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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Journal of Information Systems Engineering and Business Intelligence JUTIK : Jurnal Teknologi Informasi dan Komputer Journal of Social Work and Empowerment Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
I Wayan Agus Surya Darma
Institut Bisnis Dan Teknologi Indonesia
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Social Sciences Other

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Peramalan Jumlah Kunjungan Wisatawan Menggunakan Triple Exponential Smoothing I Wayan Agus Surya Darma; I Putu Eka Giri Gunawan; Ni Putu Sutramiani
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol. 8, No. 3, December 2020
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2020.v08.i03.p06

Abstract

Bali merupakan salah satu destinasi pariwisata terbaik di dunia. Berdasarkan berita resmi statistik yang dipublikasikan oleh Badan Pusat Statistik Bali, jumlah kunjungan wisatawan mancanegara ke Bali pada bulan Juni 2019 mencapai 549.751 kunjungan. Peramalan kunjungan wisatawan merupakan faktor yang sangat penting untuk menentukan kebijakan tempat tujuan wisata, meminimalkan ketidakpastian dan resiko investasi. Hal ini merupakan hal yang sangat penting karena sektor pariwisata merupakan tulang punggung ekonomi di Bali. Penelitian ini mengangkat topik bagaimana mengimplementasikan metode Triple Exponential Smoothing pada proses peralaman jumlah wisatawan. Kami menggunakan data historis kunjungan wisatawan ke Bali yang diperoleh dari Badan Pusat Statistik Provinsi Bali. Hasil peramalan dievaluasi menggunakan mean absolute error untuk menunjukan rata-rata kesalahan dalam perhitungan peramalan. Rata-rata Mean Absolute Error yang dihasilkan pada peramalan ini adalah 18 dengan hasil evaluasi terbaik dengan menggunakan Alpha 0.9, Beta 0 dan Gamma 0.8.
IMPLEMENTASI SUPPLY CHAIN MANAGEMENT PADA E-COMMERCE SEBAGAI STRATEGI PENGEMBANGAN UMKM JAJANAN DODOL KHAS BULELENG I Wayan Agus Surya Darma; I Gusti Agung Indrawan; Ni Putu Sutramiani
Jurnal Teknologi Informasi dan Komputer Vol 6, No 2 (2020): Jurnal Teknologi Informasi dan Komputer
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (439.92 KB)

Abstract

ABSTRACTPenglatan Village is a village that is the biggest dodol production center of Buleleng in the largestdistrict of Buleleng which is distributed to all regencies in Bali, even Buleleng dodol is also an icon oftypical souvenirs of Buleleng to be taken outside of Bali. Conventional trading has been carried out bymicro and small and medium enterprises (UMKM) entrepreneurs in the village of Penglatan who arecraftsmen of Buleleng dodol. Dodol craftsmen in the village of Penglatan, which is a home industrythat sells dodol products to collectors, then collectors distribute dodol products typical of Buleleng toall districts in Bali. This causes the home industry in the village of Penglatan difficult to developbecause it can not handle direct requests. Another problem faced is the difficulty of the craftsmen incalculating the need for raw materials in producing dodol typical of Buleleng when there is an order.Implementation of supply chain management in e-commerce that was developed to manage the supplyof dodol raw materials in accordance with orders that enter the system. This is expected to help dodolcraftsmen in the village of Penglatan in determining the need for raw materials needed to produceorders and provide electronic-based trading media. Determination of raw material requirements forproducing dodol is calculated by applying the concept of master requirements planning. Thedeveloped system can help MSMEs in managing sales transactions and calculating raw materialrequirements in producing dodol.Keywords: Supply Chain Management, Material Requirement Planning, E-Commerce, Dodol KhasBuleleng.ABSTRAKDesa Penglatan merupakan desa yang menjadi sentra produksi dodol khas Buleleng terbesar dikabupaten Buleleng yang disalurkan ke seluruh kabupaten di Bali, bahkan dodol Buleleng jugamenjadi ikon oleh-oleh khas Buleleng untuk dibawa ke luar Bali. Perdagangan secara konvesionaltelah dilakukan oleh pelaku Usaha Mikro Kecil dan Menengah (UMKM) di desa Penglatan yangmenjadi pengrajin dodol Buleleng. Pengrajin dodol di desa Penglatan yang merupakan industri rumahtangga yang menjual produk dodolnya ke pengepul, kemudian pengepul mendistribusikan dodol khasBuleleng ke seluruh kabupaten di Bali. Hal ini menyebabkan industri rumah tangga di desa Penglatansulit berkembang karena tidak bisa menangani permintaan langsung. Permasalahan lainnya yangdihadapi adalah sulitnya pengrajin dalam memperhitungkan kebutuhan bahan baku dalammemproduksi dodol khas Buleleng ketika ada pesanan. Implementasi supply chain management padae-commerce yang dikembangkan untuk mengelola pasokan bahan baku dodol sesuai dengan pesananyang masuk ke sistem. Hal ini diharapkan dapat membantu pengrajin dodol di desa Penglatan dalammenentukan kebutuhan bahan baku yang diperlukan dalam memproduksi pesanan dan memberikanmedia perdagangan berbasis elektronik. Penentuan kebutuhan bahan baku untuk memproduksi dodoldihitung dengan menerapkan konsep master requirement planning. Sistem yang dikembangkan dapatmembantu UMKM dalam mengelola transaksi penjualan dan menghitung kebutuhan bahan bakudalam memproduksi dodol.Kata kunci: Supply Chain Management, Material Requirement Planning, E-Commerce, Dodol KhasBuleleng.
Batik’s Pattern Recognition and Generation: Review and Challenges Dewa Made Sri Arsa; Anak Agung Ngurah Hary Susila; Desak Ayu Sista Dewi; Ni Putu Sutramiani; I Wayan Agus Surya Darma
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol 10 No 2 (2022): Vol. 10, No. 2, August 2022
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2022.v10.i02.p04

Abstract

Batik is one of cultural heritage acknowledged by UNESCO. Intelligence system comes as one of solution to take parts on preservation programs of this heritage. This study explores the current state of the art in application of artificial intelligence on Batik images. This research provides a systematic investigation and present the current progress and hot issues in recognition and generation area for Batik images. Furthermore, this research also presents several Batik data sets and their state of the art. As a result of the review, we are projecting several future works in the discussion.
Handwritten Balinese Script Recognition on Palm Leaf Manuscript using Projection Profile and K-Nearest Neighbor Ni Putu Sutramiani; I Wayan Agus Surya Darma; Dewa Made Sri Arsa
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol 10 No 3 (2022): Vol. 10, No. 3, December 2022
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2022.v10.i03.p02

Abstract

This paper presents a simple approach to the handwritten Balinese script characters recognition in palm-leaf lontar manuscripts. The Lontar manuscript is one of the cultural heritages found in Bali. Lontar manuscripts are written using a pengrupak, which is a kind of knife for writing on palm leaves. To give color to the results of the writing, candlenut is used so that the writing appears clear. In this paper, we apply the projection profile at the segmentation stage to get the handwritten Balinese script characters in the lontar manuscript. The palm leaf manuscript that we use is the Wariga Palalubangan palm leaf. The recognition process is carried out by implementing K-Nearest Neighbor in the recognition process. The recognition was made on the Wianjana script obtained from lontar manuscripts using 720 images consisting of 18 classes as dataset training. The test results showed that the level of recognition accuracy was obtained by 52% in the characters of handwritten Balinese scripts derived from lontar manuscripts and 92% in the characters of handwritten Balinese scripts on paper.
Pengembangan Video Dokumenter Sebagai Media Informasi Kaldera Batur Wiguna, I Putu Yoga Kresna; Parwita, Wayan Gede Suka; Darma, I Wayan Agus Surya; Santika, Putu Praba
Journal of Social Work and Empowerment Vol 3 No 3 (2024): Journal of Social Work and Empowerment - Mei 2024
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/jswe.v3i3.641

Abstract

Kaldera Gunung Batur ini memiliki tinggi kaldera ukuran 13,8 x 10 kilometer. Kaldera Gunung Batur ini merupakan salah satu yang terbesar dan terindah di dunia, sehingga perlu dijaga akeasrian lingkuangan disekitar kaldera Gunung Batur agar tidak terjadi hal-hal yang tidak diinginkan, tetapi masyarakat Desa Mengwitani, belum mengetahui bagaimana sejarah kaldera gunung batur, dengan demikian pembuatan media informasi berupa video dokumenter yang berisikan mengenai pengenalan kaldera sebagai upaya penyebaran pengetahuan. untuk teknik pengumpulan data yang digunakan berupa observasi, wawancara, kuesioner, dokumentasi dan kepustakaaan. Di dalam kaldera tersebut terdapat danau yang berbentuk bulan sabit yang menempati bagian tenggara yang dinamakan Danau Batur. Pada sisi barat laut kaldera Gunung Batur, ditemukan jejak arkeologi berupa arca-arca pada tempat suci yang sudah dimuliakan sejak peradaban Bali prasejarah. Tempat suci yang didirikan pada bukit di puncak tertinggi kaldera Gunung Batur purba ini, sering disebut Pura Penulisan. Titik pengamatan terletak di Kawasan Geopark Gunung Batur, kabupaten Bangli Provinsi Bali. Kaldera Batur merupakan salah satu kaldera terbesar dan terindah di dunia dan masih aktif sampai sekarang. Mengingat hal tersebut, maka dibutuhkan sarana yang memungkinkan masyarakat untuk mendapat edukasi tentang ilmu vulkanologi, sehingga dapat mengurangi dampak buruk jika sewaktu-waktu terjadi bencana. Di Indonesia, tepatnya di Kintamani, Bali sudah terdapat sebuah museum yang membahas tentang cabang ilmu ini, namun lebih di khususkan mengenai pembahasan Gunung Berapi. Meninjau hal tersebut diperlukan adanya digitalisasi tentang bagaimana menjaga Kaldera Gunung Batur yaitu dengan merancang sebuah media informasi yang berbasis video dokumenter. Dengan adanya video dokumenter ini diharapkan kedepannya bisa menjaga kelestarian Kaldera Gunung Batur Kintamani.
Medical Costs Estimation Using Linear Regression Method Dwikasari, Ni Made Dita; Sutramiani, Ni Putu; Putri, Komang Sri Yanisa; Kusuma, Nyoman Tri Rahaditya; Pramana, Made Dimas Aldi Dwi; Darma, I Wayan Agus Surya
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol 11 No 3 (2023): Vol. 11, No. 3, December 2023
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2023.v11.i03.p03

Abstract

Medical costs are a significant issue in the health sector. High healthcare cost lead to the need to anticipate financial risks for individuals and insurance providers. Therefore, medical cost data analysis is necessary to estimate future medical expenses. This research implements data mining techniques using Simple and Multiple Linear Regression methods to estimate medical costs. The dataset used consists of insurance claim data obtained from Kaggle, which includes attributes such as age, gender, body mass index, number of children, smoking habits, region, and medical charges. The research findings that Multiple Linear Regression outperforms Simple Linear Regression in estimating the provided dataset, with R2 value of 80% and lower ?? MSE and MAE values than Simple Linear Regression. The application of linear regression in insurance claim data analysis can provide significant benefits for patients, hospitals, and insurance providers. Overall, this research highlights the effectiveness of data mining techniques, specifically linear regression, in estimating healthcare costs.
FastText and Bi-LSTM for Sentiment Analysis of Tinder Application Reviews Dyanggi, Anak Agung Mayra Candra; Darma, I Wayan Agus Surya; Sastaparamitha, Ni Nyoman Ayu J.
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol 12 No 1 (2024): Vol. 12, No. 1, April 2024
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2024.v12.i01.p07

Abstract

Nowadays technology affects all aspects of society, one of the innovations and creativity in the field of technology is the emergence of online dating application media. The application makes it easy for users to find a partner according to their respective criteria. The most popular online dating app is Tinder. The rise of the use of online dating applications invites controversial sentiments in the community. With this problem, a sentiment analysis is needed to find out the opinions and views of users about Tinder. This study proposed the fastText and Bi-LSTM models used to determine the optimization performance of the fastText and Bi-LSTM methods in sentiment analysis and compares the performance of the fastText and Bi-LSTM models with the fastText and Bidirectional Encoder Representations from Transformers (BERT) models. Based on the experiment, fastText and Bi-LSTM produced the highest performance in the 4th fold scenario with 88% accuracy. Based on the comparison of the three model performances, the fastText and BI-LSTM models can outperform the fastText and BERT models on sentiment analysis of user review datasets in the Tinder application.
Strawberry Disease Detection Based on YOLOv8 and K-Fold Cross-Validation Pranata, I Made Dicky; Darma, I Wayan Agus Surya; Sandhiyasa, I Made Subrata; Wiguna, I Komang Arya Ganda
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol 11 No 3 (2023): Vol. 11, No. 3, December 2023
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2023.v11.i03.p06

Abstract

Strawberry plant diseases can be detected by the condition of the strawberry leaves, flowers, and fruit, but farmers still need knowledge to identify the type of strawberry disease. This study aims to develop a detection model using YOLOv8. The detection model was trained using a dataset containing 3,243 images of strawberry plant leaves, fruit, and flowers, divided into seven disease classes and one healthy plant class. This study aims to develop a more effective strawberry plant disease detection technology. The proposed method is based on YOLOv8 by applying K-Fold Cross Validation to the detection model training and applied data albumentations to produce a robust model. Based on the experimental results, it shows that the YOLOv8s model obtained the highest precision, recall, F1-score, and mean average precision values of 1.00, 0.94, 0.84, and 0.885 respectively.
Enhancing Breast Cancer Recognition in Histopathological Imaging Using Fine-Tuned CNN Darma, I Wayan Agus Surya; Sutramiani, Ni Putu
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol 12 No 3 (2024): Vol. 12, No. 3, December 2024
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2024.v12.i03.p04

Abstract

Global Cancer Statistics reports that of the 2.3 million cases of breast cancer worldwide, 600,000 result in death. Factors contributing to breast cancer in women include both genetic and lifestyle influences. One method for recognizing breast cancer is through histopathology images. Recently, deep learning has gained significant attention in machine learning due to its powerful capabilities in modeling complex data, such as images. In this study, we classify breast cancer by training a Convolutional Neural Network (CNN) model on a dataset of histopathology images annotated and validated by experts, containing two classes. We propose an optimization strategy for CNN models to enhance breast cancer recognition performance, applying a fine-tuning strategy to MobileNetV2 and InceptionResNetV2 to evaluate CNN performance in classifying breast cancer within histopathological images. The experimental results demonstrate that the model achieves optimal performance with an accuracy of 96.22%.
The Performance Comparison of DBSCAN and K-Means Clustering for MSMEs Grouping based on Asset Value and Turnover Sutramiani, Ni Putu; Arthana, I Made Teguh; Lampung, Pramayota Fane'a; Aurelia, Shana; Fauzi, Muhammad; Darma, I Wayan Agus Surya
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 1 (2024): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.1.13-24

Abstract

Background: This study focuses on the latest knowledge regarding Micro, Small and Medium Enterprises (MSMEs) as a current central issue. These enterprises have shown their significance in providing employment opportunities and contributing to the country's economy. However, MSMEs face various challenges that must be addressed to optimize their outcomes. Understanding the characteristics of this group was crucial in formulating effective strategies. Objective: This study proposed to cluster or combine micro, small, and medium enterprises (MSMEs) data in a particular area based on asset value and turnover. As a result, this study aimed to gain insights into the MSME landscape in the area and provided valuable information for decision-makers and stakeholders. Methods: This study utilized two methods, namely the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) method and the K-Means method. These methods were chosen for their distinct capabilities. DBSCAN was selected for its ability to handle noisy data and identify clusters with diverse forms, while K-Means was chosen for its popularity and ability to group data based on proximity. The study used a dataset containing MSME information, including asset values and turnover, collected from various sources. Results: The outcomes encompassed identifying clusters of MSMEs based on their closeness in the feature space within a specific region. Optimizing the clustering outcomes involved modifying algorithm parameters like epsilon and minimum points for DBSCAN and the number of clusters for K-Means. Furthermore, this study attained a deeper understanding of the arrangement and characteristics of MSME clusters in the region through a comparative analysis of the two methodologies. Conclusion: This study offered perspectives on clustering MSMEs based on asset value and turnover in a specific region. Employing DBSCAN and K-Means methodologies allowed researchers to depict the MSME landscape and grasp the business attributes of these enterprises. These results could aid in decision-making and strategic planning concerning the advancement of the MSME sector in the mentioned area. Future study may investigate supplementary factors and variables to deepen comprehension of MSME clusters and promote regional growth and sustainability.   Keywords: Asset Value, Clustering, DBSCAN, K-Means, Turnover
Co-Authors Anak Agung Ngurah Hary Susila Arsa, Dewa Made Sri Arthana, I Made Teguh Audrey Tilanov Pramasa Aurelia, Shana Desak Ayu Sista Dewi Dwikasari, Ni Made Dita Dyanggi, Anak Agung Mayra Candra I Gusti Agung Indrawan I ketut Gede Darma Putra I Nyoman Piarsa I Putu Agung Bayupati I Putu Eka Giri Gunawan Kusuma, Nyoman Tri Rahaditya Lampung, Pramayota Fane'a Made Sudarma Ni Putu Sutramiani Pramana, Made Dimas Aldi Dwi Pranata, I Made Dicky Putri, Komang Sri Yanisa Putu Andy Novit Pranartha Putu Praba Santika Sandhiyasa, I Made Subrata Sastaparamitha, Ni Nyoman Ayu J. Wayan Gede Suka Parwita Wiguna, I Komang Arya Ganda Wiguna, I Putu Yoga Kresna