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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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Penerapan Algoritma Regresi Linear Untuk Memprediksi Stok Inventaris Barang Berdasarkan Tren Musiman Dan Promosi Platform Gagah Ibnu Mutho’illah; I Kadek Dwi Nuryana
MUARA KOMPUTER : Jurnal Ilmiah Ilmu Komputer & Elektronika Vol. 2 No. 3 (2026): MUARA KOMPUTER : Jurnal Ilmiah Ilmu Komputer & Elektronika
Publisher : CV MUARA EDUKASI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64365/murakom.v2i3.511

Abstract

Toko ritel sering kesulitan menentukan jumlah stok yang tepat karena permintaan dipengaruhi oleh tren musiman dan aktivitas promosi platform yang jarang diperhitungkan secara sistematis. Penelitian ini menerapkan regresi linear berganda untuk memprediksi tingkat stok satu produk elektronik (P0009) pada satu toko, menggunakan 731 data harian dari dataset ritel publik di Kaggle. Tiga variabel bebas digunakan, yaitu tren musiman, aktivitas promosi/hari libur, dan penjualan historis, dengan pembagian 80% data latih (584 data) dan 20% data uji (147 data). Model yang dihasilkan adalah Y = 174,16 − 0,81X₁ + 1,10X₂ + 0,72X₃, dievaluasi menggunakan MAPE, MAD, dan MSE. Model menghasilkan MAPE 34,67%, MAD 74,77 unit, MSE 8.608,83, dan R² 0,38 yang menunjukkan akurasi prediksi tergolong layak. Penjualan historis dan promosi terbukti berpengaruh positif terhadap tingkat stok, sedangkan tren musiman berpengaruh negatif namun kecil. Temuan ini mengindikasikan bahwa stok perlu ditambah menjelang dan selama periode promosi serta saat tren penjualan meningkat, dan dapat dikurangi saat tidak ada promosi dan penjualan menurun. Penelitian ini menawarkan pendekatan kuantitatif yang sederhana dan mudah diinterpretasikan sebagai dasar awal perencanaan stok bagi pengelola toko.
Implementation of Knowledge Graph as a Representation of Public Sentiment Analysis Toward AI-Generated Art Finna Nur Nandia; I Kadek Dwi Nuryana
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 2 (2026): Mei-Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i2.11349

Abstract

The rapid advancement of generative AI technology has elicited diverse public responses on social media, particularly toward AI-generated art, which has significantly impacted Indonesia's creative industry. This study aims to analysed the sentiment of Indonesian society on platform X toward AI-generated art and to represent the interconnections among sentiment entities through a Knowledge Graph (KG). The approach integrates three primary methods within the Knowledge Discovery in Databases (KDD) framework: sentiment classification using the IndoBERT model, topic modelling using Latent Dirichlet Allocation (LDA), and KG construction based on Social Network Analysis (SNA). The dataset consists of 4,765 Indonesian-language tweets that underwent pre-processing. Sentiment analysis results indicate a dominance of negative sentiment (45.4%) over positive sentiment (42.5%), with the IndoBERT model achieving 69% accuracy on a three-class classification task. Topic modelling produced 18 distinctive topics (9 negative, 9 positive), validated through Two-Stage Similarity validation. Negative topics are dominated by issues of economic impact on illustrators, copyright infringement, and artistic style theft, while positive topics reflect appreciation for AI as a creative tool. The constructed KG comprises 144 nodes and 329 edges with a modularity score of 0.4556, reflecting 7 meaningful thematic communities. SNA evaluation reveals that 'ilustrasi' (illustration) is the most central entity (degree centrality = 12.621), while negative issues dominate the central positions among topic nodes. This study demonstrates that the integration of IndoBERT, LDA, and KG is capable of uncovering hidden relational patterns in public opinion that cannot be obtained through conventional sentiment analysis alone.
Analisis Pola Penyebaran Penyakit Campak Di Kabupaten Sumenep Dengan Algoritma Random Forest Berbasis Geospasial Anandito Wisnu Widya Pratama; I Kadek Dwi Nuryana
Jurnal Ilmu Ekonomi, Pendidikan dan Teknik Vol. 3 No. 6 (2026): IDENTIK - November
Publisher : CV. SINAR HOWUHOWU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70134/identik.v3i6.2003

Abstract

Measles remains a Highly contagious disease and poses a surveillance challenge in Sumenep Regency, whose administrative area consists of mainland and island subdistricts. This study aimed to analyze the spatial distribution of measles, forecast monthly cases, and implement the results in a WebGIS dashboard. The study used measles records from 2022–2025 obtained from the Sumenep Regency Health Office and subdistrict boundaries in GeoJSON format. Annual records for 2022–2024 were temporally disaggregated using the monthly distribution pattern of the detailed 2025 data, while individual 2025 records were aggregated by subdistrict and month. The resulting panel comprised 27 subdistricts, 48 monthly periods, and 1,296 observations. Spatial analysis compared Queen Contiguity and K-Nearest Neighbor weights, followed by Global Moran’s I and Local Indicators of Spatial Association with 999 permutations. Random Forest Regression used Lag-1 to Lag-4, month, and year, with a chronological split and five-fold expanding-window RandomizedSearchCV. KNN with k=5 produced a connected spatial network and significant positive autocorrelation (Moran’s I=0.3384; p=0.004). The tuned model achieved MAE=3.6021, RMSE=5.6325, and R²=0.3025, outperforming the Lag-1 baseline. Lag-1 was the dominant predictor. The integrated WebGIS presents case maps, LISA clusters, forecasts, and operational priority areas. The approach supports data-driven surveillance, although predictions remain moderate and tend to underestimate extreme increases.  
Analisis Niat Keberlanjutan Penggunaan Aplikasi Ajaib Menggunakan Expectation Confirmation Model (ECM) Dengan Penambahan Variabel Kebiasaan Aulina Naharul Kristanti; I Kadek Dwi Nuryana
Jurnal Ilmu Ekonomi, Pendidikan dan Teknik Vol. 3 No. 6 (2026): IDENTIK - November
Publisher : CV. SINAR HOWUHOWU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70134/identik.v3i6.2026

Abstract

Abstract App-based investing has widened access to Indonesia's capital market; however, attracting many users does not automatically translate into repeated platform use. This research investigates Ajaib users' intention to remain with the service by incorporating habit into the Expectation Confirmation Model (ECM). Questionnaire responses from 194 eligible users, recruited through purposive sampling, were analyzed with Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS 4. Statistical inference relied on a two-sided bootstrap test with 5,000 resamples. The model accounted for 46.3% of continuance intention. Confirmation strengthened perceived usefulness (β = 0.400; p < 0.001) and satisfaction (β = 0.486; p < 0.001), while habit was the only significant direct antecedent of continuance intention (β = 0.506; p < 0.001). No significant effect was identified from perceived usefulness to satisfaction or continuance intention, or from satisfaction to continuance intention. Thus, meeting prior expectations improves users' evaluations after adoption, whereas recurring usage patterns more directly sustain their willingness to keep using Ajaib. Incorporating habit therefore improves ECM's relevance for explaining continuance in digital investment services.  
Analisis Kelayakan Konsumsi Buah Tropis Berdasarkan Kematangan Menggunakan Convolutional Neural Network Ivander brian ramadhan; I Kadek Dwi Nuryana
Journal of Informatics and Computer Science (JINACS) Article In Press(1)
Publisher : Universitas Negeri Surabaya

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

Abstract

Abstrak— Indonesia, sebagai negara agraris beriklim tropis, memproduksi buah dalam skala besar. Namun, sekitar 40–50% komoditas buah segar mengalami kerusakan sebelum mencapai konsumen akhir. Tingginya angka susut hasil ini utamanya disebabkan oleh proses penilaian kualitas dan tingkat kematangan yang masih dilakukan secara manual dan subjektif. Kesalahan dalam identifikasi fase fisiologis buah berdampak langsung pada kerugian ekonomi dan penurunan kualitas asupan gizi masyarakat. Penelitian ini bertujuan untuk menganalisis performa arsitektur Convolutional Neural Network (CNN), secara spesifik varian EfficientNet (B0–B7), dalam mengklasifikasikan kelayakan konsumsi lima jenis buah tropis ke dalam 15 kelas secara serentak (single-stage classification). Metodologi penelitian mengadopsi standar CRISP-DM, memanfaatkan himpunan data sebanyak 14.875 citra yang dikumpulkan melalui web scraping dan pengambilan foto mandiri, serta dianotasi secara otomatis menggunakan model CLIP. Hasil komparasi dan evaluasi menunjukkan bahwa EfficientNet-B4 merupakan arsitektur paling optimal. Melalui tahap hyperparameter tuning dengan optimizer Adam, batch size 32, dan learning rate 0,001, model ini berhasil mencapai akurasi sebesar 82,29% pada pengujian 10-Fold Cross Validation. Model terbaik tersebut kemudian diimplementasikan ke dalam aplikasi berbasis web menggunakan Flask. Sistem ini dilengkapi dengan mekanisme guardrail berupa ambang batas tingkat kepercayaan (confidence threshold) sebesar 70% untuk mencegah luaran prediksi yang tidak meyakinkan, sehingga mampu menjadi solusi yang objektif dan otomatis dalam penilaian kualitas pascapanen buah tropis. Kata Kunci— klasifikasi citra, buah tropis, EfficientNet, Convolutional Neural Network, kelayakan konsumsi, CRISP-DM, single-stage classification.
Integrating Pedagogical Competence and Intrinsic Motivation in Building Inclusive Teaching Readiness among Madrasah Ibtida’iyah Teachers Ariga Bahrodin; Laily Masruroh; Evita Widiyati; Asriana Kibtiyah; Imam Muslih; I Kadek Dwi Nuryana
Berkala Ilmiah Pendidikan Vol. 6 No. 2 (2026): Berkala Ilmiah Pendidikan
Publisher : Scidac Plus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51214/bip.v6i2.1856

Abstract

This study analyzes the influence of pedagogical competence on the readiness of Madrasah Ibtidaiyah (MI) teachers to implement inclusive education, with intrinsic motivation serving as a mediating variable. Data were collected from 195 MI teachers through multistage sampling and analyzed using path analysis and the Sobel test. The research instruments were validated for reliability and construct validity through Confirmatory Factor Analysis (CFA). The results indicate that pedagogical competence significantly affects teacher readiness (β = 0.324; p < 0.001) as well as intrinsic motivation (β = 0.579; p < 0.001), while intrinsic motivation also significantly influences teacher readiness (β = 0.351; p < 0.001) and is confirmed to mediate this relationship with a coefficient of 0.262 (p < 0.05). Collectively, pedagogical competence and intrinsic motivation explain 91% of the variance in teacher readiness (R² = 0.91). The novelty of this study lies in examining a competence motivation model within the context of Madrasah Ibtidaiyah, an Islamic educational setting characterized by dual curricula and religious values distinct from prior studies that predominantly focused on general public schools. Theoretically, this study strengthens the explanatory power of Self-Determination Theory by demonstrating the psychological mechanism linking competence to readiness. Practically, the findings highlight the urgency of developing professional training for madrasah teachers that integrates the enhancement of inclusive pedagogical competence with the cultivation of intrinsic motivation.
Analisis Prediksi Penerimaan Pengguna Fitur ShopeeFood Menggunakan Algoritma Support Vector Machine Amara Indah Putri; I Kadek Dwi Nuryana
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 4 No. 3 (2023): Vol. 04 Issue 03
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v4i3.55316

Abstract

Sejalan dengan pandemi pada tahun 2020 membuat perkembangan teknologi, Internet, e-commerce, dan platform online telah menjadi semakin berkembang dan popular. Kumpulan data Measurable AI mengungkapkan bahwa permintaan konsumen untuk layanan pengiriman makanan online atau online food delivery (OFD) telah meningkat secara konsisten selama pandemi dan memasuki pascapandemi, salah satunya Indonesia. Hal ini tentunya membuat Shopee memanfaatkan kesempatan melalui layanan ShopeeFood untuk memasarkan makanan dan minuman secara online dan dalam waktu kurang dari satu tahun peluncuran ShopeeFood telah menempati posisi kedua sebagai platform layanan pesan antar makanan online yang pertama kali diingat menurut KataData.com. Kemudian, peneliti ingin memprediksi penerimaan pengguna fitur ShopeeFood menggunakan algoritma Support Vector Machine dengan bantuan tools RapidMiner. Jenis data pada penelitian ini menggunakan data primer yang diperoleh dari penyebaran kuesioner secara online dengan Google Form kepada pengguna fitur ShopeeFood di Surabaya sebanyak 275 data. Kemudian data yang didapatkan dibagi menjadi dua kelas yaitu menerima dan tidak menerima yang diolah dengan menggunakan algoritma SVM. Hasil akhir dalam penelitian ini berdasarkan hasil pengujian 10-fold cross validation dengan nilai k=3 memeroleh hasil akurasi 97.82%.
Perbandingan Metode Klasifikasi Data Mining Untuk Mengukur Tingkat Kepuasan Mahasiswa Terhadap Sistem Informasi Penilaian Nonakademik UNESA (SIPENA): Comparison Of Data Mining Classification Methods To Measure The Level Of Student Satisfaction With The Unesa Non-Academic Assessment Information System (SIPENA) Ananda Rizky Abidin; I Kadek Dwi Nuryana
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 4 No. 4 (2023): Vol. 04 Issue 04
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v4i4.56966

Abstract

Sistem Informasi Penilaian NonAkademik UNESA (SIPENA) adalah suatu sistem yang didesain untuk sarana atau fasilitas yang mendukung dalam proses penilaian nonakademik mahasiswa. Dalam upaya mengoptimalkan kepuasan mahasiswa terhadap SIPENA, dibutuhkan sebuah model dengan menggunakan metode dari machine learning dengan membandingkan algoritma terbaik untuk klasifikasi data kepuasan mahasiswa pengguna SIPENA. Pada penelitian ini, peneliti membuat dan menyebarkan kuesioner kepuasan pengguna kepada mahasiswa Universitas Negeri Surabaya berdasar dengan variabel dan indikator yang dibutuhkan yaitu, kualitas sistem, kualitas informasi, kegunaan yang dirasakan dan kepuasan pengguna itu sendiri. Hasil dari kuesioner tersebut diolah dengan perangkat lunak SPSS untuk uji validitas dan reliabilitas. Setelah didapatkan hasil yang valid dan reliabel peneliti melanjutkan mengolah dataset kepuasan pengguna SIPENA pada tools Jupyter Notebook dengan library PyCaret untuk dilakukan klasifikasi dan perbandingan.Hasil penelitian ini menunjukkan bahwa dari 16 model algoritma klasifikasi pada library PyCaret yang dibandingan pada dataset kepuasan pengguna SIPENA, model dari algoritma Extra Trees Classifier adalah yang terbaik dengan nilai akurasi 0.9743, yang kedua adalah algoritma Logistic Regression dengan nilai akurasi 0.9714, dan yang ketiga adalah algoritma Random Forest Classifier dengan nilai akurasi 0.9657.
ANALYSIS OF USER SATISFACTION ON VIDIO APPLICATIONS USING A COMBINATION OF TECHNOLOGY ACCEPTANCE MODEL (TAM) AND FRAMEWORK PIECES: ANALISIS KEPUASAN PENGGUNA PADA APLIKASI VIDIO MENGGUNAKAN KOMBINASI METODE TECHNOLOGY ACCEPTANCE MODEL (TAM) DAN PIECES FRAMEWORK Nurul - Istiqomah; I Kadek Dwi Nuryana
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 4 No. 4 (2023): Vol. 04 Issue 04
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v4i4.57517

Abstract

The development of film technology has also encouraged cinema to develop even more broadly with the presence of video streaming platforms which are now increasing, even involving entertainment industry players. Platform names such as Netflix, Video.com, Hooq, and Amazon Prime are certainly no strangers to movie lovers. The Vidio application is a video-sharing application founded by Adi Sariatmadja in October 2014 and managed by PT Kreatif Media Karya, a subsidiary of Emtek. The Vidio application allows users to upload, watch and share videos. The Vidio application has more than 50 million users on the PlayStore with a rating of 3.9/5.0 and 499 thousand reviews. With the several advantages provided by the Vidio Application, there are still problems that often occur such as a limited choice of films, especially the latest films, some find some films with slow sound and are rather difficult to watch on a cellphone. These problems will affect user satisfaction. This study uses the Technology Acceptance Model (TAM) and Pieces Framework methods to determine user satisfaction with the Vidio application. The factors discussed in the TAM method include Perceived usefulness, Perceived Ease of Use. While the factors used in the Pieces Framework method include Performances, Information and Data, Economics, Control and Security, Efficiency, and Service. Data collection was carried out by distributing questionnaires to 100 people. Based on the data analysis that was carried out using the SmartPLS software, it was found that 93.75% of the Vidio application users were in the very satisfied category.
Teks Ringkas Otomatis pada Portal Berita CNN Indonesia Menggunakan Algoritma Textrank Lizza Nur Fadhila; I Kadek Dwi Nuryana
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 5 No. 1 (2024): Vol. 05 Issue 01
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v5i1.58256

Abstract

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