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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Infotech Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI International Journal of New Media Technology Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal ULTIMA InfoSys Jurnal ULTIMATICS Jurnal ULTIMA Computing Simtek : Jurnal Sistem Informasi dan Teknik Komputer Jurnal Tekno Kompak Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Tekinkom (Teknik Informasi dan Komputer) G-Tech : Jurnal Teknologi Terapan BERNAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknologi Informasi dan Komunikasi Journal of Applied Data Sciences DEDIKASI PKM Prosiding Konferensi Nasional PKM-CSR Indonesian Community Journal Aptekmas : Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika Teknologi dan Sains (Jinteks) Journal of Scientech Research and Development Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Journal of Artificial Intelligence and Digital Business
Ni Made Satvika Iswari
Universitas Primakara
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering 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 Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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Prediksi Kesehatan Mental Pengguna Berdasarkan Konsumsi Konten Pada Media Sosial Menggunakan Metode Random Forest Putri, Ni Luh Putu Adela Sartian; Iswari, Ni Made Satvika; Juliharta, I Gede Putu Krisna
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 4 (2026): November - January
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i4.5440

Abstract

Penelitian ini bertujuan memprediksi tingkat risiko kesehatan mental pengguna media sosial berdasarkan pola konsumsi konten dan intensitas interaksi di platform digital menggunakan metode Random Forest. Fenomena meningkatnya penggunaan media sosial pada kelompok usia muda membawa dua sisi: manfaat komunikasi dan informasi, namun juga berpotensi memicu kecemasan, stres, gangguan tidur, hingga penurunan produktivitas ketika digunakan berlebihan dan tidak terkontrol. Data penelitian dikumpulkan melalui kuesioner daring (Google Form) pada responden aktif media sosial dengan variabel utama meliputi durasi penggunaan harian, waktu akses (pagi–larut malam), jenis platform yang sering digunakan (Instagram, TikTok, Twitter/X), serta frekuensi interaksi negatif. Data kemudian melalui tahapan pembersihan, transformasi, dan konversi numerik sebelum diproses pada Orange Data Mining. Model Random Forest mengklasifikasikan responden ke dalam tiga kategori risiko, yaitu Tidak Berisiko, Cenderung Berisiko, dan Risiko Tinggi. Hasil menunjukkan bahwa durasi penggunaan yang panjang (≥3,5 jam), akses pada malam hari (terutama setelah pukul 19.00–21.00), serta frekuensi interaksi negatif yang tinggi merupakan faktor paling kuat dalam meningkatkan risiko gangguan mental. Evaluasi model memperlihatkan kinerja yang baik dan stabil, ditunjukkan oleh nilai AUC yang tinggi pada tiap kelas serta akurasi yang konsisten dalam mendeteksi kondisi pengguna. Temuan ini menegaskan pentingnya pemantauan kebiasaan digital sebagai langkah deteksi dini, sekaligus menjadi dasar edukasi penggunaan media sosial yang lebih sehat untuk menjaga keseimbangan psikologis pengguna.
Rancang Bangun Game Edukasi Penyakit Rabies Menggunakan Unity Dengan Metode Game Development Life Cycle Mulyosaputro, Matthew Alden; Iswari, Ni Made Satvika; Wijaya, I Nyoman Yudi Anggara
JATISI Vol 12 No 4 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i4.13354

Abstract

Rabies, which is usually transmitted through dog bites, is one of the common diseases in Indonesia. Based on data from Indonesia Ministry of Health, there were 31,113 cases of rabies animal bites and 11 deaths caused by rabies in 2023. Although efforts to eradicate rabies have been made in Indonesia, it has not been carried out completely due to the lack of public awareness about rabies. Therefore, there needs to be a means that can be used to increase public awareness about rabies. In this study, the author designed and created a game that aims to educate the public about rabies and how to prevent it. The rabies educational game was created using the Game Development Life Cycle method and was created using Unity software. The game produced in this study is a rabies education game in the form of a story-based choice game where the player's choice will impact the player's success in preventing or failing to handle rabies. From the test results, it can be concluded that the rabies education game is able to entertain players and educate players about rabies and how to prevent it.
PENERAPAN DATA MINING UNTUK MENENTUKAN KELAYAKAN KENDARAAN SEPEDA MOTOR BEKAS MENGGUNAKAN ALGORITMA C4.5 Ni Kadek Juliani; Ni Made Satvika Iswari; Nengah Widya Utami
INFOTECH journal Vol. 11 No. 2 (2025)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v11i2.15338

Abstract

Determining the feasibility of used motorcycles is one of the challenges for companies in selecting attributes that cover various factors, such as physical condition, maintenance history, and reasonable price. In this study, the researcher aims to analyze the existing problems and provide decision results by applying the C4.5 algorithm to determine the feasibility of used motorcycles based on relevant data. The C4.5 algorithm has the capability to build decision trees to automate and improve the accuracy of the feasibility determination process. In this research, attributes such as motorcycle model, year, engine, kilometers, fuel type, modifications, engine overhaul, oil type, transmission, engine type, and displacement are used as determining variables.Furthermore, to avoid overfitting that may occur due to overly complex decision trees, the researcher also applies pruning techniques to the C4.5 algorithm. Pruning functions to trim insignificant branches of the tree so that the model becomes simpler. With pruning, it is expected that the resulting decision tree will be not only accurate but also efficient, enabling the feasibility determination process of used motorcycles to be conducted optimally. Therefore, after applying pruning techniques, the model achieved an accuracy of 72.41%, precision of 68.42%, recall of 86.67%, and F1-score of 76.47%.
RANCANG BANGUN LMS BERBASIS MOODLE UNTUK PENINGKATAN KUALITAS PENDIDIKAN DI SMP GENTA SARASWATI GIANYAR Ni Putu Gunaprya Dharmapatni; Ni Made Satvika Iswari; I Nyoman Yudi Anggara Wijaya
INFOTECH journal Vol. 11 No. 2 (2025)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v11i2.15831

Abstract

Penelitian ini bertujuan untuk merancang dan menerapkan Learning Management System (LMS) berbasis Moodle di SMP Genta Saraswati sebagai upaya mempermudah guru dalam mendistribusikan materi ajar dan mengelola tugas siswa secara daring. Pengembangan sistem dilakukan menggunakan model ADDIE yang mencakup tahap analisis, desain, pengembangan, implementasi, dan evaluasi. Evaluasi sistem dilakukan dengan melibatkan 30 responden yang terdiri atas guru dan siswa melalui kuesioner berbasis teori SEPENTES, yang menilai aspek kemudahan penggunaan, kelengkapan fungsi, aksesibilitas, dan stabilitas sistem. Hasil evaluasi menunjukkan bahwa sekitar 80% pengguna merasa LMS sangat bermanfaat dalam mendukung proses pembelajaran. Skor rata-rata tertinggi diperoleh pada aspek aksesibilitas dengan nilai 4,5 dari 5, sedangkan skor terendah terdapat pada aspek kelengkapan fungsi dengan nilai 3,9, yang menunjukkan perlunya pengembangan fitur tambahan. Selain memberikan manfaat langsung bagi sekolah, penelitian ini juga membantu pengembang memperoleh kompetensi dalam analisis kebutuhan, perancangan sistem e-learning, serta penyusunan dan analisis instrumen evaluasi. Secara keseluruhan, LMS ini diharapkan mampu menjadi solusi untuk mendukung pembelajaran digital yang efektif, interaktif, dan berkelanjutan di sekolah
RANCANG BANGUN SISTEM INFORMASI MANAJEMEN EVENT DAN KOMUNITAS BERBASIS WEBSITE (STUDI KASUS PT. BAGOES DIGITAL CREATIVE) I Made Dwi Arya Wiguna; Ni Made Satvika Iswari; Ketut Tri Budi Artani
Journal of Scientech Research and Development Vol 8 No 1 (2026): JSRD, June 2026
Publisher : Ikatan Dosen Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56670/jsrd.v8i1.1505

Abstract

Pengelolaan event dan komunitas yang efisien merupakan kebutuhan strategis bagi perusahaan di era digital, khususnya di sektor kreatif. PT. Bagoes Digital Creative, sebuah perusahaan jasa kreatif digital, selama ini menghadapi tantangan dalam mengelola berbagai acara dan interaksi komunitasnya yang masih dilakukan secara manual dan terfragmentasi. Hal ini menyebabkan inefisiensi waktu, data yang tidak terpusat, serta kurang optimalnya keterlibatan anggota komunitas. Penelitian ini bertujuan untuk merancang dan membangun sebuah Sistem Informasi Manajemen Event dan Komunitas berbasis Website guna mengatasi permasalahan tersebut. Penelitian ini menggunakan metode prototype dengan pendekatan pengembangan iteratif. Pengumpulan data dilakukan melalui observasi, wawancara, dan studi literatur di PT. Bagoes Digital Creative. Sistem dikembangkan dengan stack teknologi modern, yaitu Next.js dan TypeScript untuk frontend, serta Supabase (PostgreSQL) untuk backend dan basis data terintegrasi. Fitur utama sistem meliputi modul manajemen event (pendaftaran, penjadwalan, peserta), forum komunitas, dashboard admin, serta sistem autentikasi pengguna multi-peran. Hasil implementasi menunjukkan bahwa sistem berhasil dibangun dan berfungsi dengan baik berdasarkan pengujian fungsional (UAT) oleh pengguna internal. Sistem ini mampu menyederhanakan alur kerja, memusatkan data, dan memfasilitasi komunikasi yang lebih terstruktur. Umpan balik pengguna menunjukkan tingkat kepuasan rata-rata 4.2 dari 5. Kesimpulan dari penelitian ini adalah sistem yang dihasilkan telah memenuhi kebutuhan dasar PT. Bagoes Digital Creative dan siap digunakan Sebagai platform digital untuk meningkatkan efisiensi serta efektivitas dalam pengelolaan event dan komunitas.
PENGARUH TEKNIK AUGMENTASI DATA TERHADAP PERORMANSI MODEL CNN DALAM KLASIFIKASI CITRA KANKER KULIT I Komang Adi Galang Permana Galang; Ni Made Satvika Iswari; Ni Putu Noviyanti Kusuma
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 8 No 1 (2026): EDISI 27
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v8i1.7296

Abstract

Kanker kulit merupakan salah satu jenis kanker dengan prevalensi tinggi secara global termasuk di Indonesia yang resikonya semakin meningkat akibat penipisan lapisan ozon dan paparan sinar ultraviolet (UV) berlebih. Deteksi dini kanker kulit melalui klasifikasi citra sangat penting untuk meningkatkan peluang kesembuhan pasien. Namun, keterbatasan jumlah dan variasi data citra kanker kulit menjadi tantangan utama dalam pengembangan model klasifikasi berbasis Convolutional Neural Network (CNN). Teknik augmentasi data menjadi salah satu solusi untuk memperluas dan memperkaya keragaman dataset tanpa perlu pengumpulan data baru. Maka penelitian ini bertujuan untuk mengetahui pengaruh berbagai teknik augmentasi data terhadap performansi model CNN dalam klasifikasi citra kanker kulit menggunakan dataset Internasional Skin Imaging Collaboration (ISIC) yang terdiri dari sembilan kelas kanker kulit. Metode yang digunakan dalam penelitian ini adalah CRISP-DM yang terdiri dari enam tahapan. Berdasarkan hasil diperoleh akurasi tertinggi pada data uji menggunakan teknik augmentasi data jenis gaussian noise addition. Sementara itu, baseline model tanpa augmentasi memiliki akurasi lebih rendah. Disimpulkan teknik augmentasi data memiliki pengaruh signifikan dalam meningkatkan akurasi dan ketahanan model CNN dalam mengenali berbagai jenis kanker kulit.
Enhancing Aspect-based Sentiment Analysis in Visitor Review using Semantic Similarity Ni Made Satvika Iswari; Nunik Afriliana; Eddy Muntina Dharma; Ni Putu Widya Yuniari
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.249

Abstract

The global economy greatly depends on the tourism industry, which fosters job opportunities and stimulates economic development. With the growing reliance of tourists on online platforms for guidance, evaluations of tourist destinations have gained heightened significance. These assessments, frequently expressed through user-generated content, offer valuable perspectives on customer experiences, viewpoints, and levels of satisfaction. Nevertheless, analyzing and interpreting these reviews can pose difficulties because of the unstructured or semi-structured nature of user-generated content. Conventional sentiment analysis methods might not adequately grasp the intricacies and particular aspects of tourism encounters that users convey in their reviews. The efficacy of sentiment analysis can be augmented by integrating semantic similarity. This study explores methods to enhance aspect-based sentiment analysis within tourism reviews by utilizing semantic similarity approaches. Five aspects have been curated, representing keywords frequently reviewed by visitors to the tourist attraction. These aspects encompass scenery, dusk, surf, amenities, and sanitation. Based on the data analysis, F-Measure values with Semantic Similarity tend to increase for the scenery and dusk aspects. This is because in the sample data used, visitor reviews for the scenery and dusk categories may use other words that are semantically similar. The sample data used for these categories is also quite extensive, resulting in a better classification model for both categories. While it is valuable to analyze user-generated content data from visitor reviews, it's important to consider the limitations and potential biases associated with this data. The classification results per aspect need to be further reviewed in more depth. What aspects lead visitors to give positive reviews will certainly be maintained and even improved by stakeholders. Similarly, for negative review outcomes, it is necessary to investigate more deeply the factors contributing to visitor dissatisfaction so that they can be addressed by stakeholders.
Analyzing Student Sentiments and Insights on Generative AI for Independent Learning in Universities Ni Made Satvika Iswari; I Nyoman Yudi Anggara Wijaya; Ni Putu Widya Yuniari
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1083

Abstract

Transformations in higher education brought about by Generative AI have significantly changed how university students’ access, comprehend, and develop learning materials. This study explores Indonesian university students’ perceptions and experiences regarding the use of Generative AI for independent learning, employing qualitative surveys together with sentiment analysis powered by machine learning. Data were collected from open-ended questionnaires and analyzed using four key algorithms, such as Naive Bayes, Logistic Regression, Random Forest, and Linear SVM, to classify student sentiments towards generative AI technologies. These four classical machine learning models were employed as baseline algorithms commonly used in sentiment analysis to benchmark performance on small, imbalanced educational datasets before applying more complex transformer-based methods. In addition to quantitative analysis, this study also implements thematic analysis of open-ended responses to identify prominent issues, challenges, and student recommendations concerning the use of generative AI in learning. Evaluation results identified Linear SVM as the most consistent model, with the highest weighted F1-score (0.63), although all models showed limitations in detecting negative sentiment due to class imbalance (only three negative samples out of forty responses), which affected model generalization. Key findings indicate that students perceive Generative AI as a supportive tool that accelerates understanding, creativity, and reference searching; however, they remain wary of risks related to dependency, reduced originality, and academic integrity dilemmas. This article recommends the implementation of ethical policy, AI digital literacy training, and enhancement of campus infrastructure to ensure that AI technologies enrich the learning process without compromising student independence and integrity.
Sentiment Analysis of Student Perceptions of Generative AI using Data Augmentation and Machine Learning Models Ni Made Satvika Iswari; I Nyoman Yudi Anggara Wijaya
G-Tech: Jurnal Teknologi Terapan Vol 10 No 3 (2026): G-Tech, Vol. 10 No. 3 July 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i3.10613

Abstract

The development of Generative AI has significantly changed how students access information, understand learning materials, and generate ideas in higher education. Although Generative AI supports independent learning, it also raises concerns regarding overdependence, declining critical thinking skills, and academic integrity. This study evaluates the performance of sentiment analysis models using a processing pipeline that incorporates lexical, semantic, and generative data augmentation. The main challenge addressed in this study is class imbalance, particularly the limited number of negative sentiment samples compared to positive and neutral classes. This study applies an experimental quantitative approach consisting of dataset preparation, text preprocessing, data augmentation, feature extraction using TF-IDF, model training, and evaluation using Stratified K-Fold Cross Validation. The machine learning models evaluated include Multinomial Naive Bayes, Logistic Regression, Random Forest, and Linear Support Vector Machine. The experimental results show that Linear SVM achieved the best performance, with an average accuracy of 79.07% and a weighted F1-score of 73.90%. Compared descriptively with the non-augmented baseline, Linear SVM showed an observed increase in accuracy from 65.00% to 79.07% and in weighted F1-score from 63.03% to 73.90%. Data augmentation also enabled partial recognition of minority-class sentiment, although a substantial proportion of negative and positive samples were still misclassified as neutral. These findings indicate that hybrid data augmentation can support the performance of classical machine learning models on small and imbalanced educational text datasets, particularly when combined with TF-IDF and Linear SVM. However, a post-hoc audit identified a discrepancy between the class distribution of the original dataset and that of the final processed dataset. Therefore, the observed model performance should be interpreted as the result of the overall processing pipeline rather than as the isolated effect of data augmentation.
Analisis Perbandingan Algoritma Linear Regression dan Polynomial Regression dalam Memprediksi Durasi Rawat Inap Pasien Rumah Sakit I Putu Abdiparta; Ni Made Satvika Iswari; Nengah Widya Utami
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 14, No 3: Desember 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v14i3.3233

Abstract

The length of stay (LoS) of hospital patients is an essential indicator for measuring the efficiency of healthcare services. Accurate LoS prediction helps hospitals optimize resource management, estimate costs, and improve service quality. This study compares the performance of Linear Regression and Polynomial Regression algorithms in predicting patient LoS. The dataset, obtained from Kaggle, consists of 835 patient records that underwent preprocessing and transformation. The independent variables include gender, age, disease type, and type of service, while LoS serves as the dependent variable. The research applies the Knowledge Discovery from Data (KDD) approach, which includes the stages of selection, cleaning, transformation, data mining, and evaluation. Experiments were conducted using three data-splitting ratios (70:30, 80:20, and 90:10) with evaluation metrics MAE, MSE, RMSE, and R². The results show that Linear Regression performed slightly better, with average R² values ranging between 0.18 and 0.20, indicating its potential to support hospital management efficiency.Keywords: Length of Stay; Linear Regression; Polynomial Regression; Data Mining; Prediction. AbstrakDurasi rawat inap pasien (Length of Stay/LoS) merupakan indikator penting dalam mengukur efisiensi pelayanan rumah sakit. Prediksi LoS yang akurat membantu rumah sakit dalam pengelolaan sumber daya, estimasi biaya, dan peningkatan mutu layanan. Penelitian ini membahas perbandingan kinerja algoritma Linear Regression dan Polynomial Regression dalam memprediksi LoS pasien. Data penelitian diperoleh dari Kaggle dengan total 835 data pasien yang melalui proses preprocessing dan transformasi. Variabel independen meliputi gender, umur, jenis penyakit, dan jenis service, sedangkan LoS menjadi variabel dependen. Metode penelitian menggunakan pendekatan Knowledge Discovery from Data (KDD) yang mencakup tahapan selection, cleaning, transformation, data mining, dan evaluation. Pengujian dilakukan pada tiga rasio pembagian data (70:30, 80:20, dan 90:10) menggunakan metrik MAE, MSE, RMSE, dan R². Hasil menunjukkan Linear Regression memiliki performa sedikit lebih unggul dengan rata-rata R² sebesar 0,18–0,20, yang dapat mendukung efisiensi manajemen rumah sakit. 
Co-Authors A. A. Istri Ita Paramitha Adhi Kusnadi Adnyani, Dewa Ayu Anggi Afriliana, Nunik Agustyan Hidayat Andre Rusli Andre Utomo Martliong Anugrah Cahya Dewi, Putri Bagus Nugraha Sindhu, I Gd Putu Bimo Satriantoro David Domarco Dennis Gunawan Dewi, Putri Anugrah Cahya Dharma, Eddy Muntina Dwi Eva Pramesti, Ni Made Dwiyanti Gunaswari, Made Dyah Ayu Anggreini Tuasikal Eddy Muntina Dharma Eko K. Budiardjo Fredlina , Ketut Queena I Gede Juliana Eka Putra I Gede Juliana Eka Putra I Gede Prana Yoga I Gede Putu Krisna Juliharta I Komang Adi Galang Permana Galang I Made Dwi Arya Wiguna I Made Surya Kumara I Nyoman Yudi Anggara Wijaya I Nyoman Yudi Anggara Wijaya I Nyoman Yudi Anggara Wijaya I Nyoman Yudi Anggara Wijaya I Putu Abdiparta I Putu Eka Indrayana Juliana Eka Putra, I Gede Kadek Intan Janeta Pratiwi Ketut Queena Fredlina Ketut Tri Budi Artani Lin, Lin Andriani Luthfialmas Fakhrizki Malik Abdul Ghani Marlinda Vasty Overbeek MB Nugraha Moeljono Widjaja Mulyosaputro, Matthew Alden Muntina Dharma, Eddy Nengah Widya Utami Ni Kadek Juliani Ni Putu Gunaprya Dharmapatni Ni Putu Noviyanti Kusuma Ni Putu Widya Yuniari Nunik Afriliana Nyoman Purnama, Nyoman PERMANA, LEON FARREL AGUNG Putri, Ni Luh Putu Adela Sartian Putu Krisna Juliharta, I Gede Ranny Ranny Reza Setiabudi Risa Rahayu Setiawati, Ni Wayan Robi Rianto Ryan Adiputra Samuel Christoper Santo Syakh Alam, Helmy Wella Wella Wella Wella Widya Utami, Nengah Winarno * **, Winarno * Yaman Khaeruzzaman Zainal Arifin Hasibuan