Garuda - Garba Rujukan Digital

p-Index From 2021 - 2026

9.968

P-Index

This Author published in this journals

All Journal International Journal of Advances in Applied Sciences IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Pendidikan Fisika Undiksha Jurnal Pendidikan Teknologi dan Kejuruan Prosiding Seminar Nasional MIPA Wahana Matematika dan Sains Jurnal Sains dan Teknologi Jurnal Simetris Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Sistem dan Informatika Mimbar Pendidikan: Jurnal Indonesia untuk Kajian Pendidikan KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika JUTIK : Jurnal Teknologi Informasi dan Komputer JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer SINTECH (Science and Information Technology) Journal Jurnal Teknik Informatika UNIKA Santo Thomas Jurnal Serambi Engineering Jurnal RESISTOR (Rekayasa Sistem Komputer) International Journal of Community Service Learning Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Riset Informatika Jurnal Ilmiah Sinus EDUMATIC: Jurnal Pendidikan Informatika Jurnal Teknologi Informasi dan Pendidikan Jurnal Mantik Jurnal Teknologi Informasi dan Terapan (J-TIT) JURNAL ILMU KOMPUTER INDONESIA Jurnal Teknologi Informasi dan Multimedia Bali Medika Jurnal Indonesian Journal of Electrical Engineering and Computer Science INFOKUM Didaktik : Jurnal Ilmiah PGSD STKIP Subang Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) Journal of System and Computer Engineering J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Info Sains : Informatika dan Sains Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Locus Penelitian dan Pengabdian Malcom: Indonesian Journal of Machine Learning and Computer Science Bulletin of Network Engineer and Informatics (BUFNETS) INOVTEK Polbeng - Seri Informatika

I Gede Aris Gunadi

Universitas Pendidikan Ganesha

Author-ID : 270642

Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Nursing Physics Public Health Social Sciences Other

Published : 94 Documents Claim Missing Document

Claim Missing Document

Articles

6 7 8 9 10

Application of Bagging Ensemble Learning on Naïve Bayes Algorithm to Predict Coronary Heart Disease Nugraha, I Gusti Agung Satria; Gunadi, I Gede Aris; Dewi, Luh Joni Erawati
Jurnal Teknologi Informasi dan Pendidikan Vol. 18 No. 2 (2025): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v18i2.981

Cardiovascular health is vital, with heart disease, particularly Coronary Heart Disease (CHD), being a significant health concern in Indonesia. The 2023 Indonesian Health Survey reported 877,531 cases of heart disease. Traditional CHD diagnosis is often costly and invasive. Therefore, machine learning-based classification has emerged as a promising alternative for enhancing the accuracy and efficiency of detection. This study aims to predict CHD using a hybrid approach combining the Naïve Bayes algorithm with the Bagging ensemble method. Naïve Bayes was selected for its computational efficiency and effectiveness with high-dimensional data, while Bagging was employed to mitigate its inherent weaknesses by reducing variance and increasing prediction stability. The CRISP-DM methodology was applied to a secondary dataset of 462 rows from Kaggle. The research process included data preprocessing, method implementation, and evaluation using a confusion matrix. Results show the Bagging method with n=2 estimators achieved optimal performance, with 76.34% accuracy, 65.00% precision, and an f1-score of 70.27%. This study demonstrates that ensemble techniques can effectively improve the accuracy and stability of CHD prediction models, offering a reliable and low-cost solution for initial screening.

ANALISIS SENTIMEN MASYARAKAT TERHADAP VIRUS CORONA BERDASARKAN OPINI DARI TWITTER MENGGUNAKAN METODE NAÏVE BAYES DAN K-NEAREST NEIGHBOR Putu Wendy Ariyani; I Made Gede Sunarya; I Gede Aris Gunadi
Jurnal Pendidikan Teknologi dan Kejuruan Vol. 22 No. 2 (2025): Edisi Juli 2025
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jptk-undiksha.v22i2.103233

Sejak penyebaran Virus Corona, banyak masyarakat yang mengutarakan pendapatnya melalui media social Twitter dalam menanggapi penyebaran Virus Corona. Berbagai opini yang diutarakan masyarakat dapat menjadi sebuah acuan untuk mengetahui Sentimen Masyarakat terhadap Virus Corona. Diperlukan analisis sentiment untuk mengetahui sentiment opini yang muncul pada social media. Tujuan penelitian ini adalah untuk mengetahui implementasi dan tingkat akurasi dari metode Naïve Bayes dan K-Nearest Neighbor. Data yang digunakan diambil dari twitter mengenai Virus Corona. Jumlah data yang digunakan sebanyak 2000 data tweet. Dokumen dengan sentiment positif yaitu 1320 dan dokumen dengan sentiment negative yaitu 689. Data yang terkumpul akan dibagi untuk digunakan sebagai Data Latih dan Testing untuk proses klasifikasi. Implementasi Naïve Bayes dan KNN dalam analisis sentimen masyarakat terhadap virus corona, dimulai dari tahap preprocessing data yang terdiri Normalisasi data (menghilangkan URL dan username), Case Folding (mengubah semua huruf menjadi huruf kecil), Tokenizing (memilih menjadi beberapa kata), Stopword Removal (menghilang kata yang sering muncul), serta stemming (mengubah sebuah kata menjadi bentuk umumnya). Setelah melalui tahap preprocessing, kemudian dilakukan tahap pembobotan TF-IDF. Hasil dari pembobotan TF-IDF akan diklasifikasi menggunakan metode Naïve Bayes dan KNN, sehingga nanti akan mendapatkan hasil perbandingan klasifikasi dari kedua metode tersebut. Hasil klasifikasi menggunakan metode Naïve Bayes diperoleh akurasi sebesar 0.83 dan error rate sebesar 0.17. Sedangkan untuk hasil klasifikasi menggunakan metode KNN diperoleh akurasi sebesar 0.78 dan error rate sebesar 0.21. Perbandingan perfomansi metode Naïve Bayes dan KNN menunjukkan bahwa Naïve Bayes lebih baik dalam mengklasifikasikan data Covid-19. Kata kunci: Virus Corona, COVID-19, Twitter, Analisis Sentimen, Naïve Bayes, K-Nearest Neightbor

PERANCANGAN ARSITEKTUR ENTERPRISE SISTEM INFORMASI MENGGUNAKAN TOGAF ADM DI SMA NEGERI 1 SINGARAJA Adi Sista, Dewa Nyoman; Candiasa, I Made; Aris Gunadi, I Gede
JST (Jurnal Sains dan Teknologi) Vol. 10 No. 2 (2021)
Publisher : Universitas Pendidikan Ganesha

Penelitian ini bertujuan untuk mendefinisikan serta menguji efektivitas TOGAF ADM sebagai model arsitektur enterprise sistem informasi guna mendukung aktivitas bisnis di SMA Negeri 1 Singaraja. Secara garis besar data pada penelitian ini diambil dengan teknik wawancara dan observasi kemudian dianalisis menggunakan pemodelan TOGAF ADM. Adapun perangkat yang digunakan untuk memodelkan fase dalam TOGAF ADM adalah analisis SWOT, Value Chain, Bussiness Process Modeling and Notation, Unified Modelling Language, McFarland Strategic, dan Focus Group Discussion (FGD). Hasil penelitian ini berupa (1) rekomendasi pembuatan aplikasi, (2) rekomendasi topologi jaringan, (3) rekomendasi hardware dan software, (4) usulan urutan implementasi dan (5) roadmap implementasi dari rekomendasi-rekomendasi yang telah diberikan, serta (6) pengujian menggunakan teknik FGD. Dari hasil pengujian diperoleh hasil bahwa rekomendasi peneliti diterima dengan baik oleh peserta dan diharapkan dapat direalisasikan. Sedangkan hambatan yang akan ditemui ketika mengimplementasikan sistem informasi ini adalah pada proses mengubah kebiasaan para guru, pegawai dan siswa yang telah sekian lama mengandalkan pekerjaan secara manual. Serta faktor usia pada guru dan pegawai juga akan menjadi penghambat dalam proses penyesuaian tersebut.

PERBANDINGAN KONSTANTA ELASTISITAS VIRUS HIV-1 MATANG DAN HIV-1 BELUM MATANG Yasmini, Luh Putu Budi; Fauzi, Muhammad Rizki; Risha, Nurfa; Gunadi, I Gede Aris
JST (Jurnal Sains dan Teknologi) Vol. 11 No. 2 (2022)
Publisher : Universitas Pendidikan Ganesha

Virus pada aspek fisis masih sangat menarik untuk dikaji berdasarkan konsep Fisika. Salah satu ciri virus adalah kekakuan cangkang virus tersebut. Dalam tinjauan selanjutnya, cangkang virus dapat diasumsikan sebagai pegas. Tujuan dari penelitian ini adalah untuk menganalisis akurasi dan wawasan mengenai kekakuan cangkang virus secara teoretik dengan menggunakan selesaian persamaan Michell. Dalam artikel ini, dikaji nilai kekakuan cangkang virus secara teoretik melalui metode analitik dan simulasi dengan mengkaji berbagai sumber pustaka terkait. Metode analisis didasarkan pada teori plate & shell, serta dibahas mengenai suatu metode analitik lainnya, yakni dengan menggunakan persamaan Michell. Metode simulasi didasarkan pada metode finite element analysis (FEA). Dikaji konstanta elastisitas dua jenis virus, yakni virus HIV-1 (matang) dan HIV-1 (belum matang). Hasil penelitian menunjukkan bahwa virus HIV-1 (matang) memiliki konstanta elastisitas yang lebih kecil bila dibandingkan dengan konstanta elastisitas virus HIV-1 (belum matang). Hal tersebut sangat terkait dengan karakteristik virus tersebut, yakni ukuran, ketebalan, dan sifat instrinsik virus. Virus dengan ketebalan cangkang yang lebih kecil memiliki konstanta elastisitas yang lebih kecil, sehingga lebih efisien untuk menginfeksi sel inang dibandingkan dengan virus yang memiliki konstanta elastisitas yang lebih besar.

Deteksi Transaksi Fraud Kartu Kredit Menggunankan Oversampling ADASYN dan Seleksi Fitur SVM-RFECV Dharmana, I Wayan; Gunadi, I Gede Aris; Dewi, Luh Joni Erawati
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 1: Februari 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.20241117640

Perkembangan kejahatan transaksi fraud kartu kredit memberikan dampak kerugian finansial bagi pemegang kartu. Pengembangan model deteksi transaksi fraud menggunakan machine learning telah dilakukan, namun memiliki beberapa tantangan meliputi ketidakseimbangan data serta dimensi dataset yang besar. Penelitian ini mengusulkan pendekatan pengembangan dengan seleksi fitur menggunakan SVM-RFECV dan metode oversampling dengan ADASYN. Pendekatan ini diharapkan mampu mengatasi permasalahan dimensi data serta ketidakseimbangan data yang terjadi. Seleksi fitur dengan SVM-RFECV menghasilkan variabel optimal pada rasio data latih 70% sejumlah 390 variabel, rasio data latih 80% sejumlah 400 variabel dan rasio data latih 90% sejumlah 390 variabel. Metode ADASYN telah memperbaiki ketidakseimbangan data dengan menghasilkan data sintetis berdasarkan rasio oversampling meliputi 100%, 50% dan 25%. Model yang menggunakan data hasil oversampling mengalami peningkatan kinerja AUC dan recall. Kinerja AUC tertinggi dihasilkan sejumlah 88,08% pada data latih 70%, oversampling 100% dan algoritma LGBM. Sedangkan, kinerja recall tertinggi sejumlah 83,08% dihasilkan saat menggunakan data latih 70%, oversampling 100% dengan algoritma AdaBoost. Berdasarkan pembahasan ini, maka dapat disimpulkan bahwa penggunaan oversampling dengan ADASYN dan seleksi fitur SVM-RFECV dapat dipertimbangkan untuk meningkatkan kinerja AUC dan recall.

SURICATA ACCURACY OPTIMIZATION BASED ON LIVE ANALYSIS USING ONE-CLASS SUPPORT VECTOR MACHINE METHOD AND STREAMLIT FRAMEWORK Agus Ariwanta, I Putu Yesha; Ernanda Aryanto, Kadek Yota; Gunadi, I Gede Aris
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.2.1822

Based on data from the Checkpoint website, there are more than 10 million cyber-attacks in a single day, and the top sequence of this cyber-attack is evident in educational institutions. The IT unit of Kartini Bali Health Polytechnic has not yet conducted testing for accuracy and speed to detect suspicious activities on the computer network. The implementation of network security systems that have not undergone testing will undoubtedly have a negative impact on system providers and users. The application of Live Analysis based on a website and the One-Class Support Vector Machine (SVM) is used to optimize the capabilities of the Suricata in detecting suspicious activities on computer networks and providing visual and real-time reports. This research utilizes the Suricata for optimizing the computer network security system, with the researcher using the Streamlit Framework for Live Analysis based on a website and the One-Class Support Vector Machine (SVM) for classifying log data and visual reporting. For testing the computer network security system, tools such as Nmap, Loic, and Brutus are used. The results of the research using the One-Class Support Vector Machine (SVM) in detecting three types of attacks Port Scanning, DDOS Attack, and Brute Force Attack, show an accuracy value of 96%, precision of 95%, recall of 96%, and F1-Score of 95%. In the performance and load testing of the live analysis system using the Streamlit framework, the results show that the developed system is responsive, with CPU usage at 38%, memory usage at 62.3%, and an average system load time of 5 milliseconds.

Expert System Using Certainty Factor Method For Adjustment Of Learning Styles With Students Sanjaya, I Putu Aris; Gunadi, I Gede Aris; Indrawan, Gede
Journal of Computer Networks, Architecture and High Performance Computing Vol. 5 No. 1 (2023): Article Research Volume 5 Issue 1, January 2023
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v5i1.2068

Alignment of students with learning styles greatly affects the quality of learning of students in educational units. With good learning quality, the passing rate of students in an educational unit will also increase and can produce quality graduates. So far, the learning process implemented in this school has been going well when viewed based on the number of students graduating with the number of students present, but so far no further research has been conducted regarding this suitability so that the effectiveness of student learning is still not optimal. Based on this, the research objective is to build an Expert System with the Certainty Factor method to adjust the learning styles of students at SMK PGRI 5 Denpasar. Based on the results that will be obtained through the system designed and built in this research, it is hoped that it will make it easier for educators to prepare learning models and strategies that will be given to students from the results of determining student learning styles. The research results obtained from the test results show 100% suitability in giving dominant results to students' learning styles. In this study the students who were used as the test sample had different learning style percentage accuracy so that it could be used to determine the right learning style for each student.

Improving Butterfly Fish Image Classification Accuracy using HSV Feature Extraction and SMOTE-Based Data Balancing Putra, I Putu Arya; Wirawan, I Made Agus; Gunadi, I Gede Aris
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/vw52nn48

Class imbalance in image data can reduce the accuracy of classification models, especially when the minority class data is much smaller than the majority class. This research focuses on enhancing the classification accuracy of butterflyfish images through the application of the Synthetic Minority Over-sampling Technique (SMOTE) for data balancing, combined with the K-Nearest Neighbor (KNN) algorithm utilizing HSV-based feature extraction. The datasets were collected in two conditions, namely conditioned (controlled background and lighting) and unconditioned (varied background and natural lighting). The research stages include preprocessing, HSV feature extraction, data balancing with SMOTE, and classification using KNN with various k values (3, 5, 7, 9) and cross-validation (k-fold 5 and 10). The experimental results show that SMOTE consistently improves accuracy on both types of datasets, with the best performance at k = 3 and k-fold = 10, namely 85.32% (conditioned) and 87.59% (unconditioned). This improvement occurs because a more balanced data distribution allows the model to optimally recognize features between classes. This study proves that the integration of SMOTE and KNN is effective in overcoming class imbalance in image classification, with potential applications in the fields of digital image technology, ecosystem management, and species identification.

Analisis Segmentasi Pelanggan pada Bisnis dengan Menggunakan Metode K-Means Clustering pada Model Data RFM Sisilia Fhelly Djun; I Gede Aris Gunadi; Sariyasa Sariyasa
Jurnal Teknologi Informasi dan Multimedia Vol. 5 No. 4 (2024): February
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v5i4.434

The development of business strategies, particularly in the marketing of SMEs, requires the utilization of business intelligence as the foundation for objective decision-making. This research aims to develop a business intelligence scheme for SMEs and design targeted assistance strategies for SME support institutions. The implementation of business intelligence involves leveraging transactional data from SMEs to ascertain customer segmentation and correlating it with Customer Relationship Management (CRM) strategies. Transactional data is processed into a Recency, Frequency, Monetary (RFM) data model. Customer segmentation is achieved through a clustering process using the K-Means algorithm, and the results yield distinct profiles for SME customers. Evaluation processes are conducted to determine the optimal solution for the number of customer segments. Evaluation methods, including the Elbow Method, Silhouette Scores, and Davies–Bouldin Index, are employed to determine the optimum cluster. The evaluation results indicate that the optimum cluster is 3, with the best Silhouette Score being 0.548 and Davies–Bouldin Index at 0.76. The first customer segment exhibits the highest shopping frequency and monetary value, categorizing them as active and profitable customers. Special loyalty services are recommended for this segment. The second segment, despite having the largest number of customers, exhibits a shopping frequency of only 1-2 times, with an average recency of approximately the last 2 months. These customers require effective after-sales service. The third segment consists of customers who last shopped more than 6 months ago, making them a low-priority segment. Re-engagement strategies, such as email marketing, are suggested for this segment. Support institutions can focus on CRM assistance targeting these three identified segments.

Identifikasi Pola Komunikasi dan Kepribadian Siswa Sekolah Luar Biasa (SLB) Melalui Analisis Konten Media Sosial dengan Metode Anova dan K-Means Galih Cahyaningsih, Agung Ukki; Candiasa, I Made; Gunadi, I Gede Aris
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 6: Desember 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025126

Penelitian ini dilakukan untuk menganalisis pola komunikasi siswa Sekolah Luar Biasa (SLB) melalui aplikasi WhatsApp serta mengidentifikasi kecenderungan kepribadian mereka berdasarkan aktivitas komunikasi digital. Metode yang digunakan adalah clustering K-Means dengan tiga indikator utama: waktu respons, panjang pesan, dan frekuensi pesan untuk mengelompokkan siswa ke dalam tiga tipe kepribadian, yaitu introvert, ambivert, dan ekstrovert. Data penelitian diperoleh dari 102 siswa SLB melalui hasil penambangan pesan WhatsApp. Kualitas klaster divalidasi menggunakan Davies-Bouldin Index (DBI) dengan nilai 0,9095, yang menunjukkan bahwa hasil pengelompokan cukup baik, dengan pemisahan antar klaster yang jelas dan tingkat homogenitas internal yang tinggi. Selain itu, dilakukan analisis korelasi menggunakan metode Spearman Rank-Order untuk mengetahui hubungan antara pola komunikasi dan kepribadian siswa. Hasil uji korelasi menunjukkan koefisien ρ sebesar 0,187 dengan nilai signifikansi 0,060, yang berarti terdapat hubungan positif namun tidak signifikan secara statistik. Dengan demikian, pola komunikasi digital dapat memberikan indikasi awal mengenai kecenderungan kepribadian siswa, tetapi belum dapat dijadikan dasar prediksi yang kuat. Abstract This study was conducted to analyze the communication patterns of Special Needs School (SLB) students through the WhatsApp application and to identify their personality tendencies based on digital communication activities. The method used was K-Means clustering with three main indicators response time, message length, and message frequency to categorize students into three personality types: introvert, ambivert, and extrovert. The research data were obtained from 102 SLB students through WhatsApp message mining. The quality of the clusters was validated using the Davies-Bouldin Index (DBI), which produced a value of 0.9095, indicating that the clustering results were sufficiently good, with clear separation between clusters and high internal homogeneity. In addition, a correlation analysis using the Spearman Rank-Order method was conducted to examine the relationship between communication patterns and student personality. The results showed a correlation coefficient (ρ) of 0.187 with a significance value of 0.060, indicating a positive but statistically insignificant relationship. Therefore, digital communication patterns can provide an initial indication of students’ personality tendencies but cannot yet serve as a strong predictive basis.

Co-Authors ., Ketut Suma ., Putu Sonia Virgawati Pratiwi Adi Sista, Dewa Nyoman Agus Ariwanta, I Putu Yesha Agus Gunawan Agus Harjoko Agus Harjoko Agus Harjoko Ahmad Asroni Ahmad Asroni, Ahmad Anandita, Ida Bagus Gede Andiny T T Arditaloka, I Wayan Angga Ariasa, Komang Artama, Made Bella Eka Wahyuningtias Cipta, I Putu Agus Eka Yatna Cokorda Oka Birawidya David Juli Ariyadi Dewa Gede Hendra Divayana, Dewa Gede Hendra Dewi Oktofa Rachmawati Dharmana, I Wayan Dinata, I Made Anom Mahartha Erlangga, Anak Agung Gde Wahyu Sukma Fauzi, Muhammad Rizki Galih Cahyaningsih, Agung Ukki Gede Indrawan Gede Rasben Dantes Hajrin, M. Heryanto, I Wayan Agus I Gede Andika Diana Putra I Gusti Agung Putu Mahendra I Ketut Agus Indra Diatmika I Ketut Paramarta I Komang Adyanata I Made Arya Adinata Dwija Putra I Made Candiasa I Made Gede Sunarya I Made Gede Sunarya I Made Pradipta I Nyoman Sukajaya I Nyoman Wahyu Semeru Putra I Putu Agus Eka Yatna Cipta I Putu Aris Sanjaya I Putu Aris Sanjaya, I Putu Aris I Putu Arya Putra I Putu Dody Suarnatha I Putu Putra Damana I Wayan Agus Heryanto I Wayan Gede Suweca Antara I Wayan Pio Pratama I Wayan Rosiana I Wayan Sadia I Wayan Santyasa I Wayan Sukra Ida Ayu Mirah Cahya Dewi Ida Bagus Satriya Satriya Wibawa Jana Satvika, Gd. Aditya Kadek Kusuma Wardana Kadek Yota Ernanda Aryanto Ketut Suma Ketut Suma . Ketut Suma . Komang Ariasa Komang Setemen Lika Hanifah Luh Joni Erawati Dewi Luh Putu Budi Yasmini Luh Putu Budi Yasmini Luh Putu Risma Noviana Risma Luh Rumni Oktaria M. Hajrin M.Cs S.Kom I Made Agus Wirawan . Made Artama Made Junindra Maha Arta Sang Made Wahyu Aditya Arta Made Windu Antara Kesiman Made Windu Segara Matius Ivan Bimasena Mimin Yeli Sholekah Moh. Heri Setiawan MS Prof. Dr. Ketut Suma . N Dinda Maharani Ni Kadek Erna Supriathi Ni ketut Lisa Maheni Ni Komang Rai Mirayanti NI LUH PUTU MANIK WIDIYANTI Ni Made Dwi Andayani Ni Made Yeni Dwi Rahayu Ni Putu Eka Apriyanthi Nugraha, I Gede Pradipta Adi Nugraha, I Gusti Agung Satria Oktaria, Luh Rumni Pathni, Ida Ayu Wisma Anggaritha pramana, i gede pramana ade saputra Prof. Dr. Ketut Suma, MS . Putra, I Kadek Nurcahyo Putra, I Nyoman Wahyu Semeru Putra, I Putu Arya Putu Eka Parianthana Putu Sonia Virgawati Pratiwi . Putu Wendy Ariyani Rai Sujanem Risha, Nurfa Sandhiyasa, I Made Subrata Saputri, Ni Kadek Tesya Ari Sariyasa Sariyasa Sariyasa Sariyasa Sawitri D U Segara, Made Windu Sidik, Purnama Sisilia Fhelly Djun Sonia Dewi Parna.T Sri Hartati Suputra, I Putu Arsana Suryawan, I Made Yuda Sutarno, Erwan Sutarno, Erwan Suweca Antara, I Wayan Gede T, Andiny T U, Sawitri D Wardana, I Komang Tri Edi Wayan Eka Ariawan Yogi Duwi Antara

Title

Found 94 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 94 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 94 Documents
Search