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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal Informatika Jurnal Manajemen dan Keuangan Proceeding International Conference on Information Technology and Business International conference on Information Technology and Business (ICITB) jurnal Teknologi Informasi Magister Jurnal Manajemen Magister Darmajaya Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data) Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Artificial Intelligence Research JIM UPB (Jurnal Ilmiah Manajemen Universitas Putera Batam) JMM (Jurnal Masyarakat Mandiri) Prosiding Seminar Nasional Darmajaya Jurnal Informasi dan Komputer Jurnal Teknologi Komputer dan Sistem Informasi Building of Informatics, Technology and Science JSR : Jaringan Sistem Informasi Robotik JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Inovasi Penelitian Journal of Applied Data Sciences Jurnal Publika Pengabdian Masyarakat Jurnal INFOTEL Neraca Manajemen, Akuntansi, dan Ekonomi JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi)
Aziz, RZ. Abdul
IIB Darmajaya Bandar Lampung
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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PERBANDINGAN KINERJA LIMA ALGORITMA KLÀSIFIKASI DASAR UNTUK PREDIKSI PENYAKIT JANTUNG “CLASSIFIER: NB, DTC4.5, KNN, ANN & SVM”. Khodijah, Khodijah; Sriyanto, Sriyanto; Aziz, RZ Abdul; Suhendro, Suhendro
JSR : Jaringan Sistem Informasi Robotik Vol 8, No 2 (2024): JSR: Jaringan Sistem Informasi Robotik
Publisher : AMIK Mitra Gama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58486/jsr.v8i2.454

Abstract

Penelitian ini membandingkan kinerja lima algoritma klasifikasi dasar untuk prediksi penyakit jantung, yaitu Naive Bayes, Decision Tree C4.5, K-Nearest Neighbors, Artificial Neural Network, dan Support Vector Machine. Hasil eksperimen menunjukkan bahwa algoritma Support Vector Machine memiliki akurasi tertinggi, diikuti oleh Artificial Neural Network, K-Nearest Neighbors, Naïve Bayes, dan Decision Tree. Kurva Receiver Operating Characteristic juga menunjukkan bahwa Artificial Neural Network memiliki hasil terbaik. Penelitian ini diharapkan dapat memberikan kontribusi signifikan dalam pengembangan model prediksi penyakit jantung yang lebih handal. Tujuan dari pemilihan fitur akuisisi informasi adalah untuk memilih fitur atau atribut yang secara signifikan mempengaruhi penyakit jantung. Kata Kunci: Klasifikasi, SVM, KNN, ANN, Decision Tree, Naïve Bayes
Pengembangan Model Pengambilan Keputusan Penerima Kartu Indonesia Pintar (Kip) Dengan Metode K-Means Dan Average Linkage Clustering (Studi Kasus : SMA Negeri 1 Kotagajah) Rini Gustini; RZ Abdul Aziz
JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi) Vol 2, No 3 (2019): JTKSI
Publisher : Institut Bakti Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/jtksi.v2i3.779

Abstract

Kartu Indonesia Pintar merupakan bagian dari kebijakan dari Presiden Ir. Joko Widodo. Penerima program Kartu Indonesia Pintar (KIP)  untuk kalangan siswa-siswi yang bersekolah seringkali tidak tepat sasaran. Di dalam proses pengambilan keputusan siapa yang berhak atas KIP belum jelas aturannya khususnya di SMA Negeri 1 Kotagajah. Proses pengambilan keputusan masih menggunakan input data yang dilakukan oleh operator sekolah melalui aplikasi DAPODIK, sehingga pengambilan keputusan penerima KIP banyak yang tidak tepat sasaran. Untuk itu diperlukan suatu aplikasi sistem pendukung keputusan (SPK) yang dapat memperhitungkan segala kriteria yang mendukung pengambilan keputusan guna membantu, mempercepat dan mempermudah proses pengambilan keputusan dalam penentuan penerima KIP. Salah satu metode yang dapat digunakan untuk meningkatkan kualitas pengambilan keputusan adalah metode clustering. Metode clustering yang digunakan pada penelitian ini adalah metode K-Means Clustering dan Average Linkage Clustering.
Rencana Strategis Teknologi Informasi Menyongsong Transformasi Digital Di Dunia Pendidikan (Studi Kasus SMK Negeri 1 Sukadana Kabupaten Lampung Timur) Yoga Pratomo; RZ Abdul Aziz
JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi) Vol 2, No 3 (2019): JTKSI
Publisher : Institut Bakti Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/jtksi.v2i3.783

Abstract

Transformasi digital didefinisikan sebagai sebuah proses perubahan yang bertumpu pada teknologi informasi dalam rangka meningkatkan daya saing sebuah organisasi. Namun, transformasi digital tidak hanya tentang teknologi, tetapi mencakup seluruh aspek organisasi mulai dari SDM, proses bisnis/kerja, budaya dan teknologi. Bedasarkan masalah yang ada, pemecahan masahnya yaitu menganalisis faktor-faktor peting dari hasil Analisis Value Chain, Analisis CSF, Analisis SWOT, Analisis McFarlan Strategic Grid dan Ananlisis GAP yang diperlukan dalam mengembangkan suatu sistem infomasi yang selaras dengan rencana strategis organisasi. Dari data hasil responden siswa, staf dan guru dengan menggunakan metode versi ward dan peppard, SMK Negeri 1 Sukadana perlu merancang portofolio, mengembangkan dan mengelola sistem informasi sekolah dalam menyongsong transformasi digital untuk menghasilkan lulusan sesuai dengan kebutuhan industri di era digital.Hasil dari penyusunan perencanaan strategis Teknologi Informasi dengan menggunakan Ward dan Peppard berupa portofolio masa yang akan datang yang dijadikan sebagai pedoman dalam pelaksanan pengembangan Teknologi Informasi pada SMK Negeri 1 Sukadana 
Implementasi Algoritma Rough Set Dan Naive Bayes Untuk Mendapatkan Rule Dalam Menyeleksi Pemohon Bantuan Fasilitas Rumah Ibadah (Studi Kasus : Pemerintah Kabupaten Pringsewu) Jeprianto Jeprianto; RZ Abdul Aziz
JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi) Vol 3, No 2 (2020): JTKSI
Publisher : Institut Bakti Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/jtksi.v3i2.887

Abstract

In solving the problem of the accuracy of the selected algorithm if it is applied to a prototype application in predicting applicants for assistance with houses of worship facilities in Pringsewu District using data mining classification methods. In solving the problem using the rough set algorithm method and Naive Bayes from the results of the discussion carried out, it can draw conclusions Rough set algorithm and the resulting rule has the highest level of accuracy that is 92% Rough set algorithm model is included in the category of excellent classification and can be implemented in determining predictions more potential grant funding. The rules generated by the Rough set algorithm are applied in the prototype prediction of the grant of houses of worship grants with 92% accuracy of prototype verification testing results. Based on the accuracy of the resulting prototype shows that the methods and prototypes that are applied are good at predicting better results. Naïve Bayes algorithm has an accuracy level of 77% The Naïve Bayes algorithm model is included in the category of good classification and can be implemented in determining the prediction of grants but because the value of the rough set algorithm is higher then the naïve Bayes algorithm is not used to determine the prototype.
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.
The Classification Method is Used for Sentiment Analysis in My Telkomsel Hardiansyah, Deni; Aziz, RZ Abdul; Hasibuan, Muhammad Said
International Journal of Artificial Intelligence Research Vol 8, No 2 (2024): December 2024
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i2.1229

Abstract

User reviews significantly impact how mobile apps are perceived and provide developers with valuable insights into improving the functionality and quality of their products. Sentiment analysis of these evaluations helps identify the main issues faced by consumers, such as technical difficulties, costs, and service levels. The main objective of this study is to classify user sentiment into positive and negative categories, focusing on the MyTelkomsel app. With the use of Google Play Scraper, 39,493 reviews on various app versions and user experiences were collected. This data was analyzed using multiple machine learning models, including Support Vector Machines (SVM), Naive Bayes, Random Forest, and Gradient Boosting, alongside the Natural Language Processing (NLP) approach. The results show that 39.2% of the reviews are positive, while 60.8% reflect negative sentiment. Among the models, SVM showed the highest accuracy in sentiment classification with a value of (0.854792), while Naive Bayes (0.775541), Random Forest (0.829725), and Gradient Boosting (0.819344) also performed well in sentiment classification. These findings suggest that developers can leverage the insights gained from this analysis to proactively improve the performance and user experience of the MyTelkomsel app, by addressing technical and service-related issues identified in user reviews.
An Artificial Neural Network-Based Geo-Spatial Model for Real-Time Flood Risk Prediction Using Multi-Source High-Resolution Data Aziz, RZ Abdul; Nurpambudi, Ramadhan; Herwanto, Riko; Hasibuan, Muhammad Said
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.913

Abstract

Flood prediction presents a pressing challenge in disaster management, especially in regions vulnerable to extreme weather events. In response, this study offers a novel approach to flood risk prediction by developing a deep learning-based Geo-Spatial Artificial Neural Network (ANN). The model actively integrates high-resolution satellite imagery, meteorological data, and topographic indicators, such as rainfall, elevation, and land use to capture complex spatial and environmental relationships that influence flood risk. This study conducted data preprocessing using Principal Component Analysis (PCA) and normalization to ensure consistency across datasets. It built the ANN with multiple hidden layers and trained it using the backpropagation algorithm on historical flood data. Furthermore, it designed the ANN model with multiple hidden layers and trained it using the backpropagation algorithm. The model achieved a notable 92% prediction accuracy, significantly outperforming traditional flood prediction methods, which typically yield 75–85% accuracy. Conventional metrics were Mean Squared Error (1.41) and R-squared (0.94). It confirmed the model’s superior ability to predict high-risk flood zones. The model also effectively captured non-linear patterns that conventional statistical or deterministic methods often failed to detect. The results showed that the model generalizes well and adapts effectively, making it suitable for real-time and data-driven flood forecasting. By integrating artificial intelligence with geo-spatial analytics, this study offers a scalable, accurate, and efficient tool for early warning systems and risk management. It recommends that future research should focus on incorporating additional data sources and refining model training techniques to further enhance scalability and performance.
Implementasi Model LSTM, CNN+LSTM Hybrid, dan Transformer untuk Prediksi Cuaca Harian Berbasis Data Multivariat Wulandari, Heptyana Sri; Aziz, RZ Abdul
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7655

Abstract

Global climate change and the increasing frequency of extreme weather events demand more accurate and adaptive weather prediction systems. This study aims to implement and compare three deep learning models, Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN)+LSTM Hybrid, and Transformer for predicting next-day weather events using daily multivariate meteorological data. The dataset was obtained from the Climatology Station Class IV Lampung and includes air temperature, rainfall, humidity, solar radiation, air pressure, wind direction, and wind speed, collected in CSV format from February 2000 to March 2025. The analysis results indicate that the CNN+LSTM Hybrid model achieved the best performance, with an RMSE of 1.158, MAE of 0.521, R² Score of 0.323, accuracy of 75%, and Macro F1 score of 0.75. The LSTM model demonstrated moderate performance, while the Transformer model yielded the lowest results among the three. These findings suggest that combining CNN's spatial feature extraction with LSTM's sequential processing enhances the prediction quality of short-term weather forecasts based on multivariate data. This study is expected to contribute to the development of AI-based weather forecasting systems in Indonesia, particularly for hydrometeorological disaster mitigation.
Co-Authors -, Sukatmi Achmad Nuzul Mariyus Agustian Prakarsyah anggalia wibasuri Anggawidia Wibaselppa Ari Kurniawan Saputra Asnusa, Soraya Delli Maria Delli Maria Delli Maria Dewi, Deshinta Arrova Eka Suci Puspita Wulandari Esti Dwi Putri Firmansyah Firmansyah Hardiansyah, Deni Hasibuan, M Said Herwanto, Riko Herwanto, Riko Ikhsanudin, Anas Jeprianto Jeprianto Joko Triloka Khodijah Khodijah Kurniawan, Tri Basuki Kurniawati . M Said Hasibuan Maizary, Ary Mawarni, Rima Muhamad Galih Ramaputra Muhammad Galih Ramaputra Mutiara, Suci Neni Purwati Nurkholish Setya, Yoga Nurpambudi, Ramadhan Onno W. Purbo Pakarti Riswanto R Rizal Isnanto Ramadhan Nurpambudi Retno Dwi Handayani Retno Dwi Handayani Rini Gustini Rini Nur Listiani Rionaldi Ali Rivardi, Ganda Syahertian Rustam Rustam Satria, Fiqih Selamat Soni Harsono Wijaya Sri Karnila Sri Karnila Sri Lestari SRI RAHAYU Sriyanto . Sriyanto Sriyanto Sriyanto Sriyanto Suci Mutiara Suhendro Suhendro Tri Nurul Fajarotun Triloka, Joko Wibaselppa, Anggawidia Wibaselppa, Anggawidia Wiratama, Yunada Wulandari, Heptyana Sri Y. Suhendro Yan Aditiya Pratama Yeremia Firhan Subagja Yoga Pratomo Yulmaini Yulmaini Yuni Puspita Sari