Claim Missing Document
Check
Articles

Found 15 Documents
Search

ANALISA DAN RANCANGAN LAYANAN SMS GATEWAY BAGI SURVEILENCE AKTIF DALAM PEMANTAUAN WILAYAH SETEMPAT UNTUK KUNJUNGAN IBU HAMIL Diqi, Muhammad
Jurnal Teknologi Informasi RESPATI Vol 9, No 26 (2014)
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/jtir.v9i26.93

Abstract

Pemantauan Wilayah Setempat Kesehatan Ibu dan Anak (PWS KIA). adalah alat manajemen untuk melakukan pemantauan program KIA di suatu wilayah kerja secara terus menerus, agar dapat dilakukan tindak lanjut yang cepat dan tepat oleh surveilence aktif. Selama ini dalam pengumpulan data masih dilakukan dengan pendataan secara manual atau dengan aplikasi yang sudah disediakan. Namun hal tersebut menjadi kendala bagi daerah-daerah yang masih belum terjangkau infrasturktur jaringan komputer.Perangkat mobile merupakan perangkat telekomunikasi yang sudah umum di masyarakat yang selama ini difungsikan sebagai media komunikasi dan memberikan informasi. Dari hal tersebut diatas maka tujuan dari penelitian ini adalah menerapkan SMS Gateway dalam PWS KIA khususnya pada layanan Ibu Hamil.Dari hasil penelitian ini diharapkan dapat berguna bagi pemerintah dalam hal ini Dinas Kesehatan dan Puskesmas dalam memantau terus-menerus PWS KIA berdasarkan data yang di informasikan oleh Surveilence Aktif melalui SMS Message Kata kunci  : SMS Gateway, SMS Message, PWS KIA
Implementation of CNN for Plant Leaf Classification Mohammad Diqi; Sri Hasta Mulyani
International Journal of Informatics and Computation Vol 2 No 2 (2020): 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.v2i2.28

Abstract

Many deep learning-based approaches for plant leaf stress identification have been proposed in the literature, but there are only a few partial efforts to summarize various contributions. This study aims to build a classification model to enable people or traditional medicine experts to detect medicinal plants by using a scanning camera. This Android-based application implements the Java programming language and labels using the Python programming language to build deep learning applications. The study aims to construct a deep learning model for image classification for plant leaves that can help people determine the types of medicinal plants based on android. This research can help the public recognize five types of medicinal plants, including spinach Duri, Javanese ginseng, Dadap Serep, and Moringa. In this study, the accuracy is 0.86, precision 0.22, f-1 score 0.23, while recall is 0.2375.
Design and Building Javanese Script Classification in The State Museum of Sonobudoyo Yogyakarta Mohammad Diqi; Mujastia Muhdalifah
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.v1i2.18

Abstract

The Sonobudoyo State Museum is one of the state museums in Yogyakarta where stores historical objects like the Javanese script. This Javanese script presents in street names, especially in the city of Yogyakarta to represent local content for elementary, middle, and high schools. To read and understand Javanese script, people must learn it within a specified period, whereas with Latin letters are easier and faster to understand. The purpose of this paper is to design and build a Javanese script classification dataset to attract both adults, children, and parents as effective learning media. We construct the dataset by using Deep Learning with the Convolutional Neural Network (CNN). Stages of making a dataset are input data, the process of building models, and training can then recognize Javanese script images. We collect the dataset from the internet and several different people to train computer machines. In this paper, we construct the Javanese script classification dataset to help users to detect Javanese characters. The results of this training the application of Javanese script classification can produce a certain level of recognition of Javanese script patterns in a real application.
Measurement of Maximum Value of Dental Radiograph to Predict the Bone Mineral Density Sri Lestari; Mohammad Diqi; Rini Widyaningrum
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (433.887 KB) | DOI: 10.11591/eecsi.v4.1021

Abstract

Post-menopausal woman has a high risk to have osteoporosis. The condition of osteoporosis is characterized by the bone mineral density. The gold standard of BMD examination is using DEXA scan, but it has a problem in high cost and limited availability. So the study about the alternative to overcome the problem is necessary. The objective of this study is to measure the maximum value of periapical radiograph and determine its ability to be a predictor for bone mineral density of lumbar spine and hip.Image processing method was applied to 37 data subject that involved periapical radiograph and DEXA scan. The grayscale image was converted into binary image to observe the connectivity of the pixels. Measurement of maximum value for each radiograph has been done and continued by linier regression method between the maximum value with the BMD of lumbar spine and hip.The result of this study showed that the maximum value has a weak correlation with the BMD of lumbar spine and hip. The maximum value also cannot be the predictor for BMD of lumbar spine and hip as the significant of F is larger than 0,05 in the linier regression test.
Re-Fake: Klasifikasi Akun Palsu di Sosial Media Online menggunakan Algoritma RNN Putra Wanda; Marselina Endah Hiswati; Mohammad Diqi; Romana Herlinda
Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (SENASTINDO) Vol 3 (2021): Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo)
Publisher : Akademi Angkatan Udara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (265.653 KB) | DOI: 10.54706/senastindo.v3.2021.139

Abstract

Online Social Network (OSN) is an application that enables public communication andinformation sharing. However, fake accounts on OSN can spread false information with unknown sources. It is a challenging task to detect malicious accounts in a large OSN system. The existence of fake accounts or unknown accounts on OSN can be a serious problem in maintaining data privacy. Various communities have proposed many techniques to deal with fake accounts on OSN, including rules-based black-and-white techniques to learning approaches. Therefore, in this study we propose a classification model using RNN to detect fake accounts accurately and effectively. We carried out this research in several steps, including collecting the dataset, pre-processing, extraction, training our model using RNN. Based on the experimental results, our proposed model can produce higher accuracy than conventional learning models.
Multi-Step Vector Output Prediction of Time Series Using EMA LSTM Mohammad Diqi; Ahmad Sahal; Farida Nur Aini
JOIN (Jurnal Online Informatika) Vol 8 No 1 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i1.1037

Abstract

This research paper proposes a novel method, Exponential Moving Average Long Short-Term Memory (EMA LSTM), for multi-step vector output prediction of time series data using deep learning. The method combines the LSTM with the exponential moving average (EMA) technique to reduce noise in the data and improve the accuracy of prediction. The research compares the performance of EMA LSTM to other commonly used deep learning models, including LSTM, GRU, RNN, and CNN, and evaluates the results using statistical tests. The dataset used in this study contains daily stock market prices for several years, with inputs of 60, 90, and 120 previous days, and predictions for the next 20 and 30 days. The results show that the EMA LSTM method outperforms other models in terms of accuracy, with lower RMSE and MAPE values. This study has important implications for real-world applications, such as stock market forecasting and climate prediction, and highlights the importance of careful preprocessing of the data to improve the performance of deep learning models.
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.
Pengujian Software Pengendalian Penduduk Permanen-Nonpermanen Dengan BlackBox Test dan Evaluasi Penerimaan Metode Technology Acceptance Model Zaidir Zaidir; Bagus Subekti Nuswantoro; Indra Listiawan; Ahmad Sahal; Muhammad Diqi; Dyan Avando Meliala
Jurnal Teknologi Informasi dan Terapan Vol 10 No 1 (2023)
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v10i1.305

Abstract

Banyak variasi dan inovasi perangkat lunak tentang kependudukan telah tersedia karena kompleksitas masalah administratif dan sosial yang terkait dengan kependudukan. Penelitian ini bertujuan untuk menciptakan sistem pengendalian penduduk permanen dan non-permanen yang dapat diadaptasi dengan cepat dan meningkatkan ketersediaan informasi. Metode pengembangan yang digunakan adalah agile development methods yang memungkinkan pengembang untuk mengadaptasi sistem dengan cepat terhadap perubahan dalam bentuk apapun. Untuk menguji sistem, digunakan metode blackbox test dan evaluasi penerimaan menggunakan metode technology acceptance model. Metode pengujian blackbox yang didasarkan pada equivalence partitions digunakan untuk membantu penyusunan kasus uji dan mencoba keunggulan serta mendapatkan error yang tidak terduga. Selama pengujian, tidak ditemukan kesalahan baik pada partisi nilai masukan maupun keluaran. Berdasarkan hasil pengujian asumsi, hanya satu asumsi yang diterima, yakni dampak PU kepada ATU. Hal ini menunjukkan keyakinan bahwa software pengendalian penduduk permanen-nonpermanen bermanfaat akan mempengaruhi sikap penerimaan atau penolakan terhadap software tersebut.
Sentiment Analysis of ChatGPT Tweets Using Transformer Algorithms Sugeng Winardi; Mohammad Diqi; Arum Kurnia Sulistyowati; Jelina Imlabla
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 5, No 2 (2023): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v5i2.8632

Abstract

This study explores the application of the Transformer model in sentiment analysis of tweets generated by ChatGPT. We used a Kaggle dataset consisting of 217,623 instances labeled as "Good", "Bad", and "Neutral". The Transformer model demonstrated high accuracy (90%) in classifying sentiments, particularly predicting "Bad" tweets. However, it showed slightly lower performance for the "Good" and "Neutral" categories, indicating areas for future research and model refinement. Our findings contribute to the growing body of evidence supporting deep learning methods in sentiment analysis and underscore the potential of AI models like Transformers in handling complex natural language processing tasks. This study broadens the scope for AI applications in social media sentiment analysis.
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.