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All Journal International Journal of Electrical and Computer Engineering JOIV : International Journal on Informatics Visualization MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Teknologi Informasi dan Multimedia Jurnal SASAK : Desain Visual dan Komunikasi Jurnal Bumigora Information Technology (BITe) Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA)
Dadang Priyanto
Universitas Bumigora
Arts Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Languange, Linguistic, Communication & Media

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Analysis of earthquake hazards prediction with multivariate adaptive regression splines Dadang Priyanto; Muhammad Zarlis; Herman Mawengkang; Syahril Efendi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2885-2893

Abstract

Earthquake research has not yielded promising results, either in the form of causes or revealing the timing of their future events. Many methods have been developed, one of which is related to data mining, such as the use of hybrid neural networks, support vector regressor, fuzzy modeling, clustering, and others. Earthquake research has uncertain parameters and to obtain optimal results an appropriate method is needed. In general, several predictive data mining methods are grouped into two categories, namely parametric and non-parametric. This study uses a non-parametric method with multivariate adaptive regression spline (MARS) and conic multivariate adaptive regression spline (CMARS) as the backward stage of the MARS algorithm. The results of this study after parameter testing and analysis obtained a mathematical model with 16 basis functions (BF) and 12 basis functions contributing to the model and 4 basis functions not contributing to the model. Based on the level of variable contribution, it can be written that the epicenter distance is 100 percent, the magnitude is 31.1 percent, the location temperature is 5.5 percent, and the depth is 3.5 percent. It can be concluded that the results of the prediction analysis of areas in Lombok with the highest earthquake hazard level are Malaka, Genggelang, Pemenang, Tanjung, Tegal Maja, Senggigi, Mangsit. Meninting, and Malimbu.
Pengembangan Profil Outlet Pada Pusat Perbelanjaan Mataram Mall Lalu Arkan Zuhaedi; Dadang Priyanto
Jurnal SASAK : Desain Visual dan Komunikasi Vol 1 No 1 (2019): SASAK
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (644.86 KB) | DOI: 10.30812/sasak.v1i1.427

Abstract

Ilmu pengetahuan dan teknologi informasi berkembang cukup pesat dan menghasilkan inovasi-inovasi baru yang senantiasa terus berubah ke arah yang lebih baik. Pada kenyataannya masih banyak pusat perbelanjaan atau toko yang membuat pembelinya kesusahan untuk mengetahui dan mencari produk yang diinginkan, karena bangunan pusat perbelanjaan yang begitu luas dengan banyak toko atau outlet yang berjejer didalamnya. Berdasarkan pemaparan diatas penulis akan mengembangkan sebuah aplikasi profil outlet pada pusat perbelanjaan berbasis multimedia. Agar informasi dari masing-masing toko atau outlet tersebut dapat di sampaikan dengan lebih interaktif, menarik, dan mempermudah pengunjung mencari barang yang di butuhkan. Metode pengembangan yang digunakan oleh penulis pada penelitian ini adalah metode versi Luther Sutopo yang memiliki enam tahap yaitu tahap Concept (Konsep), Design (Perancangan), Material Collecting (Pengumpulan Bahan), Assembly (Pembuatan), Testing (Ujicoba) dan Distribution (Distribusi). Hasil atau keluaran yang akan dicapai yaitu sebuah aplikasi profil outlet pada pusat perbelanjaan mataram mall berbasis multimedia yang melibatkan elemen-elemen multimedia seperti teks, gambar, suara, dan animasi yang dikemas dalam media penyimpanan DVD yang dapat dijalankan di media elektronik yaitu PC (Personal Computer). Kesimpulan yang diperoleh selama melakukan penelitian ini, penulis dapat menyimpulkan bahwa aplikasi yang dibangun sangat membantu pengunjung dalam proses pencarian informasi toko atau outlet dengan lebih mudah dan cepat.
Implementasi Media Pembelajaran dengan Augmented Reality untuk Pengenalan Makanan Sehat Dan Bergizi Dadang Priyanto; Ahmad Deri Dustury; Apriani Apriani
Jurnal Bumigora Information Technology (BITe) Vol 4 No 2 (2022)
Publisher : Prodi Ilmu Komputer Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v4i2.2438

Abstract

The current application of Augmented Reality (AR) technology in various fields such as games, social media, business, military, medicine and including education. This research will utilize AR in learning for grade 3 students at Barujulat 1 Public Elementary School. The problem is that the 2013 revision of the 2018 curriculum is used and uses books as study guides. From interviews with teaching teachers, students have difficulty understanding various types of healthy food related to material according to the syllabus in sub-theme 2 of learning 3 about Food Important for Health, and this condition is exacerbated by the Covid-19 pandemic situation which requires students to study online and independently at home. The purpose of this study was to make a learning application for the introduction of healthy and nutritious eating with AR for class 3 of SDN 1 Barujulat. The method used in this research is the ADDIE development method developed by Dick and Carry. The results of this study, according to the syllabus used, can improve and facilitate students' understanding in participating in learning about important foods for health. The test results for grade 3 students were 21 students/respondents, it was found that 52% of respondents said this application could increase interest in learning, and 76% of respondents said that this application could facilitate understanding of healthy and nutritious food learning materials.
Improvement Performance of the Random Forest Method on Unbalanced Diabetes Data Classification Using Smote-Tomek Link Hairani Hairani; Anthony Anggrawan; Dadang Priyanto
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1069

Abstract

Most of the health data contained unbalanced data that affected the performance of the classification method. Unbalanced data causes the classification method to classify the majority data more and ignore the minority class. One of the health data that has unbalanced data is Pima Indian Diabetes. Diabetes is a deadly disease caused by the body's inability to produce enough insulin. Complications of diabetes can cause heart attacks and strokes. Early diagnosis of diabetes is needed to minimize the occurrence of more severe complications. In the diabetes dataset used, there is an imbalanced data between positive and negative diabetes classes. Diabetes negative class data (500 data) is more than diabetes positive class (268), so it can affect the performance of the classification method. Therefore, this study aims to apply the Smote-Tomeklink and Random Forest methods in the classification of diabetes. The research methodology used is the collection of diabetes data obtained from Kaggle, as many as 768 data with eight input attributes and 1 output attribute as a class, pre-processing data is used to balance the dataset with Smote-Tomeklink, classification using the random forest method, and performance evaluation based on accuracy, sensitivity, precision, and F1-score. Based on the tests conducted by dividing data using 10-fold cross-validation, the Random Forest algorithm with Smote-TomekLink gets the highest accuracy, sensitivity, precision, and F1-score compared to Random Forest with Smote. The Random Forest algorithm with Smote-Tomeklink has 86.4% accuracy, 88.2% sensitivity, 82.3% precision, and 85.1% F1-score. Thus, using Smote-Tomeklink can improve the performance of the random forest method based on accuracy, sensitivity, precision, and F1-score.
The Performance Machine Learning Powel-Beale for Predicting Rubber Plant Production in Sumatera Siska Rama Dani; Solikhun Solikhun; Dadang Priyanto
International Journal of Engineering and Computer Science Applications (IJECSA) Vol 2 No 1 (2023): March 2023
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v2i1.2420

Abstract

This study aims to predict rubber plants in Sumatra; rubber plants have a relatively high economic value; rubber sap must be cultivated because it is a product of the rubber plant, which is the raw material for the rubber industry, so in large quantities. Therefore, rubber sap, the selling value will increase so that it can increase farmers' income. Rubber production in Sumatra experiences ups and downs; therefore, this study aims to predict rubber plants using the Powell-Beale algorithm method, one of the Artificial Neural Network methods often used for data prediction, implemented using Matlab software. That supports it. This study does not discuss the prediction results. Still, it discusses the ability of the Powell-Beale algorithm to make predictions based on datasets of rubber plant production in recent years obtained from the Central Statistics Agency. Based on this data, a network architecture model will be formed and determined, including 6-10-1, 6-15-1, 6-30-1, 6-45-1 and 6-50-1. The best architecture is 6-15-1, with the lowest Performance/MSE test score of 0.00791984.
Segmentasi Hotel di Lombok Menggunakan Metode Klasterisasi Berbasis Harga, Fasilitas, dan Jarak Lokasi Eldy Waliyamursida; Dadang Priyanto; Galih Hendro Martono
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 3 (2025): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i3.722

Abstract

Lombok is one of Indonesia's premier tourist destinations, experiencing significant growth in the tourism sector. The increasing number of visitors has directly impacted the hospitality industry, resulting in a wide variety of hotels with diverse characteristics based on price, rating, and customer reviews. This diversity poses a challenge in effectively understanding hotel market segmentation. This study aims to cluster hotels in Lombok using clustering techniques to gain deeper insights into hotel segmentation patterns. The research employs the K-Means Clustering algorithm within the CRISP-DM framework, which includes the phases of Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The dataset comprises attributes such as nightly price, hotel rating, and the number of reviews, all collected from online platforms. The effectiveness of the clustering process is evaluated using the Silhouette Score metric. The results show that the K-Means algorithm delivers the best performance, with a Silhouette Score of 0.9042 (90%), indicating well-defined and distinct clusters. Therefore, K-Means Clustering is recommended as the most effective method for grouping hotels based on the attributes used in this study. This research provides valuable insights into hotel segmentation patterns in Lombok and can serve as a reference for hospitality industry stakeholders in formulating more targeted marketing strategies and business decisions. Future research may consider incorporating additional attributes such as geographic location and tourist seasons to enhance the clustering quality.
Prediksi Beban Kerja Server Secara Real-Time pada Pusat Data Cloud dengan Pendekatan Gabungan Long Short-Term Memory (LSTM) dan Fuzzy Logic Naufal Hanif; Dadang Priyanto; Neny Sulistianingsih
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 3 (2025): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i3.731

Abstract

Efficient resource management in Cloud Data Centers is essential to reduce energy waste and maintain optimal system performance. This study aims to predict server workload in real time using a hybrid approach that combines Long Short-Term Memory (LSTM) and Fuzzy Logic. CPU and RAM usage data were collected every second from a Proxmox Cluster using its API, then normalized and processed using an LSTM model to forecast future workloads. The predicted results were then classified using Fuzzy Logic into three workload categories: light, medium, and heavy. The model was evaluated using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), where the results showed an MAE of 2.48 on the training data and 3.09 on the testing data, as well as RMSE values of 5.15 and 5.57, respectively. Based on these evaluation results, the prediction system achieved an accuracy of 97.52% on the training data and 96.91% on the testing data, indicating that the model can generate accurate and stable predictions. This method enables automated decision-making such as workload-based power management, thereby improving energy efficiency and overall system performance.
Prediksi Gender Berdasarkan Nama Menggunakan Kombinasi Model IndoBERT, Convolutional Neural Network (CNN) dan Bidirectional Long Short-Term Memory (BiLSTM) Abi Mas'ud; Bambang Krismono Triwijoyo; Dadang Priyanto
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 3 (2025): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i3.736

Abstract

This study proposes a name-based gender prediction model in the Indonesian language by combining the architectures of Indonesian Bidirectional Encoder Representations from Transformers (IndoBERT), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory (BiLSTM). The non-standardized and diverse structure of Indonesian names presents a significant challenge for text-based gender classification tasks. To address this, a hybrid approach was developed to leverage the contextual representation power of IndoBERT, the local pattern extraction capability of CNN, and the sequential dependency modeling strength of BiLSTM. The dataset consists of 4,796 student names from Universitas Bumigora, collected between 2018 and 2023. The preprocessing steps include lowercasing, punctuation removal, label encoding, and train-test splitting. Evaluation results based on accuracy, precision, recall, and F1-score indicate that the IndoBERT-CNN-BiLSTM model achieved the best performance, with an accuracy of 90.94%, F1-score of 91.03%, and training stability without signs of overfitting. This model demonstrates high effectiveness in name-based gender classification and holds strong potential for applications such as population information systems, service personalization, and name-based demographic analysis.
Comparison of Naive Bayes and Dempster Shafer Methods in Expert System for Early Diagnosis of COVID-19 Nurdin Nurdin; Erni Susanti; Hafizh Al-Kautsar Aidilof; Dadang Priyanto
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i1.2280

Abstract

COVID-19 is a respiratory infection disease caused by the corona virus. Transmission of this virus can spread very quickly so that the number of cases of the corona virus continues to grow and becomes an epidemic that spreads not only in Indonesia but also in other countries in the world. The purpose of this study is to build an expert system that is able to diagnose Covid-19 early by using a comparison of the Nave Bayes method and the Dempster Shafer method. The amount of data used in this study is 550 data, consisting of 500 training data and 50 testing data. While the variables used are symptoms related to COVID-19 as many as 17 symptoms consisting of G01, G02, G03, G04, G05, G06, G07, G08, G09, G10, G11, G12, G13, G14, G15, G16, G17. The diagnostic data consists of Suspected (PDP), Non-Suspected, and Close Contact (ODP). The results of the percentage test by comparing system diagnoses with expert diagnoses, for the nave Bayes method it has an accuracy of 96% with 48 diagnoses according to expert diagnoses from 50 tested data. Meanwhile, the Dempster Shafer method has an accuracy of 40% with 20 diagnoses according to expert diagnoses from 50 tested data. Based on the results of this study, the Naive Bayes and Dempster Shafer methods can be applied to an expert system for early diagnosis of COVID-19, from the results of the system testing the Naive Bayes method has better accuracy than the Dempster Shafer method.
Data Mining Earthquake Prediction with Multivariate Adaptive Regression Splines and Peak Ground Acceleration Dadang Priyanto; Bambang Krismono Triwijoyo; Deny Jollyta; Hairani Hairani; Ni Gusti Ayu Dasriani
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 3 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i3.3061

Abstract

Earthquake research has not yielded promising results because earthquakes have uncertain data parameters, and one of the methods to overcome the problem of uncertain parameters is the nonparametric method, namely Multivariate Adaptive Regression Splines (MARS). Sumbawa Island is part of the territory of Indonesia and is in the position of three active earth plates, so Sumbawa is prone to earthquake hazards. Therefore, this research is important to do. This study aimed to analyze earthquake hazard prediction on the island of Sumbawa by using the nonparametric MARS and Peak Ground Acceleration (PGA) methods to determine the risk of earthquake hazards. The method used in this study was MARS, which has two completed stages: Forward Stepwise and Backward Stepwise. The results of this study were based on testing and parameter analysis obtained a Mathematical model with 11 basis functions (BF) that contribute to the response variable, namely (BF) 1,2,3,4,5,7,9,11, and the basis functions do not contribute 6, 8, and 10. The predictor variables with the greatest influence were 100% Epicenter Distance and 73.8% Magnitude. The conclusion of this study is based on the highest PGA values in the areas most prone to earthquake hazards in Sumbawa, namely Mapin Kebak, Mapin Rea, Pulau Panjang, and Pulau Saringi.
Co-Authors Abi Mas'ud Ahmad Deri Dustury Alyauma Hajjah Anthony Anggrawan Apriani Apriani Ayu Dasriani, Ni Gusti Azhar, Raisul Deny Jollyta Efendi, Syahril Eldy Waliyamursida Erni Susanti Galih Hendro Martono Hafizh Al Kautsar Aidilof Hairani Hairani Herman Mawengkang Lalu Arkan Zuhaedi Marlim, Yulvia Nora Muhammad Hairul Abror Muhammad Nur Muhammad Zarlis Musta’an Musta’an Naufal Hanif Neny Sulistianingsih Nurdin Nurdin Prihandoko . Siska Rama Dani Solikhun Solikhun Triwijoyo, Bambang Krismono