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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Lontar Komputer: Jurnal Ilmiah Teknologi Informasi 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)
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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Developing a smart system for infant incubators using the internet of things and artificial intelligence Aryanto, I Komang Agus Ady; Maneetham, Dechrit; Triandini, Evi
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2293-2312

Abstract

This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
Neobots: an open-source platform for a low-cost neonatal incubator with internet of things approach Aryanto, I Komang Agus Ady; Maneetham, Dechrit; Triandini, Evi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1817-1837

Abstract

A baby incubator implements the internet of things (IoT) with an architectural design combining several scientific fields, such as networks, software, and hardware. Furthermore, this research develops an open-source platform called Neobots, including open-source program code to create a baby incubator. Then an overview of the system includes sending sensor data to the IoT Broker with the message queuing telemetry transport (MQTT) protocol and automatically storing data in the database. The results of the comparison value on each temperature sensor with a temperature sensor at the midpoint with an error of less than 0.7°C. Then testing the fuzzy between the Neobots program and the simulation in MATLAB got an error rate of 0-28.27%. In addition, in less than 10 minutes, the system response can adjust the temperature conditions to a setpoint value of 34°C from 29°C, and the average error value is 0.35°C during 1 hour of the Fuzzy implementation on the incubator. Then transfer data from the incubator to the database in a room without noise and full noise to get results for lost data less than 16.41% and 42.14%, delay rates between 0-6 seconds and 0-7 seconds with testing for 1 hour at every 1 second.
Pengembangan Database E-commerce De Janggelan Menggunakan Metode Database Life Cycle Sugiarto, Sugiarto; Triandini, Evi
Jurnal Sistem dan Informatika (JSI) Vol 16 No 2 (2022): Jurnal Sistem dan Informatika (JSI)
Publisher : Bagian Perpustakaan dan Publikasi Ilmiah - Institut Teknologi dan Bisnis (ITB) STIKOM Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30864/jsi.v16i2.478

Abstract

E-commerce merupakan media digital pemasaran yang saat ini telah banyak digunakan oleh perusahaan untuk mempromosikan maupun memasarkan produknya. Aplikasi e-commerce menyediakan beberapa fungsionalitas yang mendukung proses bisnis perusahaan. Aplikasi E-commerce juga digunakan oleh salah satu produsen Janggelan. Data base yang dimiliki oleh e-commerce tersebut masih dibuat secara umum. Berdasarkan uraian tersebut diatas, maka pengembangan database e-commerce De Janggelan perlu dilakukan untuk mempermudah pengelolaan produk janggelan yang akan dijual oleh beberapa komunitas penjual janggelan. Tujuan penelitian ini yaitu menghasilkan database yang efisien dan mudah diakses untuk keperluan pengelolaan usaha janggelan. Metode yang digunakan untuk pengembangan database dalam penelitian ini yaitu metode Data Base Life Cycle (DBLC). Pembangunan database e-commerce De Janggelan menggunakan metode DBFL menghasilkan model-model data sesuai dengan tahapan pengerjaan dalam metode tersebut. Penggunaan metode ini telah memberikan kemudahan, ketelitian serta kesesuaian antara keperluan data-data yang diperlukan sesuai proses bisnis De Janggelan dengan implementasi rancangan database dalam DBMS yang digunakan. Rancangan Model Data yang dihasikan dalam penelitian ini akan mempermudah programmer untuk membangun aplikasi e-commerce De Janggelan.
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.
Co-Authors Abdul Karim Achmad Syaifudin Agus Gian Angga Permana Alifah Putri, Athirah Hersyadea Arie Indrawan Arif Djunaidy Artana, I Gede Edy Aryanto, I Komang Agus Ady Ayu Chrisniyanti Bayu Iswara Budaya, I Gede Bintang Arya Cahya Ayuu Pertami Candra Ahmadi Chusak, Thassaporn Dandy Pramana Hostiadi Daniel Oranova Siahaan 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 Indrawan, Arie Indrianto Indrianto Iswara, Bayu Jafari, Nadya Paramitha 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 Wayan Deriani, Ni Wayan Ni Wayan Ni Wayan Novia Ari Sandra Nur Rochmah, Nur Nurfalah, Rizal Farhan Nabila Nuryananda, Praja Firdaus Pamungkas, Yuri Perwitasari, Rayi Kurnia Puji Purwatiningsih, Aris Putra, Chrystia Aji Putra, I Gd Windu Sara Adi 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 Reza Fauzan Rijal, Muhammad Syamsu Riko Setya Wijaya Rusli, M Rusli, M Sadu Jayanatha Sangsawang, Thosporn Saputra, Kadek Surya Setiawan , I Wayan Agus Hery Setini, Made Shofwan Hanief Siti Rochimah Suardana, Gede Sugiarto Sugiarto S Sugiarto Sugiarto Sugiarto Sugiarto Suniantara , I Ketut Putu Suradarma, IB Suradarma, IB Tedy Apriawan Thwe, Yamin Uda, Muhammad Nur Afnan Wawan Yunanto Werla Putra, Ganda Widari Upadani, I Gusti Ayu Wijaya, I Gusti Ngurah Satria Wulandari, Riza Yohanes Priyo Atmojo Zulaikha, Ellya