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All Journal Jurnal Simantec JIEET (Journal of Information Engineering and Educational Technology) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Journal of Robotics and Control (JRC) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Journal of Applied Data Sciences ITIJ
Mas Diyasa, I Gede Susrama
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Mathematics Mechanical Engineering

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PEMILIHAN KEGIATAN ORGANISASI MAHASISWA MENGGUNAKAN ALGORITMA PROBABILITAS BAYES Mas Diyasa, I Gede Susrama; Nadhira, Firya; Ardianto, Taruna; Mumtaz, Ahmad Naufal
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol 9, No 1 (2020)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v9i1.23032

Abstract

Organisasi mahasiswa adalah organisasi yang beranggotakan mahasiswa untuk mewadahi bakat, minat dan potensi mahasiswa yang dilaksanakan di dalam kegiatan ko dan ekstra kurikuler, akan tetapi permasalahan muncul ketika mahasiswa dihadapkan untuk memilih organisasi yang sebaiknya diikuti. Kondisi mahasiswa dan banyaknya organisasi yang ada membuat sulit dalam menentukan pilihan, sehingga perlu suatu sistem yang dapat digunakan mahasiswa untuk menentukan pilihan tersebut. Untuk itu pada penelitian ini dibuat suatu sistem dengan tujuan memberikan rekomendasi organisasi yang sebaiknya dipilih oleh mahasiswa dengan menggunakan pendekatan Algoritma Probabilitas Bayes dan konsep Case Based Reasoning (CBR) pada proses retrieval dan similarity berdasarkan data parameter penilaian (pp) dan data class Kelayakan, dengan teknik mengumpulkan data dilakukan dengan cara memberikan kuesioner kepada mahasiswa kemudian dilakukan wawancara, tujuh parameter parameter antara lain nama, IPK, jarak kos/rumah, transportasi, semester, uang saku dan kerja sampingan.  Dari ketujuh fitur tersebut diolah dengan menggunakan metode CBR (Case Based Reasoning), kemudian dilakukan proses klasifikasi dengan menggunakan algoritma Probabilitas Bayes, dan hasilnya berupa rekomendasi kegiatan yang bisa diikuti oleh mahasiswa bersangkutan dengan nalai probabilitas retrieval dan similarity, sebesar 0,00037656 pada kelayakan di class C2.
Wireframe Creation on SIOBEL Application User Interface Design using User Centered Design Wibawani, Sri; Terza Damaliana, Aviolla; Setiawan, Ariyono; Mas Diyasa, I Gede Susrama; Dwi Kusuma, Irma
Information Technology International Journal Vol. 1 No. 2 (2023): Information Technology International Journal
Publisher : Magister Teknologi Informasi UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/itij.v1i2.12

Abstract

The development of an interactive SIOBEL (Sistem Informasi Bela Neagara) application requires the application of User Centered Design in the UI/UX design phase. In this context, User Centered Design becomes an important cornerstone in ensuring an optimal user experience and meeting the needs of users when using the SIOBEL application as a platform to integrate state defense values with oubound. By applying UCD, the UI/UX wireframe design of the SIOBEL application can create a better and satisfying user experience. Users will feel engaged and comfortable when using the application.
Long Short Term Memory Method and Social Media Sentiment Analysis for Stock Price Prediction Mas Diyasa, I Gede Susrama; Mustika, Agung; Amanullah , Nurkholis
Information Technology International Journal Vol. 2 No. 1 (2024): Information Technology International Journal
Publisher : Magister Teknologi Informasi UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/itij.v2i1.13

Abstract

The stock market is a complex arena of interest yet uncertainty. Trading stocks, binaries, gold, and bitcoin is growing in popularity, but is prone to price fluctuations influenced by economic and political factors. Social media, particularly Twitter, is where views on companies are shared. Social media sentiment analysis can provide additional insights to evaluate potential future stock price movements, preventing unwanted speculation. The purpose of this research is to develop a Tesla stock price prediction model by integrating the Long Short-Term Memory (LSTM) method and social media sentiment analysis from Twitter to improve prediction accuracy. Stock price data is obtained from Kaggle and Twitter sentiment data is processed through pre-processing. Evaluation values such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) are lower in the model with sentiment indicating the ability of the model to more accurately model the dynamics of stock price movements. Lower MSE and RMSE indicate that the model's predictions are closer to the true values, and therefore, the model can be considered more reliable in projecting future stock price changes. These results provide support for the use of Twitter sentiment analysis as a useful source of additional information in improving the prediction accuracy of LSTM regression models in the context of stock market analysis
ANALISIS HUBUNGAN ANTARA PARAMETER METEOROLOGI DAN KONSUMSI ENERGI LISTRIK MENGGUNAKAN ALGORITMA HDD Wafiqotul Azizah, Nabila; Yulia Puspaningrum, Eva; Mas Diyasa, I Gede Susrama
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 3 (2024): JATI Vol. 8 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i3.9749

Abstract

Listrik menjadi satu diantara elemen yang bersifat krusial dalam kehidupan, mengingat sebagian besar aktivitas manusia bergantung kepada listrik. Sehingga tidak heran, apabila listrik mengalami peningkatan yang pesat khususnya pada era globalisasi seperti saat ini. Peningkatan ini juga dipengaruhi oleh faktor meteorologi. Beberapa penelitian telah dilakukan untuk mengetahui kecenderungan penggunaan listrik yang dipengaruhi oleh parameter meteorologi. Penelitian ini bertujuan untuk mengetahui pemakaian listrik pada kehidupan sehari-hari yang dipengaruhi oleh faktor meteorologi. Pemilihan faktor ini disebabkan faktor meteorologi menjadi faktor yang mempunyai keterikatan yang sangat erat dengan kehidupan manusia. Sejalan dengan hal tersebut, penelitian ini menggunakan dataset yang diperoleh dari BMKG dan PLN. Pada kesempatan kali ini, peneliti menggunakan CRISP-DM dan algoritma HDD. Metode CRISP-DM berguna untuk menggambarkan siklus data mining sehingga prosesnya bisa lebih teratur, sedangkan metode HDD berguna untuk mengetahui korelasi parameter meteorologi terhadap konsumsi listrik pada musim kemarau. Sejalan dengan itu, penelitian ini menghasilkan proyeksi konsumsi listrik selama periode 2023-2030 dengan menggunakan algoritma HDD, serta menghasilkan prediksi konsumsi listrik pada bulan Desember 2023. Prediksi tersebut menghasilkan nilai MAPE sebesar 1,3%, nilai tersebut menyatakan bahwa akurasi dari hasil relative tinggi
ANALYSIS OF CLUSTERING METHODS ON THE CAUSAL FACTORS OF DIABETES MELLITUS WITH FUZZY C MEANS METHOD Adiwidyatma, Afdhal Reshanda; Mas Diyasa, I Gede Susrama; Trimono, Trimono
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 2 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i2.611

Abstract

This study focuses on the effectiveness of clustering algorithms, namely Fuzzy C-Means by using k-Means algorithm as a supporting method, in the factors that cause Diabetes Mellitus. Diabetes mellitus is a chronic disease characterized by high levels of sugar (glucose) in the blood. Indonesia ranks 5th with the highest diabetes Mellitus patients in the world. This study aims to understand the pattern of factors causing Diabetes Mellitus and test the effectiveness of the clustering algorithm used. The data analysis methods include data collection, data pre-processing, distribution of cluster numbers, algorithm implementation, model adjustment, model training, model evaluation, and analysis of results. The results showed that the Fuzzy C-Means algorithm gets a coefficient of Fuzzynes score of 0.23 with a validation score of 0.40, while for supporting methods used K-Means algorithm gets a validation score of 0.32. This result shows that Fuzzy C-Means algorithm is superior in clastering the factors that cause Diabetes mellitus. The results of what variables have the most effect on cluster values 0 and 1. Where cluster 0 is a cluster that shows which variables are more at risk of diabetes, while cluster 1 is a cluster whose value shows what variables are far from the risk of causing diabetes mellitus. Then based on the results of the cluster that has been done, random blood sugar variables become the most influential variable on the risk of developing diabetes mellitus, followed by blood sugar variables 2 hours PP, and fasting blood sugar
Detection of Abnormal Human Sperm Morphology Using Support Vector Machine (SVM) Classification Mas Diyasa, I Gede Susrama; Prasetya, Dwi Arman; Cahyani Kuswardhani, Hajjar Ayu; Halim, Christina
Information Technology International Journal Vol. 2 No. 2 (2024): Information Technology International Journal
Publisher : Magister Teknologi Informasi UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/itij.v2i2.36

Abstract

Abnormal sperm morphology is a key indicator of male infertility, making its accurate detection crucial for reproductive health assessments. This study explores the application of Support Vector Machine (SVM) classification to automatically detect abnormalities in human sperm morphology. A dataset of microscopic sperm images was collected and labelled based on normal and abnormal morphological features, including head shape, midpiece defects, and tail irregularities. Feature extraction techniques were employed to quantify key morphological characteristics, which were then used to train the SVM model. The proposed SVM-based approach demonstrated high accuracy in classifying normal versus abnormal sperm morphology, significantly reducing the time and error associated with manual analysis. This method provides an efficient, automated solution for andrology laboratories and fertility clinics, enhancing diagnostic consistency and reliability. By incorporating machine learning techniques, this system holds promise for improving the precision of sperm morphology analysis, ultimately contributing to better fertility treatments and outcomes
Balinese Script Handwriting Recognition Using CNN and ELM Hybrid Algorithms Mas Diyasa, I Gede Susrama; Wijaya, Pandu Ali; via, Yisti Vita
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 1 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i1.87524

Abstract

One of the foundational scripts used in Balinese culture is the Balinese script, known as “Aksara Bali”. In its writing, Aksara Bali follows specific rules regarding distinctive stroke shapes that must be carefully maintained to preserve authenticity and readability. This study proposes the use of a hybrid algorithm combining Convolutional Neural Network (CNN) and Extreme Learning Machine (ELM) to recognize handwritten Balinese script characters. The preprocessing stage includes dataset splitting, rescaling, data augmentation, batch size adjustment, and visualization of class distribution. The training stage utilizes the Adam Optimizer to enhance model accuracy. Using 1,691 images of various Balinese script characters, the dataset is divided into an 80:10:10 ratio for training, validation, and testing. Experimental results show that the best accuracy achieved is 91%, indicating that the CNN-ELM hybrid model effectively recognizes Balinese script characters.
Implementation Of Hybrid EfficientNet V2 And Vision Transformer for Apple Leaf Diseases Classification Santoso, Sri Fuji; Hadi, Surjo; Nugroho, Budi; Mas Diyasa, I Gede Susrama
Information Technology International Journal Vol. 3 No. 1 (2025): Information Technology International Journal
Publisher : Magister Teknologi Informasi UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/itij.v3i1.42

Abstract

The apple farming industry faces challenges in managing apple leaf diseases. Current manual detection methods have limitations in expertise variability, time required, potential delays in identification leading to disease spread, and difficulty distinguishing diseases with similar visual symptoms. This research aims to develop an accurate, efficient, and automated apple leaf disease classification system using a hybrid approach that combines EfficientNet V2 architecture and Vision Transformer. The main objectives are to improve disease detection accuracy, reduce computational requirements, facilitate more effective plant management, and support modern agricultural practices in the apple industry. This research uses a hybrid deep learning model that integrates EfficientNet V2 and Vision Transformer components. Experiments were conducted on an apple leaf disease dataset to evaluate model performance. Results show the effectiveness of this method in classifying apple leaf diseases, achieving 98.56% accuracy and an F1 score of 0.9856 on test data. The proposed model has 15.6 million parameters, lighter than the original EfficientNetV2S model with 20 million parameters. Training time was reduced to 6 minutes 32 seconds compared to the original EfficientNetV2S model that required 8 minutes 41 seconds for 5 epochs on the same dataset.
Penerapan Gated Recurrent Unit dengan Bayesian Optimization dalam Prediksi Harga Saham Sektor FMCG Mas Diyasa, I Gede Susrama; Akmal, Mohammad Faizal; Junaidi, achmad
JIEET (Journal of Information Engineering and Educational Technology) Vol. 9 No. 1 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v9n1.p36-41

Abstract

Peningkatan partisipasi investor muda terutama dari Generasi Z dan Milenial menciptakan kebutuhan mendesak untuk menggunakan metode prediksi yang lebih akurat guna meminimalkan risiko investasi. Penelitian ini bertujuan untuk mengembangkan model prediksi harga saham pada sektor Fast-Moving Consumer Goods (FMCG) di Indonesia dengan memanfaatkan algoritma Gated Recurrent Unit (GRU) yang dioptimalkan menggunakan teknik Bayesian Optimization. Metode penelitian ini dimulai dengan pembagian data saham PT Hanjaya Mandala Sampoerna Tbk (HMSP) dari tahun 2019 hingga 2025, yang dibagi menjadi data train (60%), data validation (20%), dan data test (20%). Selanjutnya, dilakukan preprocessing data berupa normalisasi dan sequencing untuk mempersiapkan data. Model GRU yang diterapkan diuji dengan menggunakan metrik evaluasi seperti Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), dan Mean Absolute Percentage Error (MAPE), yang menghasilkan akurasi prediksi yang tinggi dengan RMSE 17.07, MAE 11.50, dan MAPE 1.48%. Penelitian ini menunjukkan bahwa penerapan Bayesian Optimization dapat memberikan efektivitas pemilihan hyperparameter menghasilkan model yang lebih presisi dalam memprediksi harga saham FMCG di Indonesia dan memberikan panduan yang lebih andal bagi investor dalam pengambilan keputusan investasi
MRI image enhancement of the brain using U-NET Etniko Siagian, Pangestu Sandya; Puspaningrum, Eva Yulia; Wan Awang, Wan Suryani; Mas Diyasa, I Gede Susrama
Jurnal Simantec Vol 13, No 2 (2025): Jurnal Simantec Juni 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/simantec.v13i2.29775

Abstract

The quality of Magnetic Resonance Imaging (MRI) images is often compromised by various types of noise, such as salt, pepper, salt-and-pepper, and speckle noise, caused by technical or environmental disturbances. This study aims to develop a brain MRI image denoising model based on the U-Net architecture, capable of effectively removing different types of noise. The methodology includes collecting normal brain MRI datasets, applying data augmentation to increase variability, and introducing artificial noise to simulate possible noise conditions. The U-Net model is trained and evaluated using the Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) metrics. The novelty of this study lies in its combination of augmentation techniques, multi-intensity artificial noise variations, and its exclusive focus on normal brain MRI images. The results demonstrate that the U-Net model achieves optimal performance on salt-and-pepper noise at an intensity of 0.1, marked by the highest PSNR value of 37.2047 dB and the lowest MSE value of 0.000207. Conversely, the model shows the lowest performance on high-intensity speckle noise, indicating greater challenges in addressing multiplicative noise. This study contributes a systematic and empirically tested approach to improving the quality of brain MRI images with high efficiency, supporting the development of image-based diagnostic systems in the medical field.Keywords: Deep Learning, Denoising, Image Enhancement, Noise, U-Net.
Co-Authors Achmad Junaidi Adiwidyatma, Afdhal Reshanda Akmal, Mohammad Faizal Alhamda, Denisa Septalian Amanullah , Nurkholis Ardianto, Taruna Aryananda, Rangga Laksana Budi Nugroho Cahyani Kuswardhani, Hajjar Ayu Dewi, Deshinta Arrova Dewi, Deshinta Arrowa Dwi Arman Prasetya Dwi Kusuma, Irma Etniko Siagian, Pangestu Sandya Eva Yulia Puspaningrum Fara Disa Durry Fatmah Sari, Allan Ruhui Gunawan, Ellexia Leonie Hadi, Surjo Halim, Christina Humairah, Sayyidah Jauharis Saputra, Wahyu Syaifullah Mumtaz, Ahmad Naufal Mustika, Agung Nadhira, Firya Santoso, Sri Fuji Setiawan, Ariyono Sri Wibawani, Sri Sugiarto S Sukri, Hanifudin Terza Damaliana, Aviolla Trimono, Trimono Wafiqotul Azizah, Nabila Wahyu Caesarendra Wahyu Dwi Lestari Wan Awang, Wan Suryani Wijaya, Pandu Ali Yisti Vita Via