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All Journal Telematika : Jurnal Informatika dan Teknologi Informasi Indonesian Journal of Artificial Intelligence and Data Mining International Journal of Informatics and Computation Prosiding Seminar Nasional Multidisiplin Ilmu Respati Jurnal Computer Science and Information Technology (CoSciTech) Journal of Soft Computing Exploration
Wijaya, Nurhadi
Universitas Respati Yogyakarta
Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions Other

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PENERAPAN ALGORITMA KLASIFIKASI NAIVE BAYES UNTUK DATA STATUS HUNI RUMAH BANTUAN DANA REHABILITASI DAN REKONSTRUKSI PASCA BENCANA ERUPSI GUNUNG MERAPI 2010 Wijaya, Nurhadi
Prosiding Seminar Nasional Multidisiplin Ilmu Prosiding Seminar Nasional Multidisiplin Ilmu
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (627.548 KB)

Abstract

Bencana Erupsi gunung Merapi berikut susulan material lahar hujan pada Tahun 2010 mengakibatkan kerusakan rumah dan infrastruktur di wilayah Kabupaten Sleman D.I.Yogyakarta dan Kabupaten Magelang Jawa Tengah. Melalui Perka BNPB No.5 Tahun 2011, pemerintah menginstruksikan rencana dan aksi rehabilitasi dan rekonstruksi pasca erupsi dilakukan dengan skema program Rehabilitasi dan Rekonstruksi Masyarakat dan Permukiman Berbasis Masyarakat. Skema program ini telah membangun rumah sebanyak 2.516-unit bagi warga yang terdampak erupsi Merapi dan lahar hujan. Menurut Key Performance Indikator (KPI) The World Bank, status huni rumah terbangun merupakan salah satu indikator kinerja program rehab rekon. Pelaksanaan program rehab dan rekon ini sebagian besar didokumentasikan secara digital dan terekam ke dalam basis data. Dalam Ilmu Teknologi Informasi dibidang data mining, basis data merupakan aset yang dapat digunakan sebagai bahan pengenalan dan penemuan pola-pola data yang dapat dipelajari dan diteliti guna menyelesaikan permasalahan. Basis data yang dimiliki Satker rehab rekon merekam sebanyak 2.146-unit rumah huntap sudah dihuni dan 370 rumah belum dihuni. Hasil penelitian/eksperimen menunjukkan bahwa penerapan algoritma klasifikasi Naive Bayes dapat diterapkan terhadap data status huni rumah bantuan dana rehabilitasi dan rekonstruksi pasca erupsi Merapi 2010 dengan hasil nilai akurasi klasifika si mencapai sebesar 89,59% dan nilai performa klasifikasi AUC mencapai 0,826Kata kunci : Erupsi Merapi, Data Mining, Naive Bayes, Klasifikasi, Rehab Rekon, Status huniDisaster Eruption of Mount Merapi and the following a mixture of lava rain material in 2010 resulted in damage to homes and infrastructure in the Sleman Regency of D.I.Yogyakarta and Magelang District of Central Java. Through Perka BNPB No.5 of 2011, the government instructed plans and actions for rehabilitation and reconstruction after the eruption was carried out with the scheme of the Community Rehabilitation and Reconstruction and Community Based Settlement program. The program scheme has built 2,516-unit houses for residents affected by Merapi and rain lava eruptions. According to The World Bank's Key Performance Indicator (KPI), the occupancy status of built houses is one of the indicators of the performance of the rehabilitation and reconstruction program. The implementation of the rehabilitation and reconstruction program is mostly digitally documented and recorded in the database. In Information Technology in the field of data mining, the database is an asset that can be used as an introduction and discovery of data patterns that can be studied and researched to solve problems. The database owned by the reconstruction rehabilitation work unit recorded 2,146 housing units has been occupied and 370 houses have not been occupied. The results of the research / experiment show that the application of the Naive Bayes classification algorithm can be applied to the occupancy status data of houses for rehabilitation and reconstruction assistance after the 2010 Merapi eruption with the classification accuracy reaching 89.59% and the AUC classification perf ormance value reaching 0.826Keywords: Merapi Eruption, Data Mining, Naive Bayes, Classification, Reconstruction Rehabilitation, Occupied status
SISTEM INFORMASI ADMINISTRASI BEASISWA MAHASISWA UNIVERSITAS RESPATI YOGYAKARTA Wijaya, R. Nurhadi
Jurnal Teknologi Informasi RESPATI Vol 10, No 28 (2015)
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (569.431 KB) | DOI: 10.35842/jtir.v10i28.280

Abstract

ABSTRAK Biro Administrasi Kemahasiswaan dan Carrier Center merupakan unit yang ada Universitas Respati Yogyakarta dibawah tanggung jawab Wakil Rektor III dalam mengelola kegiatan kemahasiswaan. Salah satu kegiatan rutin yang dilakukan adalah kegiatan penerimaan beasiswa baik bersumber dari yayasan, badan pemerintah maupun instansi swasta. Dalam proses administrasi beasiswa mahasiswa selama ini masih dilakukan secara komputerisasi dengan menggunakan aplikasi perkantoran dan berkas masih disimpan secara manual pada rak penyimpanan yang tentunya beresiko pada kerusakan dokumen. Masalah lain yang timbul adalah data yang diolah belum terekam dengan basisdata sehingga apabila membutuhkan riwayat penerima beasiswa pada tingkat program studi atau Fakultas membutuhkan waktu yang lama. Selain itu sering terjadi seorang mahasiswa memperoleh beasiswa ganda dikarenakan tidak adanya monitoring dari penerima beasiswa.Tujuan penelitian adalah mengembangkan Sistem Informasi Administrasi Beasiswa Mahasiswa Universitas Respati Yogyakarta. Pengembangan Sistem Administrasi Beasiswa Mahasiswa nantinya berbasis Web dengan Bahasa pemrograman PHP dan DBMS MySql sebagai basisdata. Hasil penelitian diharapkan memberikan manfaat bagi unit beasiswa untuk dapat mempermudah dalam pengelolaan dokumen administrasi penerimaan beasiswa mahasiswa di Universitas Respati Yogyakarta.  Kata kunci  : Beasiswa, Mahasiswa, Sistem Informasi
PENERAPAN ALGORITMA DECISION TREE C.45 UNTUK KLASIFIKASI DATA STATUS HUNI RUMAH REHABILITASI PASCA ERUPSI MERAPI Mujatia Feliati, Nurhadi Wijaya, Marselina Endah,
Prosiding Seminar Nasional Multidisiplin Ilmu Vol 2, No 1 (2020): Tetap Produktif dan Eksis Selama dan Pasca Pandemi COVID-19
Publisher : Universitas Respati Yogyakarta

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Abstract

Erupsi gunung Merapi berikut lahar hujan di Tahun 2010 berdampak pada kerusakan infrastruktur berikut ribuan hunian rumah di Kabupaten Sleman D.I.Yogyakarta dan Kabupaten Magelang Jawa Tengah. Melalui Peraturan Kepala BNPB No.5 Tahun 2011, rehabilitasi dan rekonstruksi perumahan yang terdampak erupsi Merapi, dilakukan dengan skema program Rehabilitasi dan Rekonstruksi Masyarakat dan Permukiman Berbasis Komunitas. Skema tersebut telah membangun rumah hunian sebanyak 2.516-unit. Berdasarkan Key Performance Indikator (KPI) oleh The World Bank, status huni rumah merupakan indikator keberhasilan kinerja skema program ini. Pelaksanaan progam rehabilitasi rumah pasca erupsi Merapi didokumentasikan dan terekam ke dalam basis data. Dibidang data mining, basis data merupakan aset yang dapat digunakan sebagai bahan pengenalan dan penemuan pola-pola data yang dapat dipelajari dan diteliti guna menyelesaikan permasalahan baik pengelompokan data maupun klasifikasi data. Pada penelitian ini dilakukan penerapan algoritma decision tree C.45 untuk mengklasifikasi data status huni rumah rehabilitasi pasca erupsi gunung Merapi. Hasil klasifikasi penelitian diperoleh angka nilai tingkat akurasi klasifikasi mencapai 91.34%, dengan demikian terjawab bahwa algoritma decision tree C.45 dapat diterapkan untuk mengklasifikasi data status huni rumah rehabilitasi pasca erupsi gunung Merapi.
Evaluation of Naïve Bayes and Chi-Square performance for Classification of Occupancy House Nurhadi Wijaya
International Journal of Informatics and Computation Vol 1 No 2 (2019): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v1i2.20

Abstract

Occupancy status is one indicator of the rehabilitation and reconstruction program to support eruption victims in Indonesia. It needs to establish the rehabilitation and reconstruction in digital system with structured database. In this paper, we provide dataset 2,146 occupied and 370 unoccupied houses. We utilize a naive Bayes classifier to classify the objects and implement a chi-square algorithm to measure comparison data to actual observed data. This research uses a combination of Naive Bayes and Chi-Square by applying weighting to the dataset attributes. Our study conclude that the combination of the algorithms can achieve a promosing result in classifying the occupancy houses status. The combination of the proposed technique gain 89.59% accuracy and ROC-AUC value 0.839. Therefore, our approach is better than the standard Naive Bayes without combination with the Chi-Square approach
Harnessing the Power of Stacked GRU for Accurate Weather Predictions Mohammad Diqi; Ahmad Wakhid; I Wayan Ordiyasa; Nurhadi Wijaya; Marselina Endah Hiswati
Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 2 (2023): September 2023
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v6i2.24769

Abstract

This research proposed a novel approach using Stacked GRU (Gated Recurrent Unit) models to address the problem of weather prediction and aimed to improve forecasting accuracy in sectors like agriculture, transportation, and disaster management. The key idea involved leveraging the temporal dependencies and memory management capabilities of Stacked GRU to model complex weather patterns effectively. Comprehensive data preprocessing ensured data quality and fine-tuning of the model architecture and hyperparameters optimized performance. The research demonstrated the Stacked GRU model's effectiveness in accurately forecasting temperature, pressure, humidity, and wind speed, validated by low RMSE and MAE scores and high R2 coefficients. However, challenges in forecasting humidity and a percentage discrepancy in wind speed predictions were observed. Overfitting and computational complexity were identified as potential limitations. Despite these constraints, the study concluded that the Stacked GRU model showed promise in weather forecasting and warranted further refinement for broader applications in time-series prediction tasks.
AdaBoost Classification for Predicting Residential Habitation Status in Mount Merapi Post-Eruption Rehabilitation NURHADI WIJAYA; MOHAMMAD DIQI; IKHWAN MUSTIADI
Computer Science and Information Technology Vol 4 No 2 (2023): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v4i2.5141

Abstract

This research paper explores the use of the AdaBoost algorithm for predicting residential habitation status in the aftermath of the Mount Merapi eruption. Using a dataset from the Rehabilitation and Reconstruction Task Force, with 2516 instances and 11 attributes, the AdaBoost model was trained and evaluated. The model demonstrated a robust performance with an accuracy of 92%, though it struggled with correctly identifying all 'No Habited' instances. These findings underscore the potential of machine learning algorithms in disaster management, particularly in optimizing resource allocation and expediting recovery times. Future research should aim to improve the model's performance on imbalanced datasets and explore the incorporation of temporal dimensions for more dynamic and accurate predictions.
Enhancing cirrhosis detection: A deep learning approach with convolutional neural networks Marselina Endah H; R. Nurhadi Wijaya; Hilmi Khotibul Ahsan
Journal of Soft Computing Exploration Vol. 4 No. 4 (2023): December 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i4.226

Abstract

Cirrhosis, a prevalent and life-threatening liver condition, demands early detection for effective intervention. This study investigates the potential of machine learning algorithms, including Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Decision Trees, K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Gradient Boosting (GBoost), in cirrhosis prediction using a dataset from Kaggle containing 418 observations and 20 attributes. Performance evaluation involves metrics like accuracy, precision, recall, and F1-score, revealing CNN's superior performance with an 84% accuracy rate. The study highlights the importance of algorithm selection and feature engineering in medical diagnosis. Moreover, a comparison with traditional machine learning techniques underscores CNN's prowess in this domain. Beyond cirrhosis, CNNs offer promise for automating feature extraction from medical imagery and recognizing complex patterns, potentially transforming diagnostic accuracy in healthcare.
Identifying Types of Waste as Efforts in Plastic Waste Management Based on Deep Learning Irawadi Buyung; Agus Qomaruddin Munir; Nurhadi Wijaya; Latifah Listyalina
Telematika Vol 20, No 3 (2023): Edisi Oktober 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i3.10804

Abstract

Purpose: This research aims at designing a computer algorithm for automatic waste sorting.Design/methodology/apprach: This research is quantitative and uses secondary data, specifically images of various types of waste. The images will be classified into organic and inorganic waste types with the assistance of a deep learning model. In this research, we propose the EfficientNet method for Waste Type Identification as an Effort in Plastic Waste Management. Experiments were conducted on a secondary dataset from Kaggle.com, which involved classifying various types of waste into "Plastic" and "Non-Plastic" categories, showing the effectiveness of the proposed method.Findings/result: The measurement is performed to compute the accuracy of the designed deep learning model in classifying waste images into the appropriate waste types. Based on the research results, our system achieved the highest accuracy of 97% during testing.Originality/value/state of the art: The designed method can perform fast and automatic waste sorting, which is useful in reducing the increasing amount of waste accumulating each year. 
Co-Authors Agus Qomaruddin Munir Ahmad Wakhid Hilmi Khotibul Ahsan I Wayan Ordiyasa Ikhwan Mustiadi Irawadi Buyung Latifah Listyalina Marselina Endah H Marselina Endah Hiswati Mohammad Diqi