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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Majalah Ilmiah Teknologi Elektro Jurnal Pendidikan Teknologi dan Kejuruan Techno.Com: Jurnal Teknologi Informasi Ekuitas: Jurnal Pendidikan Ekonomi JUTIK : Jurnal Teknologi Informasi dan Komputer WIDYABHAKTI Jurnal Ilmiah Populer IJISTECH (International Journal Of Information System & Technology) Indonesian Journal of Data and Science Journal of Computer Networks, Architecture and High Performance Computing IJISTECH Jurnal Puan Indonesia Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) Jurnal Pengabdian Masyarakat Sains dan Teknologi Ekuitas Jurnal Pnedidikan Ekonomi Majalah Ilmiah Teknologi Elektro
I Gusti Agung Indrawan
Institut Bisnis Dan Teknologi Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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Decision Support System for Sentiment Analysis of Youtube Comments on Government Policies I Putu Agus Eka Darma Udayana; I Gusti Agung Indrawan; I Putu Dwi Guna Ambara Putra
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.v5i1.1999

Abstract

Sentiment analysis is the process of classifying a text dataset as positive, negative or neutral. Youtube is one of the popular media used to provide responses to a problem. In the Jokowi era, infrastructure development was carried out massively and evenly, one of which was in Bali Province, namely the construction of the Mengwi-Gilimanuk Toll Road. The construction of the Mengwi-Gilimanuk Toll Road consumed a lot of people's agricultural land, which resulted in various pro and con responses from the community. From these problems, sentiment analysis is carried out to get community reviews related to the object being analyzed by utilizing algorithms to be able to classify opinions, in the construction of this system the naïve bayes algorithm is used with testing methods namely accuracy, precision, and recall. From the sentiment analysis conducted by utilizing 18 video links on YouTube with 701 comments, it produces positive sentiment as much as 50.64%, negative sentiment as much as 7.70% and neutral sentiment as much as 39.23%.
PENERAPAN VIDEO PEMBELAJARAN BAGIAN DAN FUNGSI PADA TANAMAN UNTUK KELAS 4 SD DI SD MUHAMMADIYAH 3 DENPASAR: Implementation on Video Tutorial of Plants Parts and Function for 4th Grade Elementary School Students at Muhammadiyah 3 Elementary School Denpasar Welda Welda; Aniek Suryanti Kusuma; Ayu Manik Dirgayusari; I Nyoman Agus Suarya Putra; I Gusti Agung Indrawan
PUAN INDONESIA Vol. 5 No. 1 (2023): Jurnal Puan Indonesia Vol 5 No 1 Juli 2023
Publisher : ASOSIASI IDEBAHASA KEPRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37296/jpi.v5i1.148

Abstract

Technological advances and the current pandemic situation require SD Muhammadiyah 3 Denpasar to evaluate the current learning system. The current learning system is less effective due to advances in technological developments, as well as the prolonged pandemic situation that has forced the learning system to be carried out online. Students are still confused about participating in online learning, and are often left behind in effective online learning because the gadgets students usually use for online learning are sometimes being used by their parents or other circumstances. Students need a source of literature or other sources of teaching materials that allow students to review the teaching materials after study hours. Therefore, this community service activity is carried out to provide an effective alternative to be applied to students, namely by making animated videos covering natural science subject matter regarding the functions and parts of plants. This community service activity begins with survey and preparation activities, analysis and development of learning videos, and then conducts outreach and training on how to use these learning videos. Based on the results of the training conducted, students and teachers can properly use this learning video as an alternative reference for learning materials.
Optimization Strategy on Deep Learning Model to Improve Fruit Freshness Recognition I Gusti Agung Indrawan; Putu Andy Novit Pranartha; I Wayan Agus Surya Darma; I Putu Eka Giri Gunawan
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 14 No 1 (2023): Vol. 14, No. 1 April 2023
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2023.v14.i01.p01

Abstract

The high fruit production during the harvest season is a challenge in the process of sorting fresh fruit and rotten fruit in plantations. Automatic fruit freshness classification based on deep learning can speed up the sorting process. However, building a model with high accuracy requires the right strategy based on the dataset's characteristics. This research aims to apply optimization strategies to deep learning models to improve model performance. The optimization strategy is implemented by optimizing the model using fine-tuning strategy by selecting the best parameters based on learning rate, optimizers, transfer learning, and data augmentation. The transfer learning process is applied based on the dataset's characteristics by training some parameters with a size of 30% and 60%, which were tested in four scenarios. The fine-tuning strategy is applied to three Deep Learning models, i.e., MobileNetv2, ResNet50, and InceptionResNetV2, which have various parameter sizes. Based on test results, fine-tuning strategy produces the best performance up to 100% with a learning rate of 0.01, the SGD optimizers on the InceptionResNetV2 model are trained on 60% of the parameters.
Analisis Faktor yang Mempengaruhi Customer Loyalty dalam Menggunakan Uang Elektronik Berbasis Server Ni Made Mila Rosa Desmayani; I Gusti Agung Indrawan
Ekuitas: Jurnal Pendidikan Ekonomi Vol. 10 No. 1 (2022)
Publisher : Fakultas Ekonomi Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/ekuitas.v10i1.30306

Abstract

Electronic money providers can be divided into two. First is a provider of electronic money from banks in the form of a physical chip card. Examples are e-money from Bank Mandiri and Flazz from BCA. The second is a server-based, non-banking e-money provider. Examples of products are GoPay, OVO and DANA. Competition is very tight to dominate the electronic money market share, encouraging server-based electronic money providers to compete to expand services, and provide facilities and discounts to its users. This has the potential to seize competitors' marketshare, where electronic money users will switch to other providers.  This article aims to analyze the factors that affect customer loyalty of server-based electronic money users in Denpasar, Bali. The object of this research is the factors that affects customer loyalty in using server-based electronic money. This research uses modified technology acceptance model. The collection of data on the perception of electronic money was carried out using a questionnaire. The results obtained from this questionnaire are: (1) perceived user convenience, (2) perceived usefulness, (3) perceived price suitability, (4) perceived discount has a positive dan significant effect on user loyalty in using server-based e-money.
PKM Optimalisasi Pengelolaan Sampah Melalui Pemilahan Pada Sumber Timbulannya di Desa Pecatu, Kecamatan Kuta Selatan, Kabupaten Badung, Bali Sandika, I Kadek Budi; Ariasih, Ni Kadek; Sutarwiyasa, I Ketut; Lesmana, Putu Surya Wedra; Ginantra, Ni Luh Wiwik Sri Rahayu; Widiartha, Komang Kurniawan; Marlinda, Ni Luh Putu Mery; Indrawan, I Gusti Agung
Jurnal Pengabdian Masyarakat Sains dan Teknologi Vol. 1 No. 4 (2022): Desember : Jurnal Pengabdian Masyarakat Sains dan Teknologi
Publisher : Fakultas Teknik Universitas Cenderawasih

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58169/jpmsaintek.v1i4.61

Abstract

Pemerintahan desa Pecatu belum mampu menangani sampah pada unit jasa pengelolaan sampah secara maksimal karena sampah belum terpilah di sumber timbulannya, mesin yang kurang bagus, serta belum menemukan formula tepat menangani masalah residu. Tujuan Kegiatan PKM yaitu melakukan optimalisasi pengelolaan sampah melalui proses pemilihan. proses edukasi kepada masyarakat sebagai target dari transfer knowledge tata cara pemilahan sampah, karena masyarakat merupakan sumber timbulan dari pengelolaan sampah. Kegiatan PKM ini memberikan solusi yang ditawarkan dengan sumber daya yang dimiliki adalah sosialisasi, edukasi dan pendampingan pemilahan sampah di sumber timbulan sampah/pelanggan. Hasil penelitian berfokus pada kegiatan pendampingan program pemilahan sampah ditargetkan pada 5% pelanggan unit jasa pengelolaan sampah, Tahapan pengabdian yang direncanakan adalah persiapan, FGD unsur pemerintahan desa dinas dan desa adat, sosialisasi pemilahan sampah, monitoring pelaksanaan pemilahan sampah disumbernya, serta evaluasi kegiatan. Hasil PKM menunjukkan tingkat pemahaman dan konsistensi masyarakat pada proses pemilahan sampah rerata adalah 63%.
ANALISIS PEMILIHAN CLUSTER OPTIMAL DALAM SEGMENTASI PELANGGAN TOKO RETAIL Murpratiwi, Santi Ika; Agung Indrawan, I Gusti; Aranta, Arik
Jurnal Pendidikan Teknologi dan Kejuruan Vol. 18 No. 2 (2021): Edisi Juli 2021
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (435.37 KB) | DOI: 10.23887/jptk-undiksha.v18i2.37426

Abstract

Saat ini pemanfaatan data menjadi fokus dalam bidang pemasaran khususnya untuk menyusun strategi. Agar strategi pemasaran bisa tepat sasaran dibutuhkan segmentasi pelanggan. Data mining khususnya clustering mampu membantu proses segmentasi pelanggan. Dalam penelitian ini, data mining diimplementasikan untuk segmentasi pelanggan UD. XYZ dengan metode K-Means, K-medoids, dan Means.. Tujuan penelitian ini adalah mencari metode dan nilai k terbaik yang dihasilkan dari tiga metode clustering. Penelitian ini menyajikan proses Data Mining dengan menggabungkan model RFM dengan algoritma clustering K-Medoids, X-Means, dan K-Means. Dataset yang telah diimplementasikan ke dalam model RFM digunakan sebagai bahan pengolahan data. Data transaksi dengan jumlah 153.492 diimplementasikan ke dalam model RFM menjadi 10.145 data untuk dilakukan identifikasi pelanggan potensial. Inisialisasi cluster awal pada metode K-Medoids, X-Means, dan K-Means dilakukan secara random. Nilai k dalam penelitian ini diinisialisasi dari 1 sampai 10. Nilai k diimplementasikan secara berulang dan dihitung validasi cluster menggunakan metode David Bouldin Index (DBI) dan jaraj rata-rata cluster dengan centroid. Hasil penelitian menunjukkan K-medoids memiliki nilai validitas yang lebih baik dibandingkan dengan X-Means dan K-Means. Rata-rata nilai DBI yang dihasilkan metode K-Medoids adalah 0,540778. Jumlah cluster terbaik yang dihasilkan adalah 5 cluster, hal ini ditentukan dengan mempertimbangkan jumlah persebaran data pada k = 5 yang menghasilkan nilai sama pada metode K-Medoids, X-Means, dan K-Means. Tingkatan pelanggan yang terbentuk adalah About To Sleep, Customer Needing Attention, Recent Customer, Potential Loyalist, dan Loyal Customers.
Comparison of Deep Learning Methods for Detecting Tuberculosis Through Chest X-Rays Udayana, I Putu Agus Eka Darma; Indrawan, I Gusti Agung; Prawira, I Made Karang Satria
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4345

Abstract

Chronic diseases are the leading cause of death worldwide, accounting for 73% of deaths in 2020. Tuberculosis (TB), caused by the bacterium Mycobacterium tuberculosis, is one of these diseases and has a significant impact on countries with a high TB burden due to a lack of radiologists and medical equipment. Early diagnosis of TB is crucial but challenging because of its similarity to lung cancer and the shortage of radiologists. A semi-automatic TB detection system is needed to support medical diagnosis and improve public health services. Deep learning technology, such as Convolutional Neural Networks (CNN), offers an effective solution for disease diagnosis with high accuracy. This study compares deep learning methods using an 8-layer CNN and VGG-19, both enhanced with Histogram Equalization (HE) for improved image quality. The study utilizes chest X-ray images of normal lungs and TB-affected lungs from Kaggle. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Results indicate that the VGG-19 model outperforms the 8-layer CNN across all evaluation metrics, achieving an accuracy of 72.00% compared to 65.00% for the 8-layer CNN. VGG-19 also demonstrates better precision, recall, and F1-score, making it a more suitable choice for TB detection with enhanced image quality.
Implementasi Algoritma FP-Growth dengan Closure Table untuk Penemuan Frequent Itemset pada Keranjang Belanja I Gusti Agung Indrawan; Made Sudarma; Lie Jasa
Jurnal Teknologi Elektro Vol 17 No 2 (2018): (May - Agustus) Majalah Ilmiah Teknologi Elektro
Publisher : Program Studi Magister Teknik Elektro Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.2018.v17i02.P06

Abstract

Algoritma FP-growth adalah algoritma data mining yang digunakan untuk menemukan frequent itemset pada data keranjang belanja. Frequent itemset adalah kelompok barang yang sering dibeli bersamaan dalam satu keranjang belanja. Analisa frequent itemset akan menghasilkan aturan asosiasi. Algoritma FP-growth menemukan frequent itemset dengan mengkompresi data keranjang belanja ke struktur data tree yang disebut FP-tree. FP-tree kemudian dianalisa untuk mengekstrak frequent itemset. Data keranjang belanja selalu bertambah jumlahnya untuk setiap transaksi yang terjadi, sehingga proses penemuan frequent itemset akan membuat FP-tree dari awal secara berulang-ulang setiap kali algoritma FP-growth dijalankan. Agar FP-tree tidak perlu dibuat ulang dari awal setiap kali algoritma FP-growth dijalankan, maka FP-tree perlu disimpan pada media penyimpanan menggunakan format yang sesuai dengan struktur data tree. Penelitian ini menyimpan FP-tree ke tabel pada database dengan struktur closure table. Struktur closure table memiliki beberapa keunggulan sehingga cocok digunakan untuk menyimpan struktur data tree.
Classification of Cavendish Banana Ripeness With CNN Method Tjokorda Istri Agung Pandu Yuni Maharani; I Gusti Agung Indrawan; Gede Dana Pramitha; Christina Purnama Yanti; I Made Marthana Yusa
Indonesian Journal of Data and Science Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.259

Abstract

Cavendish bananas are one of the most widely consumed tropical fruits in Indonesia due to their sweet taste and high nutritional content. However, as they ripen, the sugar content in bananas increases, which can be a problem for diabetics. To help diabetics choose bananas with the right level of ripeness, this study developed a Cavendish banana ripeness classification model using artificial intelligence technology, namely the ResNet50 Convolutional Neural Network (CNN) architecture. The banana data is divided into five ripeness categories: green, yellowish green, yellow, spotted yellow, and spotted brownish yellow. The model was trained with two approaches, with and without data augmentation, using two types of training algorithms (optimizers), namely Adam and SGD, as well as a k-fold cross-validation method to ensure accurate results. The results showed that the ResNet50 model produced the highest accuracy of 98% when trained using data augmentation and the Adam optimizer with a learning rate setting of 0.0001.
Classification Of Bougainvillea Flower Varieties Using Variant Of CNN: Resnet50 I Gede Agung Chandra Wijaya; I Gusti Agung Indrawan; I Nyoman Anom Fajaraditya; Ayu Gede Wildahlia; Ida Bagus Ary Indra Iswara
Indonesian Journal of Data and Science Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.266

Abstract

Bougainvillea is a tropical ornamental plant renowned for its vibrant colors and variety of cultivars, yet classifying its species remains challenging due to morphological similarities. This study aims to develop an automated classification system using the ResNet50 deep learning architecture to identify Bougainvillea flower varieties based on visual imagery. The dataset consists of 700 images from seven distinct classes, captured under natural lighting using a smartphone camera. The research process includes image preprocessing (resizing to 224x224 pixels), geometric data augmentation to increase dataset diversity, and evaluation using K-Fold Cross Validation to ensure robust model assessment. The model was trained using transfer learning, and its performance was compared between augmented and non-augmented datasets through evaluation metrics such as accuracy, precision, recall, and F1-score. The results show that augmentation significantly improved the model's performance, achieving an average accuracy of 99.67% on augmented data compared to 93.39% on non-augmented data. The augmented model also exhibited greater consistency across all folds, with several achieving perfect scores. These findings highlight that combining ResNet50 with transfer learning and image augmentation produces a highly accurate and reliable Bougainvillea classification system. This research contributes to the field of AI-based plant phenotyping and lays the groundwork for future applications in horticulture, biodiversity conservation, and education. Further development is recommended to explore larger and more diverse datasets, investigate advanced architectures such as EfficientNet or Vision Transformers, and build real-time mobile-based classification tools for practical field usage
Co-Authors Aniek Suryanti Kusuma, Aniek Suryanti Aranta, Arik Aristamy, I Gusti Ayu Agung Mas Atmaja, Ketut Jaya Ayu Gede Wildahlia Ayu Manik Dirgayusari Christina Purnama Yanti I Gede Agung Chandra Wijaya I Gusti Ayu Anom I Komang Arya Ganda Wiguna I Made Marthana Yusa I Nyoman Agus Suarya Putra I Nyoman Anom Fajaraditya I Putu Agus Eka Darma Udayana, I Putu Agus Eka I Putu Dwi Guna Ambara Putra I Putu Eka Giri Gunawan I Wayan Agus Surya Darma Ida Bagus Ary Indra Iswara Lie Jasa Made Sudarma Murpratiwi, Santi Ika Ni Kadek Ariasih Ni Kadek Ariasih, Ni Kadek NI LUH PUTU MERY MARLINDA Ni Luh Wiwik Sri Rahayu Ginantra Ni Made Mila Rosa Desmayani, Ni Made Mila Rosa Ni Putu Sutramiani Pramitha, Gede Dana Prawira, I Made Karang Satria Putra, I Putu Satria Udyana Putu Andy Novit Pranartha Putu Surya Wedra Lesmana Putu Wirayudi Aditama Qinantha, I Kadek Mahesa Chandra Sandika, I Kadek Budi Santi Ika Murpratiwi Sutarwiyasa, I Ketut Tjokorda Istri Agung Pandu Yuni Maharani Welda Widiartha, Komang Kurniawan Willdahlia, Ayu Gede