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All Journal Jurnal Matematika Jurnal Sains dan Teknologi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JIEET (Journal of Information Engineering and Educational Technology) JUTIK : Jurnal Teknologi Informasi dan Komputer JOURNAL OF APPLIED INFORMATICS AND COMPUTING SINTECH (Science and Information Technology) Journal Jurnal Matematika UNAND Jurnal Ekonomi Manajemen Sistem Informasi Jurnal Cahaya Mandalika Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Locus Penelitian dan Pengabdian Jurnal Pengabdian Informatika (JUPITA) International Journal of Applied Mathematics and Computing. Konstanta : Jurnal Matematika dan Ilmu Pengelatuan Alam
Wijayakusuma, I Gusti Ngurah Lanang
Universitas Udayana
Aerospace Engineering Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Immunology & microbiology Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Comparison of EfficientNet-B0 and ResNet-50 for Detecting Diseases in Cocoa Fruit Maylianti, Ni Putu; Wijayakusuma, I Gusti Ngurah Lanang; Arta Wiguna, I Putu Chandra
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8868

Abstract

Cocoa is a plant that is very susceptible to disease. One of the diseases that often attacks cocoa is black spots on the fruit. Detecting diseases in cocoa fruit is usually done manually by experts, which has limitations in providing information and is very expensive. this study proposes a model for detecting cocoa fruit diseases based on deep learning, namely convolution neural networks (CNN). This study compares CNN architectures, namely EfficientNetB0 and ResNet50 because these two architectures are very popular. EfficientNetB0 is known to be efficient in utilizing model parameters and the ability to achieve high accuracy, while ResNet50 uses Residual block recognition which allows deeper and more accurate model training. The dataset used is 3344 healthy cocoa fruit images, 943 black pod rot images and 103 pod borer images. From this study, the results for the accuracy of both methods are equally superior with an accuracy of 96% while for the precision of the EfficientNetB0 architecture is superior to ResNet50 with a value of 95.7% while for recall and f1-score ResNet50 is superior with a recall value of 94.7% and f1-score 93.3%. Based on the Confusion Matrix, it can be seen that ResNet50 is able to predict pod borer accurately so it can be concluded that in this study ResNet 50 is superior to EfficientNetB0. However, ResNet50 requires more parameters than EfficientNetB0 so ResNet50 requires a very large amount of data and when using a small amount of data EfficientNetB0 is more suitable for use.
Mutual Fund Performance Analysis Using Information Ratio, STJ Ratio and Value at Risk Ni Putu Leony Putri Paramita; Komang Dharmawan; I Gusti Ngurah Lanang Wijaya Kusuma
International Journal of Applied Mathematics and Computing Vol. 2 No. 1 (2025): International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v2i1.66

Abstract

Measuring performance solely by relying on returns is probably not enough, it is important to consider both returns and risks. Some measurement methods that consider both of these factors are the Sharpe Ratio index, Treynor Ratio, Jensen Alpha, and Information Ratio. Risk analysis using Value at Risk Monte Carlo simulation is also important to determine the potential for extreme risks. The purpose of this study is to provide a good understanding of the performance and risk of mutual fund investments. Based on the performance results, Schroder is the most superior mutual fund, with the highest Information Ratio, Sharpe Ratio, and Jensen Ratio, indicating that they are able to generate good returns considering the risks taken. However, Schroder also has the highest VaR, meaning it has the potential for large losses in the worst market conditions. On the other hand, MNC is at the bottom in almost all performance methods, indicating poor performance with low returns and lower risks.
Application of Conditional Monte Carlo Simulation in Determining European Option Contract Pricing (Case Study on Toyota Motor Corporation (TM) Stock) Fransisca Emmanuella Aryossi; Komang Dharmawan; I GN Lanang Wijayakusuma
International Journal of Applied Mathematics and Computing Vol. 2 No. 1 (2025): International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v2i1.97

Abstract

When making investment decisions, it is crucial for investors to consider various risks that may arise, both in the short and long term. One method to measure risk is through volatility. Volatility represents a statistical measurement of the degree of price variation over a specific period, expressed as volatility (σ) (Aklimawati & Wahyudi, 2013). This study aims to discuss the pricing of European option contracts using Conditional Monte Carlo simulation and the Black-Scholes method. The data used in this study is secondary data obtained from Yahoo Finance. The data consists of quantitative information, namely the monthly closing prices of Toyota Motor Corporation (TM) stock, spanning 5 years from July 1, 2019, to July 1, 2024, yielding 60 data points. In this research, the pricing of European call option contracts was calculated using Conditional Monte Carlo simulation and the Black-Scholes method. The study concludes that European option contract pricing can be determined using two methods: Conditional Monte Carlo simulation and the Black-Scholes method. Conditional Monte Carlo simulation can be employed to calculate European option prices in a structured manner, utilizing stochastic volatility estimated through the Ordinary Least Squares (OLS) method. The two methods yield differing option prices; Conditional Monte Carlo simulation produces lower option price estimates with relatively lower error values compared to the Black-Scholes method at every strike price. The lower estimates from Conditional Monte Carlo simulation are due to its consideration of stochastic changes in volatility, whereas the Black-Scholes method results in higher prices due to its assumption of constant volatility. The comparison demonstrates that Conditional Monte Carlo simulation provides cheaper price estimates under market conditions with non-constant volatility, despite requiring higher computational time compared to the Black-Scholes method. ,
Detection of Political Hoax News Using Fine-Tuning IndoBERT Jocelynne, Charlotte; Wijayakusuma, IGN Lanang; Harini, Luh Putu Ida
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.8989

Abstract

Indonesia has experienced a surge in the spread of political hoax news, posing a potential threat to democratic and social stability. This study aims to develop a model for detecting political hoax news in the Indonesian language using IndoBERT, a language model optimized for Indonesian text. The dataset was sourced from Kaggle and comprises 20,928 factual news articles and 2,251 hoax news articles from major Indonesian media outlets, including CNN, Kompas, Tempo, and Turnbackhoax. The imbalance between factual and hoax news articles was addressed through undersampling, resulting in 1,302 samples for each class. The research stages include data collection, preprocessing, IndoBERT model training, and model evaluation. Results indicate that fine-tuning IndoBERT can detect political hoax news with an accuracy of 94.1% and an ROC AUC of 0.991, demonstrating high performance in accuracy and generalization capability. This research is expected to contribute to minimizing the spread of political hoax news in Indonesia and enhance media literacy among the public.
Comparison of Machine Learning Methods for Menstrual Cycle Analysis and Prediction Khairunisa, Mutiara; Putri, Desak Made Sidantya Amanda; Wijayakusuma, I Gusti Ngurah Lanang
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9076

Abstract

This study compares three machine learning methods—Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Decision Tree—for analyzing and predicting menstrual cycles. The dataset consists of 1,665 samples with 80 attributes encompassing information related to menstrual health. These methods were evaluated using accuracy, Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) metrics. The results show that LSTM achieved the highest accuracy (91.3%), followed by CNN (88.9%) and Decision Tree (85.2%). LSTM excelled in capturing complex temporal patterns in menstrual cycle data, while CNN effectively identified key patterns, and Decision Tree offered interpretability despite lower performance. This study concludes that LSTM is the most effective model for menstrual cycle prediction. The findings highlight the potential for improved accuracy in reproductive health tracking, with future research opportunities to incorporate additional variables such as hormonal history and lifestyle factors, as well as a focus on data privacy.
Analisis Pemilihan Parameter pada Algoritma DBSCAN untuk Pengelompokan Titik Api di Indonesia Driyandita, Bernadeta; Kencana, I Putu Eka Nila; Wijayakusuma, I Gusti Ngurah Lanang
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 3 (2025): JPTI - Maret 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.703

Abstract

Kebakaran hutan, gambut, wilayah pertanian, maupun wilayah urban dan industri menjadi salah satu ancaman terbesar bagi lingkungan, menyebabkan kerusakan ekosistem dan kerugian ekonomi yang signifikan. Penelitian ini mengusulkan optimalisasi pengelompokkan titik api di Indonesia sebagai salah upaya pencegahan kebakaran dengan menggunakan algoritma Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Metodologi yang digunakan dalam penelitian mencakup pemrosesan data, pemilihan parameter tuning algoritma, dan evaluasi hasil klaster menggunakan metrik validasi internal yaitu Silhouette Coefficient (SC). Data penelitian diambil dari web FIRMS NASA berjumlah 12.708, dengan variabel yang digunakan yaitu longitude, latitude, bright_t31, brightness, fire radiative power, scan, dan track. Pada tahap pemrosesan dilakukan pembersihan dan standarisasi data memakai algoritma z-score. Selanjutnya percobaan klasterisasi dijalankan dengan mengubah-ubah nilai pada parameter epsilon (eps) sebesar 0.2, 2, 3, 4, 5, dan 10, yang dikombinasikan dengan nilai minimal point (MinPts) dari 2 sampai dengan 6.  Hasil pengelompokkan optimal yang ditemukan adalah pada saat percobaan dengan nilai eps 4 dan MinPts 4. Kondisi optimal tersebut menghasilkan 2 klaster dan 3 noise, dengan nilai SC sebesar 0.8022. Penelitian ini memberikan wawasan baru dalam pemetaan risiko kebakaran serta dapat digunakan untuk sistem pemantauan berbasis kecerdasan buatan.
Aspect-Based Sentiment Analysis of Reviews for Pandawa Beach Using Naive Bayes and SVM Methods Putri, Made Ayu Asri Oktarini; Sumarjaya, I Wayan; Wijayakusuma, I Gusti Ngurah Lanang
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9083

Abstract

The presence of digital technology, especially online platforms such as Google Maps, has changed the way people search for information about tourist destinations, including reviews and ratings from previous visitors. Aspect-based sentiment analysis becomes a very useful tool to understand people's views and feelings towards a place or product based on the reviews given and identify aspects of interest to tourists visiting Pandawa Beach, by utilizing Naive Bayes and Support Vector Machine (SVM) methods. The main objective of this research is to identify sentiment patterns based on aspects such as attraction, accessibility, amenities, and ancillary. Data was collected and labeled according to sentiment and aspects, then processed using preprocessing techniques, extracted by bag-of-words method, and chi-square feature selection. The model evaluation results showed that SVM produced the highest F1-Score value of 79,625%, while the Naive Bayes method reached 73.29%.
Named Entity Recognition for Medical Records of Heart Failure Using a Pre-trained BERT Model Manurung, Mikael Triartama; I Gusti Ngurah Lanang Wijayakusuma; I Putu Winada Gautama
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9170

Abstract

This study aims to develop a Named Entity Recognition (NER) model based on a pre-trained BERT model for medical records of heart failure patients. The focus of this research is to classify essential medical entities from unstructured medical record texts. The classification covers four categories: objective data (patient identity, laboratory test results, and objective examination data), subjective data (patient complaints), prescriptions, and diagnoses (diagnosis codes and descriptions). The methodology employs Natural Language Processing (NLP) techniques using Transformer-based architectures, such as Bidirectional Encoder Representation from Transformers (BERT). The developed model is evaluated based on entity label prediction accuracy and medical entity classification performance. The results indicate that the BERT-based NER model performs well, achieving an entity prediction accuracy of 84.82%. Furthermore, the model effectively classifies medical entities from input texts in alignment with expected medical entities. This research is expected to contribute significantly to medical data management, assist healthcare professionals in clinical decision-making, and serve as a reference for the development of AI-based healthcare technology in Indonesia.
Perbandingan Metode Machine Learning untuk Analisis dan Prediksi Siklus Menstruasi Putri, Desak; Khairunisa, Mutiara; Wijayakusuma, I Gusti Ngurah Lanang
JIEET (Journal of Information Engineering and Educational Technology) Vol. 8 No. 2 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v8n2.p111-115

Abstract

Penelitian ini membandingkan metode machine learning—Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), dan Decision Tree—untuk analisis dan prediksi siklus menstruasi. Menggunakan data sekunder, model-model ini dievaluasi berdasarkan akurasi, Mean Absolute Percentage Error (MAPE), dan Root Mean Square Error (RMSE). Hasil menunjukkan bahwa LSTM memiliki akurasi tertinggi (91,3%), efektif menangkap pola temporal kompleks pada data menstruasi, sedangkan CNN dan Decision Tree kurang konsisten. Hasil ini mendukung LSTM sebagai model yang disarankan untuk pelacakan siklus menstruasi, yang bermanfaat bagi pemantauan kesehatan reproduksi. Penelitian selanjutnya disarankan menambah variabel lain, seperti riwayat kesehatan hormonal dan gaya hidup, untuk meningkatkan akurasi prediksi serta memperhatikan privasi data pada aplikasi pelacakan menstruasi.
Perancangan Inovatif UI/UX Fitur Kesehatan BNI Mobile Banking Berbasis User Centered Design Karina Maharani Bernis; I Gusti Ngurah Lanang Wijayakusuma
Jurnal Ekonomi Manajemen Sistem Informasi Vol. 6 No. 3 (2025): Jurnal Ekonomi Manajemen Sistem Informasi (Januari - Februari 2025)
Publisher : Dinasti Review

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/jemsi.v6i3.3724

Abstract

Salah satu aspek krusial dalam perjalanan kehidupan manusia adalah kesehatan. Integrasi antara layanan kesehatan dan keuangan melalui penciptaan fitur kesehatan di BNI Mobile Banking tidak hanya memberikan kemudahan, tetapi juga mendorong masyarakat untuk lebih memperhatikan kesehatan mereka sekaligus memiliki pengaturan keuangan yang baik dan terstruktur serta bisa dilacak. Penelitian ini menciptakan fitur kesehatan pada aplikasi BNI Mobile Banking dengan menerapkan metode User Centered Design. Melalui wawancara dan survei yang dilakukan terhadap sejumlah pengguna aplikasi perbankan tersebut, didapatkan bahwa lebih dari 50% pengguna menginginkan sebuah integrasi BNI Mobile Banking dengan aplikasi kesehatan dan gaya hidup yang mereka gunakan sehari-hari. Pengujian prototype yang dikembangan menggunakan aplikasi Figma memberikan suatu hasil penelitian bahwa tercetak skor sebesar 80 serta skor SUS yang didapat setelah perancangan  fitur  baru  meningkat  dari angka 52,5 menjadi 80. User yang melakukan testing menyatakan cukup puas dengan rancangan UI/UX yang dihasilkan dan memberikan kemudahan serta efektivitas dalam pengeluaran aspek kesehatan.
Co-Authors Agustina, Ni Putu Dina Anak Agung Kompiang Oka Sudana Arta Wiguna, I Putu Chandra Astutik, Dian Br Sebayang, Virna Dalira Chandra, Veronica Celine Damayanthi, Ni Wayan Rita Desak Made Sidantya Amanda Putri Driyandita, Bernadeta EKA N. KENCANA Fransisca Emmanuella Aryossi G K Gandhiadi I Gusti Ayu Made Srinadi I ketut Gede Darma Putra I Ketut Restu Wiranata I KOMANG GDE SUKARSA I MADE EKA DWIPAYANA I Nyoman Widana I Putu Eka Nila Kencana I Putu Winada Gautama I PUTU WINADA GAUTAMA I WAYAN SUMARJAYA IM Suyana Utama Isabel Divya Georgiana Walewangko Jocelynne, Charlotte Karina Maharani Bernis Kencana, Eka N. Ketut Jayanegara Khairunisa, Mutiara Komang Dharmawan LUH PUTU IDA HARINI LUH PUTU IDA HARINI Made Sudarma Manurung, Mikael Triartama Maylianti, Ni Putu Minho Jo Ni Kadek Emik Sapitri Ni Luh Putu Suciptawati Ni Nyoman Bintang Marscelina Ni Putu Leony Putri Paramita Pratama, Halim Meliana Pratiwi Tentriajaya, I Dewa Ayu Pradnya Putri, Desak Putri, Desak Made Sidantya Amanda Putri, Made Ayu Asri Oktarini Putu Veri Swastika Raharja, Made Agung Ratna Sari Widiastuti Riandika Fathur Rochim Sagun Chandra Yowani Siden, Hagia Sofia Swastika, Putu Veri Tobing, Charlotte Jocelynne L Ulfatun Farika Novitasari Widiastuti, Ratna Sari Yohanes Kristianto Kristianto