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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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Implementation of AI Number Generator and A Using GDLC in Android Games Mochammad Ilham Study Wartana Ilham; 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.v6i4.72061

Abstract

The rapid development of information technology has brought significant changes in various aspects of life, including cultural preservation through digital media. Traditional Indonesian games such as sack racing and hide-and-seek have experienced a decline in interest among the younger generation due to modernization. This research aims to develop Android-based offline games of sack racing and hide-and-seek with the implementation of artificial intelligence (AI) using the Random Number Generator (RNG) algorithm for sack racing games and the A* pathfinding model algorithm for hide-and-seek games. The development methodology used is Game Development Life Cycle (GDLC) with Unity as the main game engine. The implementation of RNG in the sack racing game serves to produce dynamic and unpredictable NPC behavior, such as variations in jump speed and movement patterns. While the A* algorithm pathfinding model in hide-and-seek game allows the searcher NPC to find the optimal path in searching for hiding players, creating a realistic and challenging gaming experience. This research uses functional, performance, and user experience testing to evaluate the effectiveness of the AI implementation.
Analysis Of User Satisfaction Towards The Telegram Application Using The EUCS (End User Computing Satisfaction) Method Yulius Candra Akmala; I Kadek Dwi Nuryana
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 7 No. 1 (2026): Vol. 07 Issue 01
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v7i1.72708

Abstract

Telegram is a messaging app that works over the cloud and focuses on being fast and secure. It has useful features like big group chats, public channels, bots, and strong security. Many people use it for talking to friends, working, and sharing information with groups. This study looked at how happy users are with Telegram and what makes them satisfied. The research used an online survey given to Telegram users who are part of the Information Systems Study Program at Surabaya State University's Faculty of Engineering. The study used a method called End User Computing Satisfaction (EUCS). The results showed that users were happy with the content (3.95), accuracy (3.91), format (3.89), ease of use (3.91), and format(3.84). The main things that really affect user satisfaction are how easy the app is to use and the format of the messages.
IOT-BASED AUTOMATIC WATERING SYSTEM FOR TOMATO PLANTS WITH SOIL MONITORING Devanda Yudha Bharagus; I Kadek Dwi Nuryana
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 7 No. 2 (2026): Vol. 07 Issue 02
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v7i2.76210

Abstract

The development of the Internet of Things (IoT) technology has significantly contributed to plant maintenance, particularly in automated irrigation systems. Tomato plants require optimal soil conditions, especially soil moisture and soil pH, to grow properly. The ideal soil pH for tomato plants ranges from 6.0 to 6.8, while soil moisture levels should be maintained between 60% and 80%. However, manual tomato plant care often encounters problems due to a lack of understanding of plant requirements and improper irrigation timing. This study aims to design and develop an automatic tomato irrigation system based on the Internet of Things (IoT) with real-time monitoring of soil moisture and soil pH through a web-based interface. The system uses an ESP32 microcontroller as the main controller, along with a soil moisture sensor and a soil pH sensor. Sensor data are processed to automatically control the water pump based on a predetermined soil moisture threshold. System testing was conducted ten times under various soil conditions. The results show that when the soil moisture value exceeds 80%, the water pump automatically turns off, whereas when the value falls below 80%, the pump turns on to perform irrigation. These results indicate that the system operates according to the defined threshold and is able to maintain optimal soil moisture conditions. The implementation of this system improves the efficiency, effectiveness, and control of tomato plant maintenance.
Analysis of Information Security Awareness Levels Using Multiple Criteria Decision Analysis : Case Study of Digital Transaction Application Users in Surabaya Fauzan Ali Ghofur; I Kadek Dwi Nuryana
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 7 No. 2 (2026): Vol. 07 Issue 02
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v7i2.76571

Abstract

The rapid growth of digital transaction applications has transformed financial activities and increased reliance on electronic payment systems. However, the widespread use of these platforms also exposes users to various information security threats, including phishing, malware, and social engineering attacks that often exploit human vulnerabilities. Despite the increasing adoption of digital financial services, the level of information security awareness among users remains a critical issue that requires systematic evaluation. This study aims to measure and analyze the level of information security awareness among digital transaction application users in Surabaya. This research employs a quantitative approach using Multiple Criteria Decision Analysis (MCDA) integrated with the Knowledge, Attitude, and Behavior (KAB) model. The assessment framework is based on the Human Aspects of Information Security Questionnaire (HAIS-Q), which includes seven focus areas: password management, email usage, internet usage, social media usage, device usage, information handling, and incident reporting. Data were collected through an online questionnaire distributed to digital transaction users in Surabaya, resulting in 102 valid respondents. The results indicate that most focus areas fall into the low awareness category, including password management (53.73%), email usage (23.44%), internet usage (15.99%), social media usage (47.34%), device usage (47.63%), and incident reporting (45.57%). Only the information handling dimension reached a moderate awareness level with a score of 78.17%. These findings highlight the need for improved cybersecurity education and awareness programs to encourage safer digital transaction practices.
Implementasi Sistem Informasi Perpustakaan pada Sekolah Indonesia Davao Filipina Menggunakan CMS SLiMS Bonda Sisephaputra; I Kadek Dwi Nuryana; Aries Dwi Indriyanti; Ghea Sekar Palupi
Abimanyu : Jornal of Community Engagement Vol 6 No 2 (2025): August 2025
Publisher : Universitas Negeri Surabaya

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Abstract

This community service aims to develop a Library Information System at Indonesian School of Davao, Philippines, to improve the efficiency of library management and the accessibility of learning resources for students and teachers. The system was built using the Content Management System (CMS) SLiMS, an open-source platform designed for managing library operations digitally, including inventory recording, book lending, and returns. In implementing this Community Service Program (PKM), the methods used to address the problems at Indonesian School of Davao (SID) were the Diffusion of Science and Technology (Ipteks) and Training. This activity also involved continuous evaluation to ensure the success of the program. The results showed increased efficiency in library management, easier access to information, and improved skills of library staff in using CMS SLiMS. In conclusion, the application of information technology through CMS SLiMS in the library of Indonesian School of Davao has contributed positively to enhancing the teaching and learning process.
Prediksi Kelulusan Tepat Waktu Mahasiswa Menggunakan Artificial Neural Network Berdasarkan Nilai Akademik Dan Kepuasan Penggunaan E-Learning (Studi Kasus: Universitas Negeri Surabaya) Rizky Pratama Syahrul Ramadhan; I Kadek Dwi Nuryana
Journal of Informatics and Computer Science (JINACS) Vol. 7 No. 03 (2026)
Publisher : Universitas Negeri Surabaya

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Abstract

Abstrak— Salah satu elemen utama yang mempengaruhi mutu pendidikan tinggi adalah tingkat kelulusan mahasiswa tepat waktu. Melalui algoritma Jaringan Syaraf Tiruan (ANN), studi ini berfokus untuk menciptakan model yang mampu memprediksi kelulusan mahasiswa dalam waktu yang sesuai pada program Sistem Informasi dan Ilmu Komputer di Universitas Negeri Surabaya. Prediksi didasarkan pada kombinasi data nilai akademik (IPK semester 1-4) dan kepuasan penggunaan e-learning yang diukur menggunakan metode End User Computing Satisfaction (EUCS). EUCS atas lima aspek: content, accuracy, format, ease of use, dan timeliness. Informasi yang dipakai dalam penelitian ini berasal dari 68 siswa angkatan 2018–2022. Untuk menangani keterbatasan jumlah data, pengembangan model melibatkan pengoptimalan hyperparameter menggunakan Optuna dan evaluasi menggunakan Stratified 5-Fold Cross-Validation. Hasil pengujian menunjukkan bahwa model ANN yang dikembangkan sangat akurat, dengan akurasi rata-rata 95,38%, ketepatan 93,33%, recall 96,00%, dan skor F1 94,55%. Hasil ini menunjukkan bahwa integrasi data akademik dan kepuasan pengguna terhadap teknologi pembelajaran dapat menjadi dasar strategi intervensi yang efektif bagi institusi pendidikan.   Kata Kunci— Artificial Neural Network, EUCS, Kelulusan Tepat Waktu, E-learning, Data Mining.
Penyisipan Teks ke dalam Citra Digital menggunakan Kombinasi Beaufort Cipher dan Steganografi Least Significant Bit Muhammad Aswiandi; I Kadek Dwi Nuryana
Journal of Informatics and Computer Science (JINACS) Vol. 7 No. 03 (2026)
Publisher : Universitas Negeri Surabaya

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Abstract

Abstrak— Penelitian ini menerapkan teknik pengamanan pesan teks berlapis dengan mengkombinasikan algoritma kriptografi Beaufort Cipher dan steganografi Least Significant Bit (LSB) pada citra digital. Pesan teks terlebih dahulu dienkripsi menggunakan Beaufort Cipher menghasilkan ciphertext, kemudian ciphertext disisipkan ke dalam citra cover berformat JPG (RGB Color Model) menggunakan metode LSB. Implementasi dilakukan pada aplikasi desktop berbasis Java yang mendukung proses enkripsi, steganografi, ekstraksi, serta fitur chat rahasia real-time. Pengujian dilakukan terhadap variasi panjang pesan 1000, 3000, dan 5000 karakter serta lima citra uji berbeda. Hasil pengujian menunjukkan nilai MSE sangat rendah dengan nilai antara 0.0012 - 0.2047 dan nilai rata-rata PSNR berkisar 55–77 dB (kategori Excellent), serta analisis histogram citra stego tetap seragam. Hal ini membuktikan bahwa citra hasil stego tidak mengalami perubahan visual signifikan dan sulit terdeteksi keberadaan pesan tersembunyi. Metode kombinasi Beaufort Cipher dan LSB efektif memberikan perlindungan berlapis terhadap pesan rahasia pada komunikasi digital.   Kata Kunci— Steganografi, Least Significant Bit, Beaufort Cipher, Citra Digital, Keamanan Informasi, PSNR, MSE
Penerapan Business Intelligence untuk Analisis Penjualan dan Segmentasi Pelanggan Toko Bangunan XYZ Irsyad Adi Rochman; I Kadek Dwi Nuryana
Journal of Informatics and Computer Science (JINACS) Vol. 7 No. 03 (2026)
Publisher : Universitas Negeri Surabaya

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Abstract

Abstrak— Penelitian ini bertujuan untuk mengimplementasikan sistem Business Intelligence (BI) guna meningkatkan kapabilitas analitik pada Toko Bangunan XYZ, dengan fokus pada analisis kinerja penjualan dan segmentasi perilaku pelanggan berbasis data. Metodologi yang diterapkan mencakup tahapan perancangan data warehouse menggunakan metode Nine-Step Kimball dengan MySQL sebagai sistem manajemen basis data, dilanjutkan dengan proses Extract, Transform, Load (ETL) untuk menjamin integritas dan kesiapan data. Analisis dilakukan dengan pendekatan Online Analytical Processing (OLAP) untuk eksplorasi data multidimensi, serta penerapan teknik Data Mining berupa model Recency, Frequency, Monetary (RFM) untuk melakukan segmentasi pelanggan secara kuantitatif. Hasil implementasi menunjukkan bahwa analisis OLAP berhasil mengidentifikasi determinan kinerja penjualan, seperti produk dengan kontribusi volume tertinggi, kategori dengan margin profitabilitas optimal, dan pola temporal dalam tren pembelian. Sementara itu, segmentasi RFM menghasilkan pengelompokan pelanggan yang berbeda secara statistik ke dalam klaster High Value, Potential, Loyal dan Dormant. Seluruh temuan kemudian diwujudkan dalam bentuk dashboard analitik interaktif menggunakan Looker Studio, yang berfungsi sebagai alat bantu keputusan bagi manajemen. Simpulan penelitian mengindikasikan bahwa implementasi sistem BI ini secara efektif mentransformasi data operasional menjadi insight strategis, sehingga mendukung pengambilan keputusan yang lebih terinformasi dan berbasis bukti.   Kata Kunci— Business Intelligence, RFM, OLAP, Data Mining, Looker Studio.
Implementasi Algoritma K-Means untuk Analisis Segmentasi Pelanggan dengan Menggunakan Model RFM dan CRISP-DM Yuninda Intan; I Kadek Dwi Nuryana
Journal of Informatics and Computer Science (JINACS) Article In Press(1)
Publisher : Universitas Negeri Surabaya

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Abstract

Abstrak— Segmentasi pelanggan merupakan salah satu strategi yang dapat digunakan perusahaan untuk memahami karakteristik pelanggan sehingga penyusunan strategi pemasaran menjadi lebih efektif dan tepat sasaran. Penelitian ini bertujuan untuk menerapkan model Recency, Frequency, Monetary (RFM) dan algoritma K-Means dalam melakukan segmentasi pelanggan menggunakan pendekatan Cross Industry Standard Process for Data Mining (CRISP-DM) pada Online Retail Refined Dataset. Tahapan penelitian meliputi Business Understanding, Data Understanding, Data Preparation, Modeling, dan Evaluation. Data yang digunakan dibatasi pada atribut CustomerID (ID Pelanggan), InvoiceDate (Tanggal Transaksi), InvoiceNo (Nomor Transaksi), dan TotalPrice (Harga Total) untuk membentuk nilai RFM. Penentuan jumlah cluster dilakukan menggunakan Elbow Method, sedangkan kualitas hasil clustering dievaluasi menggunakan Silhouette Score. Hasil penelitian menunjukkan bahwa jumlah cluster terbaik adalah tiga cluster dengan nilai Silhouette Score sebesar 0,4172 dan Davies-Bouldin Index  sebesar  0,8169, yang menunjukkan kualitas cluster cukup baik. Ketiga cluster yang dihasilkan terdiri atas Pelanggan Loyal, Pelanggan Potensial, dan Pelanggan Tidak Aktif, yang masing-masing memiliki karakteristik berbeda berdasarkan nilai Recency, Frequency, dan Monetary. Hasil segmentasi ini menunjukkan bahwa kombinasi model RFM dan algoritma K-Means mampu mengelompokkan pelanggan sesuai karakteristik perilaku pembelian sehingga dapat menjadi dasar dalam mendukung pengambilan keputusan pemasaran yang lebih tepat sasaran.   Kata Kunci— Data Mining, Segmentasi Pelanggan, CRISP-DM, RFM, K-Means.
Implementasi XGBoost Pada Prediksi Penjualan Produk Untuk Pengoptimalan Manajemen Persediaan Khiena Salsabiila Susanty; I Kadek Dwi Nuryana
Journal of Informatics and Computer Science (JINACS) Article In Press(1)
Publisher : Universitas Negeri Surabaya

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Abstract

Abstrak— Pengelolaan persediaan yang kurang optimal dapat menyebabkan terjadinya kelebihan maupun kekurangan stok sehingga berdampak pada efisiensi operasional bisnis ritel. Salah satu pendekatan yang dapat digunakan untuk mendukung pengelolaan persediaan adalah memanfaatkan prediksi penjualan berbasis machine learning. Penelitian ini bertujuan mengimplementasikan algoritma eXtreme Gradient Boosting (XGBoost) untuk memprediksi penjualan produk sebagai dasar dalam penyusunan ilustrasi rekomendasi stok. Penelitian menggunakan metodologi Cross-Industry Standard Process for Data Mining (CRISP-DM) yang meliputi tahap Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, dan Deployment. Model dibangun menggunakan atribut Price, Rating, Product_Category, Product_Brand, Month, dan Day sebagai variabel prediktor, sedangkan Quantity digunakan sebagai variabel target. Hasil evaluasi menunjukkan nilai Mean Absolute Error (MAE) sebesar 1,2414, Root Mean Squared Error (RMSE) sebesar 1,4363, dan Coefficient of Determination (R²) sebesar 0,0023. Nilai R² yang rendah menunjukkan bahwa atribut yang digunakan belum memiliki daya prediktif yang memadai untuk menjelaskan variasi jumlah penjualan. Pada tahap deployment, hasil prediksi dimanfaatkan untuk menghasilkan ilustrasi rekomendasi stok berdasarkan kategori dan merek produk sebagai bentuk implementasi metodologi CRISP-DM. Namun, ilustrasi tersebut belum dapat dijadikan dasar pengambilan keputusan operasional karena kemampuan prediksi model masih terbatas. Hasil penelitian menunjukkan bahwa penambahan fitur yang lebih relevan, seperti data deret waktu (time-series) dan faktor eksternal lainnya, diperlukan untuk meningkatkan performa model prediksi penjualan.   Kata Kunci— eXtreme Gradient Boosting (XGBoost), Prediksi Penjualan, Manajemen Persediaan, CRISP-DM, Machine Learning.
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