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All Journal International Journal of Electrical and Computer Engineering MATEMATIKA JURNAL SISTEM INFORMASI BISNIS Jurnal Sains dan Teknologi Jurnal Simetris Jurnal Sarjana Teknik Informatika Jurnal Teknik Sipil dan Perencanaan Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Systems Engineering and Business Intelligence EMITTER International Journal of Engineering Technology JOIV : International Journal on Informatics Visualization INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Penelitian Pendidikan IPA (JPPIPA) Jurnal Teknologi Sistem Informasi dan Aplikasi Journal of Computer Science and Informatics Engineering (J-Cosine) Aptisi Transactions on Technopreneurship (ATT) Indonesian Journal of Electrical Engineering and Computer Science JPKMI (Jurnal Pengabdian Kepada Masyarakat Indonesia) Scientific Journal of Informatics
Dinar Mutiara Kusumo Nugraheni
Departemen Ilmu Komputer, Fakultas Sains Dan Matematika, Universitas Diponegoro
Agriculture, Biological Sciences & Forestry Humanities 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 Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics Public Health Social Sciences

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A Comparative Analysis of Naïve Bayes and Logistic Regression for Student Satisfaction Prediction in Microsoft Teams Wulandari, Dewi Arianti; Soeprobowati, Tri Retnaningsih; Nugraheni, Dinar Mutiara Kusumo
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.3197

Abstract

Student satisfaction reflects educational quality, influences retention, and enhances institutional reputation. This study examines the impact of student performance and motivation in online learning using Naïve Bayes and Logistic Regression. Data from 316 respondents at PLN Institute of Technology, collected during the COVID-19 pandemic via Microsoft Teams, were divided into 80% training and 20% testing. The process included questionnaire distribution, data labeling, parameter determination, and normalization to ensure completeness and reliability.  Questionnaire data is stored in Excel format and was processed using Python for programming, Pandas for data manipulation, and Kaggle for dataset management, before being analyzed with Naïve Bayes and Logistic Regression. Finally, the processed data is tested for accuracy using confusion matrix. The results show high precision, recall, f1-score, and accuracy for both methods, with Naïve Bayes achieving an accuracy 93.75% to 97.44% and Logistic Regression achieving 98.95%. In summary, Naïve Bayes can be optimized with threshold adjustments, but Logistic Regression is more reliable than consistent, maintaining high accuracy across different thresholds. Institutions can update their strategies using the latest data to enhance learning experiences. From those results, it can be concluded that Naïve Bayes method should be enhanced, while Logistic Regression is proven reliable. In the future, researchers are encouraged to use more diverse datasets while also considering external factors such as technological infrastructure and psychological support.
Oriented Enterprise Architecture for Enhancing Digital Governance and Technopreneurship in Regional Governments Rahmadani, Rizki Galang; Nurhayati, Oky Dwi; Nugraheni, Dinar Mutiara Kusumo
Aptisi Transactions On Technopreneurship (ATT) Vol 7 No 3 (2025): November
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v7i3.769

Abstract

This study examines disparities in the implementation of Indonesia’s Electronic-Based Government System (SPBE) which remain fragmented across regional governments and hinder efficient service delivery. The purpose of this research is to develop a hybrid enterprise architecture planning framework that combines TOGAF, Zachman, FEAF, and Gartner models to achieve more harmonized and interoperable SPBE adoption. A quantitative explanatory method was used with Partial Least Squares Structural Equation Modeling (PLS-SEM), involving data from 337 valid respondents across nine district and city governments in West Java Province. Four main constructs were examined, including SPBE implementation standardization, operational procedure clarity, technology harmonization, and institutional collaboration. The findings show that all constructs significantly improve digital governance effectiveness, with standardization being the strongest influencing factor with a coefficient value of 0.423 and significance level below 0.01. In addition to statistical validation, the study presents an operational framework containing procedural flow, logic matrix, and pseudocode that bridges theoretical concepts and practical implementation in SPBE planning. This hybrid framework provides a structured but also flexible approach suitable for Indonesia’s decentralized governance by enhancing interoperability, transparency, and coordination between agencies. Overall, the research contributes both theoretically and practically by demonstrating how the integration of enterprise architecture principles can strengthen SPBE implementation and support sustainable digital transformation within local governments.
Integration of UTAUT 2 and Delone & McLean to Evaluate Acceptance of Video Conference Application Bayastura, Shahnilna Fitrasha; Warsito, Budi; Nugraheni, Dinar Mutiara Kusumo
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 6 No 2 (2022): August 2022
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v6i2.17897

Abstract

This article explores how college students adopt video conferencing software for distance education. This research aims to examine the factors that influence the spread of video conferencing programs in Indonesia. A video conferencing application is a multimedia program that generates audio and visual content to facilitate real-time, two-way communication between its users. Because of COVID-19, classes of all kinds are now being taken online. As a result, more people are turning to tools like video conferencing. Therefore, learning how to access student video conferencing software is crucial. The UTAUT 2 and Delone & McLean models will be integrated into the analysis. A total of 327 people answered the survey. Next, we used the PLS-SEM technique in smart pls 3.0 to analyze the data collected from the respondents. The R-Square value of 26.2% for the retention intent variable and 62.3% for the user satisfaction variable demonstrate that independent variables in the study can explain endogenous variables and that the remaining variance is influenced by factors external to the survey.
Customer Segmentation Based on Recency, Frequency, Monetary Analysis Using K-Means Algorithms in Apple Ecosystem Edwin Setiawan; Bayu Surarso; Dinar Mutiara Kusumo Nugraheni
Jurnal Penelitian Pendidikan IPA Vol 11 No 2 (2025): February
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i2.10011

Abstract

One of the companies in Semarang engaged in gadget sales services has an Apple Ecosystem information system for selling products from an exclusive brand, Apple. Inside there are sales transactions and also service devices iPad, Macbook Air, Macbook Pro, AirPods, Mac, and Apple Accsessories. This research uses purchase transaction data from Apple Ecosystem customers for the period 2023. The use of RFM (Recency, Frequency, Monetary) analysis helps in determining the attributes used for customer segmentation. To determine the optimal number of clusters from the RFM dataset, the Elbow method is applied. The dataset generated from RFM is grouped using the K-Means algorithm, the quality of the algorithm will be compared in cluster formation using the Silhouette Coefficient method. All procedures will be loaded into the Customer Segmentation App (RFM Clustering) web application. Customer segmentation from RFM datasets that have been clustered produces 3 optimal clusters, namely Cluster 2 is High Spenders with 326 customers, Cluster 0 is VIP Customers, Cluster 1 is Frequent Buyers. Cluster validation of k-means using the silhouette coefficient produces a value of 0.3524.
Contact, Fulfillment, and Privacy as Key Drivers of Mobile Commerce Success: A SOR-Extended M-S-QUAL Analysis Nurdin, Alya Aulia; Nugraheni, Dinar Mutiara Kusumo; Waspada, Indra
Scientific Journal of Informatics Vol. 13 No. 1: February 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i1.34382

Abstract

Purpose: M-commerce has become crucial for facilitating grocery shopping through delivery services, yet challenges like delayed orders and systems failures continue to hinder user satisfaction and loyalty. There are still lack of research that have investigated in depth both mobile apps service quality and paid attention to the perspective of user behavior in a structured way. This study addresses a gap by uniquely integrating the Mobile Service Quality (M-S-QUAL) and Stimulus Organism Response (SOR) to find out the key drivers for enhancing m-commerce grocery shopping services quality, and analyzing the influence of the m-commerce services quality factors as Stimulus in m-commerce apps to satisfaction felt by users as Organism, and their relationship with loyalty and E-WOM as user Response. Methods: Using a quantitative approach, 362 Indonesian m-commerce user responses from online survey were analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM). Outer model and inner model was carried out to test the significance between the construct and the strength of the model. Result: Results show that Contact ( =0.229), Fulfillment ( =0.192), and Privacy ( =0.166) are the most influential factors driving perceived m-commerce service quality that has strong predictive power (R2=0.804). These findings, which repositions these specific dimensions as primary stimulus within the SOR framework significantly impacts user satisfaction as organism and positively drive both loyalty and E-WOM as response. Novelty: This study provides valuable insights and a structured perspective to explain post-adoption user behavior in m-commerce delivery. The study offers novel academic insights and practical strategies for enhancing customer service, delivery reliability, and data protection through user-centered design with more attention to factors such as Contact, Fulfillment, and Privacy (Stimulus) to drive user satisfaction (Organism), loyalty and E-WOM (Response).
User Satisfaction Analysis of SUS Method of Virtual Tours Website for Semarang Chinatown Tourism Experience Setiawan, Muhammad Reza; Nugraheni, Dinar Mutiara Kusumo; Nurhayati, Oky Dwi
Jurnal Penelitian Pendidikan IPA Vol 11 No 1 (2025): January
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i1.9891

Abstract

This article analyzes the satisfaction of website-based virtual tour users using the usability scale system method for tourism experiences in Chinatown Semarang for respondents who use the virtual tour application both in the initial and final applications. This research aims to compare user satisfaction by analyzing the score calculation results from 80 respondents from each virtual tour application using the system usability scale method where the virtual tour in the initial application and the virtual tour in the final application have differences in the addition of a simple interactive website and a guide in the form of a manual book and video tutorial in the final application. The system usability scale d calculation score was obtained with an increase in value of 7.875. This proves that modifying the application and adding a guide feature in the form of a manual book and video tutorial can help respondents use a virtual tour in Semarang's Chinatown village. The advice obtained in this research is that it is hoped that for future research, you can use paid hosting because, in the study here, the virtual tour application still uses free hosting, which still has limitations in processing files, so it takes longer for users to access the virtual tour in Semarang Chinatown Village.
Perbandingan Model Machine Learning berbasis Badir Framework untuk Deteksi Fraud dan Prediksi Keterlambatan Pengiriman pada Supply Chain Eriya, Eriya; Widodo, Catur Edi; Nugraheni, Dinar Mutiara Kusumo
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 17 No. 1 (2026): JURNAL SIMETRIS VOLUME 17 NO 1 TAHUN 2026
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Rantai pasokan rentan terhadap berbagai risiko operasional, khususnya fraud dan keterlambatan pengiriman, yang dapat mengganggu efisiensi logistik, kinerja perusahaan, dan kepuasan pelanggan. Big Data Analytics dan Machine learning menawarkan pendekatan prediktif yang efektif untuk mengidentifikasi risiko tersebut secara dini, namun kinerja setiap model berbeda sehingga diperlukan evaluasi komparatif untuk menentukan algoritma paling optimal. Penelitian ini bertujuan untuk membandingkan kinerja berbagai model klasifikasi machine learning dalam mendeteksi fraud dan memprediksi keterlambatan pengiriman pada supply chain dengan menerapkan BADIR Framework sebagai pendekatan analitik terstruktur. Dataset publik DataCo Global digunakan sebagai sampel dengan tahap analisis meliputi identifikasi business question, perencanaan analisis, pengumpulan dan pembersihan data, visualisasi data, insight, dan rekomendasi. Sembilan algoritma klasifikasi diuji, termasuk Logistic Regression, Support Vector Machines, K-Nearest Neighbors, Random Forest, XGBoost, dan Decision Tree. Hasil menunjukkan bahwa Decision Tree merupakan model dengan kinerja terbaik, dengan nilai F1 sebesar 80,35% untuk deteksi fraud dan 99,41% untuk prediksi keterlambatan pengiriman. Temuan ini menegaskan bahwa integrasi BADIR Framework dan machine learning mampu mendukung pengambilan keputusan berbasis data dalam mitigasi risiko supply chain, terutama dalam pengawasan transaksi dan optimalisasi ketepatan pengiriman barang.
Co-Authors Ahmad Zaini Alfajri, Willy Bima Andi Kurniawan Nugroho Anita Ratnasari Rakhmatulloh Aris Puji Widodo Barid, Aziz Jihadian Bayastura, Shahnilna Fitrasha Bayu Surarso Benawan, Ivan Beta Noranita Budi Warsito Catur Edi Widodo Dewi Arianti Wulandari Dewi Arianti Wulandari Diah Intan Kusumo Dewi Djalal Er Riyanto Edwin Setiawan Edy Suharto Eko Adi Sarwoko Eriya, Eriya Guruh Aryotejo Guruh Aryotejo, Guruh Harsani, Nafia Arlila I Ketut Eddy Purnama Indra Waspada Kusuma, Teja Mauridhi Hery Purnomo Merdekawati, Utami Muhammad Ikhsan Nafia Arlila Harsani Nurdin Bahtiar Nurdin, Alya Aulia Nurul, Hanan Oky Dwi Nurhayati Reza Setiawan Rina Fiati Rismiyati Rismiyati Rizki Galang Rahmadani Safitri, Adila Terawan Agus Putranto Tri Retnaningsih Soeprobowati Wahyul Amien Syafei Widowati, Widowati Wirawan, Panji Wisnu Wiryadinata, Doni Yasmin Yasmin