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All Journal Jurnal Buana Informatika Telematika : Jurnal Informatika dan Teknologi Informasi Wikrama Parahita : Jurnal Pengabdian Masyarakat Indonesian Journal of Artificial Intelligence and Data Mining Applied Technology and Computing Science Journal International Journal of Informatics and Computation Prosiding Seminar Nasional Multidisiplin Ilmu Respati Jurnal Computer Science and Information Technology (CoSciTech) Journal of Computer, Electronic, and Telecommunication (COMPLETE) Jurnal Ilmu Komputer dan Teknologi (IKOMTI)
Wijaya, Nurhadi
Universitas Respati Yogyakarta
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences 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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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
Pooling Comparison in CNN Architecture for Javanese Script Classification Mujastia Feliati Muhdalifah
International Journal of Informatics and Computation Vol 3 No 2 (2021): 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.v3i2.30

Abstract

Javanese script is evidence of the past culture, which contains various current language learning, including script recognition. However, learning traditional scripts becomes less attractive to the students. Thus, we propose a learning method to enable character recognition among students to deal with the issues. We offer a novel CNN architecture and compare different pooling layers for Javanese script classification. We calculate the separate pooling layer to reduce extensive feature extraction of the image. We present the model comparison results in Javanese character classification to convince our development.
The Design Of Augmented Reality Media Koi Fish Literacy Using Fast Corner Algorithm Mohammad Rofi Rahman
International Journal of Informatics and Computation Vol 3 No 1 (2021): 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.v3i1.32

Abstract

Ornamental fish that are quite famous and in demand in the market is the koi fish. This fish has a relatively high economic value, and its demand is increasing. There are still many difficulties in maintaining this fish so that it can cause the growth of disease and even death in the fish. It is due to the lack of public attention in terms of literacy about koi fish. Researchers used augmented reality technology to design koi fish literacy media based on these problems using the FAST Corner algorithm. So it is hoped that it could help improve public literacy about koi fish by introducing real-time information. The Fast Corner detection algorithm is helpful to accelerate the computational time when detecting corners in real-time with the markerless Augmented Reality technique. In this technique, the marker used for object tracking has been replaced with pattern recognition or pattern recognition of an object. The study results showed that experiments using this algorithm could track targets with good and faster performance and a maximum level of accuracy.
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.
Stacked Gated Recurrent Units and Indonesian Stock Predictions: A New Approach to Financial Forecasting MOHAMMAD DIQI; MARSELINA ENDAH HISWATI; NURHADI WIJAYA
Jurnal IT UHB Vol 5 No 1 (2024): Jurnal Ilmu Komputer dan Teknologi
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/ikomti.v5i1.1106

Abstract

This research paper introduces a novel approach to predicting stock prices using a Stacked Gated Recurrent Unit (GRU) model. The model was trained on historical data from the top 10 companies listed on the Indonesia Stock Exchange, covering the period from July 6, 2015, to October 14, 2021. The performance of the model was evaluated using key metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R-squared (R2). The results demonstrated promising performance, with average RMSE, MAE, and MAPE values of 0.00592, 0.00529, and 0.01654, respectively, indicating a high level of accuracy in the model's predictions. The average R2 value of 0.97808 further suggests a high degree of predictive power, with the model able to explain a significant proportion of the variance in the stock prices. These findings highlight the effectiveness of the Stacked GRU model in capturing stock price patterns and making accurate predictions. The practical implications of this research are significant, as the model provides a powerful tool for forecasting future stock price trends, which can be utilized in investment decision-making, financial analysis, and risk management. Future research could explore other deep learning architectures, incorporate additional features, or consider different evaluation metrics to enhance the model's performance further.
Machine Learning for Environmental Health: Optimizing ConcaveLSTM for Air Quality Prediction Diqi, Mohammad; Hamzah; Ordiyasa, I Wayan; Wijaya, Nurhadi; Martin, Benedicto Reynaka Filio
Jurnal Buana Informatika Vol. 15 No. 01 (2024): Jurnal Buana Informatika, Volume 15, Nomor 01, April 2024
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v15i1.8707

Abstract

This study investigates the optimization of the ConcaveLSTM model for air quality prediction, focusing on the interplay between input sequence lengths and the number of LSTM units to enhance forecasting accuracy. Through the evaluation of various model configurations against performance metrics such as RMSE, MAE, MAPE, and R-squared, an optimal setup featuring 50 input steps and 300 neurons was identified, demonstrating superior predictive capabilities. The findings underscore the critical role of model parameter tuning in capturing temporal dependencies within environmental data. Despite limitations related to dataset representativeness and environmental variability, the research provides a solid foundation for future advancements in predictive environmental modeling. Recommendations include expanding dataset diversity, exploring hybrid models, and implementing real-time data integration to improve model generalizability and applicability in real-world scenarios.
Co-Authors Agus Qomaruddin Munir Ahmad Wakhid Anang Faktchur Rachman Evrita Lusiana Utari, Evrita Lusiana Hamzah Hamzah Hamzah I Wayan Ordiyasa Ikhwan Mustiadi Imam Mahfudz I'tisyam Irawadi Buyung, Irawadi Joko Aryanto kasmawaru kasmawaru Listiawan, Indra Listyalina, Latifah Marselina Endah Hiswati Martin, Benedicto Reynaka Filio Mohammad Diqi Muliani, Sri Hasta Nurain, Maisarah Ordiyasa, I Wayan Putra Wanda, Putra Rike Pradila Sri Hasta Mulyani Trinidya, Fike