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All Journal IJORER : International Journal of Recent Educational Research Journal of Informatics and Computer Science (JINACS) Data Sciences Indonesia (DSI) Media Konstruksi : Jurnal Ilmiah Teknik Sipil Journal of Emerging Information Systems and Business Intelligence (JEISBI) Inovate : Jurnal Ilmiah Inovasi Teknologi Informasi
I Kadek Dwi Nuryana
Universitas Negeri Surabaya
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Library & Information Science Mathematics Physics 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.
Co-Authors 'Ulhaq, Arafat A'izzatul Khiyana Adi Duta Saputra P., I Gede Aininnisa, Firda Akhmad Hilmy Zakaria Andini Pramesti Andrik Santoso, Muhammad Anggung Mestuti Kaprawiran, Immas Ardhini Aarih Utami Augusta Jannatul Firdaus, Reza Billah, Hilmi Almuhtade Firmanda Himawan, Ahmad Fitrah Amaliah Ghea Sekar Palupi Hadi Sucipto, Hadi Iftitaahul Mufarrihah Innayah, Tifanny Maulida Intan Novita Sari Noer Qholby Maulidiyah Maharani, Herlina Syafhita Mahrus Ali Margaretha Ekaristi Yobella Mohammad Aris Saputra Muhammad Hafizh Ferdiansyah Muhammad Naufal Ammar Rizqi Muhammad Naufal Baharudin Mujianto, Ahmad Heru Nugroho, Meriana Wahyu Oktaviana Tri Wulanndari Seriusman Waruwu Shuffy, Muhandis Suhartanto, Martin Sultan Triabidin, Muchammad Titin Sundari Totok Yulianto Vania Nadhiya Tsary Zahra, Salsabila Nur Zahra