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All Journal Journal of Information Systems and Informatics IJORER : International Journal of Recent Educational Research Abimanyu: Journal of Community Engagement Journal of Informatics and Computer Science (JINACS) Data Sciences Indonesia (DSI) Journal of Artificial Intelligence and Digital Business 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
Universitas Negeri Surabaya
Arts Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Physics Social Sciences Other

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Rancang Bangun Aplikasi Ealah E- Learning Pengenalan Perograman Dasar Berbasiss Web Dengan Menggunakan Algoritma Edit Distance Pada Koreksi Otomatis Jawaban Essay Firmanda Himawan, Ahmad; Dwi Nuryana, I Kadek; Ali, Mahrus
Inovate Vol 5 No 2 (2021): Maret
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33752/inovate.v5i2.3119

Abstract

E-learning is an innovative learning medium that must be optimized, so that education delivery will continue to develop. By using E-learning, the problems of space and time that have been an obstacle in learning are no longer a barrier. Apart from that, the existence of e-learning which has the feature of being able to correct essay answers automatically is felt to be able to help teachers to provide grades to students, and students can learn independently and can get more learning material and can communicate with teachers outside the classroom. In this research the USDP (unified software development process) method, Edit Distance algorithm, and Cosine Simmiliarity are used to match the students' answers with the teacher's answer key, the counting process starts from Text prepocessing, Case floading, filtering, and tokenizing, then processing of Edit Distance and Cosine Simmiliarity. The results of this study are in the form of a web-based e-learning application which is expected to help learning, especially for teachers, to make it easy to give grades to students in exams or essay assignments. In the tests conducted by the author with data from 5 essay questions along with the answer keys and student answer data, and the authors conducted 2 application tests using the same data by applying the edit distance algorithm the value = 94.98 while without the edit distance algorithm it got a value of = 78, 04 and the difference in value = 16.94 from the total value = 100. Keywords: E-learning, USDP, unified software development process, Cosine Simmiliarity, Edit Distance, Levenstein Distance, Essay Automatic Correction.
Penerapan Metode Exponential Smoothing Pada Prediksi Dana Donatur Di Lembaga Amil Zakat Ummul Quro Kabupaten Jombang Anggung Mestuti Kaprawiran, Immas; Dwi Nuryana, I Kadek; Augusta Jannatul Firdaus, Reza
Inovate Vol 5 No 2 (2021): Maret
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33752/inovate.v5i2.3122

Abstract

Donor Fund Prediction is a prediction system that aims to predict donor funds at the Ummul Quro Amil Zakat Institution, Jombang Regency. The Donor Fund Prediction is used to predict the next year based on previous year's data. This study focuses on using exponential smoothing while for the prediction method initially using moving averages. The calculated data is 2014-2018 while 2019 is used for testing prediction errors. In predicting exponential smoothing, alpha constant value which has the smallest error is needed. To get it, several stages are needed, namely, predicting 2014-2019 using a moving average with a constant defined by the user, predicting 2014-2018 with exponential smoothing with an alpha value between 0 to 1, looking for the MAPE (Mean Absolute Percentage Error) value at each value alpha used. After obtaining alpha with the smallest MAPE, the alpha value is used to predict 2019. The test results explain that calculations using a program with the Moving average method and Exponential smoothing successfully predict with an accuracy of 93.32% or only have an error of 6.68% instead of using only the method. The moving average only has an accuracy of 90.25%. Keywords: Prediction, Exponential smoothing, Moving average
Perancangan Sistem Informasi Prediksi Curah Hujan Pada Kabupaten Jombang Menggunakan Metode Fuzzy Time Series Suhartanto, Martin; Dwi Nuryana, I Kadek; Heru Mujianto, Ahmad
Inovate Vol 6 No 1 (2021): September
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33752/inovate.v6i1.3167

Abstract

Weather and climate information is very important for some people to support life. In agriculture, for example, climate change has a major effect on changing planting patterns. Fuzzy Time Series (FTS), Rainfall prediction is used to understand the intensity of future rainfall that serves as a reference in making the right decisions and preparing to address future problems. In this study using the Fuzzy Time Series (FTS), this method can be used to solve forecasting problems with linguistic historical data and real numbers by converting the real number data into linguistic variables. The results of this study are a website-based system that can predict rainfall in the coming year by using previous rainfall history data as a reference for prediction calculations. from the calculation done by the system in predicting rainfall by using the Fuzzy Time Series method as an example in this silver sub-district produces an average mape value of 0.90 which means it has excellent performance because it produces an average mape forecasting error value of less than 10. Key words: Prediction, Rainy, FTS, website
Rancang Bangun Sistem Pendukung Keputusan Pemilihan Pondok Pesantren Menggunakan Metode Analytical Hierarchy Process (AHP) Berbasis Web Shuffy, Muhandis; Dwi Nuryana, I Kadek; Sucipto, Hadi
Inovate Vol 6 No 2 (2022): Maret
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33752/inovate.v6i2.3171

Abstract

The college under the auspices of the Tebuireng Islamic Boarding School is to produce religious students who are scientists and religious scientists with the motto "The Real University of Pesantren and Entrepreneurship". However, data and information about Pondok Pesantren in the campus environment have not been given specific recommendations from the campus for students. To overcome this problem, it is necessary to develop a decision support system for the selection of Pondok Pesantren by applying the Analytical Hierarchy Process (AHP) method as a new breakthrough in decision support systems in informal institutions, namely the selection of Pondok Pesantren. The Analytical Hierarchy Process (AHP) method describes complex problems into a hierarchical model so that it will be more systematic and produce consistent data. This research method uses qualitative methods, according to Sugiyono (2009), data collection is carried out in natural conditions by means of observation, interviews and documentation. From the results of testing the accuracy data obtained 99.99% based on the ranking data of Pondok Pesantren. The results of testing the accuracy data indicate the effectiveness of AHP in implementing the decision support system for the selection of Pondok Pesantren. Keywords: SPK AHP, pondok pesantren selection, php website, mysql.
Sistem Rekomendasi Gaya Rambut Personal Berdasarkan Analisis Wajah dan Rambut 'Ulhaq, Arafat; Dwi Nuryana, I Kadek
Journal of Informatics and Computer Science (JINACS) Vol. 7 No. 02 (2025)
Publisher : Universitas Negeri Surabaya

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

Abstract

Abstrak— Pemilihan gaya rambut seringkali bersifat subjektif dan tidak didasarkan pada karakteristik visual yang objektif, sehingga mengurangi tingkat personalisasi dan kepuasan pengguna. Penelitian ini bertujuan untuk mengembangkan sistem identifikasi gaya rambut berbasis website yang mampu memberikan rekomendasi personal berdasarkan analisis data visual. Untuk mencapai tujuan tersebut, penelitian ini menerapkan pendekatan deep learning dengan mengembangkan dua arsitektur Convolutional Neural Network (CNN) pada EfficientNetB0 untuk tugas klasifikasi bentuk wajah dan ResNet50 untuk klasifikasi jenis rambut. Metode penelitian yang digunakan adalah Cross-Industry Standard Process for Data Mining (CRISP-DM), yang mencakup tahapan pemahaman bisnis, pemahaman data, persiapan data dengan augmentasi, pemodelan menggunakan transfer learning, evaluasi, hingga implementasi. Hasil dari penelitian ini adalah sebuah aplikasi web fungsional yang mampu melakukan klasifikasi bentuk wajah dan jenis rambut dari gambar yang diunggah pengguna. Sistem ini berhasil mengintegrasikan kedua model untuk memberikan identifikasi gaya rambut yang lebih akurat dan personal, sehingga dapat menjadi solusi objektif dalam industri kecantikan digital. Kata Kunci— Identifikasi Gaya Rambut, Klasifikasi Gambar, EfficientNet, ResNet, Personalisasi Rambut.
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.
Co-Authors 'Ulhaq, Arafat A'izzatul Khiyana Achmad Asrori Adi Duta Saputra P., I Gede Aininnisa, Firda Akhmad Hilmy Zakaria Andini Pramesti Andrik Santoso, Muhammad Anggung Mestuti Kaprawiran, Immas Ardhini Aarih Utami Aries Dwi Indriyanti Augusta Jannatul Firdaus, Reza Avikatria Cahyaningrum Bagas Setya Wicaksono Billah, Hilmi Almuhtade Bonda Sisephaputra Cendra Devayana Putra Devanda Yudha Bharagus Faizatul Mukaromah Fauzan Ali Ghofur Finna Nur Nandia Firmanda Himawan, Ahmad Fitrah Amaliah Gagah Ibnu Mutho’illah Gerin Azharani Ghea Sekar Palupi Ghea Sekar Palupi Hadi Sucipto, Hadi Hilal Hindi Saputra Iftitaahul Mufarrihah Innayah, Tifanny Maulida Intan Novita Sari Noer Qholby Maulidiyah Irsyad Adi Rochman Khiena Salsabiila Susanty Khoirotun Nisa Lintang Iqhtiar Dwi Mawarni Maharani, Herlina Syafhita Mahrus Ali Margaretha Ekaristi Yobella Mochammad Ilham Study Wartana Ilham Mohammad Aris Saputra Monica Cinthya Muhammad Aswiandi Muhammad Hafizh Ferdiansyah Muhammad Naufal Ammar Rizqi Muhammad Naufal Baharudin Mujianto, Ahmad Heru Nadya Kumalasari Nugroho, Meriana Wahyu Oktaviana Tri Wulanndari Rizky Pratama Syahrul Ramadhan Seriusman Waruwu Shuffy, Muhandis Suhartanto, Martin Sultan Triabidin, Muchammad Titin Sundari Totok Yulianto Vania Nadhiya Tsary Yulius Candra Akmala Yuninda Intan Zahra, Salsabila Nur Zahra