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All Journal Prosiding SNATIKA Vol 01 (2011) Telematika Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal CoreIT 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 for Applied Information Management Jurnal Abdi Masyarakat Indonesia Journal of Social Research Scientific Journal of Informatics Journal of Digital Market and Digital Currency
Taqwa Hariguna
STMIK AMIKOM Purwokerto
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences

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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.
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.
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.
Academic Performance Prediction from Student–VLE Bipartite Interaction Graphs Using Centrality Features A Comparative Study with Classical Classifiers Sumiati, Ai Irma; Hariguna, Taqwa; Barkah, Azhari Shouni
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15798

Abstract

The rapid growth of digital learning platforms has increased the availability of student academic records and fine-grained interaction logs, creating opportunities for Educational Data Mining (EDM) to support early academic monitoring. However, many predictive models still rely mainly on individual tabular attributes and underutilize relational signals embedded in learning interactions. This study proposes a graph-mining feature approach for predicting student academic performance using a bipartite Student–VLE interaction graph. Centrality measures—degree, weighted degree, HITS hub, PageRank, and eigenvector centrality—are extracted to form a centrality feature set and combined with standard student information features. Using the public OULAD dataset, we compare three supervised classifiers: Random Forest, Support Vector Machine, and XGBoost. Experiments show that adding the centrality feature set consistently and substantially improves performance across all models compared to baseline tabular features. On the test set, XGBoost achieves the strongest results with accuracy 0.842, ROC-AUC 0.922, PR-AUC 0.902, and MCC 0.684, while Random Forest is close behind (accuracy 0.834, ROC-AUC 0.916, PR-AUC 0.894, MCC 0.672). The SVM model also benefits (accuracy 0.800, ROC-AUC 0.869, PR-AUC 0.811, MCC 0.599), confirming the robustness of the graph-derived signal. Scientifically, this study provides empirical evidence that a multi-centrality representation offers more systematic and transferable predictive value than relying on a single graph metric, across multiple classical model families under the same evaluation protocol. These findings indicate that graph-mining centrality features capture complementary structural information about learning engagement that is not represented by tabular attributes alone, and they offer a practical, interpretable enhancement to classic EDM pipelines for academic performance prediction.
An Empirical Study to Understanding Students Continuance Intention Use of Multimedia Online Learning Hariguna, Taqwa
International Journal for Applied Information Management Vol. 1 No. 2 (2021): Regular Issue: July 2021
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v1i2.10

Abstract

The purpose of this study was to assess students' ongoing intentions towards online multimedia learning such as perceived usefulness, ease of use, and flow experience. The sample of this study was 523 students who used off-campus/online learning resources and examined the content of online learning resources and their multimedia aspects. The Extended of Technology Acceptance Model (TAM) was used to predict students' continuing intentions. The results showed that students' intentions were positively influenced by their perceived usefulness, ease of use, and flow experience. It is suggested that the designer of multimedia online learning should be more specific in determining the target users to receive and cultivate a more positive sustainable intention.
Evaluasi Ensemble Learning untuk Prediksi Nilai Matematika Siswa Sekolah Menengah Asikin, Zaenal; Tahyudin, Imam; Hariguna, Taqwa
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 12 (2025): JPTI - Desember 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.858

Abstract

Prediksi dini performa matematika siswa sekolah menengah sangat penting untuk merancang intervensi pendidikan yang lebih adaptif dan efektif sebelum ujian akhir resmi dilaksanakan. Penelitian ini bertujuan untuk mengevaluasi kinerja tiga model machine learning Random Forest (RF), Gradient Boosting Regressor (GBR), dan Multi-Layer Perceptron (MLP) dalam memprediksi nilai matematika siswa di Indonesia, serta mendokumentasikan proses tuning hyperparameter secara sistematis untuk setiap model. Dataset yang digunakan terdiri dari skor matematika, membaca, menulis, serta variabel demografis meliputi jenis kelamin, latar belakang pendidikan orang tua, jenis layanan makan, dan keikutsertaan kursus persiapan. Proses tuning hyperparameter untuk RF dan GBR dilakukan menggunakan RandomizedSearchCV dengan 5-fold cross-validation, menguji rentang nilai untuk jumlah estimator, kedalaman maksimum pohon, dan laju pembelajaran (learning rate). Sedangkan pada Multi-Layer Perceptron, GridSearchCV diterapkan dengan variasi arsitektur hidden_layer_sizes, laju pembelajaran awal (learning_rate_init), dan faktor regularisasi (alpha) pada 5-fold CV. Model diukur menggunakan Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), dan koefisien determinasi (R²). Hasil eksperimen menunjukkan bahwa GBR memberikan performa terbaik dengan MAE sebesar 11,61 poin, RMSE 15,23 poin, dan R² 0,10. Random Forest menempati urutan kedua (MAE 12,34; RMSE 16,05; R² 0,64), diikuti MLP (MAE 13,10; RMSE 17,20; R² 0,60). Analisis feature importance mengungkap bahwa skor membaca dan menulis bersama-sama menyumbang lebih dari 60 % kontribusi prediksi, sedangkan faktor demografis seperti latar belakang pendidikan orang tua dan keikutsertaan kursus berperan sekunder namun tetap signifikan. Temuan ini mengindikasikan bahwa model ensemble learning tidak hanya unggul dalam akurasi prediksi, tetapi juga memberikan wawasan mendalam tentang variabel kunci yang memengaruhi performa matematika siswa. Implementasi model ini memungkinkan guru dan pihak sekolah untuk mengidentifikasi siswa yang berisiko rendah secara lebih cepat, merancang program remedial atau pengayaan yang tepat sasaran, serta memanfaatkan sumber daya pendidikan secara lebih efisien. Untuk penelitian lanjutan, disarankan penambahan variabel perilaku siswa seperti durasi belajar mandiri dan kehadiran serta eksplorasi model sekuensial (RNN/Transformer) untuk menangkap dinamika pembelajaran dari waktu ke waktu.
Transformasi Portal Data Pemerintah di Indonesia dengan Large Language Model dan Retrieval-Augmented Generation: Tinjauan Pustaka Sistematis Hadie, Agus Nur; Tahyudin, Imam; Hariguna, Taqwa
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 12 (2025): JPTI - Desember 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.1175

Abstract

Integrasi kecerdasan buatan (Artificial Intelligence/AI) seperti Large Language Model (LLM) dan Retrieval-Augmented Generation (RAG) berpotensi mentransformasi portal data pemerintah, namun implementasinya terhambat oleh kurangnya tinjauan sistematis dan kerangka evaluasi yang spesifik. Penelitian ini bertujuan untuk mengidentifikasi, mengevaluasi, dan mensintesis literatur terkini mengenai metodologi, keberhasilan, dan tantangan integrasi teknologi tersebut melalui tinjauan pustaka sistematis. Metode ini diterapkan dengan pencarian terstruktur pada basis data Google Scholar, Scopus, dan IEEE Xplore, diikuti proses penyaringan bertahap. Hasil tinjauan menunjukkan bahwa teknologi AI terbukti efektif meningkatkan komunikasi pemerintah-warga, efisiensi layanan, dan akurasi pengambilan data, di mana penyesuaian model menjadi faktor penting. Namun, implementasinya masih menghadapi tantangan signifikan terkait tata kelola, kualitas data, dan masalah etis. Hasil penelitian ini menekankan pentingnya pengembangan kerangka kerja tata kelola yang komprehensif untuk memastikan penerapan AI yang akuntabel dan selaras dengan kepentingan publik.
Co-Authors Abdul Azis Abdul Hakim Prima Yuniarto Abidin, Uun Adi Suryaputra Paramita Anton Satria Prabuwono Asikin, Zaenal Azhari Shouni Barkah Baihaqi, Wiga Maulana Bangkit, Jeffri Prayitno Berlilana Berlilana Chen, Shih-Chih Cheng, Tsang-Hsiang Dhanar Intan Surya Saputra Eko Winarto El Emary, Ibrahiem M. M. Fitriani, Radifa Rahma Giat Karyono Hadie, Agus Nur Hananto, Andhika Rafi Harahap, Eka Purnama Heru Supriyanto Hidayat, Muhammad Taufik Nur Husni Teja Sukmana Imam Tahyudin Johan Reimon Batmetan Kim, Jong Il Muflikhatun, Siti Muhamad Yusup NAWANGNUGRAENI, DEVI ASTRI Nazwan, Nazwan Nugroho, Khabib Adi 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 Salsabila, Salsabila Sarmini Sarmini Setiawan, Ito Subhan Mahendrasyah, Muhammad Sumiati, Ai Irma Susanti, Nindi Nofi Syahrizal, Hendrawan Tikaningsih, Ades Tri Wahyuningsih Uswatun Hasanah Wahid, Arif Mu'amar Waluyo, Retno Yarsasi, Sri Yi, Ding