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All Journal Prosiding SNATIKA Vol 01 (2011) Telematika Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal CoreIT SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Applied Information System and Management Technomedia Journal Aptisi Transactions on Technopreneurship (ATT) Building of Informatics, Technology and Science IJIIS: International Journal of Informatics and Information Systems Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) ADI Bisnis Digital Interdisiplin (ABDI Jurnal) Journal of Applied Data Sciences Jurnal Pendidikan dan Teknologi Indonesia International Journal of Engineering, Science and Information Technology Jurnal Abdi Masyarakat Indonesia Journal of Social Research Scientific Journal of Informatics Journal of Digital Market and Digital Currency Journal of Current Research in Blockchain International Journal Research on Metaverse
Taqwa Hariguna
STMIK AMIKOM Purwokerto
Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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Assessing The Acceptance and Trust in Student Information Systems Through a Modified TAM Perspective Hidayat, Muhammad Taufik Nur; Hariguna, Taqwa; Saputra, Dhanar Intan Surya
Applied Information System and Management (AISM) Vol. 8 No. 1 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i1.42324

Abstract

The rapid development of information technology has motivated universities to implement technology-based student information systems to enhance the efficiency and effectiveness of student data management. This research seeks to evaluate acceptance and trust in student information systems at universities using a modified version of the Technology Acceptance Model, incorporating perceived trust as an additional variable. The study involved a sample of 200 active university students, with data analyzed using the structural equation modeling approach. Findings from the analysis show that both perceived usefulness and perceived ease of use significantly impact students’ intention to adopt the system, which in turn influences actual system usage. Additionally, perceived trust emerged as a critical factor in reinforcing both the intention to use and the subsequent actual use of the student information system. The results indicate that the intention to use the system acts as an essential mediator in the relationships between students’ perceptions of usefulness, ease of use, trust, and their actual usage behavior. These results have significant implications for universities aiming to improve the adoption of student information systems. Enhancing user experience, building system trust, and ensuring robust security should be prioritized in the development and refinement of such systems. By focusing on these aspects, institutions can foster higher acceptance and sustained usage, leading to more effective student data management and a better overall educational experience.
MLP Model Optimization for Heart Attack Risk Prediction: A Systematic Literature Review Supriyanto, Heru; Hariguna, Taqwa; Barkah, Azhari Shouni
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 3 (2025): Article Research July 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i3.15027

Abstract

Heart disease remains a leading cause of global mortality, making the development of accurate predictive models a clinical priority. While Multilayer Perceptron (MLP) models offer significant potential, their application is hindered by challenges in optimization, data imbalance, and interpretability. This systematic literature review aims to address these issues by synthesizing current research on MLP model optimization for heart disease prediction, focusing on strategies for handling class imbalance and achieving model transparency with SHapley Additive exPlanations (SHAP). Following PRISMA guidelines, a structured search of major scientific databases resulted in the in-depth analysis of 30 peer-reviewed studies. The findings indicate that MLP optimization is increasingly sophisticated, employing automated hyperparameter tuning and novel architectures. For class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is the predominant data-level solution, though a trend towards advanced algorithm-level techniques is emerging. The application of SHAP has successfully validated models by confirming the importance of known clinical risk factors like age and chest pain type, while also demonstrating potential for new discovery. This review concludes by providing a comprehensive roadmap for researchers, highlighting a critical need for comparative studies on imbalance techniques, deeper applications of explainable AI for local-level analysis, and a stronger focus on validation using large-scale, real-world clinical data to develop truly robust and trustworthy predictive systems.
Information System Evaluation Framework to Improve Teacher and Education Personnel Competency (GTK Room): Extended Hot-Fit Framework Approach Waluyo, Retno; Hariguna, Taqwa; Setiawan, Ito
Scientific Journal of Informatics Vol. 12 No. 2: May 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i2.23841

Abstract

Purpose: This study aims to identify the factors that influence users in the implementation of the GTK (Teachers and Educational Staff) Room System among elementary school teachers in Banyumas Regency, Central Java, Indonesia. Methods: This study employed the HOT-Fit (Human, Organization, and Technology Fit) Framework approach, with the addition of the 'Behavioral Intention to Use' variable on the Human dimension and the 'Organizational Culture' variable on the Organizational dimension. The sample consisted of 147 elementary school teachers from Banyumas Regency, Central Java, Indonesia. Data were analyzed using SmartPLS to identify the variables that influence user behavior. Result: The results of this study indicate that certain relationships between variables do not have a significant influence on others. Specifically, User Satisfaction and Behavioral Intention to Use do not significantly affect Net Benefit. Additionally, Information Quality does not have a significant effect on System Use. Furthermore, System Quality does not significantly influence User Satisfaction or Behavioral Intention to Use. Meanwhile, other variable relationships were found to significantly impact the successful implementation of the GTK (Teachers and Educational Staff) Room system. The model’s goodness-of-fit shows an NFI (Normed Fit Index) value of 0.632, indicating that the proposed model explains 63.2% of the variance in the data. Novelty: This research presents several significant novelties that contribute to the evaluation of the implementation of the GTK (Teachers and Education Personnel) Room System in primary education. The traditional HOT-Fit (Human, Organization, Technology-Fit) model was enhanced by adding two new variables, Behavioral Intention to Use and Organizational Culture, resulting in a more comprehensive and contextually relevant evaluation framework. The study was conducted within a specific local context, focusing on primary school teachers in Banyumas Regency, Central Java, Indonesia, thereby providing empirical insights into the implementation dynamics at the local level, which have been rarely explored in previous research. The findings reveal that system success is influenced not only by technical factors but also by behavioral dynamics and social contexts, such as organizational culture.
Application of Augmented Reality Technology in a Custom Car Ride Selection Application (Case Study: Impala Auto Fashion) Syahrizal, Hendrawan; Hariguna, Taqwa
Journal of Social Research Vol. 2 No. 12 (2023): Journal of Social Research
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/josr.v2i12.1597

Abstract

The use of augmented reality technology as an auxiliary medium in the wheel selection process is proof of the very rapid development of technology. An effective way to make work easier is by utilizing augmented reality technology, namely combining two-dimensional objects or three-dimensional objects that are applied to the real world. In other words, augmented reality is a combination of two or three-dimensional objects with the real world. A lot of time and energy is simply spent during the process of replacing car rims because the car owner does not feel comfortable with how to disassemble the rims, therefore the augmented reality application is a breakthrough to simplify and speed up the process of selecting rims, the method used is concept, design, assembly, testing, and distribution. The result of the research is an augmented reality application to simplify the process of selecting and replacing car rims on Impala auto fashion.
Perancangan Ajri Learning Journal Center Menggunakan Tools Invision Untuk Mewujudkan Creative Innovation Soft Skill Hariguna, Taqwa; Wahyuningsih, Tri
ADI Bisnis Digital Interdisiplin Jurnal Vol 1 No 1 (2020): ADI Bisnis Digital Interdisiplin (ABDI Jurnal)
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/abdi.v1i1.107

Abstract

Platform penyedia layanan pelatihan dan materi penulisan karya ilmiah secara online untuk memotivasi civitas akademik untuk melakukan penelitian. Dengan memanfaatkan metode pengembangan sistem yaitu waterfall dalam menganalisis agar platform yang dikembangkan berjalan sesuai dengan apa yang direncanakan. Pada saat ini sangat jarang penyedia layanan dalam pelatihan penulisan yang dilakukan secara online. Biasanya dilakukan conference yang mengharuskan seseorang hadir dalam ruangan dan mendengarkan pembicara berjam-jam. Maka pelatihan secara online dianggap sangat efektif untuk mendorong civitas akademik dalam membuat karya ilmiah karena dapat dilakukan dimanapun dan kapanpun. Dengan diimplementasikannya platform ini akan ada 2 manfaat yaitu (1) Termotivasinya civitas akademik untuk melakukan penelitian, karena ada banyak kemudahan untuk berlatih dalam penulisan yang dapat dilakukan dimanapun dan kapanpun. (2) Jumlah penelitian di Indonesia akan meningkat dalam rangka mendukung program Tridharma Perguruan Tinggi. Penelitian ini akan diimplementasikan pada sebuah website yang mampu diakses secara online, yang berisi pelatihan penulisan secara online berupa video pembelajaran dengan beberapa coach yang profesional. Disediakan pula materi pembelajaran bagaimana cara menulis sebuah karya ilmiah sesuai dengan standar yang benar.
Pelatihan penulisan artikel ilmiah internasional bereputasi pada dosen Universitas Amikom Purwokerto Hariguna, Taqwa; Waluyo, Retno; Lestari, Dwi Puji; Hani, Nurul
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 8, No 3 (2024): September
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v8i3.25850

Abstract

AbstrakPenulisan artikel ilmiah merupakan suatu elemen yang memegang peranan sentral dan vital dalam ekosistem penelitian dan pengembangan ilmu pengetahuan. Seorang dosen ataupun pihak yang mempublikasikan suatu penelitian dapat dikatakan memiliki kapasitas atau kualitas yang tinggi apabila mampu mempublikasikan penelitian dalam jurnal nasional dan jurnal internasional yang bereputasi. Pengindeks Scopus dipandang sebagai tolok ukur yang signifikan bagi jurnal ilmiah, dan kuantitas serta kualitas jurnal yang terindeks di Scopus dapat berdampak pada penulis, perguruan tinggi dan negara. Oleh karena itu, keterampilan menulis artikel ilmiah yang baik dan pemahaman mendalam tentang persyaratan SCOPUS menjadi sangat penting bagi dosen. Banyak akademisi maupun peneliti mungkin merasa terhambat dalam proses menulis artikel ilmiah yang memenuhi kriteria Scopus. Hal ini seringkali melibatkan tantangan dalam memahami prosedur, teknik penulisan yang efektif, dan kriteria yang diperlukan untuk diterbitkan di jurnal Scopus. Agar artikel ilmiah di jurnal internasional bereputasi yang terindeks Scopus, harus melewati berbagai persyaratan. Teknik, sistem dan metodenya harus benar-benar diikuti. Untuk mengatasi hal tersebut, perlu dilakukan suatu kegiatan pengabdian kepada masyarakat pelatihan penulisan artikel ilmiah international bereputasi. Tujuannya meningkatkan kemampuan dosen dalam membuat artikel ilmiah internasional bereputasi. Hasil dari kegiatan pengabdian telah meningkatkan pengetahuan dosen mengenai cara penulisan artikel ilmiah yang memenuhi standar internasional bereputasi. Kata kunci: pelatihan; artikel ilmiah; internasional Abstract Writing scientific articles is an element that plays a central and vital role in the scientific research and development ecosystem. A lecturer or party who publishes research can be said to have high capacity or quality if they are able to publish research in reputable national and international journals. The Scopus indexer is seen as a significant benchmark for scientific journals, and the quantity and quality of journals indexed in Scopus can have an impact on authors, universities and the country. Therefore, good scientific article writing skills and an in-depth understanding of SCOPUS requirements are very important for lecturers. Many academics and researchers may feel hampered in the process of writing scientific articles that meet Scopus criteria. This often involves challenges in understanding procedures, effective writing techniques, and the criteria required to publish in a Scopus journal. In order for scientific articles in reputable international journals to be indexed by Scopus, they must pass various requirements. The techniques, systems and methods must be strictly followed. To overcome this, it is necessary to carry out community service activities, training in writing reputable international scientific articles. The aim is to improve lecturers' abilities in creating reputable international scientific articles. The results of service activities have increased lecturers' knowledge regarding how to write scientific articles that meet reputable international standards. Keywords: training; scientific articles; international
Comparison of K-Means and DBSCAN Algorithms for Customer Segmentation in E-commerce Paramita, Adi Suryaputra; Hariguna, Taqwa
Journal of Digital Market and Digital Currency Vol. 1 No. 1 (2024): Regular Issue June 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v1i1.3

Abstract

Customer segmentation is crucial for e-commerce businesses to effectively target and engage specific customer groups. This study compares the effectiveness of two popular clustering algorithms, K-Means and DBSCAN, in segmenting e-commerce customers. The primary objective is to evaluate and contrast these algorithms to determine which provides more meaningful and actionable customer segments. The methodology involves analyzing a comprehensive e-commerce customer dataset, which includes various features such as customer ID, gender, age, city, membership type, total spend, items purchased, average rating, discount applied, days since last purchase, and satisfaction level. Initial data preprocessing steps include handling missing values, encoding categorical variables, and normalizing numerical features. Both K-Means and DBSCAN algorithms are implemented, and their performance is evaluated using metrics such as silhouette score, Davies-Bouldin index, and Calinski-Harabasz score. The results indicate that K-Means achieved a silhouette score of 0.546, a Davies-Bouldin index of 0.655, and a Calinski-Harabasz score of 552.9. In contrast, DBSCAN achieved a higher silhouette score of 0.680, a Davies-Bouldin index of 1.344, and a Calinski-Harabasz score of 1123.9. These findings suggest that while DBSCAN performs better in terms of silhouette score, indicating more distinctly separated clusters, its higher Davies-Bouldin index reflects fewer compact clusters. The discussion highlights that K-Means is suitable for applications requiring clear and well-defined segments of customers, as it produces balanced cluster sizes. DBSCAN, with its strength in identifying clusters of varying densities and handling noise, is more effective in detecting niche markets and unique customer behaviors. This study's findings have significant practical implications for e-commerce businesses looking to enhance their customer segmentation strategies. In conclusion, both K-Means and DBSCAN demonstrate their respective strengths and weaknesses in clustering the e-commerce customer dataset. The choice of algorithm should be based on the specific requirements of the segmentation task. Future research could explore hybrid methods combining the strengths of both algorithms and incorporate additional data sources for a more comprehensive analysis.
Customer Segmentation and Targeted Retail Pricing in Digital Advertising using Gaussian Mixture Models for Maximizing Gross Income Hariguna, Taqwa; Chen, Shih Chih
Journal of Digital Market and Digital Currency Vol. 1 No. 2 (2024): Regular Issue September
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v1i2.11

Abstract

This study investigates the application of Gaussian Mixture Models (GMM) for customer segmentation and targeted pricing strategies in the retail industry to maximize gross income. Using a dataset of 1000 transaction records, the analysis focused on attributes such as unit price, quantity, total amount, and payment methods. The dataset was preprocessed to handle missing values, encode categorical features, and scale numerical features. The optimal number of components for the GMM was determined using the Bayesian Information Criterion (BIC), resulting in the selection of 10 clusters. Model training was conducted using the Expectation-Maximization (EM) algorithm, achieving convergence after 18 iterations. Customer segments were identified and analyzed based on their purchasing behaviors and demographic traits. For instance, Segment 0 preferred bulk purchases of lower-priced items, while Segment 1 favored higher-priced items in smaller quantities, resulting in a higher average purchase value of 2274.19. Conversely, Segment 2 showed a high frequency of returns, indicated by a negative average purchase value of -2608.40. Targeted pricing strategies were developed for each segment, aiming to maximize gross income. The effectiveness of the segmentation and pricing strategies was evaluated using metrics such as the silhouette score, with training and testing scores of 0.175 and 0.015 respectively, highlighting areas for improvement in clustering quality. This study underscores the potential of GMM in uncovering distinct customer segments and tailoring pricing strategies to enhance profitability. Future research should explore alternative clustering techniques and extend the analysis to other retail domains and larger datasets to validate and improve the findings. The practical implications for retail businesses include the need for iterative testing and refinement of pricing strategies based on customer segmentation to achieve sustainable growth and customer satisfaction.
Uncovering Key Service Improvement Areas in Digital Finance: A Topic Modeling Approach Using LDA on User Reviews Othman, Jalel Ben; Hariguna, Taqwa
Journal of Digital Market and Digital Currency Vol. 2 No. 4 (2025): Regular Issue December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i4.47

Abstract

The rapid expansion of digital finance has transformed the way financial services are accessed and utilized, particularly in emerging markets such as Indonesia. This study aims to uncover key service improvement areas within the Easycash mobile lending platform by analyzing user reviews through topic modeling using Latent Dirichlet Allocation (LDA). The research employed a data-driven approach, combining text preprocessing in Bahasa Indonesia using the Sastrawi library, TF-IDF vectorization, and sentiment classification with machine learning models including Naive Bayes, K-Nearest Neighbors (KNN), and XGBoost. The XGBoost model achieved the highest performance with an F1-score of 0.9709, effectively distinguishing between positive, neutral, and negative sentiments. LDA analysis identified five major topics: Loan Limits and Repayment, Customer Gratitude and Satisfaction, Loan Application Process and Interest Rates, App Quality and Customer Service, and Data Management and Account Issues. Results indicate that while Easycash users generally express positive sentiment toward ease of use and service speed, concerns persist regarding high interest rates, customer service responsiveness, and data privacy. These findings provide actionable insights for fintech companies to enhance user satisfaction through targeted service improvements and continuous feedback analysis.
Unsupervised Anomaly Detection in Digital Currency Trading: A Clustering and Density-Based Approach Using Bitcoin Data Hariguna, Taqwa; Al-Rawahna, Ammar Salamh Mujali
Journal of Current Research in Blockchain Vol. 1 No. 1 (2024): Regular Issue June
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v1i1.12

Abstract

This study investigates the application of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for detecting anomalies in Bitcoin trading data. With the growing significance of Bitcoin in the financial market, identifying irregular trading patterns is crucial for maintaining market integrity and preventing market manipulation. Utilizing a dataset from Kaggle, which includes features such as date, timestamp, open, high, low, close, volume, and number of trades, the data was aggregated from minute-by-minute to hourly intervals for more manageable analysis. The DBSCAN algorithm effectively identified a primary cluster comprising 29,612 data points and flagged 2 points as anomalies, achieving a precision of 1.0, recall of 0.0068, F1-score of 0.0135, and an AUC-ROC of 0.5034. The optimal parameters, determined through sensitivity analysis, were epsilon (ε) = 0.1 and min_samples = 3, yielding the highest silhouette score of 0.21499. These results underscore the algorithm's ability to accurately label anomalies while highlighting the challenge of comprehensive anomaly detection. The study contributes to the field of financial anomaly detection by demonstrating the effectiveness of DBSCAN in analyzing high-dimensional, noisy datasets. It also addresses gaps in the literature regarding the application of density-based clustering methods to Bitcoin trading data. Despite its contributions, the study acknowledges limitations, such as potential data aggregation impact and the need for further validation with different datasets. Future research directions include integrating additional features like social media sentiment and exploring hybrid approaches that combine supervised and unsupervised methods.
Co-Authors Abbas, Intesar Abdul Azis Abdul Hakim Prima Yuniarto Abidin, Uun Adi Suryaputra Paramita Al-Rawahna, Ammar Salamh Mujali Anton Satria Prabuwono Azhari Shouni Barkah Baihaqi, Wiga Maulana Bangkit, Jeffri Prayitno Berlilana Berlilana Chen, Shih Chih Chen, Shih-Chih Cheng, Tsang-Hsiang Dhanar Intan Surya Saputra Dwi Puji Lestari, Dwi Puji Eko Winarto El Emary, Ibrahiem M. M. Fitriani, Radifa Rahma Giat Karyono Hadie, Agus Nur Hananto, Andhika Rafi Hani, Nurul Harahap, Eka Purnama Heru Supriyanto Hidayat, Muhammad Taufik Nur Husni Teja Sukmana Hussain, Refat Taleb Imam Tahyudin Izumi, Calvina Jawad, Haider Mahmood Johan Reimon Batmetan Kim, Jong Il Mohammed, Ismail Abdulaziz Muflikhatun, Siti Muhamad Yusup NAWANGNUGRAENI, DEVI ASTRI Nazwan, Nazwan Nurwanti, Aulia Othman, Jalel Ben Pratama, Satrya Fajri Priyadi, Agung PUJI LESTARI Qurotul Aini R Rizal Isnanto Rahayu, Silvia Anggun Ramadani, Nevita Cahaya Riawan, Fendi Rozak, Rofik Abdul Ruangkanjanases, Athapol Sabah, Sura Salsabila, Salsabila Sarmini Sarmini Setiawan, Ito Subhan Mahendrasyah, Muhammad Susanti, Nindi Nofi Syahrizal, Hendrawan Tikaningsih, Ades Tri Wahyuningsih Uswatun Hasanah Wahid, Arif Mu'amar Waluyo, Retno Yarsasi, Sri Yi, Ding