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All Journal Journal of Information Systems and Informatics Ed-Humanistics : Jurnal Ilmu Pendidikan IJORER : International Journal of Recent Educational Research Jurnal Abdimas Kartika Wijayakusuma Abimanyu: Journal of Community Engagement Berkala Ilmiah Pendidikan Journal of Informatics and Computer Science (JINACS) Data Sciences Indonesia (DSI) Journal of Artificial Intelligence and Digital Business Journal Research of Social Science, Economics, and Management Indonesian Research Journal on Education Scientica: Jurnal Ilmiah Sains dan Teknologi Neraca: Jurnal Ekonomi, Manajemen dan Akuntansi Jurnal Ilmu Ekonomi, Pendidikan dan Teknik (IDENTIK) Media Konstruksi : Jurnal Ilmiah Teknik Sipil Journal of Emerging Information Systems and Business Intelligence (JEISBI) Inovate : Jurnal Ilmiah Inovasi Teknologi Informasi MURAKOM
I Kadek Dwi Nuryana, I Kadek Dwi
Universitas Negeri Surabaya
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

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Segmentasi Pelanggan Berbasis Clustering Menggunakan LRFM dan Variabel Transaksi Untuk Mendukung Strategi Pemasaran Andini Pramesti; I Kadek Dwi Nuryana
Data Sciences Indonesia (DSI) Vol. 6 No. 1 (2026): Article Research Volume 6 Issue 1, Juni 2026
Publisher : Yayasan Cita Cendikiawan Al Kharizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dsi.v6i1.8430

Abstract

CV. Restu Tani Jaya marupakan salah satu pelaku usaha yang bergerak di sektor pertanian yang berlokasi di Nganjuk, Jawa Timur. Berdasarkan hasil wawancara CV. Restu Tani Jaya mengalami permasalahan dalam pengelolaan data dan strategi pemasaran yang kurang tepat sasaran. Data penjualan online CV. Restu Tani Jaya saat ini digunakan untuk arsip laporan penjualan saja dan belum diolah secara mendalam untuk menghasilkan informasi penting dalam memahami perilaku pelanggan. Oleh karena itu, diperlukan segmentasi pelanggan berdasarkan Length, Recency, Frequency, dan Monetary (LRFM) dan variabel perilaku transaksi tambahan. Penelitian dilakukan dengan mengikuti tahapan Knowledge Discovery in Database (KDD). Data yang digunakan yaitu data penjualan online dari bulan Januari 2022 hingga September 2025 dengan total 2.393 baris. Metode yang digunakan untuk mengelompokkan pelanggan menggunakan tiga algoritma clustering yaitu K-Means, K-Medoids, dan Agglomerative Hierarchical Clustering (AHC). Hasil algoritma yang menghasilkan performa yang paling stabil Adalah algoritma K-Means yaitu 2 cluster dengan nilai Silhouette Score adalah 0.51161 dan Davies Bouldin Index adalah 0.77046 dengan berdasarkan Customer Loyalty Matrix dan Customer Value Matrix cluster 0 merupakan kelompok pelanggan Core-Spender dengan strategi yang diberikan strategi retensi, sedangkan kelompok pelanggan pada cluster 1 merupakan Lost-Frequent dengan strategi reaktivasi. Hasil dari segmentasi disajikan dalam visualisasi dashboard untuk mempermudah perusahaan dalam pengambilan keputusan.
Design and Development of Financial Reporting Information System for Taman Pendidikan AlQuran Governance Achmad Asrori; I Kadek Dwi Nuryana
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 7 No. 3 (2026): Vol. 07 Issue 03
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v7i3.66512

Abstract

The Al-Qur'an Education Park (TPQ) plays a significant role in shaping character and understanding of Islam from an early age. However, TPQ financial management is typically conducted manually, increasing the risk of recording errors, lack of transparency, and difficulties in decision-making. This study aims to develop a computerized financial reporting information system for more effective and efficient TPQ governance in accordance with the Ministry of Religious Affairs (Kemenag) standards. The prototype method is used in system development, allowing for iterative improvements based on user feedback. The system is designed to record financial transactions, generate financial reports, and present relevant financial information for TPQ administrators. System testing is conducted using the black-box method to evaluate functionality from the user’s perspective and the white-box method to examine the internal structure and logic. This study demonstrates that the financial reporting information system improves the accuracy, efficiency, and transparency of TPQ financial management in compliance with Kemenag standards. Additionally, the system facilitates financial analysis and better decision-making for administrators. Therefore, this system is expected to contribute to the overall improvement of TPQ governance.
Public Opinion on MyTelkomsel Using DeLone and McLean Model on X Bagas Setya Wicaksono; Cendra Devayana Putra; I Kadek Dwi Nuryana; Monica Cinthya
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 7 No. 3 (2026): Vol. 07 Issue 03
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v7i3.78043

Abstract

The MyTelkomsel application is a digital service used by Telkomsel customers to access telecommunications information and services. The high number of users is accompanied by the emergence of various user opinions and complaints expressed through social media. This study aims to analyze user satisfaction with the MyTelkomsel application based on public opinions on the X (Twitter) platform using the DeLone and McLean Information Systems Success Model. The research data consist of 1,500 Indonesian-language tweets collected through a crawling process. The data then underwent a text preprocessing stage to improve analysis quality. Sentiment analysis was conducted using the RoBERTa model to classify user opinions into positive, neutral, and negative sentiments. Subsequently, each tweet was labeled into six dimensions of the DeLone and McLean model, namely System Quality, Information Quality, Service Quality, Use, User Satisfaction, and Net Benefits. Sentiment scores were used as quantitative values for each dimension. The relationships among variables were analyzed using the Structural Equation Modeling–Partial Least Squares (SEM-PLS) method. The results indicate that System Quality and Information Quality significantly influence User Satisfaction, while Service Quality shows a lower level of influence. This study is expected to provide academic contributions to the application of the DeLone and McLean model based on social media data and offer practical insights for the development of the MyTelkomsel application in improving service quality and user experience. Keywords : MyTelkomsel, Sentiment Analysis, Social Media, DeLone and McLean, User Satisfaction, SEM-PLS
Sentiment Analysis And UTAUT2 Classification On Maxim Application User Reviews Using IndoBERT And Zero-Shot Hilal Hindi Saputra; Cendra Devayana Putra; I Kadek Dwi Nuryana; Monica Cinthya
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 7 No. 3 (2026): Vol. 07 Issue 03
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v7i3.78304

Abstract

The rapid growth of ride-hailing services has intensified competition, making user feedback on digital platforms a critical asset for service improvement. This study addresses the challenge of managing and extracting actionable insights from large volumes of unstructured user reviews on the Google Play Store for the Maxim application. To overcome this, a comprehensive text-mining framework is proposed, integrating sentiment analysis and technology acceptance modeling. A dataset of 2.000 Indonesian-language user reviews from July to September 2025 was retrieved via web scraping. Data preprocessing was executed using case folding, filtering, and normalization. Subsequently, sentiment classification was performed using the IndoBERT model, while the mapping of user text to the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework was automated using a Zero-Shot Classification approach. Finally, Structural Equation Modeling–Partial Least Squares (SEM-PLS) via SmartPLS 4.0 was utilized to test the structural hypotheses. The analytical findings reveal that negative sentiments slightly dominate the dataset (48.05%), heavily driven by system stability and sudden fare adjustments. Furthermore, the structural model proves that behavioral intention, effort expectancy, facilitating conditions, habit, performance expectancy, price value, and social influence exert positive and significant effects on adoption, whereas hedonic motivation exhibits no significant influence.
Predicting Student Performance to Support Adaptive Content Delivery: A Random Forest Approach I Kadek Dwi Nuryana; Lintang Iqhtiar Dwi Mawarni
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.v8i3.1663

Abstract

This study addresses the prediction-to-action gap in student performance analytics by proposing an interpretable framework that transforms predictive risk scores into adaptive content recommendations. Rather than only identifying at-risk students, the framework integrates performance prediction, interpretable rule extraction, and decision-support simulation to guide adaptive learning interventions. The study used the Open University Learning Analytics Dataset (OULAD), comprising 6,937 student records after filtering and preprocessing from the original 32,593 records. A Random Forest-based framework was adopted because of its interpretability and rule-extraction capability, although XGBoost achieved slightly higher predictive performance. The framework consists of three components: student performance prediction, interpretable decision rule extraction, and a decision-engine simulation for adaptive content recommendation. The predictive model achieved 87.22% accuracy and an AUC-ROC of 0.932. Rule extraction generated 20 human-readable rules with an average of 2.0 conditions per rule, an interpretability score of 1.000, and 81.6% fidelity to the full Random Forest model. The decision-engine simulation classified students by risk level and produced corresponding adaptive recommendations. An estimated Adaptation Gain metric indicated a potential 53.54% improvement in projected student success rates under conservative simulation assumptions. The proposed framework connects prediction with actionable recommendations to support educational decision-making, although real-world intervention validation remains necessary.
Business Process Digitization in “Marbil Collection” Home Industry Using Business Process Model and Notation Nadya Kumalasari; I Kadek Dwi Nuryana
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 4 (2025): Vol. 06 Issue 04
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i4.71001

Abstract

Digital transformation has become an essential requirement for small and medium enterprises, including home industries engaged in handicrafts such as bag production. However, most home industry players still run business processes manually, potentially causing various operational problems such as production delays, distribution inaccuracies, and order recording errors. This research aims to analyze and re-model business processes at Marbil Collection, a home-based business that produces bags on a make-to-order basis. The approach used was Business Process Model and Notation (BPMN), a visual standard for systematically describing business workflows. This research identified four main processes in operations, namely ordering, procurement of raw materials and production, distribution and order completion, and employee payroll. These processes were mapped in the current business process model (as-is), and then redesigned into a proposed model (to-be) that supports automation and digitization. The results of the modeling showed significant gaps in the manual system used, especially in terms of service speed, transparency, and documentation. The proposed digital process design provides a structured solution to improve efficiency, accuracy, and customer experience. This research is expected to be the basis for developing a simple information system that suits the needs of the home industry, as well as making a practical contribution to similar businesses that want to start digitalization from an understanding of their own business processes.
Clustering of Goat Buyers in West Java with K-Means Algorithm Faizatul Mukaromah; I Kadek Dwi Nuryana
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 4 (2025): Vol. 06 Issue 04
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i4.72022

Abstract

The advancement of information technology has encouraged the utilization of data as a strategic resource across various fields, including the livestock sector. This study aims to implement the K-Means algorithm to segment goat buyers in West Java Province based on demographic characteristics such as age, marital status, geographic location, type of goat purchased, and transaction methods. This segmentation is expected to assist business actors in understanding purchasing patterns and designing more targeted marketing and distribution strategies. The study uses 1,250 transaction records and follows the stages of selection, preprocessing, transformation, data mining, and interpretation using the Knowledge Discovery in Databases (KDD) approach. Geographic distances between buyer locations and reference points were calculated using the Haversine formula. To determine the optimal number of clusters, the Elbow Method and Silhouette Score were used, with the best result obtained at a Silhouette score of 0.16 for 3 clusters. Each cluster was analyzed based on modal characteristics such as age, marital status, district, type of goat purchased, number of goats per transaction, purchase purpose, delivery method, payment method, as well as Recency, Frequency, and Monetary (RFM). The results indicate that the K-Means algorithm is effective in grouping goat buyers into relevant and meaningful segments. This information can be used by farmers and stakeholders to improve distribution efficiency, stock optimization, and data-driven marketing strategies. This study also emphasizes the importance of integrating technologies such as Python and Streamlit for interactive visualization and ID-based buyer tracking in advanced analytics.  
Implementation of Business Intelligence for Sales Analysis and Customer Segmentation at XYZ Store Gerin Azharani; I Kadek Dwi Nuryana
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 4 (2025): Vol. 06 Issue 04
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i4.72050

Abstract

XYZ Retail Store has a large amount of transaction data, but it has not been optimally utilized for strategic decision making. The research aims to implement a Business Intelligence. Business Intelligence helps analyze sales data and segment key business entities, namely customers, suppliers, and products. The research methodology includes designing a datawarehouse using Kimball's Nine Step method with MySql as the database platform. Extract, Transform, Load (ETL) process is performed to prepare the data before processing with Online Analytical Processing (OLAP) approach for multidimensional sales analysis, and Data Mining with K-Means clustering algorithm to perform segmentation. The results obtained from the entire analysis, visualized using the tools of tableau.
Healthcare Data Analysis Through Business Intelligence: A Case Study With Power BI Avikatria Cahyaningrum; I Kadek Dwi Nuryana
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 4 (2025): Vol. 06 Issue 04
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i4.72051

Abstract

This research aims to analyze hospital health data with the application of Power BI-based Business Intelligence (BI) to support a more precise and efficient decision-making process. The research data is taken from a public repository that provides a hospital management system database structure with complex inter-table relationships. The initial stages were carried out with the ETL (Extract, Transform, Load) process to integrate and clean the data before being entered into the data warehouse with the star and galaxy schema model. Next, analysis was conducted using Online Analytical Processing (OLAP) for medical service usage and other trends. In addition, the application of data mining using the Random Forest algorithm is also carried out for the classification of hospital busyness levels and prediction of patient re-visits based on historical data.
Business Intelligence Implementation For Hotel Room Reservation Data Analysis Khoirotun Nisa; I Kadek Dwi Nuryana
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 4 (2025): Vol. 06 Issue 04
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i4.72053

Abstract

The growth of the hotel industry in Indonesia is driven by increasing mobility for business and tourism purposes. However, this growth presents new challenges in hotel management, particularly in analyzing customer behavior, optimizing room availability, and making strategic decisions. Manual management of reservations and customer data is considered ineffective. This study implements Business Intelligence (BI) for analyzing hotel reservation data from 2022–2024 using OLAP and data mining techniques. BI enables the analysis of room popularity, active customer identification, payment method trends, and customer profiles. Additionally, this study applies clustering for room segmentation and forecasting methods to predict future income and reservation trends. Data is processed using ETL into a star schema-based data warehouse, visualized through Power BI dashboards. Results show that BI provides valuable insights into customer behavior, room occupancy trends, and financial performance, supporting management in improving operational efficiency and revenue.
Co-Authors 'Ulhaq, Arafat A'izzatul Khiyana Achmad Asrori Ahmad Shihabudin Aininnisa, Firda Aisyiah, Jamilatul Akhmad Hilmy Zakaria Alifia Octaviany Bashir Amara Indah Putri Ananda Rizky Abidin Anandito Wisnu Widya Pratama Andini Pramesti Andrik Santoso, Muhammad Anggung Mestuti Kaprawiran, Immas Anis Maulidatur Rizqiyah ANITA ANDRIANI, ANITA Ardhini Aarih Utami Ardiansyah, Fernando Aries Dwi Indriyanti Aries Dwi Indriyanti, Aries Dwi Arif Hidayatullah, Arif Ariga Bahrodin Asriana Kibtiyah Augusta Jannatul Firdaus, Reza Aulia Mufidatur Rosida Aulina Naharul Kristanti Avikatria Cahyaningrum Aziz Bagas Setya Wicaksono Bagus Laksono Yudo Atmojo Bagus Bashir, Alifia Octaviany Billah, Hilmi Almuhtade Bonda Sisephaputra Burhan Hidayatulloh Cendra Devayana Putra Daniswara, Anak Agung Aryasatya Darren Waluya Ardianto Devanda Yudha Bharagus Devi Riskhi Kurniawati Egar Caesario Firmansyah Evita Widiyati Faizatul Mukaromah Fauzan Ali Ghofur Ferdani, Happy Septian Finna Nur Nandia Firmanda Himawan, Ahmad Fitrah Amaliah Gagah Ibnu Mutho’illah Galang Maftuh Nur Alian Gerin Azharani Ghea Sekar Palupi Ghea Sekar Palupi Hadi Sucipto, Hadi Hadi, Febria Erliana Hamdani, Hilman Hanif, Zidny Hasan, Jamal Hilal Hindi Saputra Husnul Mubaroq I Gede Adi Duta Saputra P. I Gusti Lanang Putra Eka Prismana, I Gusti Lanang Putra Eka Iftitaahul Mufarrihah Imam Muslih Intan Novita Sari Noer Qholby Maulidiyah Intan Rahma Diana Putri Irsyad Adi Rochman Ivander brian ramadhan Jasica Ardana Herviyandasari Jatminto, Joko Khiena Salsabiila Susanty Khoirotun Nisa Kurrotul Uyun Lailatul Mukharromatus Sa'diyah Laily Masruroh Lintang Iqhtiar Dwi Mawarni Lizza Nur Fadhila Madani, Heru Galang Ardi Reda Maharani, Herlina Syafhita Mahrus Ali Mairatul Lailia Margaretha Ekaristi Yobella Maulana Auliyaurroshidin Mochammad Ilham Study Wartana Ilham Moerdyanto, Octarian Prasetya Moh. Fatihul Farras Dzulfaqqor Mohammad Aris Saputra Mohammad Dandi Arsydi Mohammad Ulil Kirom Monica Cinthya Muchammad Sultan Triabidin Muchtarotun Novia Ustadha Muhammad Aswiandi Muhammad Hafizh Ferdiansyah Muhammad Naufal Ammar Rizqi Muhammad Naufal Baharudin Muizadin, Irwan Mujianto, Ahmad Heru Mukhtarul Fata An Nadwi Nadya Kumalasari Niasmara, Jeptika Herni Nugroho, Meriana Wahyu Nurul - Istiqomah Oki Kurniasari, Serly Oktaviana Tri Wulanndari Pramudita, Genta Prismala, Darisva Puspita Westi Erlitiya Ningrum Rafif Rafeda Ramma Ramadhan, Gemilang Idam Rizky Pratama Syahrul Ramadhan Robbiatul Adawiyah Rohmanialuhri Rengganis Rosida, Aulia Mufidatur Santoso, M Haries Eko Sari, Devit Etika Seriusman Waruwu Shuffy, Muhandis Suhartanto, Martin Suhendi, Laizim Tifanny Maulida Innayah Titin Sundari Totok Yulianto Ulumudin, Febri Nur Utomo, Ilham Wahyu Vania Nadhiya Tsary Wicaksono, Satria Adi Yulius Candra Akmala Yuninda Intan Zahra, Salsabila Nur Zahra