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All Journal Journal of Food and Pharmaceutical Science Jurnal Pendidikan Matematika Undiksha Scan : Jurnal Teknologi Informasi dan Komunikasi Sinkron : Jurnal dan Penelitian Teknik Informatika JUTIK : Jurnal Teknologi Informasi dan Komputer Akta Kimia Indonesia WIDYABHAKTI Jurnal Ilmiah Populer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Journal of Computer Networks, Architecture and High Performance Computing JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Jurnal Tika JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia) Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Emasains : Jurnal Edukasi Matematika dan Sains Prioritas : Jurnal Pengabdian Kepada Masyarakat Dharma Jnana Jurnal Ilmiah Ilmu Terapan Universitas Jambi Jurnal Pengabdian Kepada Masyarakat Widya Mahadi Journal of Global Pharma Technology Jurnal Kendali Teknik dan Sains Journal of Food and Pharmaceutical Sciences Jurnal Media Akademik (JMA) Jurnal Bisnis Inovatif dan Digital Router : Jurnal Teknik Informatika dan Terapan Repeater: Publikasi Teknik Informatika dan Jaringan Fusion : Journal of Research in Engineering, Technology and Applied Sciences Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika Saturnus: Jurnal Teknologi dan Sistem Informasi KOMET Router : Jurnal Teknik Informatika dan Terapan

I WAYAN SUDIARSA

STMIK STIKOM Indonesia

Author-ID : 1238975

Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Neuroscience Public Health Social Sciences Other

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Analisis Fitokimia dan Aktivitas Antibakteri Minyak Atsiri Kayu Manis dengan Bakteri Staphylococcus aureus Ni Wayan Karitha Pradnyandari; Sanjiwani, Ni Made Sukma Sanjiwani; I Made Agus Sunadi Putra; Wayan Surya Rahadi; I Wayan Sudiarsa; Ni Nyoman Yudianti Mendra
Emasains : Jurnal Edukasi Matematika dan Sains Vol. 15 No. 1 (2026): Maret 2026 (On Progress)
Publisher : Program Studi Pendidikan Matematika dan Pendidikan Biologi Universitas PGRI Mahadewa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59672/emasains.v15i1.6058

Tanaman kayu manis yang berkembang di Indonesia, terutama jenis Cinnamomum burmanii Blumea. Dalam penelitian ini salah satu tanaman yang digunakan dalam pengobatan yaitu minyak atsiri kayu manis. Minyak atsiri dikenal sebagai minyak eteris atau minyak terbang (volatile oil) adalah senyawa berbentuk cair yang umumnya diperoleh dari berbagai bagian tanaman seperti akar, kulit, batang, daun, buah, biji, maupun bunga. Banyaknya manfaat dari minyak atsiri kayu manis, maka dilakukan penelitian dengan tujuan untuk mengetahui senyawa kimia yang terkandung dalam minyak atsiri kayu manis dan untuk menguji aktivitas antibakteri dengan bakteri Staphylococcus aureus didalam minyak atsiri kayu manis. Penelitian ini merupakan desain pengujian laboratorium dengan tujuan verikatif yaitu untuk menguji adanya metabolit sekunder dan aktivitas antibakteri dari minyak atsiri kayu manis (Cinnamomum burmannii) dengan bakteri Staphylococcus aureus menggunakan metode difusi cakram dengan media agar. Penelitian minyak atsiri kayu manis dibuat dengan dua sampel dalam pembelian toko berbeda. Hasil yang didapatkan dijelaskan dalam analisis deskriptif. Hasil penelitian ini menunjukkan bahwa minyak atsiri kayu manis (Cinnamomum burmannii) mengandung senyawa triterpenoid dan minyak atsiri kayu manis berpotensi menghambat aktivitas antibakteri Staphylococcus aureus dengan diameter 25,93 mm dan 26,13 mm dengan kategori sangat kuat. Keterbaruan dari penelitian ini adalah menguji kandungan senyawa metabolit sekunder dan menganalisis aktivitas antibakteri pada minyak atsiri kayu manis dengan metode difusi cakram.

Optimasi Prediksi Harga Saham BBNI melalui Integrasi Proses ETL dan Algoritma Long Short-Term Memory I Gusti Ngurah Rangga Mahesa; I Wayan Sudiarsa; I Putu Dicky Dharma Suryasa; Putu Agus Aditya Putra; Yulianus Kevin Dharmawa Sagur
Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 4 No. 1 (2026): Januari: Repeater : Publikasi Teknik Informatika dan Jaringan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/repeater.v4i1.795

Stock price prediction remains a complex challenge due to the dynamic and non-linear nature of financial markets, especially for banking stocks like PT Bank Negara Indonesia (Persero) Tbk (BBNI). This study aims to optimize BBNI stock price forecasting by integrating an automated Extract, Transform, Load (ETL) pipeline with the Long Short-Term Memory (LSTM) algorithm within a data engineering framework. Historical data from 2019 to 2025 were processed through a structured ETL sequence—including data cleaning, feature engineering, and MinMaxScaler normalization—to ensure high data quality. The dataset was partitioned into 80% for model training and 20% for testing to ensure rigorous evaluation. The results demonstrate that the systematic ETL approach significantly enhances model stability and predictive accuracy compared to conventional methods. The LSTM model effectively captured long-term temporal dependencies, providing reliable trend forecasts with an impressive test accuracy, achieving a Root Mean Squared Error (RMSE) of 0.0354. This research underscores that integrating robust data engineering practices with deep learning is essential for building resilient financial decision-support systems.

Implementasi Pipeline ETL dan Pemodelan Prediktif ARIMA dalam Memetakan Pola Pembelian Konsumen pada Dataset Marketplace I Wayan Manik Mas Sri Dantya; I Wayan Sudiarsa; I Putu Kabinawa Raesa Putra; Brian Adi Sapurta; I Komang Hari Sastrawan
Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 4 No. 1 (2026): Januari: Repeater : Publikasi Teknik Informatika dan Jaringan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/repeater.v4i1.799

In the rapidly evolving digital economy, the ability to anticipate transaction surges is a strategic asset for marketplace platforms to maintain operational efficiency. This research aims to build an accurate daily transaction volume forecasting system thru the implementation of an Extract, Transform, and Load (ETL) pipeline and Autoregressive Integrated Moving Average (ARIMA) predictive modeling. The dataset used is sourced from dataset_olshop.csv, which includes transaction history for the entire year of 2025. The ETL stage focused on data cleaning and handling missing values, while time series analysis began with the Augmented Dickey-Fuller (ADF) stationarity test, which yielded a significant p-value of 0.000006. The parameter model was optimized using the auto_arima algorithm, which determined the ARIMA(2,0,0) configuration as the best model. The evaluation results of the model show fairly stable performance with a Root Mean Squared Error (RMSE) value of 2.002 and a Mean Absolute Error (MAE) of 1.704 on the test data. Research findings reveal a consistently higher purchasing pattern during the mid-month and end-of-month periods, with an average of 5.52 daily transactions, compared to the beginning of the month, which saw 5.48 transactions. The 30-day forecast results provide valuable insights for online store managers to proactively adjust inventory and logistics workforce allocation strategies. This research concludes that integrating data engineering techniques and statistical analysis can provide predictive solutions for the dynamics of the digital market.

Production of Solid Soap from Arabica Coffee Grounds (Coffea arabica L.) with Antibacterial Properties Sanjiwani, Ni Made Sukma; Ariani, Komang; Sunadi Putra, I Made Agus; Rahadi, I Wayan Surya; Mirah Mariati, Ni Putu Ayu; Sudiarsa, I Wayan; Udayani, Ni Nyoman Wahyu
Journal of Food and Pharmaceutical Sciences Vol 14, No 1 (2026): J.Food.Pharm.Sci
Publisher : Integrated Research and Testing Laboratory (LPPT) Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jfps.25035

Coffee shops are now a favourite hangout for people of all ages, so many entrepreneurs are developing coffee shop businesses because of their high profit potential. Arabica coffee is the type of coffee most commonly used in coffee shops. The coffee-making process produces waste in the form of coffee grounds. Coffee grounds can be used as an ingredient in beauty products such as soap. Soap is the result of saponification, which is a reaction between a base or alkali and fatty acids, which acts as a skin cleanser. This study aims to examine the presence of secondary metabolite compounds in Arabica coffee grounds (Coffea arabica L.), test the physical quality of solid soap made from Arabica coffee grounds (C arabica L.), and assess the soap's ability to inhibit the growth of Staphylococcus aureus bacteria. Arabica coffee grounds were first analysed through qualitative phytochemical screening tests using test tubes and various reagents, then formulated into solid soap with different concentrations, namely 4%, 7%, and 9%. After that, physical properties testing and antibacterial activity evaluation against Staphylococcus aureus were carried out using the disc diffusion method. The research results data were presented using descriptive analysis. The tests revealed that Arabica coffee residues contain secondary metabolites in the form of alkaloids, flavonoids, tannins, saponins, and triterpenoids. Solid soap made from Arabica coffee grounds meets the physical quality test standards in accordance with SNI 3532:2021 and that solid soap made from Arabica coffee grounds with a percentage of 7% (formula 2) and 9% (formula 3) has the potential to inhibit the activity of Staphylococcus aureus bacteria with a moderate category. It can be concluded that Arabica coffee grounds contain secondary metabolites such as alkaloids, flavonoids, tannins, saponins, and triterpenoids. Solid soap with Arabica coffee grounds as an ingredient meets the physical quality requirements in accordance with SNI 3532:2021. Soap formulas with a coffee grounds concentration of 7% (F2) and 9% (F3) have the potential to inhibit the growth of Staphylococcus aureus with moderate efficacy.

Data Pipeline Engineering untuk LSTM Forecasting Seismisitas Melalui Integrasi Proses ETL Katalog Gempa Indonesia Dewa Gde Agung Wisnu Anantha; I Wayan Sudiarsa; I Kadek Adi Erawan; I Ketut Okta Suastika; Gde Wardika Nugraha
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 4 No. 1 (2026): Januari : Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v4i1.1426

Indonesia, as a country with the highest seismicity in the world, requires an accurate earthquake prediction system through the use of the BMKG earthquake catalogue. This research aims to implement ETL-based data pipeline engineering to process 92,887 earthquake catalog entries for the 2008-2023 period into ready-to-use daily time series for the LSTM seismicity forecasting model. The ETL process includes raw data extraction, cleaning of 97% missing values columns on focal mechanism parameters, datetime conversion, daily resampling producing 5,200 entries with earthquake count, total magnitude, and average magnitude features, as well as Min-Max Scaler normalization for LSTM compatibility. The dataset was processed using Google Colab with a stacked LSTM architecture of two layers of 50 and 25 units, dropout 0.2, Adam optimizer, and a sequence window of 30 days to predict the daily earthquake count. The model trained for 100 epochs shows the ability to capture stable seismic activity trends with a consistent decrease in MSE loss, although it shows deviations in extreme spikes due to aftershock sequences. The ETL pipeline proved crucial in ensuring temporal consistency, 100% data completeness, and relevant physics representation, resulting in a reproducible end-to-end framework for disaster mitigation.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jfps.25035

Analisis Tingkat Kemiskinan di Indonesia Dengan Metode DBSCAN Mitan, Maria M; Sudiarsa, I Wayan; Koda, Andrianus; Koda, Stanisilia D. Wero; Koda, Moh M. Azmi
Fusion : Journal of Research in Engineering, Technology and Applied Sciences Vol. 2 No. 2 (2025): Fusion - Oktober
Publisher : PT. Faaslib Serambi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66341/fusion.v2i2.314

Kemiskinan merupakan permasalahan multidimensional yang masih menjadi fokus utama pembangunan di Indonesia. Penelitian ini bertujuan untuk menganalisis tingkat kemiskinan kabupaten/kota di Indonesia menggunakan metode Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Data dianalisis menggunakan Google Colaboratory dengan parameter DBSCAN berupa nilai epsilon (ε) sebesar 0,5 dan minimum points (MinPts) sebesar 5.Hasil pengujian menunjukkan bahwa metode DBSCAN menghasilkan 2 klaster utama dan 7 data teridentifikasi sebagai noise (cluster −1). Klaster pertama mencakup 68 kabupaten/kota dengan karakteristik tingkat kemiskinan relatif sedang, sedangkan klaster kedua terdiri dari 34 kabupaten/kota dengan tingkat kemiskinan tinggi. Keberadaan data noise menunjukkan wilayah dengan karakteristik kemiskinan yang bersifat ekstrem dan berbeda dari pola umum.Hasil ini membuktikan bahwa DBSCAN mampu mengelompokkan wilayah berdasarkan kepadatan karakteristik kemiskinan serta mengidentifikasi wilayah outlier yang memerlukan perhatian kebijakan khusus.

Analisis Klasifikasi Pengaruh Kegagalan dan Keterbatasan Metode Pembayaran Digital terhadap Churn Pelanggan Menggunakan Decision Tree Dewa Ayu Putu Angelina Dewi; I Wayan Sudiarsa; Ni Made Dwi Junita Sariyani; Yuvensia Armelia Sumu; Gusti Ngurah Abhimanyu
Jurnal Bisnis Inovatif dan Digital Vol. 3 No. 1 (2026): Januari : Jurnal Bisnis Inovatif dan Digital
Publisher : Asosiasi Riset Ilmu Manajemen Kewirausahaan dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/jubid.v3i1.1232

The rapid development of digital technology has led to an increased adoption of digital payment methods in online transaction-based businesses. However, in practice, failures and limitations in the implementation of digital payment systems still occur, potentially disrupting transaction processes and reducing customer convenience. Payment related obstacles may result in transaction cancellations and increase the risk of customer churn. This study aims to analyze the impact of failures and limitations in digital payment methods on customer churn using a classification-based approach. The data used in this research are secondary e-commerce customer data obtained from the Kaggle platform, including transaction information, payment methods, customer behavior, and historical transaction records. The research methodology consists of data preprocessing, time-based feature engineering, and classification modeling using logistic regression, decision tree, and random forest algorithms. Model performance is evaluated using accuracy, precision, recall, F1-score, and confusion matrix metrics. The results indicate that the decision tree model demonstrates superior capability in identifying churn customers compared to the other models, although it does not always achieve the highest accuracy. In addition to digital payment methods, other factors such as purchase value, transaction frequency, purchase timing patterns, and product return rates also influence customer churn. The findings highlight the importance of optimizing digital payment systems as part of customer experience enhancement strategies and customer retention efforts in online transaction–based businesses.

Implementasi Algoritma Random Forest untuk Klasifikasi Rentang Harga Ponsel Berdasarkan Spesifikasi Teknis Yustinus Liguori; I Wayan Sudiarsa; I Made Jagat Dita; I Gusti Ngurah Galih Jimbar Baskara; Pande Wisnu Wijaya Putra
Router : Jurnal Teknik Informatika dan Terapan Vol. 3 No. 4 (2025): Desember : Router : Jurnal Teknik Informatika dan Terapan
Publisher : Asosiasi Profesi Telekomunikasi dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/router.v3i4.796

The rapid development of smartphone technology today creates challenges for consumers and manufacturers in determining an objective price range based on highly varied technical specifications. This study aims to implement the Random Forest algorithm in classifying smartphone price ranges into four main categories, namely low, mid-range, high, and flagship. The research method was carried out systematically through the stages of loading a dataset of 2,000 entries, exploratory data analysis (EDA) to ensure data integrity, and model training with a training and testing data split of 80:20. The results showed that the Random Forest model achieved a significant overall accuracy rate of 89%. Based on feature importance analysis, it was found that RAM capacity was the most dominant determining factor, contributing 47% to prediction accuracy, followed by battery power and screen resolution as supporting features. These findings have strategic implications for manufacturers to prioritize memory capacity upgrades in determining product pricing in the market, as well as providing guidance for consumers in assessing the fairness of a device's price based on its technical capabilities.

Analisis Klasifikasi Keputusan Belanja Konsumen Pada Toko Online XX Menggunakan Algoritma Decision Tree Putri Maria Theresia Kehi; I Wayan Sudiarsa; Maria Oktaviani Suryati; Yosefina Dehadi; Maria Karlinda
Saturnus: Jurnal Teknologi dan Sistem Informasi Vol. 4 No. 1 (2026): Januari : Saturnus: Jurnal Teknologi dan Sistem Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/saturnus.v4i1.1436

This study aims to analyze consumer purchasing behavior on e-commerce platforms using the Decision Tree algorithm as an easily interpretable classification method. The dataset used consists of 12,330 transaction records with 18 attributes representing visitor characteristics and user activities during interactions with the e-commerce platform. The research stages include data exploration to identify initial patterns, data preprocessing to handle missing values and class imbalance, splitting the data into training and testing sets, training the Decision Tree model, evaluating model performance, and visualizing the tree structure to analyze decision rules.The test results show that the Decision Tree model with a maximum depth of 3 achieves fairly good performance, with an average accuracy of 89.78%, precision of 69.82%, recall of 59.95%, and an F1-score of 64.51% for the buyer class. The visualization of the decision tree provides clear interpretation of the main attributes influencing purchasing decisions, thereby facilitating understanding for non-technical decision makers. Overall, this study demonstrates that the Decision Tree method is effective in modeling consumer purchasing behavior in e-commerce and can be utilized as a basis for data-driven business decision making, particularly in marketing strategies and improving sales conversion rates.

Co-Authors A. A. Gde Ekayana Agung Ari Chandra Wibawa Agung Narayana Adhi Putra Andika, I Gede Aniek Suryanti Kusuma, Aniek Suryanti Ariana, Anak Agung Gede Bagus Ariani, Komang Aslin Thanelab Nope Augreselia Novita Nuer Brian Adi Sapurta Dewa Ayu Ika Pramitha Dewa Ayu Putu Angelina Dewi Dewa Ayu Sri Handani Dewa Gde Agung Wisnu Anantha Dewa Putu Yudhi Ardiana Dirgayusari, Ayu Manik Gandika Supartha, I Kadek Dwi Gde Wardika Nugraha Gede Agus Santiago Giri, Putu Agus Semara Putra Gusti Ngurah Abhimanyu I Dewa Made Krishna Muku I Dewa Putu Juwana I Gede Adnyana I Gede Andika I Gede Iwan Sudipa I Gusti Ayu Anom I Gusti Made Aditya Putra I Gusti Ngurah Agung Putra Wijaya I Gusti Ngurah Galih Jimbar Baskara I Gusti Ngurah Rangga Mahesa I Kadek Adi Erawan I Kadek Adi Gunawan I Kadek Yukiarta Putra I Ketut Okta Suastika I Komang Dika Setiawan I Komang Hari Sastrawan I Komang Sukendra I Made Jagat Dita I Made Suarta I Made Suarta I Nyoman Agus Suarya Putra I Nyoman Buda Hartawan I P.Fajar Adi Pradipta I Putu Dicky Dharma Suryasa I Putu Diva Naratama I Putu Kabinawa Raesa Putra I Wayan Dharma Suryawan I Wayan Eka Saputra I Wayan Manik Mas Sri Dantya I Wayan Sumandya I Wayan Surya Rahadi Ida Ayu Agung Ekasriadi Indra Pratistha Jepri Martana, I Nyoman Kadek Bagus Karunia Dwi Dharmayasa Kadek Suryati Koda, Andrianus Koda, Moh M. Azmi Koda, Stanisilia D. Wero Made Hanindia Prami Swari Maharianingsih, Ni Made Maria Karlinda Maria Oktaviani Suryati Mitan, Maria M NI KADEK RINI PURWATI Ni Luh Putu Sandrya Dewi Ni Made Dwi Junita Sariyani Ni Made Lisma Martarini Ni Made Sukma Sanjiwani Ni Nyoman Padmawati Ni Nyoman Wahyu Udayani Ni Nyoman Yudianti Mendra Ni Putu Ayu Mirah Mariati NI PUTU AYU MIRAH MARIATI Ni Putu Kania Mahadina Ni Putu Sri Indah Wulandari Ni Wayan Karitha Pradnyandari Ni Wayan Sunita Pande Wisnu Wijaya Putra Pande, Ni Kadek Nita Noviani Pramana, I Made Wisnu Yoga Puguh Santoso Putra , I Dewa Putu Gede Wiyata Putra, I Made Agus Sunadi Putri Maria Theresia Kehi Putu Agus Aditya Putra Putu Paramita Rusaldi PUTU SUGIARTAWAN Rahadi, I Wayan Surya Sanjiwani, Ni Made Sukma Sanjiwani, Ni Made Sukma Sanjiwani Sastaparamitha, Ni Nyoman Ayu J. Satwika, I Kadek Susila Setya Cahyani, I Gusti Ayu Agung Dwita Socatama, I Putu Yoga Suradana, I Made Suyitno, Yoga Kristian Syamsiar, Syamsiar Tebai, Elisabeth Lydia Wardani, Ni Wayan Wayan Surya Rahadi Willdahlia, Ayu Gede Wiyata Putra, I Dewa Putu Gede Yosefina Dehadi Yulianus Kevin Dharmawa Sagur Yustinus Liguori Yuvensia Armelia Sumu Zamzak , M.Arif

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