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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmiah Kursor 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 Minfo Polgan (JMP) Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Journal of Artificial Intelligence and Digital Business Prosiding Seminar Nasional CORISINDO
Dadang Priyanto
Program Studi Ilmu Komputer, Universitas Bumigora
Arts Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences

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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.
THE INFLUENCE OF DATA CATEGORIZATION AND ATTRIBUTE INSTANCES REDUCTION USING THE GINI INDEX ON THE ACCURACY OF THE CLASSIFICATION ALGORITHM MODEL Willy Fernando; Jollyta, Deny; Dadang Priyanto; Dwi Oktarina
Jurnal Ilmiah Kursor Vol. 12 No. 3 (2024)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v12i3.372

Abstract

Numerical data problems are typically caused by a failure to comprehend the data and the outcomes of its processing. In order to give richer context and a deeper understanding of the facts, numerical data must be transformed into categories. On the other hand, changes in data have a significant impact on the analysis's outcomes. The purpose of this study is to see how transforming numerical data into categories affects the model produced by the classification algorithms. The dataset used in this study is the Maternal Health Risk. Categorization refers to formal arrangements. Categorization is also accomplished by using the Gini Index to limit the number of instances of an attribute. The classification is carried out using the Random Forest (RF), K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) algorithms to produce a model. The influence of data modifications to model can be observed in the confusion matrix with 5 different data splitting. The study results suggested that changing numerical data to categories data significantly improved the performance of the SVM model from 76.92% to 80.77% at a data splitting percentage of 95/5.
Pendekatan Aritificial Neural Network untuk Prediksi Hasil Panen Kopi dengan Metode Backpropagation Guntara, Muhammad; I Gusti Ayu Diah Gita Kartika Santi; Dadang Priyanto
CORISINDO 2025 Vol. 1 (2025): Prosiding Seminar Nasional CORISINDO 2025
Publisher : CORISINDO 2025

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/corisindo.v1.5362

Abstract

Indonesia merupakan salah satu produsen kopi terbesar di dunia, dengan Provinsi Nusa Tenggara Barat (NTB) sebagai salah satu daerah penghasil utama. Produksi kopi di NTB mengalami fluktuasi tahunan yang dipengaruhi oleh berbagai faktor, seperti luas lahan, kondisi iklim, dan teknik budidaya. Untuk mendukung perencanaan dan pengambilan keputusan, diperlukan metode prediksi yang akurat. Penelitian ini bertujuan memprediksi hasil produksi kopi menggunakan pendekatan Artificial Neural Network (ANN) dengan algoritma Backpropagation, yang mampu mempelajari pola non-linear antara variabel input dan output. Dataset yang digunakan berasal dari Badan Pusat Statistik (BPS) Provinsi NTB untuk periode 2015–2024, dengan variabel Tahun, Luas Lahan (Ha), dan Produksi (Ton). Tahapan penelitian meliputi preprocessing data dengan Min-Max Scaling, perancangan arsitektur ANN dengan struktur 2–8–8–1, pelatihan model menggunakan optimizer Adam, serta evaluasi dengan metrik MSE, RMSE, MAE, dan MAPE. Hasil evaluasi menunjukkan bahwa model terbaik memiliki nilai MAPE sebesar 6.93%, yang termasuk kategori akurasi sangat baik. Prediksi produksi untuk periode 2025–2030 menunjukkan tren peningkatan, dari 7.748 ton pada tahun 2025 menjadi 10.262 ton pada tahun 2030. Hasil ini membuktikan bahwa ANN dengan algoritma Backpropagation efektif digunakan untuk memprediksi hasil produksi kopi dan berpotensi mendukung pengambilan keputusan di sektor pertanian.
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.
Co-Authors Abi Mas'ud Ahmad Deri Dustury Alyauma Hajjah Anthony Anggrawan Apriani Apriani Ayu Dasriani, Ni Gusti Azhar, Raisul Cong Dai Nguyen Deny Jollyta Dwi Oktarina Efendi, Syahril Eldy Waliyamursida Erni Susanti Galih Hendro Martono Guntara, Muhammad Hafizh Al Kautsar Aidilof Hairani Hairani Herman Mawengkang Husain Husain I Gede Yoga Sudarma Yasa I Gusti Ayu Diah Gita Kartika Santi Lalu Arkan Zuhaedi M Rizki Marlim, Yulvia Nora Muhamad Zaril Akbar Muhammad Hairul Abror Muhammad Nur Muhammad Zarlis Musta’an Musta’an Naufal Hanif Neny Sulistianingsih Neny Sulistianingsih Nindya Alifia Khumaira Nurdin Nurdin Prihandoko . Ria Rismayati Siska Rama Dani Solikhun Solikhun Syahrir, Moch. Triwijoyo, Bambang Krismono Willy Fernando