema (JIP) International Journal Research on Metaverse
Budi Sunarko
Universitas Negeri Semarang
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Library & Information Science Social Sciences Other

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Comparison of Ensemble Learning Methods for Mining the Implementation of the 7 Ps Marketing Mix on TripAdvisor Restaurant Customer Review Data Sunarko, Budi; Hasanah, Uswatun; Hidayat, Syahroni
International Journal of Artificial Intelligence Research Vol 7, No 2 (2023): December 2023
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i2.1096

Abstract

The 7P marketing mix encompasses various business facets, notably the Process element governing internal operations from production to customer service. With the surge in online customer feedback, assessing machine learning efficacy, especially ensemble learning, in classifying 7P-related customer review data has gained prominence. This research aims to fill a gap in existing literature by evaluating ensemble learning’s performance on 7P classification, an area not extensively explored despite prior sentiment analysis studies. Employing a methodology merging Natural Language Processing (NLP) with ensemble learning, the study processes restaurant reviews using NLP techniques and employs ensemble learning for precision and accuracy. Findings demonstrate that DESMI yielded the highest performance metrics with accuracy at 0.697, precision at 0.699, recall at 0.697, and an F1-score of 0.684. These outcomes underscore ensemble learning's potential in handling complex datasets, signifying its relevance for marketers and researchers seeking comprehensive insights from customer reviews within the 7P marketing mix domain. This study sheds light on how ensemble learning outperforms its foundational methods, indicating its prowess in extracting meaningful insights from diverse and intricate customer feedback.
Penerapan Stacking Ensemble Learning untuk Klasifikasi Efek Kesehatan Akibat Pencemaran Udara Sunarko, Budi; Hasanah, Uswatun; Hidayat, Syahroni; Muhammad, Naufal; Ardiansyah, Muhammad Irfan; Ananda, Briska Putra; Hakiki, Muhammad Khikam; Baroroh, Luluk Taufiqul
Edu Komputika Journal Vol 10 No 1 (2023): Edu Komputika Journal
Publisher : Jurusan Teknik Elektro Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukomputika.v10i1.72080

Abstract

Pencemaran udara merupakan masalah serius yang berdampak negatif pada kesehatan manusia. Berbagai jenis polutan udara seperti partikel halus, sulfur dioksida, nitrogen oksida, dan ozon dapat menyebabkan gangguan pernapasan, penyakit jantung, kanker paru-paru, dan masalah kesehatan lainnya. Untuk memahami dampak kesehatan pencemaran udara, klasifikasi efek kesehatan akibat pencemaran udara menjadi penting. Metode klasifikasi ini membagi efek kesehatan berdasarkan jenis polutan, dosis, dan waktu paparan. Penelitian ini mengusulkan penerapan metode klasifikasi dengan ensemble learning untuk mengidentifikasi polutan berdampak dan tingkat risiko kesehatannya. Ensemble learning adalah teknik pembelajaran mesin yang menggabungkan beberapa model untuk meningkatkan akurasi prediksi. Stacking ensemble learning merupakan salah satu metode yang digunakan dalam klasifikasi efek kesehatan pencemaran udara dengan mengintegrasikan beberapa model dasar seperti Logistic Regression, Decision Tree, K-Nearest Neighbor, Support Vector Machine, dan Multi-Layer Perceptron. Hasil penelitian menunjukkan bahwa model Stacking memberikan performa tertinggi dengan akurasi sekitar 99,9% pada dataset baik yang seimbang maupun tidak seimbang. Namun, model Decision Tree dan K-Nearest Neighbor juga berhasil memberikan performa yang sangat baik. Waktu pelatihan model menjadi pertimbangan penting, di mana K-Nearest Neighbor dan Decision Tree memiliki waktu yang jauh lebih singkat dibandingkan dengan model Stacking.
Analisis Area Wajah Berdasarkan Tekstur Wajah untuk Mengidentifikasi Risiko Penyakit Jantung Koroner Budi Sunarko; Agung Adi Firdaus; Yudha Andriano Rismawan; Anan Nugroho
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 1: Februari 2025
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v14i1.13658

Abstract

Early screening for coronary heart disease (CHD) remains insufficiently addressed, underscoring the need for a more effective screening tool. Previous studies have reported a classification accuracy of only 72.73%, which is inadequate. This study aimed to develop and evaluate a machine learning model or diagnose CHD using facial texture features and to compare the performance across different facial regions to provide recommendations for improvement. The research involved constructing a machine learning model that extracted texture features from six facial regions of interest (ROIs) using the gray level co-occurrence matrix (GLCM) and employed an artificial neural network (ANN) algorithm. The datasets were full-face images of CHD patients (positive) and healthy people (negative). The face parts identified were the right crow’s feet, right canthus, nose bridge, forehead, left canthus, and left crow’s feet. A total of 132 (72 positive and 60 negative CHD) datasets were divided into 80% (n = 106) training data and 20% (n = 26) testing data. The developed model achieved a notable accuracy of 76.9%. The findings revealed that two facial regions—canthus and forehead—demonstrated excellent accuracy of 80.97% and 90%, respectively. Meanwhile, the crow’s feet and nose bridge regions showed good accuracies at 73.50% and 65%, respectively. Based on the results, this research has proven to be able to become a model for early CHD screening with good accuracy and faster execution.
Integration of Sentiment Analysis and RFM in Restaurant Customer Segmentation: A 7P-Based CRM Model with Clustering Sunarko, Budi; Hasanah, Uswatun; Hidayat, Syahroni; Rachmawati, Rina
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.633

Abstract

The increasing use of digital platforms like Tripadvisor has created opportunities to transform customer review data into strategic insights for Customer Relationship Management (CRM). This study proposes a novel CRM model by integrating the Recency, Frequency, Monetary (RFM) framework with the 7P marketing mix to segment restaurant customers more effectively. Using 3,716 Tripadvisor reviews, annotated based on 7P elements and clustered through unsupervised learning, three key customer segments were identified: acquisition, retention, and win-back. Evaluation metrics show strong clustering performance with a Silhouette Score of 0.73 and a Davies-Bouldin Score of 0.08. The acquisition cluster (Product) demonstrates the highest Frequency (37,664) and Monetary value (64.94), signifying high engagement and revenue potential. The retention cluster (Physical Evidence, Place, Process, Promotion, Traveler) shows stable interaction patterns with Recency values of 1261–1262 and moderate Frequency (378–2,079). The win-back cluster (Price, People) reflects lower Frequency (198–946) but equal Recency (1259), indicating recent but infrequent activity, which is ideal for reactivation strategies. By mapping customer reviews to 7P labels and analyzing them using RFM, the model uncovers specific behavioral patterns tied to service quality, pricing, and promotions. This integration allows restaurants to apply tailored strategies: offering loyalty rewards to high-frequency customers, promotional incentives for those with high Recency, and prioritizing high-monetary customers for exclusive programs. The novelty of this research lies in its combined use of sentiment-based review analysis and RFM–7P segmentation, offering a scalable, data-driven framework for enhancing customer satisfaction, loyalty, and long-term business growth in the restaurant industry.
Support Vector Machine (SVM) for Tomato Leaf Disease Detection Ibrahim, Shafaf; Mohd Fuad, Nur Afiqah; Md Ghani, Nor Azura; Aminuddin, Raihah; Sunarko, Budi
AGRIVITA Journal of Agricultural Science Vol 47, No 2 (2025)
Publisher : Faculty of Agriculture University of Brawijaya in collaboration with PERAGI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17503/agrivita.v47i2.3746

Abstract

Tomatoes rank among the top five most globally demanded crops and serve as a key ingredient in numerous dishes. However, productivity may decline due to challenges such as diseases, pest infestations, and climate change. Therefore, automatic disease detection is essential to identify early signs of illness during the growth period. This study proposes a method for detecting tomato leaf diseases using image processing techniques. The approach involves image enhancement, feature extraction, and classification. Initially, leaf disease images were enhanced using the Contrast Adjustment technique. Subsequently, color and texture features were extracted using Color Moments and the Gray-Level Co-occurrence Matrix (GLCM), respectively. Disease detection was carried out using a Support Vector Machine (SVM). The method was tested on 50 images each for healthy leaves and four types of tomato leaf diseases: Bacterial Spot, Yellow Leaf Curl Virus, Early Blight, and Late Blight. The performance of the disease detection system was evaluated using a confusion matrix, achieving an overall accuracy, sensitivity, and specificity of 96%, 90%, and 97.5%, respectively. These results demonstrate the effectiveness of the proposed SVM-based approach for tomato leaf disease detection.
Peran Mediasi Kemampuan Penyelesaian Proyek dalam Peningkatan Hasil Belajar Analisis Data Rahmawati, Desi; Hudallah, Noor; Supraptono, Eko; Sunarko, Budi
SAP (Susunan Artikel Pendidikan) Vol 9, No 1 (2024)
Publisher : Universitas Indraprasta PGRI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/sap.v9i1.23773

Abstract

This study aims to determine the effect of student understanding and student activity on learning outcomes in informatics subjects through the mediator of project completion ability. The study used a quantitative approach with the Structural Equation Modelling analysis method with 168 students as respondents. The data collection instrument was a questionnaire containing positive and negative statements covering indicators including, understanding (translation, interpretation, extrapolation), activity (participation, gathering information, discussion, assessing self-ability), learning outcomes (cognitive, affective, psychomotor improvements), project completion skills (problem solving, critical thinking, project management). The results showed that student understanding had a significant effect on learning outcomes (percentage 25.2%, p-value 0.001), and project completion skills (percentage 32.7%, p-value 0.000). Student activity does not affect learning outcomes (percentage of 2%, p-value 0.802), but affects the skills to complete projects (percentage 50.5%, p-value 0.000). The skills to complete projects have a significant effect on learning outcomes (percentage 57.7%, p-value 0.000). In addition, student understanding and student activity have a significant indirect effect on learning outcomes through the mediator of project completion skills with each percentage (18.9%, p-value 0.000 and 29.2%, p-value 0.000). Thus, student understanding and project completion skills have a direct effect on student learning outcomes, while student activity only has an indirect effect through project completion skills.
Automated Ultrasound Object Segmentation Using Combinatorial Active Contour Method Anan Nugroho; Sunarko, Budi; Wibawanto, Hari; Mulwinda, Anggraini; Fauzi, Anas; Oktaviyanti, Dwi; Savitri, Dina Wulung
Jurnal Ilmu Komputer dan Informasi Vol. 17 No. 2 (2024): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v17i2.1298

Abstract

Active Contour (AC) is an algorithm widely used in segmentation for developing Computer-Aided Diagnosis (CAD) systems in ultrasound imaging. Existing AC models still retain an interactive nature. This is due to the large number of parameters and coefficients that require manual tuning to achieve stability. Which can result in human error and various issues caused by the inhomogeneity of ultrasound images, such as leakage, false areas, and local minima. In this study, an automatic object segmentation method was developed to assist radiologists in an efficient diagnosis process. The proposed method is called Automatic Combinatorial Active Contour (ACAC), which combines the simplification of the global region-based CV (Chan-Vese) model and improved-GAC (Geodesic Active Contour) for local segmentation. The results of testing with 50 datasets showed an accuracy value of 98.83%, precision of 95.26%, sensitivity of 86.58%, specificity of 99.63%, similarity of 90.58%, and IoU (Intersection over Union) of 82.87%. These quantitative performance metrics demonstrate that the ACAC method is suitable for implementation in a more efficient and accurate CAD system.
Classification of Game Genres Based on Interaction Patterns and Popularity in the Virtual World of Roblox Hasanah, Uswatun; Sunarko, Budi; Hidayat, Syahroni; Rachmawati, Rina
International Journal Research on Metaverse Vol. 2 No. 3 (2025): Regular Issue September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i3.30

Abstract

The rapid growth of user-generated virtual environments has elevated the importance of understanding player behavior and content dynamics in metaverse platforms. This study investigates the relationship between game genres and user engagement in Roblox, one of the largest and most interactive virtual worlds. Utilizing a dataset of over 300 game entries, we analyzed engagement metrics including visits (ranging from thousands to over 2.8 billion), likes (up to 1,000,000), favorites (up to 3.4 million), and active user counts (as high as 22,155). Descriptive statistics and correlation analysis revealed that action-oriented genres—particularly Action, Shopping, and Obby & Platformer—consistently outperform others in attracting and retaining users. The strong positive correlation between likes and favorites (r = 0.95) indicates that user satisfaction strongly predicts long-term interest, while negative feedback (dislikes) shows minimal correlation with other variables. In contrast, genres such as Education and Entertainment demonstrated significantly lower averages, with visits below 1 million, and active user counts typically under 1,000. These findings provide practical insights for developers and platform administrators seeking to optimize content strategies and offer a foundation for future research involving clustering analysis, sentiment mining, and temporal behavior modeling to enhance recommendation systems and genre personalization within metaverse ecosystems.
Co-Authors Agung Adi Firdaus Alfa Faridh Suni Alfian Arsyad Aminuddin, Raihah Anan Nugroho Ananda, Briska Putra Anggraini Mulwinda Ardiansyah, Muhammad Irfan Baroroh, Luluk Taufiqul Citra Nur Aini Djuniadi Djuniadi Dwi Oktaviyanti, Dwi FAUZI, ANAS Hakiki, Muhammad Khikam Hanrian Rossa Hari Wibawanto Hudallah, Noor Ibrahim, Shafaf Khakim Assidiqi Nur Hudaya Md Ghani, Nor Azura Mohd Fuad, Nur Afiqah Muhammad, Naufal Mukhamad Farkhan Noor Hudallah Noor Hudallah Nugroho, Anan Nugroho, Anan Nur Iksan Rahmawati, Desi Ria Wulansarie, Ria Riana Defi Mahadji Putri Rina Rachmawati Savitri, Dina Wulung Supraptono, Eko Suswitaningrum, Ema Syahroni Hidayat Uswatun Hasanah Uswatun Hasanah Vera Noviana Sulistyawan Wiwik Handayani Yudha Andriano Rismawan