Article Per Year (5 Year)
p-Index From 2021 - 2026
3.566
P-Index
This Author published in this journals
All Journal Jupiter Jurnal Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Artificial Intelligence Research INTEGER: Journal of Information Technology Jurnal Penelitian Pendidikan IPA (JPPIPA) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Informasi dan Komputer Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science JSR : Jaringan Sistem Informasi Robotik Journal of Computer System and Informatics (JoSYC) Journal of Computer Networks, Architecture and High Performance Computing Soeropati: Journal of Community Service Journal of Applied Data Sciences TRANSFORMASI : JURNAL PENGABDIAN PADA MASYARAKAT Jurnal Publika Pengabdian Masyarakat Jurnal INFOTEL Jurnal Ilmu Siber dan Teknologi Digital Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) IJISCS (International Journal of Information System and Computer Science)
Triloka, Joko
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

Published : 26 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 26 Documents
Search

 1   2   3 
PENERAPAN DATA MINING UNTUK PREDIKSI PENDAFTARAN PDB DI SMKN3 METRO MENGGUNAKAN MACHINE LEARNING Effendi, Mukhammad Khoirul; Sriyanto, Sriyanto; Goesderilidar, Goesderilidar; Nugroho, Handoyo Widi; Triloka, Joko
JSR : Jaringan Sistem Informasi Robotik Vol 9, No 1 (2025): JSR: Jaringan Sistem Informasi Robotik
Publisher : AMIK Mitra Gama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58486/jsr.v9i1.482

Abstract

Penelitian ini bertujuan untuk menerapkan teknik data mining dalam memprediksi jumlah pendaftar Penerimaan Peserta Didik Baru (PPDB) di SMKN3 Metro menggunakan algoritma machine learning, khususnya Decision Tree (C4.5). Masalah utama yang dihadapi adalah tantangan pengelolaan data historis dan keterbatasan kapasitas sekolah dalam merencanakan penerimaan siswa secara efektif. Metode penelitian meliputi pengumpulan data historis pendaftaran, pra-pemrosesan data, penerapan algoritma machine learning, serta evaluasi kinerja model menggunakan metrik seperti Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), dan koefisien determinasi (R²).Hasil penelitian menunjukkan bahwa model Decision Tree (C4.5) memiliki performa terbaik dibandingkan algoritma lain, dengan nilai MSE sebesar 290,948, RMSE 17,057, MAE 11,096, dan R² sebesar 0,893. Akurasi prediksi yang tinggi ini menunjukkan potensi besar algoritma tersebut dalam mendukung pengelolaan PPDB secara lebih efisien. Penelitian ini diharapkan dapat menjadi solusi inovatif bagi SMKN3 Metro dalam merencanakan penerimaan siswa baru dan optimalisasi sumber daya sekolah. Selain itu, model ini dapat menjadi referensi bagi institusi pendidikan lain dalam mengadopsi teknologi serupa.Kata Kunci: Data Mining, Prediksi Pendaftar, PPDB, Decision Tree, SMKN3 MetroAbstractThis research focuses on implementing data mining to predict the number of registrants for new student admissions (PPDB) at SMKN3 Metro using the C4.5 machine learning algorithm. The study aims to address annual challenges in data management and school capacity limitations. By leveraging historical registration data, an accurate predictive model is developed to assist the school in planning student admissions more effectively. The methodology includes data collection and preprocessing, application of the C4.5 algorithm, and model performance evaluation based on prediction accuracy. Preliminary results indicate that the C4.5 algorithm outperforms other models, achieving a Mean Squared Error (MSE) of 290.948, Root Mean Squared Error (RMSE) of 17.057, and a coefficient of determination (R²) of 0.893. These findings demonstrate the model's reliability in estimating the number of registrants for key competencies such as Software Engineering and Computer Network Engineering. This implementation is expected to improve the efficiency of the PPDB process and resource planning at SMKN3 Metro, while providing a practical application of data mining and machine learning in educational management.Keywords: Data Mining, PPDB Prediction, Machine Learning, C4.5 Algorithm, SMKN3 Metro
Integrating Convolutional Neural Networks into Mobile Health: A Study on Lung Disease Detection Hasibuan, Muhammad Said; Isnanto, R Rizal; Dewi, Deshinta Arrova; Triloka, Joko; Aziz, RZ Abdul; Kurniawan, Tri Basuki; Maizary, Ary; Wibaselppa, Anggawidia
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.660

Abstract

This study presents the development and evaluation of a Convolutional Neural Network (CNN) model for lung disease detection from chest X-ray images, complemented by a mobile application for real-time diagnosis. The CNN model was trained on a diverse dataset comprising images labeled as "NORMAL" and "PNEUMONIA," achieving an overall accuracy of 96%. Compared to traditional machine learning methods such as Support Vector Machine (SVM) and Random Forest, which typically achieve accuracies ranging from 85% to 92%, the proposed CNN model demonstrates superior performance in classifying lung conditions. The model achieved high precision (0.98) and recall (0.96) for pneumonia detection, as well as precision (0.89) and recall (0.95) for normal cases, ensuring both sensitivity and specificity in diagnostic performance. These results indicate that the model minimizes false positives and false negatives, which is crucial for reducing misdiagnoses and improving patient outcomes in clinical settings. To enhance accessibility, an Android-based application was developed, allowing users to upload chest X-ray images and receive instant diagnostic results. The application successfully integrated the trained CNN model, offering a user-friendly interface suitable for healthcare professionals and patients alike. User testing demonstrated reliable performance, facilitating timely and accurate lung disease detection, particularly in areas with limited access to radiologists. These findings highlight the potential of CNNs in medical imaging and the critical role of mobile technology in expanding healthcare accessibility. This innovative approach not only improves diagnostic accuracy but also enables real-time disease detection, ultimately supporting clinical decision-making. Future research will focus on expanding the dataset, incorporating additional lung conditions, and optimizing the model for enhanced robustness in diverse clinical scenarios.
Evaluasi Kinerja Model Deep Learning dalam Memprediksi Kejadian Hujan Di Wilayah Panjang Bandar Lampung Tarjono; Triloka, Joko; Mutiara, Suci
Jurnal Informatika Vol 25 No 1 (2025): Jurnal Informatika
Publisher : Institut Informatika Dan Bisnis Darmajaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30873/jurnalinformatika.v25i1

Abstract

Global warming and climate change have increased the frequency and intensity of extreme weather events, significantly impacting human life and the environment. Urban areas such as Kecamatan Panjang in Bandar Lampung City frequently experience flooding due to extreme rainfall and poor drainage systems. This study compares the effectiveness of three deep learning model architectures- Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Transformers — in predicting rainfall events in Kecamatan Panjang. The data used includes key meteorological variables such as air temperature, dew point, humidity, and air pressure, collected from the Maritime Meteorology Station in Panjang (BMKG) over the past three years. The models were trained using this historical data, with the data divided into training and testing sets. The study results indicate that the Transformer model performs best with the highest accuracy compared to CNN and RNN. The Transformer model efficiently captures long-term dependencies in sequential data, providing more accurate and timely predictions. Model performance evaluation was conducted using accuracy, F1 score, precision, recall, ROC AUC, RMSE, and MAE metrics. The use of deep learning models in rainfall prediction is expected to assist in flood risk mitigation and planning for adaptation to increasingly frequent extreme weather due to climate change. This research significantly advances more accurate and efficient weather prediction systems for urban areas prone to hydrological disasters.
Efforts to Improve the Welfare of Coastal Communities in Batu Menyan Village through the Establishment and Training of Waste Banks: Upaya Peningkatan Kesejahteraan Masyarakat Pesisir Pantai Di Desa Batu Menyan Melalui Pendirian dan Pelatihan Bank Sampah Fadila, Kurnia; Triloka, Joko; Badri, Rico Elhando; Saputra, Muhammad
Jurnal Soeropati Vol 6 No 1: November 2023
Publisher : LPPM Universitas Yudharta Pasuruan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35891/js.v6i1.4255

Abstract

The aim of the service activities carried out is to utilize waste as an economic resource through the establishment of a Waste Bank. The method used in this community service activity uses a participatory approach with participatory rural assessment techniques. Implementation of activities is based on stages that are adapted to POAC management principles. The results of the implementation of the activities were considered achieved, this can be seen in the results of the pretest and posttest assessments. The results of the pretest and posttest of the first session regarding the Introduction to the Concept of Waste Banks and the Formation of a Waste Bank Organizational Structure showed an increase in scores with an average pretest score of 40 to 85. Improvement was also visible. from the results of the training in the second session regarding training on the Waste Bank financial management system with an average pretest score of 35 to 75. The service activities produced three outcomes, namely 1) Establishment of a waste bank with the name "Dewi Pelita Waste Bank, Batu Menyan Village; 2) Establishment of the management structure of the Dewi Pelita waste bank in Batu Menyan Village based on the Decree of the Head of Batu Menyan Village. 3) Understanding in managing the financial management system of the Dewi Pelita Waste Bank.
PREDIKSI KETERLAMBATAN TERHADAP PRESTASI SISWA SMK TELKOM LAMPUNG MENGGUNAKAN ARTIFICIAL NEURAL NETWORK Susanti, Desi; Triloka, Joko
JURNAL INFOTEL Vol 16 No 3 (2024): August 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i3.1163

Abstract

The analysis of student performance is crucial in vocational schools because it helps identify the challenges students face in preparing themselves for the workforce. By integrating data mining techniques such as Artificial Neural Networks (ANN), educators can enhance their understanding of factors that improve student learning outcomes. An artificial neural network (ANN) is composed of interconnected artificial neurons that can learn from input data and make complex predictions, including academic achievements. The structure and function of the human brain inspire ANN. This study compares the effec- tiveness of the artificial neural network (ANN) method with other methodologies, such as support vector regression (SVR), to predict student achievement at SMK Telkom Lampung. Primary data collected from SMK Telkom Lampung includes 4939 examples with 550 cases, 26 features, and 4 meta-attributes. Performance evaluation involves metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2). The coefficient of determination (R2) value of the Neural Network at 0.001 is higher than the R2 value of SVR, which reaches -0.036. Research find- ings indicate that the Artificial Neural Network model slightly outperforms the Support Vector Regression model, with lower prediction error rates and better ability to explain data variability.
SYSTEM USABILITY EVALUATION OF THE DIGITAL AUTOMATIC WEATHER SYSTEM AT BMKG LAMPUNG PROVINCE Rintiana, Rintiana; Triloka, Joko
IJISCS (International Journal of Information System and Computer Science) Vol 9, No 2 (2025): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v9i2.1848

Abstract

The Digital Automatic Weather System (AWS) is a vital tool for real-time meteorological data collection, utilized by the Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG) to support weather monitoring activities. However, the effectiveness of this technology largely depends on its perceived usability among users. This study aims to evaluate the usability level of the Digital AWS implemented at BMKG Lampung Province by applying the System Usability Scale (SUS) method. Data were collected through SUS questionnaires completed by AWS users within the BMKG environment. The analysis revealed an average SUS score of 59.9, which falls into the Marginal Low category and below the industry standard benchmark of 68. These findings suggest that, although the Digital AWS is functional, several aspects require improvement, particularly in interaction simplicity, interface consistency, and usage efficiency. Recommendations are directed toward enhancing interface design, simplifying navigation, and providing user training to ensure that the Digital AWS can optimally support BMKG’s operational activities.
Perbandingan Algoritma SVM dan CNN menggunakan PCA untuk Klasifikasi Kematangan Jeruk Keprok Sunarso, Sunarso; Chairani, Chairani; Triloka, Joko; Kurniawan, Rio
Jurnal Ilmu Siber dan Teknologi Digital Vol. 3 No. 2 (2025): Mei
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/jisted.v3i2.5034

Abstract

Purpose: This study aims to compare the SVM and CNN machine learning algorithms by combining PCA as data reduction to see which level of accuracy is higher with orange objects. Methodology/approach: created using the waterfall model, the system used to create the model is matlab ver r2022a, using the help of the python programming language to separate the datasets used, the datasets used come from kaagle including the following (https://www.kaggle.com/datasets/raghavrpotdar/fresh-and-stale-images-of-fruits-and-vegetables), and Orange disease dataset(https://www.kaggle.com/datasets/jonathansilva2020/orange-diseases-dataset). Results/findings: The results obtained from the Matlab test using the CNN and PCA algorithms obtained an accuracy of 76.4% and the SVM and PCA classification models obtained an accuracy of 98.89%. Conclusions: This research was successful with the results of combining the SVM and PCA algorithms which had high accuracy results compared to CNN and PCA. Limitations: In this study, the focus is only on comparing the SVM and CNN algorithms with the help of PCA to see which one has the higher level of accuracy between the two. The dataset was only taken from Kaagle, and the software used to create the model was Matlab. Contribution: This research is expected to be a reference for creating models in the future that can be applied to the classification process of automated products.
Prediction of Tuberculosis Treatment Outcomes in Indonesia Using Support Vector Machine and Random Forest Triloka, Joko; Sugianto, Dian
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10018

Abstract

Tuberculosis (TB) remains a global health challenge, particularly in developing countries such as Indonesia, which ranks third worldwide in the number of TB cases. This study aims to evaluate the performance of Support Vector Machine (SVM) and Random Forest (RF) algorithms in predicting TB patient recovery rates based on clinical data obtained from healthcare facilities in Indonesia. Evaluation results indicate that the model achieved very high precision scores (100%) for the "Deceased," "Transferred," and "Default" categories; however, these findings require critical interpretation due to the likely class imbalance in those categories. In contrast, for the "Recovered" and "Completed" categories—where data instances were more numerous—the model exhibited lower precision and recall values (below 90%), reflecting challenges in accurately predicting majority classes. These results suggest that despite seemingly high numerical performance, model predictions can be biased if class distribution is not appropriately considered. The main contribution of this research lies in providing a comparative analysis of two widely used machine learning algorithms in predicting TB recovery outcomes, while emphasizing the importance of addressing data imbalance issues in clinical predictive modeling. The findings provide a practical basis for integrating predictive algorithms into clinical workflows, enabling more accurate monitoring of patient recovery and timely adjustments of TB treatment plans in Indonesia.
Application of Naïve Bayes Classifiers for Family Risk Identification and Stunting Intervention Planning Kurniawan, Wildan Indra; Triloka, Joko
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10721

Abstract

Stunting remains a significant public health concern influenced by a combination of social, economic, and environmental factors. This study aims to implement the Naïve Bayes algorithm to support the determination of appropriate intervention strategies for families identified as being at risk of stunting in Metro City. Risk data were obtained from the BKKBN Metro City and underwent preprocessing steps, including handling missing values, encoding categorical variables, and feature selection. The dataset was then divided into training, validation, and testing subsets to develop and evaluate models using three Naïve Bayes variants: Gaussian, Multinomial, and Bernoulli. Evaluation metrics of accuracy, precision, recall, and F1-score indicate that the Multinomial Naïve Bayes model achieved the best performance with 99% accuracy, followed by the Bernoulli Naïve Bayes model with 98% accuracy. Both models effectively classified families at risk of stunting with minimal misclassification, while the Gaussian Naïve Bayes variant demonstrated lower performance with an accuracy of 60%. These results highlight the potential of the Naïve Bayes algorithm, particularly the Multinomial and Bernoulli models, as practical and efficient tools to support data-driven decision-making for stunting interventions.
AUGMENTED REALITY RUMAH SAKIT BERBASIS ANDROID MENGGUNAKAN METODE MULTIMEDIA DEVELOPMENT LIFE CYCLE Triloka, Joko; Setiawan, Agustinus Eko; Andika, Tahta Herdian; Aras, Irsan
Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) Vol 7, No 1 (2023): SEMNAS RISTEK 2023
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/semnasristek.v7i1.6280

Abstract

Implementasi Augmented Reality dalam memvisualisasikan ruangan dirumah sakit merupakan sebuah inovasi baru untuk mendukung kegiatan dirumah sakit dengan cara yang lebih efektif dan efisien. AR menawarkan visualisasi tiga dimensi suatu objek yang ditumpangkan pada lingkungan nyata yang disediakan oleh seperangkat teknologi untuk menciptakan realitas campuran secara real time antara elemen nyata dan virtual. Penggunaanya melalui media Android lebih mempermudah masyarakat umum dalam mengakses dan memperoleh informasi seputar ruangan di rumah sakit yang disajikan dalam bentuk virtual dengan mengikuti panduan penggunaan yang ada pada aplikasi. Penelitian ini menghasilkan aplikasi Augmented Reality (AR) berbasis android menggunakan metode Multimedia Development Life Cycle (MDLC). Berdasarkan pengujian yang telah dilakukan dapat disimpulkan bahwa aplikasi dapat beroperasi dengan baik pada perangkat smartphone berbasis Android dengan menampilkan informasi dan objek 3D tentang ruangan Rumah Sakit.
Co-Authors Andani, Mia Andika, Tahta Herdian Andini, Dwi Yana Ayu Aras, Irsan Arie Ardiansyah, Arie Aris Kurniawan Assidik, Reksa Qodri Aviv Fitria Yulia Aziz, RZ. Abdul Badri, Rico Elhando Bintoro, Panji Chairani Chairani Desi Susanti Dewi, Deshinta Arrova Effendi, Mukhammad Khoirul Fadila, Kurnia Fitriana, Jesi Goesderilidar handoyo widi nugroho Hartono Hartono Indera Indera Kurnia Muludi Kurniawan Kurniawan Kurniawan, Tri Basuki Kurniawan, Wildan Indra M Said Hasibuan Maizary, Ary Mutiara, Suci Nouvan, M. Ahadi Novita Sari Pramodiyo Wilantara, Rangga Priyatna, Aziz R Rizal Isnanto Rachmadi, Rachmadi Rintiana, Rintiana Rio Kurniawan Saputra, Muhammad Setiawan, Agustinus Eko Seyhan Siti Maesaroh Sriyanto Sriyanto Sugianto, Dian Sunarso Sunarso Sutedi Sutedi Tarjono Wasilah Wasilah Wibaselppa, Anggawidia Y. Suhendro Yusuf Irianto, Suhendro Zulkifli