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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) IPTEK The Journal for Technology and Science Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) CommIT (Communication & Information Technology) Jurnal Ilmiah Kursor Prosiding SNATIKA Vol 01 (2011) Jurnal Sistem dan Informatika EKOMBIS REVIEW: Jurnal Ilmiah Ekonomi dan Bisnis MIX : Jurnal Ilmiah Manajemen Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Information System Jurnal Eksplora Informatika MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer WIDYABHAKTI Jurnal Ilmiah Populer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) CSRID (Computer Science Research and Its Development Journal) CCIT (Creative Communication and Innovative Technology) Journal SENSITEK E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Buletin Ilmiah Sarjana Teknik Elektro Journal of Robotics and Control (JRC) Journal of Applied Data Sciences International Journal of Robotics and Control Systems ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Bulletin of Social Informatics Theory and Application Jurnal Pengabdian Masyarakat (JURDIMAS) Prosiding Seminar Nasional CORISINDO
Evi Triandini
Institut Teknologi Dan Bisnis STIKOM Bali
Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Nursing Social Sciences Other

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Penguatan Pemasaran Produk Kopi Kelompok Triguna Karya Kintamani Bangli Widari Upadani, I Gusti Ayu; Kadek Surya Adi Saputra; Ayu Chrisniyanti; Gusti Ngurah Aditya Krisnawan; Evi Triandini; I Nyoman Suraja Antarajaya
WIDYABHAKTI Jurnal Ilmiah Populer Vol. 7 No. 1 (2024): Nopember
Publisher : Direktorat Penelitian, Pengabdian Masyarakat, dan HKI Institut Teknologi dan Bisnis STIKOM Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30864/widyabhakti.v7i1.417

Abstract

Kelompok “Triguna Karya” adalah kelompok subak yang bergerak dalam bidang pengolahan dan pemasaran Kopi Arabika di Kabupaten Bangli. Jenis produk kopi yang diolah dan dipasarkan adalah sebagian besar dalam bentuk kopi beras, namun ada beberapa kelompok juga melakukan pengolahan kopi dalam bentuk kopi sangrai dan kopi bubuk. Beberapa permasalahan dihadapi kelompok seperti teknik pemasaran sebagian besar dilakukan secara konvensional serta kegiatan promosi produk belum dilakukan secara intensif. Tujuan kegiatan pengabdian untuk meningkatkan pengetahuan dan keterampilan peserta  kelompok melalui penguatan dalam bidang pemasaran produk kopi dan penggunaan media sosial. Metode yang digunakan dalam kegiatan pengabdian  adalah 1) pemberian teori, 2) diskusi dan tanya jawab, 3) praktek cara penggunaan media sosial (Facebook, Instagram) melalui webprofile yang akan dibuatkan oleh tim pelaksana dari ITB Stikom Bali 4) dokumentasi dan 5) monitoring dan evaluasi kegiatan.  Pemberian teori disampaikan secara langsung kepada peserta yang berjumlah 20 orang (sebagai perwakilan). Target luaran yang  dicapai melalui kegiatan pengabdian ini adalah peningkatan pengetahuan dan keterampilan anggota kelompok dalam bidang pemasaran produk kopi dan promosi melalui webprofile serta perbaikan desain kemasan. Publikasi akan dilakukan pada media massa lokal, video kegiatan berdurasi 5 menit pada Youtube, publikasi pada WIDYABHAKTI Jurnal Ilmiah Popular STIKOM Bali.
Data mining for forecasting community mobility denpasar city with long short-term memory method Setiawan , I Wayan Agus Hery; Triandini, Evi; Suniantara , I Ketut Putu; Kuswanto , Djoko
Bulletin of Social Informatics Theory and Application Vol. 8 No. 2 (2024)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v8i2.670

Abstract

Denpasar City has a high potential for community mobility, this is supported by many public facilities. High and highly volatile human mobility causes the transmission of the COVID-19 virus to spread very quickly, so forecasting is needed to find out a picture of future community mobility using data mining techniques. Data mining is the process of solving problems by analyzing data that already exists in the database. Denpasar City community mobility data for the period September 1, 2021 – October 31, 2021 show that most of the high mobility is in the junior high school sector. The Long Short-Term Memory method was chosen as a method that can assist in forecasting community mobility. Long Short-Term Memory has the advantage of dealing with missing gradient problems and can be used on all types of data patterns, whether trend, cyclical, seasonal, or horizontal patterns. Hyperparameter tests were carried out including LSTM_units representing the number of Long Short-Term Memory units in each layer, Dropout, and Optimizer to obtain the optimal prediction method. this combination yields a total of 45 methods. The best hyperparameter obtained is at LSTM_units of 128, Dropout of 0.1, and Optimizer is Adam. The results obtained with this hyperparameter are the Root Mean Square Error (RMSE) value of 971,438687. This method results in forecasting the mobility of the people of Denpasar City from November 1, 2021 to November 7, 2021, reaching 9.550 total checkins which is close to the actual value of 10.219
Optimization of XGBoost Algorithm Using Parameter Tunning in Retail Sales Prediction Wijaya, Hendra; Hostiadi, Dandy Pramana; Triandini, Evi
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.82214

Abstract

In retail companies, the owner needs sales analysis to make decisions in the company's business processes. Several previous studies have introduced forecasting techniques using regression analysis, and classification approaches that need optimization. This article proposes a new approach to sales prediction using XGBoost, which is optimized by comparing the best performance from three optimization methods: Random search, grid search, and Bayesian optimization. The aim is to obtain the best comparative analysis and increase prediction accuracy. The novelty of the proposed model is determining the best value for each optimization method using XGBoost. The results of the evaluation show that the best results were achieved by the grid search optimization technique in the XGBoost model with an increase in the evaluation value R^2 from 97.31 to 98.41. The results of the proposed model analysis can help retail business owners in accurate sales predictions to determine the development of business processes.
Digital Marketing Strategy As A Catalyst For SME Growth In The Modern Era Setini, Made; Amerta, I Made Suniastha; Indiani, Ni Luh Putu; Laksmi, Putu Ayu Sita; Triandini, Evi; Purwatiningsih, Aris Puji; Suardana, Gede
EKOMBIS REVIEW: Jurnal Ilmiah Ekonomi dan Bisnis Vol 13 No 1 (2025): Januari
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/ekombis.v13i1.7888

Abstract

This study aims to explore the influence of digital marketing strategies on the performance of Small and Medium Enterprises (SMEs) in Bali. Through a quantitative approach, this study tests the hypothesis that various digital marketing strategies-including search engine optimisation, content marketing, social media marketing, email marketing, and influencer marketing-significantly predict SME performance. The findings show that the implementation of these strategies contributes to increased sales, market share, and better customer relationships. Although the hypothesis regarding dynamic environment moderation did not show significant effects, the study identified challenges that SMEs face, such as resource limitations and digital literacy deficits. The results recommend that SMEs should utilise digital channels more effectively and conduct data-driven analysis to optimise marketing strategies. This research confirms the importance of digital marketing as a vital tool for the growth and success of SMEs in Bali.
Impact of Hyperparameter Tuning on ResNet-UNet Models for Enhanced Brain Tumor Segmentation in MRI Scans Pamungkas, Yuri; Triandini, Evi; Yunanto, Wawan; Thwe, Yamin
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1802

Abstract

Brain tumor segmentation in MRI scans is a crucial task in medical imaging, enabling early diagnosis and treatment planning. However, accurately segmenting tumors remains a challenge due to variations in tumor shape, size, and intensity. This study proposes a ResNet-UNet-based segmentation model using LGG dataset (from 110 patients), optimized through hyperparameter tuning to enhance segmentation performance and computational efficiency. The proposed model integrates different ResNet architectures (ResNet18, ResNet34, ResNet50, ResNet101, and ResNet152) with UNet, evaluating their performance under various learning rates (0.01, 0.001, 0.0001), optimizer types (Adam, SGD, RMSProp), and activation functions (Sigmoid). The methodology involves training and evaluating each model using Loss Function, Mean Intersection over Union (mIoU), Dice Similarity Coefficient (DSC), and Iterations per Second as performance metrics. Experiments were conducted on MRI brain tumor datasets to assess the impact of hyperparameter tuning on model performance. Results show that lower learning rates (0.0001 and 0.001) improve segmentation accuracy, while Adam and RMSProp outperform SGD in minimizing segmentation errors. Deeper models (ResNet50, ResNet101, and ResNet152) achieve the highest mIoU (up to 0.902) and DSC (up to 0.928), but at the cost of slower inference speeds. ResNet50 and ResNet34 with RMSProp or Adam provide an optimal trade-off between accuracy and computational efficiency. In conclusion, hyperparameter tuning significantly impacts MRI segmentation performance, and selecting an appropriate learning rate, optimizer, and model depth is crucial for achieving high segmentation accuracy with minimal computational cost.
Enhancing Diabetic Retinopathy Classification in Fundus Images using CNN Architectures and Oversampling Technique Pamungkas, Yuri; Triandini, Evi; Yunanto, Wawan; Thwe, Yamin
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i1.25331

Abstract

Diabetic Retinopathy (DR) is a severe complication of diabetes mellitus that affects the retinal blood vessels and is a leading cause of blindness in productive-age individuals. The global increase in diabetes prevalence requires an effective DR classification system for early detection. This study aims to develop a DR classification system using several CNN architectures, such as EfficientNet-B4, ResNet-50, DenseNet-201, Xception, and Inception-ResNet-v2, with the application of the SMOTE oversampling technique to address data class imbalance. The dataset used is APTOS 2019, which has an unbalanced class distribution. Two scenarios were tested, the first without data balancing and the second with SMOTE implementation. The test results show that in the first scenario, Xception achieved the highest accuracy at 80.61%, but model performance was still limited due to majority class dominance. The application of SMOTE in the second scenario significantly improved model accuracy, with EfficientNet-B4 achieving the highest accuracy of 97.78%. Additionally, precision and recall increased dramatically in the second scenario, demonstrating SMOTE's effectiveness in enhancing the model's ability to detect minority classes and reduce prediction errors. DenseNet-201 achieved the highest precision at 99.28%, while Inception-ResNet-v2 recorded the highest recall at 98.57%. Overall, this study proves that the SMOTE method effectively addresses class imbalance in the fundus dataset and significantly improves CNN model performance. Although data balancing can help improve model quality by dealing with data imbalances, it comes at a higher computational cost. Using data balancing techniques with SMOTE significantly increased the iteration time per round on all tested CNN architectures.
The Promotion Strategy for Virgin Coconut Oil Products in The Pangsan Ayu Group, Petang District, Badung District Upadani, I Gusti Ayu Widari; Triandini, Evi; Srinadi, Ni Luh Putri; Krisnawan, Gusti Ngurah Aditya; Saputra, Kadek Surya
MIX: JURNAL ILMIAH MANAJEMEN Vol 15, No 1 (2025): MIX : Jurnal Ilmiah Manajemen
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/jurnal_mix.2025.v15i1.018

Abstract

Objectives: Virgin Coconut Oil (VCO) is oil obtained from the processing of coconuts, this VCO has many benefits, especially for health and beauty. However, this product still faces obstacles in the marketing process. Digital marketing is one solution that can be offered to the Pangsan Ayu group. The purpose of this research is to determine the marketing system used and to determine the appropriate marketing method used by the Pangsan Ayu group.Methodology The research was conducted using a survey method through the use of questionnaires and in-depth interviews. The quantitative and qualitative data obtained were analyzed using statistical analysis with SPSS version 25 application. Primary data was obtained from questionnaires given to 96 respondents, consisting of 90 consumers, 4 producers (Pangsan Ayu group) and 2 representatives of the Pangsan Village government.Finding: The research results show that: The group has carried out the planning stage before carrying out the production process 83.3%. Apart from that, the group has also received training (33.3%) and is skilled in processing VCO (33.3%) and the group has divided work tasks (66.7%) in carrying out production.Conclusion: All consumers, 100% of the 90 people who were respondents, said they had used Pangsan Ayu VCO, had purchased it by order, so they recommended using VCO. Most of the 66.7% VCO oil is sold at IDR 35,000, this price is considered cheap at 34.4% and some say the price is moderate at 61.1%. VCO quality is good at 44.4%, attractive at 44.4% and the information conveyed on the packaging label is complete. Most of Pangsan Ayu's VCO products are 51.1% for beauty and 44.4% for health.
Application of Formal Concept Analysis and Clustering Algorithms to Analyze Customer Segments Budaya, I Gede Bintang Arya; Dharmendra, I Komang; Triandini, Evi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6184

Abstract

Business development cannot be separated from relationships with customers. Understanding customer characteristics is important both for maintaining sales and even for targeting new customers with appropriate strategies. The complexity of customer data makes manual analysis of the customer segments difficult, so applying machine learning to segment the customer can be the solution. This research implements K-Means and GMM algorithms for performing clustering based on the Transaction data transformed to the Recency, Frequency, and Monetary (RFM) data model, then implements Formal Concept Analysis (FCA) as an approach to analyzing the customer segment after the class labeling. Both K-Means and GMM algorithms recommended the optimal number of clusters as the customer segment is four. The FCA implementation in this study further analyzes customer segment characteristics by constructing a concept lattice that categorizes segments using combinations of High and Low values across the RFM attributes based on the median values, which are High Recency (HR), Low Recency (LR), High Frequency (HF), Low Frequency (LF), High Monetary (HM), and Low Monetary (LM). This characteristic can determine the customer category; for example, a customer that has HM and HR can be considered a loyal customer and can be the target for a specific marketing program. Overall, this study demonstrates that using the RFM data model, combined with clustering algorithms and FCA, is a potential approach for understanding MSME customer segment behavior. However, special consideration is necessary when determining the FCA concept lattice, as it forms the foundation of the core analytical insights.
Deep Learning Approach to Lung Cancer Detection Using the Hybrid VGG-GAN Architecture Pamungkas, Yuri; Kuswanto, Djoko; Syaifudin, Achmad; Triandini, Evi; Hapsari, Dian Puspita; Nakkliang, Kanittha; Uda, Muhammad Nur Afnan; Hashim, Uda
International Journal of Robotics and Control Systems Vol 5, No 3 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i3.1923

Abstract

Lung cancer ranks among the primary contributors to cancer-related deaths globally, highlighting the need for accurate and efficient detection methods to enable early diagnosis. However, deep learning models such as VGG16 and VGG19, commonly used for CT scan image classification, often face challenges related to class imbalance, resulting in classification bias and reduced sensitivity to minority classes. This study contributes by proposing an integration of the VGG architecture and Generative Adversarial Networks (GANs) to improve lung cancer classification performance through balanced and realistic synthetic data augmentation. The proposed approach was evaluated using two datasets: the IQ-OTH/NCCD Dataset, which classifies patients into Benign, Malignant, and Normal categories based on clinical condition, and the Lung Cancer CT Scan Dataset, annotated with histopathological labels: Adenocarcinoma, Squamous Cell Carcinoma, Large Cell Carcinoma, and Normal. The method involves initial training of the VGG model without augmentation, followed by GAN-based data generation to balance class distribution. The experimental results show that, prior to augmentation, the models achieved relatively high overall accuracy, but with poor performance on minority classes (marked by low precision and F1-scores and FPR exceeding 8% in certain cases). After augmentation with GAN, all performance metrics improved dramatically and consistently across all classes, achieving near-perfect precision, TPR, F1-score, and overall accuracy of 99.99%, and FPR sharply reduced to around 0.001%. In conclusion, the integration of GAN and VGG proved effective in overcoming data imbalance and enhancing model generalization, making it a promising solution for AI-based lung cancer diagnostic systems.
Performance Analysis of Prediction Methods on Tokyo Airbnb Data: A Comparative Study of Hyperparameter-Tuned XGBoost, ARIMA, and LSTM Nurfalah, Rizal Farhan Nabila; Hostiadi, Dandy Pramana; Triandini, Evi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i2.30631

Abstract

The rapid growth of the digital economy has increased the importance of accurately predicting Airbnb property occupancy rates, especially in dynamic and competitive markets such as Tokyo, Japan. Property owners face significant challenges in forecasting occupancy rates due to seasonal patterns, non-linear trends, and complex temporal dependencies within the data. Addressing these challenges, this study investigates the performance of ARIMA, XGBoost, and LSTM models in predicting Airbnb occupancy rates in Tokyo. The dataset is collected from Airbnb listings and includes relevant features such as location, price, customer reviews, and historical occupancy rates. The models were optimized using Grid Search for ARIMA and Random Search for XGBoost and LSTM to identify the best hyperparameter configurations. Evaluation metrics included Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R²), which are more appropriate for regression tasks. The results indicate that XGBoost achieves the highest R² (0.23), followed by LSTM (0.19) and ARIMA (0.03). However, the low R² values suggest that the models struggle to capture occupancy rate variations, indicating the potential influence of unmodeled external factors such as seasonality and policy changes. This study highlights the importance of hyperparameter tuning in improving prediction accuracy and contributes by providing an in-depth comparison of regression-based models for Airbnb occupancy forecasting.
Co-Authors Abdul Karim Achmad Syaifudin Adisurya, Ida Bagus Agus Gian Angga Permana Arie Indrawan Arif Djunaidy Artana, I Gede Edy Artanaya, I Made Dwi Darma Aryanto, I Komang Agus Ady Ayu Chrisniyanti Bayu Iswara Budaya, I Gede Bintang Arya Budhiarta, Dewa Gede Eka Cahya Ayuu Pertami Candra Ahmadi Ciptahadi, Ketut Gus Oka Dandy Pramana Hostiadi Daniel Oranova Siahaan Daru Asih Dian Puspita Hapsari DwAyu Agung Indra Swari EDWAR EDWAR Fajar Astuti Hermawati Forca, Adrian Jaleco Franky Rawung Ganda Werla Putra Gde Sastrawangsa Gusti Ngurah Aditya Krisnawan Hashim, Uda Hendra Wijaya Hisbiyah, Yuni I Gede Putu Krisna Juliharta I Gede Suardika I Gusti Ayu Widari Upadani I Gusti Bagus Wiksuana I Ketut Dedy Suryawan I Ketut Putu Suniantara I Ketut Suniantara I Komang Dharmendra I Komang Rinartha Yasa Negara I Made Dwi Darma Artanaya I Made Suniastha Amerta I Nyoman Suraja Antarajaya Indra Swari, DwAyu Agung Indrawan, Arie Indrianto Indrianto Iswara, Bayu Jayanatha, Sadu Kabnani, Ezra Tifanie Gabriela Kadek Surya Adi Saputra Karolita, Devi Krisnawan, Gusti Ngurah Aditya Kuswanto , Djoko Kuswanto, Djoko Made Pradnyana Ambara, Made Pradnyana Maneetham, Dechrit Marco Ariano Kristyanto Muhammad Faizi, Muhammad Nakkliang, Kanittha Ni Ketut Dewi Ari Jayanti Ni Luh Putri Srinadi Ni Luh Putu Indiani Ni Made Wahyuni Ni Wayan Deriani, Ni Wayan Ni Wayan Ni Wayan Novia Ari Sandra Nur Rochmah, Nur Nurfalah, Rizal Farhan Nabila Nuryananda, Praja Firdaus Perwitasari, Rayi Kurnia Puji Purwatiningsih, Aris Putra, Chrystia Aji Putra, I Gd Windu Sara Adi Putra, Ida Bagus Udaya Putu Adi Guna Permana Putu Ayu Sita Laksmi Putu Suarma Widiada Rangkuti, Rahmah Yasinta Ratna Kartika W Ratna Kartika Wiyati Ravi Vendra Rishika Reza Fauzan Rijal, Muhammad Syamsu Riko Setya Wijaya Rusli, M Rusli, M Sadu Jayanatha Sandra, Novia Ari Sangsawang, Thosporn Saputra, Kadek Surya Setiawan , I Wayan Agus Hery Setini, Made Shofwan Hanief Siti Rochimah Suardana, Gede Sugiarto S Sugiarto Sugiarto Sugiarto, Sugiarto Suniantara , I Ketut Putu Suniantara, I Ketut Putu Suradarma, IB Suradarma, IB Thwe, Yamin Uda, Muhammad Nur Afnan Wawan Yunanto Werla Putra, Ganda Widari Upadani, I Gusti Ayu Widiada, Putu Suarma Wijaya, I Gusti Ngurah Satria Wulandari, Riza Yohanes Priyo Atmojo Yuri Pamungkas