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All Journal Jurnal Pendidikan Teknologi dan Kejuruan KARMAPATI (Kumpulan Artikel Mahasiswa Pendidikan Teknik Informatika) ISSN: 2252-9063 Jurnal Sains dan Teknologi Techno.Com: Jurnal Teknologi Informasi Jurnal Pseudocode Jurnal Informatika dan Teknik Elektro Terapan CESS (Journal of Computer Engineering, System and Science) Sistemasi: Jurnal Sistem Informasi Jurnal Pilar Nusa Mandiri JUTIK : Jurnal Teknologi Informasi dan Komputer SINTECH (Science and Information Technology) Journal INTECOMS: Journal of Information Technology and Computer Science MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal RESISTOR (Rekayasa Sistem Komputer) INFOMATEK: Jurnal Informatika, Manajemen dan Teknologi International Journal of Engineering and Emerging Technology Jurnal Ekonomi Manajemen Sistem Informasi INSERT: Information System and Emerging Technology Journal Prosiding SNAST Jurnal Info Sains : Informatika dan Sains Andalasian International Journal of Applied Science, Engineering, and Technology Jurnal Pendidikan Sains dan Komputer Jurnal Krisnadana Journal of Artificial Intelligence and Digital Business Jurnal Krisnadana Paradharma: Jurnal Aplikasi IPTEK Jurnal ilmiah teknologi informasi Asia JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
I Gusti Ayu Agung Diatri Indradewi
Universitas Pendidikan Ganesha
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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Recognize The Polarity of Hotel Reviews using Support Vector Machine Ni Wayan Sumartini Saraswati; I Gusti Ayu Agung Diatri Indradewi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i1.1848

Abstract

A brand is very dependent on consumer perceptions of the product or services. In assessing consumer perceptions of products and services, companies are often faced with data analysis problems. One of the data that is very useful to produce a picture of consumer perceptions of the products and services is review data. So that the company's ability to process review data means that the company has a picture of the strength of the brand it has. Some of the most popular machine learning algorithms for creating text classification models include the naive Bayes family of algorithms, support vector machines (SVM) and deep learning algorithms. In this research, SVM has been proven to be a reliable method in pattern recognition. In particular, this study aims to produce a model that can be used to classify the polarity of hotel reviews automatically. The experimental data comes from review data on hotels in Europe sourced from TripAdvisor with a total of 38000 reviews. We also measure the quality of the classification engine model. The test results of the SVM model built from hotel review data are quite good. The average accuracy of the classification engine is 92.48%. Because the recall and precision values ​​are balanced, the accuracy value is considered sufficient to describe the quality of the classification.
Comparison of MOORA and WASPAS in the Banyuwangi Nature Tourism Selection DSS Febiasterina, Dyapradita Eka; Indradewi, I Gusti Ayu Agung Diatri; Mahendra, Gede Surya
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 1 (2026): Februari - April
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i1.6063

Abstract

This study compares two multi-criteria Decision Support System (DSS) methods, Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) and Weighted Aggregated Sum Product Assessment (WASPAS), for ranking natural tourism destinations in Banyuwangi Regency, Indonesia. Using a quantitative design, survey data were collected from 50 respondents who assessed 48 destinations using five criteria like facilities, entrance fee, safety, travel distance, and cleanliness. The analysis followed the CRISP-DM framework from business understanding through evaluation and interpretation. The MOORA method applied vector normalization and benefit cost optimization, while the WASPAS method combined weighted sum and weighted product models to produce preference scores. Results show that Bangsring Underwater emerged as the most competitive destination overall, achieving preference values of 0.1932 using MOORA and 0.6837 using WASPAS for Decision Maker 1. Sensitivity testing across ten weight variation scenarios indicated that WASPAS showed stronger individual level dominance, ranking the top alternative first in 8 of 10 scenarios, while MOORA ranked first in 7 of 10 scenarios. However, when extended to all respondents, MOORA demonstrated higher population level robustness and slightly higher average accuracy at 51.61% than WASPAS at 50.32%. These findings indicate a trade-off between stability and responsiveness. MOORA is preferable for generalized tourism planning involving diverse stakeholders, while WASPAS is better suited for adaptive or personalized recommendation contexts.
EVALUASI KUALITAS LAYANAN PLATFORM MICROBLOGGING MENGGUNAKAN WEBQUAL 4.0 DAN IMPORTANCE PERFORMANCE ANALYSIS: STUDI KOMPARATIF WEBSITE X (TWITTER) DAN THREADS Gusti Putu Yastika Putra; I Gusti Lanang Agung Raditya Putra; I Gusti Ayu Agung Diatri Indradewi
INTECOMS: Journal of Information Technology and Computer Science Vol. 9 No. 2 (2026): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/ya9r5j42

Abstract

Penelitian ini bertujuan mengevaluasi dan membandingkan kualitas layanan platform microblogging X (Twitter) dan Threads dengan menerapkan kerangka kerja WebQual 4.0 yang mencakup tiga dimensi utama: usability, information quality, dan service interaction quality. Pendekatan penelitian bersifat kuantitatif dengan melibatkan 100 responden mahasiswa Universitas Pendidikan Ganesha yang aktif menggunakan kedua platform tersebut. Analisis data dilakukan melalui Importance Performance Analysis (IPA) dan uji Paired Sample t-Test menggunakan IBM SPSS Statistics 25. Hasil penelitian menunjukkan bahwa skor rata-rata keseluruhan platform X sebesar 4,12 melampaui Threads yang memperoleh 3,78. Pada dimensi usability, X meraih 4,18 berbanding 3,85 untuk Threads; pada information quality, X memperoleh 4,10 sedangkan Threads 3,72; dan pada service interaction quality, X mencapai 4,08 berbanding 3,76. Uji Paired Sample t-Test menghasilkan nilai signifikansi 0,000 (p < 0,05), mengkonfirmasi perbedaan yang bermakna antara kedua platform. Pemetaan IPA menunjukkan mayoritas atribut X berada di Kuadran II (pertahankan kinerja), sementara beberapa atribut Threads masih berada di Kuadran I (prioritas perbaikan). Temuan ini mengindikasikan bahwa X dinilai memiliki standar kualitas yang lebih tinggi meskipun Threads lebih banyak dipilih sebagai platform utama dalam pra-survei.
Comparative Analysis of Machine Learning Models for Early Dengue Detection Using Clinical Symptoms Made Dwi Aprillia Kusuma Wiryani; I Gusti Ayu Agung Diatri Indradewi; Putu Hendra Suputra
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.v15i2.115451

Abstract

Early detection of dengue fever remains a clinical challenge without prior laboratory examination, due to its overlapping symptoms with other febrile conditions, most notably high body temperature. By observing these symptom similarities, this study proposes a comprehensive comparison of machine learning algorithms using a classification-based early detection approach with Random Forest, Gradient Boosting, Support Vector Machine, and Decision Tree. The dataset was obtained from a regional hospital with ethical clearance, consisting of 212 records collected in January 2025, with 11 selected clinical features including body temperature, fever duration, pain, nausea, vomiting, cough, flu, rash, headache, nosebleed, and gum bleed. The models were evaluated using accuracy, precision, recall, and F1-score, both before and after hyperparameter tuning. GridSearchCV was applied to identify the optimal hyperparameter combination for each model. In this study, recall is prioritized as the primary evaluation metric to ensure the model performs well in minimizing missed dengue cases. The results demonstrated that SVM achieved the best performance across all metrics except precision, both before and after tuning. These findings suggest that SVM is the most suitable model for clinical early detection of dengue fever using symptom-based data.
PERBANDINGAN PERFORMA TF-IDF DAN BOW PADA ANALISIS SENTIMEN BPJS KESEHATAN MENGGUNAKAN XGBOOST Made Donita Maharani; I Gusti Ayu Agung Diatri Indradewi; I Nyoman Saputra Wahyu Wijaya
Jurnal Pendidikan Teknologi dan Kejuruan Vol. 23 No. 1 (2026): Edisi Januari 2026
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jptk-undiksha.v23i1.109604

Abstract

Penelitian ini bertujuan untuk membandingkan kinerja metode ekstraksi fitur Term Frequency–Inverse Document Frequency (TF-IDF) dan Bag of Words (BoW) menggunakan algoritma XGBoost dalam analisis sentimen masyarakat terhadap BPJS Kesehatan berdasarkan ulasan pada platform X. Tahapan penelitian meliputi preprocessing teks, ekstraksi fitur, penanganan ketidakseimbangan kelas, serta pengujian model menggunakan tujuh skenario dengan validasi 10-fold dan pembagian data 80:20. Hasil pengujian menunjukkan bahwa kombinasi TF-IDF dan XGBoost memperoleh rata-rata akurasi sebesar 78,63%, presisi 70,30%, recall 65,68%, dan F1-score 67,12%. Sementara itu, kombinasi BoW dan XGBoost menghasilkan akurasi 78,52%, presisi 68,53%, recall 70,23%, dan F1-score 69,14%. Hasil visualisasi word cloud menunjukkan bahwa sentimen negatif lebih dominan dengan kata-kata seperti “lama”, “antri”, dan “buruk”, sedangkan sentimen positif didominasi oleh kata “baik” dan “bagus”. Secara keseluruhan, TF-IDF lebih unggul dalam akurasi dan presisi, sedangkan BoW lebih efektif dalam meningkatkan recall dan F1-score pada analisis sentimen BPJS Kesehatan.
Klasifikasi Severity Level Diabetic Macular Edema Berbasis ResNet-50 I Made Dendi Maysanjaya; Putu Yudia Pratiwi; I Gusti Ayu Agung Diatri Indradewi
Jurnal Pseudocode Vol 13 No 1 (2026): Volume 13 Nomor 1 Februari 2026
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/pseudocode.13.1.9-13

Abstract

Diabetes is one of the most common diseases people suffer from today, and it can lead to complications such as blindness, heart disease, and kidney failure. The condition of blindness caused by this disease is known as diabetic retinopathy (DR). An ophthalmologist will use a fundus camera to examine the retina, looking for several clinical features, such as microaneurysms (MA), hemorrhages (HM), cotton-wool spots (CWS), and exudates. Based on these clinical symptoms, clinicians then determined the patient's level of diabetic macular edema (DME) severity. Although several studies have applied CNN-based architectures for diabetic retinopathy detection, limited attention has been given to the impact of dataset imbalance handling on DME severity classification, particularly using ResNet-50. This study highlights the significant impact of extensive data augmentation on classification performance in imbalanced DME datasets. Evaluate performance using the accuracy, precision, and recall metrics. We used the IDRiD dataset, which consists of 516 images split into a training set of 413 and a test set of 103. IDRiD divides the dataset into three classes, namely normal, moderate DME, and severe DME. In the preprocessing stage, we enhanced contrast using CLAHE and resized the images to 224x224 pixels. To address the imbalance, we applied 11 data augmentation methods. We experimented by comparing the performance of two models: one with and one without dataset augmentation. Based on the test results, the best performance was obtained with the model that included dataset augmentation, achieving an accuracy of 0.5961, a precision of 0.63, and a recall of 0.61, while the baseline model (without dataset augmentation) gained 0.4553, 0.36, and 0.34 for the accuracy, precision, and recall, respectively.
Indonesian News Website Accessibility Evaluation Based on WCAG 2.2 for Visually Impaired User Desak Putu Putri Dharmayanti; Putu Yudia Pratiwi; I Gusti Ayu Agung Diatri Indradewi
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.v5i2.10271

Abstract

Web accessibility is essential to ensure equal access to online information for all users, including individuals with visual impairments. Despite the increasing reliance on online news platforms, the accessibility status of Indonesian news websites remains underexplored, particularly with respect to the latest Web Content Accessibility Guidelines (WCAG) 2.2. This study evaluates the accessibility compliance of the three most visited Indonesian news websites, namely Detik.com, Kompas.com, and Tribunnews.com, from the perspective of users with visual impairments. The evaluation was conducted using WCAG 2.2 as the accessibility standard, the Website Accessibility Conformance Evaluation Methodology (WCAG-EM) as the evaluation method, and three automated accessibility evaluation tools: WAVE, axe DevTools, and Siteimprove Accessibility Checker. The findings indicate that all evaluated websites still contain accessibility barriers that may hinder access for visually impaired users. Keyboard accessibility violations were identified as the most dominant issue, followed by violations related to non-text content and color contrast. The results also reveal that homepage pages generally exhibit more accessibility issues than article pages due to their higher complexity and larger number of interactive and advertisement-related components. Among the evaluated websites, Tribunnews.com demonstrated the best overall accessibility performance, followed by Kompas.com and Detik.com. Furthermore, advertisement and promotional components were found to contribute substantially to the identified accessibility issues. These findings highlight the need for Indonesian news website providers to prioritize accessibility throughout the design and development process to promote more inclusive access to online news content.
Analisis Sentimen terhadap Perencanaan Pemilu Elektronik di Indonesia Menggunakan Pendekatan Soft Voting Ensemble I Putu Dennis Prana Arta; Putu Hendra Suputra; I Gusti Ayu Agung Diatri Indradewi
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.v5i2.10298

Abstract

Pemilu elektronik merupakan salah satu inovasi yang berpotensi meningkatkan efisiensi, transparansi, dan akurasi dalam penyelenggaraan pemilihan umum. Namun, implementasinya masih menimbulkan beragam tanggapan dari masyarakat yang banyak disampaikan melalui media sosial. Penelitian ini bertujuan untuk menganalisis sentimen masyarakat terhadap implementasi pemilu elektronik di Indonesia menggunakan metode Soft Voting Ensemble. Penelitian menerapkan kerangka kerja CRISP-DM dengan memanfaatkan data yang diperoleh dari platform X dan TikTok. Dari 9.715 data yang berhasil dikumpulkan, diperoleh 3.979 data relevan setelah proses seleksi, yang kemudian dilabeli secara manual oleh tiga ahli bahasa Indonesia menjadi 1.446 sentimen positif dan 2.533 sentimen negatif. Tahapan penelitian meliputi preprocessing, pembobotan TF-IDF, penanganan ketidakseimbangan data menggunakan Borderline-SMOTE, klasifikasi menggunakan Logistic Regression, Random Forest, dan Soft Voting Ensemble, serta evaluasi menggunakan confusion matrix dan 5-Fold Cross Validation. Hasil penelitian menunjukkan bahwa model Soft Voting Ensemble dengan optimasi awal dan Borderline-SMOTE menghasilkan performa terbaik dengan akurasi sebesar 80,78%. Selain itu, analisis topik menggunakan Latent Dirichlet Allocation (LDA) berhasil mengidentifikasi topik-topik dominan yang memengaruhi sentimen positif dan negatif masyarakat terhadap pemilu elektronik. Hasil penelitian juga diimplementasikan dalam bentuk dashboard interaktif untuk mendukung visualisasi dan pemanfaatan informasi oleh pemangku kepentingan. Temuan penelitian menunjukkan bahwa pendekatan Soft Voting Ensemble efektif dalam menganalisis sentimen publik terhadap implementasi pemilu elektronik di Indonesia.
Pengaruh Hyperparameter Tuning Gridsearch pada Random Forest untuk Klasifikasi Diabetes Melitus Resa Astari Br Tarigan; I Gusti Ayu Agung Diatri Indradewi; Gede Surya Mahendra
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.v5i2.11967

Abstract

Diabetes Melitus merupakan salah satu penyakit kronis yang memerlukan deteksi dini dan klasifikasi yang akurat untuk mendukung pengambilan keputusan dalam bidang kesehatan. Salah satu algoritma yang banyak digunakan untuk tugas klasifikasi adalah Random Forest, namun performanya dipengaruhi oleh pemilihan hyperparameter yang tepat. Penelitian ini bertujuan untuk menganalisis pengaruh hyperparameter tuning menggunakan metode GridSearchCV terhadap kinerja algoritma Random Forest dalam klasifikasi Diabetes Melitus. Dataset yang digunakan diperoleh dari Mendeley Data dan terdiri atas 826 data setelah melalui tahap preprocessing. Penelitian dilakukan dengan membandingkan performa model Random Forest sebelum dan sesudah proses hyperparameter tuning menggunakan metrik accuracy, precision, recall, dan F1-score. Hasil pengujian menunjukkan bahwa model tanpa tuning menghasilkan accuracy sebesar 97,59%, precision 99,07%, recall 85,75%, dan F1-score 90,93%. Setelah dilakukan tuning menggunakan GridSearchCV, diperoleh parameter terbaik yaitu criterion = gini, max_depth = 15, min_samples_leaf = 1, max_features = sqrt, dan n_estimators = 300, yang menghasilkan accuracy sebesar 98,19%, precision 99,30%, recall 89,91%, dan F1-score 93,98%. Hasil penelitian menunjukkan bahwa penerapan GridSearchCV memberikan pengaruh positif terhadap performa Random Forest, terutama dalam meningkatkan kemampuan model mengenali kelas minoritas yang ditunjukkan oleh peningkatan nilai recall dan F1-score. Dengan demikian, hyperparameter tuning menggunakan GridSearchCV dapat digunakan untuk mengoptimalkan kinerja Random Forest dalam klasifikasi Diabetes Melitu gayanya.
Co-Authors Adnyana, I Ketut Widhi Andika, I Gede Arakian, Laurensia Anjelina Tutosili Bawatanaya, Anak Agung Gede Wisnu Cahyadi, Kadek Wawan Desak Putu Putri Dharmayanti Devi, Made Diah Arista Dewi, Ni Putu Bimantari Dewi, Ni Putu Dita Ariani Sukma Febiasterina, Dyapradita Eka Gede Arna Jude Saskara Gede Surya Mahendra Gusti Putu Yastika Putra I Gde Prayoga Wibawa I Gede Mahendra Darmawiguna I Gusti Lanang Agung Raditya Putra I Kadek Wahyu Dananjaya I ketut Gede Darma Putra I Ketut Resika Arthana I Made Ardwi Pradnyana I Made Gede Sunarya I Md. Dendi Maysanjaya I Nyoman Saputra Wahyu Wijaya I Putu Agung Bayupati I Putu Dennis Prana Arta Ida Bagus Nyoman Pascima Kertiyasa, Made Medhi Premana Komang Pramayasa Made Donita Maharani Made Dwi Aprillia Kusuma Wiryani Made Suci Ariantini Made Windu Antara Kesiman Mitha, I Made Ardha Premana Ni Kadek Ariasih, Ni Kadek Ni Luh Wiwik Sri Rahayu Ginantra Ni Putu Sri Mulyani Ni Wayan Wardani Putra, I Kadek Andika Putra, I Wayan Ari Pramana Putri, Vany Adelia Putu Hendra Suputra Putu Yudia Pratiwi Raditia, Kadek Rama Rajasyah, Alfatino Yohan Resa Astari Br Tarigan Saraswati, Ni Wayan Sumartini Sari, Ni Luh Pangestu Widya suryani, rika Sutrisna, Ketut Dita Ari Wardani, Ni Wayan Wayan Gede Suka Parwita