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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Jurnal Pendidikan Teknologi dan Kejuruan KARMAPATI (Kumpulan Artikel Mahasiswa Pendidikan Teknik Informatika) ISSN: 2252-9063 Jurnal Sains dan Teknologi Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Informatika dan Teknik Elektro Terapan Jurnal Sistem dan Informatika Jurnas Nasional Teknologi dan Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Artificial Intelligence and Data Mining SINTECH (Science and Information Technology) Journal International Journal of Natural Science and Engineering Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JURNAL ILMU KOMPUTER INDONESIA Jurnal Teknologi Informasi dan Multimedia Jurnal Sistem Komputer & Kecerdasan Buatan Jurnal Pendidikan Teknik Mesin Undiksha Jurnal Teknik Informatika (JUTIF) INSERT: Information System and Emerging Technology Journal Brilliance: Research of Artificial Intelligence MATRIX : JURNAL MANAJEMEN TEKNOLOGI DAN INFORMATIKA Paradigma YASIN: Jurnal Pendidikan dan Sosial Budaya Malcom: Indonesian Journal of Machine Learning and Computer Science STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Journal of Artificial Intelligence and Digital Business Journal Of Artificial Intelligence And Software Engineering
Kadek Yota Ernanda Aryanto
Universitas Pendidikan Ganesha
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Social Sciences Other

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Android-based decision support system using MAGIQ-MARCOS for digital bank selection Gede Surya Mahendra; Mahendra, Gede Surya; I Nyoman Indhi Wiradika; Wiradika, I Nyoman Indhi; Aryanto, Kadek Yota Ernanda
Matrix : Jurnal Manajemen Teknologi dan Informatika Vol. 15 No. 3 (2025): Matrix: Jurnal Manajemen Teknologi dan Informatika
Publisher : Unit Publikasi Ilmiah, P3M, Politeknik Negeri Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31940/matrix.v15i3.148-162

Abstract

Choosing a digital bank is a challenge for anyone, especially due to cognitive biases that influence decision making. This study aims to develop an Android-based Decision Support System (DSS) using the MAGIQ MARCOS method to provide recommendations for digital banks that suit users’ preferences. The MAGIQ method is used to weight the main criteria, namely Application Performance (C1), Financial Reports (C2), and User Experience (C3), while MARCOS is used to rank digital banking alternatives. This study includes data collection through surveys and interviews, data processing using MAGIQ for weighting, and ranking alternatives using MARCOS. The results indicate that Jenius ranked first with a preference value of 0.7632 followed by Seabank with 0.7164 and Krom Digital Bank with 0.6983. These findings show that the system is able to differentiate alternatives based on user priorities. The system achieved an accuracy of 80.39 percent compared with students’ manual selections confirming that the recommendations align with actual user preferences. The recommendations generated by the system are consistent with the priorities of decision makers who value application quality and user experience. Use case testing also shows that all test scenarios function as expected. This research contributes to the development of technology based DSS to help students make more rational and data driven decisions in choosing a digital bank. Future work may integrate real time data updates and predictive analysis to improve recommendation accuracy and expand the MAGIQ-MARCOS method to other sectors that require multi criteria decision making.
SEGMENTASI BANGUNAN PERKOTAAN PADA CITRA SATELIT BERESOLUSI TINGGI: CNN, U-NET (VGG16), DAN DEEPLABV3+ (RESNET-50) Putu Haryaka Setadewa; Kadek Yota Ernanda Aryanto; Luh Joni Erawati Dewi
STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Vol. 4 No. 4 (2025): November
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/storage.v4i4.6552

Abstract

Seiring meningkatnya laju urbanisasi di Indonesia, kebutuhan pemetaan bangunan yang akurat menjadi semakin penting untuk mendukung perencanaan tata ruang, mitigasi bencana, dan pengelolaan infrastruktur perkotaan. Pendekatan konvensional berbasis survei manual dinilai kurang efisien, terutama di wilayah dengan pertumbuhan pesat. Oleh karena itu, pemanfaatan citra satelit dan Deep learning menjadi solusi potensial untuk identifikasi bangunan secara otomatis. Penelitian ini membandingkan performa tiga model segmentasi bangunan pada citra satelit resolusi tinggi: CNN konvensional (CNN-K), U-Net berbasis VGG16 (U-VGG), dan DeepLabV3+ dengan ResNet-50 (DL-ResNet). Dataset terdiri atas 1.216 patch citra dari kawasan Bali Selatan yang telah dilabeli dan diaugmentasi. Evaluasi dilakukan menggunakan metrik akurasi, IoU, dice coefficient, precision, recall, dan F1-score. Hasil menunjukkan U-VGG unggul (dice 89%, IoU 81%) dengan keseimbangan presisi dan efisiensi, sementara DL-ResNet mendekati hasilnya (dice 85%, IoU 80%) tetapi memerlukan sumber daya komputasi lebih besar. CNN-K mengalami overfitting dengan performa terendah.
OPTIMASI MODEL PREDIKSI EXTREME GRADIENT BOOSTING DENGAN GENETIC ALGORITHM UNTUK PRODUKSI DAN PRODUKTIVITAS PADI Wirakusuma, Kadek Ardy; Ni Putu Novita Puspa Dewi; Kadek Yota Ernanda Aryanto
STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Vol. 4 No. 4 (2025): November
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/storage.v4i4.6633

Abstract

Produksi padi di Kabupaten Buleleng meningkat dari tahun 2021 hingga 2023, namun produktivitas justru menurun hingga 8%. Kondisi ini berpotensi mengganggu stabilitas pasokan beras di tengah pertumbuhan penduduk sebesar 4,59% per tahun. Diperlukan pendekatan prediktif berbasis kecerdasan buatan untuk memodelkan hubungan kompleks antar variabel pertanian. XGBoost dipilih karena kemampuannya dalam menangkap pola non-linear dan sering digunakan dalam analisis data pertanian, sementara Genetic Algorithm (GA) digunakan untuk menentukan kombinasi hiperparameter optimal guna meningkatkan performa model. Model XGBoost tanpa optimasi diterapkan sebagai pembanding untuk mengevaluasi efektivitas pendekatan hybrid. Hasil analisis menunjukkan bahwa optimasi hiperparameter berpengaruh signifikan terhadap hasil prediksi. Model GA-XGBoost menghasilkan tingkat kesalahan lebih rendah, dengan penurunan nilai MAPE sekitar 2,98% untuk prediksi produksi padi dan 0.21% untuk produktivitas dibandingkan dengan model standar atau default.
ANALISIS KOMPARATIF U-NET ATTENTION DAN RESNET-50 UNTUK SEGMENTASI SEMANTIK SUNGAI PADA CITRA PENGINDERAAN JAUH Wijaya, Gede Andra Rizqy; Ni Putu Novita Puspa Dewi; Kadek Yota Ernanda Aryanto
STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Vol. 4 No. 4 (2025): November
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/storage.v4i4.6637

Abstract

Pemantauan potensi banjir sungai merupakan langkah penting dalam mitigasi risiko bencana dan perencanaan tata ruang wilayah. Dengan perkembangan zaman dan meningkatnya ketersediaan data citra satelit dengan resolusi tinggi yang mengikutinya, pemantauan potensi banjir bisa dilakukan dengan metode yang terkini, seperti dengan menggunakan metode deep learning, terutama pada semantic segmentation. Dengan menggunakan model semantic segmentation yang berbasiskan deep learning, pemantauan sungai dalam langkah mitigasi risiko bencana alam bisa dilakukan dengan lebih fleksibel. Dengan menggunakan arsitektur ResNet-50 dan U-Net Attention, didapatkan akurasi yang tinggi, dimana masing-masing arsitektur mencapai tingkat akurasi hingga lebih dari 90%. Model dilatih menggunakan data sungai yang sudah dilakukan masking pada bagian sungainya saja, tidak menghiraukan wilayah lainya seperti genangan air besar lain yang berada diluar area sungai. Dalam penelitian ini juga dapat disimpulkan bahwa arsitektur U-Net Attention memiliki akurasi dan juga ketepatan yang lebih baik dibandingkan dengan arsitektur ResNet-50. Implementasi hasil dari penelitian ini diharapkan dapat mendukung pengembangan sistem pemantauan banjir berbasis teknologi, sehingga bisa meningkatkan kesiapsiagaan dan efisiensi dalam mitigasi bencana alam.
IMPLEMENTASI SISTEM INFORMASI TOKO ONLINE PADA TOKO LOVELACE BALI Riswan Alfin Jaya Arsana; Kadek Yota Ernanda Aryanto; Ni Wayan Marti
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 1 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i1.9110

Abstract

Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem informasi toko online pada Toko LoveLace Bali sebagai solusi digital dalam menghadapi tantangan era modern. Metode yang digunakan adalah model pengembangan perangkat lunak Waterfall yang mencakup tahapan kebutuhan sistem, perancangan, implementasi dan pengujian, integrasi, serta pemeliharaan. Sistem dirancang menggunakan berbagai diagram seperti Use Case Diagram, Activity Diagram, Sequence Diagram, ERD, serta UI Wireframe dan arsitektur sistem. Teknologi yang diadopsi meliputi Laraveluntuk backend, ReactJS dengan InertiaJS untuk frontend, serta MySQL sebagai basis data. Pengujian dilakukan menggunakan metode Blackbox dan System Usability Scale (SUS) dengan melibatkan 23 responden. Hasil pengujian menunjukkan skor SUS sebesar 71,41 yang termasuk dalam kategori “baik”, menandakan bahwa sistem memiliki tingkat kegunaan yang tinggi dan dapat memberikan pengalaman pengguna yang memadai. Sistem ini mendukung operasional toko seperti manajemen produk, proses pemesanan, hingga promosi online. Dengan demikian, sistem informasi toko online ini berhasil memenuhi kebutuhan fungsional dan non-fungsional, serta dapat digunakan sebagai sarana untuk meningkatkan efisiensi dan efektivitas penjualan di Toko LoveLace Bali.
Development of a Web Extended Reality (WEBXR) Based Smart Farming Simulation Game for Soybean Cultivation Gede Sri Yuniarta; I Gede Partha Sindu; Kadek Yota Ernanda Aryanto
YASIN Vol 6 No 3 (2026): YASIN: Jurnal Pendidikan dan Sosial Budaya
Publisher : Lembaga Yasin AlSys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/yasin.v6i3.10173

Abstract

Although digital simulation media in agriculture have received considerable scholarly attention, limited research has specifically examined the development of Web Extended Reality (WebXR)-based smart farming educational simulations to address the low regeneration of young farmers. This study aims to design and develop a WebXR-based smart farming simulation game for soybean cultivation as an interactive learning medium accessible through WebXR-enabled browsers and to evaluate its feasibility and user experience. This study employed a Research and Development (R&D) approach using the Multimedia Development Life Cycle (MDLC) model, consisting of concept, design, material collection, assembly, testing, and distribution stages. Data were collected through observation, interviews, and literature review, involving 50 student respondents from Universitas Pendidikan Ganesha through direct application testing. User experience data were obtained using the Game Experience Questionnaire (GEQ), covering In-Game and Post-Game modules. The findings show that all system functions operated properly based on black box testing, and the application was declared highly valid by content experts (1.00) and media experts (1.00). GEQ results for the In-Game components showed competence at 2.88 (high/good), sensory and imaginative immersion at 2.90 (high/good), flow at 2.37 (moderate/fair), challenge at 2.74 (high/good), tension/annoyance at 1.33 (low), positive affect at 3.21 (very high), and negative affect at 0.43 (very low). The Post-Game components showed positive experience at 3.07 (high/good), negative experience at 0.30 (very low), tiredness at 0.44 (very low), and returning to reality at 0.95 (low). The study concludes that the WebXR-based smart farming simulation game is feasible for broader implementation and effective in providing an immersive, enjoyable, and interactive learning experience for soybean cultivation. These findings contribute to agricultural education technology by demonstrating the potential of WebXR-based simulation games as modern digital learning media to support smart farming literacy and encourage youth engagement in agriculture.
Pemanfaatan ARCore Geospatial untuk Simulasi Banjir sebagai Media Edukasi Kebencanaan I Gede Dhananjaya; Kadek Yota Ernanda Aryanto; I Gede Partha Sindu
YASIN Vol 6 No 3 (2026): YASIN: Jurnal Pendidikan dan Sosial Budaya
Publisher : Lembaga Yasin AlSys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/yasin.v6i3.10421

Abstract

Although Augmented Reality (AR) technology has been widely applied in various educational contexts, studies that specifically integrate ARCore Geospatial technology for location-based flood simulation as a disaster education medium remain limited. This study aims to develop and evaluate an ARCore Geospatial-based flood simulation application as a disaster education medium. This study used a Research and Development (R&D) approach with the Multimedia Development Life Cycle (MDLC) model, involving 30 students selected using a convenience sampling technique. Data were collected through black-box testing, expert validation instruments, and the User Experience Questionnaire (UEQ), then analyzed using functional testing results, Gregory validity analysis, and descriptive UEQ analysis. The results show that all application features function according to the development objectives. The expert validation results show that the media and application design aspects received very high ratings, although several suggestions were provided to improve the content aspect. In addition, the UEQ results show a positive user experience across all evaluated dimensions, namely attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty. These findings contribute to the development of disaster education media by demonstrating the potential of geospatial-based Augmented Reality in supporting contextual and interactive learning experiences. The conclusion of the study affirms that location-based flood simulation through ARCore Geospatial can support disaster education and increase flood mitigation awareness through contextual and interactive visualization. The implications of this study provide practical contributions for educational institutions and disaster stakeholders in developing educational strategies that are more engaging, innovative, and location-based. Future research is recommended to integrate real-time environmental data and expand the application implementation to broader geographical areas.
Pengembangan Model Hybrid Untuk Deteksi Serangan XSS Menggunakan Algoritma Random Forest dan BERT Anggaradiva Bendesa; Kadek Yota Ernanda Aryanto; I Nyoman Saputra Wahyu Wijaya
KARMAPATI (Kumpulan Artikel Mahasiswa Pendidikan Teknik Informatika) Vol. 15 No. 2 (2026): Karmapati Vol 15 No 2 Tahun 2026
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/karmapati.v15i2.116372

Abstract

Cross-Site Scripting (XSS) merupakan serangan pada aplikasi web yang memanfaatkan kelemahan validasi input untuk menyisipkan skrip berbahaya. Variasi payload seperti event handler, encoding, obfuscation, dan JavaScript URI menyebabkan proses deteksi menjadi lebih kompleks. Penelitian ini bertujuan mengembangkan model hybrid untuk deteksi payload XSS dengan menggabungkan Random Forest dan BERT. Random Forest dilatih menggunakan TF-IDF berbasis karakter dan fitur statistik, sedangkan BERT digunakan melalui proses fine-tuning untuk klasifikasi teks. Model hybrid dibangun dengan menggabungkan skor prediksi dari kedua model. Hasil evaluasi menunjukkan bahwa Random Forest dan BERT memperoleh akurasi sebesar 99,21%, sedangkan model hybrid memperoleh akurasi sebesar 99,28% pada data uji. Hasil ini menunjukkan bahwa pendekatan hybrid dapat digunakan sebagai metode pendukung deteksi payload XSS.
Development Of a YOLOV4-Based Orange Fruit Detection Model for Orange Counting Using the Coco Dataset I Gusti Ngurah Surya Ardika Dinataputra; Kadek Yota Ernanda Aryanto; Anak Agung Gede Yudhi Paramartha; I Made Gede Sunarya
INSERT : Information System and Emerging Technology Journal Vol. 7 No. 1 (2026)
Publisher : Information System Study Program, Faculty of Engineering and Vocational, Undiksha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/insert.v7i1.117660

Abstract

Orange counting during harvest season is important because pricing decisions depend on estimated yield, and miscalculation can disadvantage both farmers and traders. This study developed an orange fruit detection model using YOLOv4 and training data derived from the Microsoft COCO dataset. The problem context was established through interviews with Siam orange farmers in Belantih, Kintamani, Bangli, where counting errors were reported to reduce expected income. For model development, two training configurations were compared: a one-class model trained only on orange images and a two-class model trained on orange and apple images. Field testing images were collected in an orchard in Penikit, Badung, using the same Siam orange type targeted in the study. Evaluation was divided into three subsets: 30 orange-only images for one-class mAP evaluation, 60 mixed orange-apple images for two-class mAP evaluation, and 20 orange images for final manual counting validation. At IoU 0.50, the one-clas model achieved 85.78% mAP, whereas the two-class model achieved 33.52%. Manual counting on 20 images yielded 82% accuracy for the one-class model and 55% for the two-class model. These results show that a focused one-class formulation is more suitable for orchard-side orange counting under the constraints of this study.
Implementation of MQTT Broker and Gemini API in an Internet of Things Based Indoor Air Pollution Monitoring System I Putu Tude Rama Prasatya; Ketut Agus Seputra; Kadek Yota Ernanda Aryanto
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.v5i2.10874

Abstract

Indoor air pollution from pollutants such as Carbon Monoxide (CO) and Particulate Matter (PM2.5) poses significant health risks to room occupants, particularly in enclosed spaces with poor ventilation where pollutants can accumulate to hazardous concentrations. To address this challenge, this study designed and implemented an IoT-based indoor air quality monitoring system integrating an MQTT broker for real-time data transmission and the Gemini API for intelligent data interpretation. The system adopts a three-layer architecture spanning hardware, backend, and application layers. The hardware layer utilizes an ESP32 as a wireless gateway and an Arduino Nano for sensor acquisition, employing the Sharp GP2Y1010AU0F sensor for particulate matter and the MQ-7 sensor for carbon monoxide. The backend, built with Laravel, manages data through a dual-database approach, where MySQL handles structured user data and InfluxDB stores continuous sensor readings. A Flutter mobile application, built with the MVVM pattern, serves as the user interface, delivering real-time air quality information. The Gemini API further enhances the system by automatically generating air quality classifications and actionable health recommendations. Calibration testing demonstrated an average error of 6.45% for the MQ-7 sensor and a notably low 0.44% error for the Sharp GP2Y1010AU0F sensor, indicating high measurement accuracy. A 24-hour continuous stress test revealed a system uptime of 83.3%, confirming reasonable operational reliability. Finally, a usability evaluation using the SUS method involving 30 respondents yielded an average score of 70.67, placing the system in Grade B with a "Good" interpretation, confirming the system is functional and easy to use.
Co-Authors ., I Wayan Adi Sumertama A. A. Gede Yudhi Paramartha Agus Ariwanta, I Putu Yesha Agus Seputra I Ketut Anak Agung Gede Yudhi Paramartha Anggaradiva Bendesa Aryani, Luh Nitra Budiana, I Wayan Gabriel Nathanael Purba Gede Aditra Pradnyana Gede Indrawan Gede Rasben Dantes Gede Sri Yuniarta Gede Surya Mahendra Gede Surya Mahendra Gede Suweken GUSTI NGURAH MADE AGUS WIBAWANTARA . I Gede Aris Gunadi I Gede Dhananjaya I Gede Mahendra Darmawiguna I Gede Nyoman Agung Jayarana I Gusti Agung Ayu Sekarini I Gusti Made Wahyu Krisna Widiantara I Gusti Ngurah Surya Ardika Dinataputra I Ketut Eddy.P I Ketut Resika Arthana I Made Agus Widiana Putra I Made Candiasa I Made Edy Listartha I Made Gede Sunarya I Nyoman Indhi Wiradika I Nyoman Saputra Wahyu Wijaya I Nyoman Sukajaya I Putu Eka Sutariawan I PUTU EKA SUTARIAWAN . I Putu Tude Rama Prasatya I WAYAN GEDE SABDANA, S.KOM . Ida Bagus Prayoga Bhiantara Indriyani, Ni Luh Putu Ratih Indriyani, Ni Luh Putu Ratih Kadek Rihendra Dantes Kafabi, Moh Iqbal Luh Joni Erawati Dewi Luh Nitra Aryani Luh Putu Wiwien Widhyastuti M.Cs S.Kom I Made Agus Wirawan . Made Ari Sucahyana Made Windu Antara Kesiman Ni Ketut Pradani Gayatri Sarja Ni Made Rai Masita Dewi Ni Nyoman Mestri Agustini Ni Putu Novita Puspa Dewi Ni Wayan Marti Ony Andewi, Putu Purba, Gabriel Nathanael Pusparani, Diah Ayu Putu Alan Arismandika Putu Gede Surya Cipta Nugraha Putu Haryaka Setadewa Riswan Alfin Jaya Arsana Sanjaya, Kadek Oki Sanjaya, Kadek Oki Sari, Ni Ketut Ayu Purnama Sindu, I Gede Partha Siti Saibah Pua Luka Siti Saibah Pua Luka Sukajaya, I N. Sumertama, I Wayan Adi Suryaningsih, Gusti Ketut Suryaningsih, Gusti Ketut Taufik Akbar Taufik Akbar Trywanto Rina Wiani, Ni Wayan Yulya Widhiyanti Metra Putri, Dewi Arum Widhiyanti, Anak Agung Sandatya Widiantara, I Gusti Made Wahyu Krisna Wijaya, Gede Andra Rizqy Wiradika, I Nyoman Indhi Wirakusuma, Kadek Ardy Yudistira, Bagus Gede Krishna