Article Per Year (5 Year)
p-Index From 2020 - 2025
3.441
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Teknologi Telematika : Jurnal Informatika dan Teknologi Informasi Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Wikrama Parahita : Jurnal Pengabdian Masyarakat JITK (Jurnal Ilmu Pengetahuan dan Komputer) JSiI (Jurnal Sistem Informasi) ILKOMNIKA: Journal of Computer Science and Applied Informatics Prosiding Seminar Nasional Multidisiplin Ilmu Respati Jurnal Teknik Informatika (JUTIF) JUSTIN (Jurnal Sistem dan Teknologi Informasi) ETAM: Jurnal Pengabdian kepada Masyarakat Jurnal Masyarakat Madani Indonesia Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen
Agus Qomaruddin Munir
Fakultas Teknik, Universitas Negeri Yogyakarta
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

Published : 29 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 29 Documents
Search

 1   2   3 
Conv-Tire: Tire Condition Assessment using Convolutional Neural Networks Latifah Listyalina; Irawadi Buyung; Agus Qomaruddin Munir; Ikhwan Mustiadi; Dhimas Arief Dharmawan
Telematika Vol 19, No 3 (2022): Edisi Oktober 2022
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v19i3.7697

Abstract

Purpose: In this study, the authors designed an algorithm based on convolutional neural networks that can automatically assess tire quality.Design/methodology/approach: The proposed algorithm is built through several stages as follows. In the first stage, the tire images, which are the input of the designed algorithm, are acquired. Further, the acquired images are divided into two sets, namely training and testing sets. The training set contains tire images used in the training phase of several convolutional neural networks (CNN) architectures such as ResNet-50, MobileNetV2, Inception V3, and DenseNet-121. The training phase is carried out in a number of epochs, and at each epoch, the cross entropy loss function will be calculated which expresses the performance of the CNN architecture in classifying tire images. For this reason, the training stage requires a label or reference that shows the feasibility of the tires displayed in each image.Findings/result: In the testing phase, trained CNN architectures are used to classify tire images from the test set. Classification performance in the test set is also expressed in terms of cross-entropy loss function value. In addition, the accuracy value has also been calculated which shows the percentage of the number of tire images that are successfully classified correctly to the total number of tire images in the test set, namely the DenseNet-121 model has the best accuracy of 92.62%.Originality/value/state of the art: Given the high accuracy achieved by our algorithm, this work can be used as a reference by other researchers, specifically to benchmark their tire quality classification methods developed in the future.
INTEGRATED POPULATED SERVICES SYSTEM USING AGILE APPROACH Agus Qomaruddin Munir; Evrita Lusiana Utari; Desty Ervira Puspaningtyas; Bayu Indra Wahyudi
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 1 (2023): JUTIF Volume 4, Number 1, February 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.1.657

Abstract

The significant and uneven population growth in Indonesia in each region triggers problems for implementing development. The government has tried to overcome various population problems, one of which is using electronic media to record and update population biodata in population administration in Indonesia, namely establishing a Population Administration Information System (SIAK). The goal is that all people can be recorded and recorded accurately in the SIAK database and have a Population Identification Number (NIK) which is helpful as a reference for the government in providing public services to the community, one of which is population services at the village level. Kelurahan is a government agency under the sub-district, led by the village head. Kelurahan consists of several RW, and each RW consists of several RT. For this reason, a government agency requires a system that can assist and facilitate agency devices, so a system is needed, namely a population information system in the Village. This study aims to make an Integrated Population Information System at the Kalurahan level. The Population Information System includes data services for new residents, birth data, death data, moving data, arrival date, and marriage data. The information system is accessed using mobile devices that have easy access for residents. The software development method used is a serial waterfall which starts from the system's planning, analysis, design, and implementation process. The output of this research is an applicative work for using research-based information technology to assist population services where the Integrated Population Service System can be accessed using a mobile application. Digitized data can be used as a basis for information retrieval, one of which is the opportunity to make efforts to facilitate population services through the information collected.
GEOGRAPHIC INFORMATION SYSTEMS FOR AGRICULTURAL SUITABLE LAND AT KABUPATEN SLEMAN Agus Qomaruddin Munir; Indra Listiawan; Evrita Lusiana Utari; Muh. Ridho Wahid Solihin
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 1 (2023): JUTIF Volume 4, Number 1, February 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.1.759

Abstract

Indonesia is an agricultural country, the growth rate of various food and crops tends to be stable. However, some plants in certain areas are different from what is expected. This study aims to investigate land suitability for crops such as rice, analysis of development potential, and develop recommendations for land use for agricultural types, Sleman Regency, Special Region of Yogyakarta. Landsat 8 imagery is used for land use extraction as input for making land use directions for agricultural and forestry plant species. The method used in this study is a combination of analysis of Landsat 8 imagery and field data processed using a Geographic Information System in making thematic maps of the physical characteristics of the land to obtain land suitability maps for agricultural crop species and with a weighting method in stages based on the physical parameters of the conditions for growing crops. The analysis results are used to formulate land use directions for the types of crops in Sleman Regency. An area very ideal for the type of rice plant and land planted. For crops, it is directed based on the room's function, namely in the cultivation area. Sites that have the potential to be developed for crops are produced and set in buffer zones because more emphasis is placed on conservation aspects and increasing crop productivity. This geographic information system is one of the recommendations to be used as a reference by farmers in increasing the productivity of agricultural land.
EFFECTIVE BREAST CANCER DETECTION USING NOVEL DEEP LEARNING ALGORITHM Irawadi Buyung; Agus Qomaruddin Munir; Putra Wanda
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 8 No. 2 (2023): JITK Issue February 2023
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1363.386 KB) | DOI: 10.33480/jitk.v8i2.4077

Abstract

Ultrasound is one of the most common screening tools for breast cancer detection. However, the lack of qualified radiologists causes the diagnosis process to become a challenging task. Deep learning's promising achievement in various computer vision problems inspires us to apply the technology to medical image recognition problems. We propose a detection model based on the Rapid-CNN to detect breast cancer quickly and accurately. We conduct this experiment by collecting breast cancer datasets, pre-processing, training models, and evaluating the model performance. This model can detect breast cancer with bounding boxes based on the experiment result. In this model, it is possible to detect the bounding box that is more than what it should be, so we applied NMS to eliminate the prediction of the bounding box that is less precise to increase accuracy.
RAINFALL PREDICTION USING SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE AND GEOGRAPHIC INFORMATION SYSTEM APPROACH Agus Qomaruddin Munir; Heru Ismanto
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 1 (2023): JITK Issue August 2023
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i1.4180

Abstract

Rainfall is one indicator to determine the estimated adequacy of groundwater on agricultural land. The groundwater availability produced by rain can determine cropping patterns in an area. The availability of rainfall data depends on the accuracy of information on current climate conditions. This case causes the related parties to find difficulty determining the classification of cropping patterns in the future. Accurate rainfall prediction models are needed to overcome the problem of shifting rain patterns. Rainfall prediction models in determining cropping patterns are recommended by FAO, such as linear regression, which is still widely used today. This study aims to develop a new model of rainfall prediction by using the method SARIMA to determine cropping patterns to increase crop yields. Rainfall data was used from 2010 to 2020 from seven rainfall collection stations in Sleman Regency, and they are used as training data to predict future rainfall. The output of the data analysis is a prediction of rainfall in the range of January-April, which is predicted to be high, May-August, which is predicted to be low; and September-December, which is predicted to be moderate. In addition, based on the identified cropping patterns, recommendations can be given to farmers to set cropping schedules and strategies to increase the productivity of the farmland. The testing of accuracy forecasting used relative mean absolute error (RMAE) for 12 months. The results of the forecasting accuracy test for 12 months in Sleman Regency showed RMAE average of 1.46 was considered low, for it was still below 10%.
Klasifikasi Citra Rontgen Covid-19 dengan menggunakan Deep Learning Evrita Lusiana Utari; Prastowo Kristiyanto; Agus Qomaruddin Munir
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 11, No 3 (2023)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v11i3.61111

Abstract

Citra adalah representasi dari suatu obyek yang ditulis ulang pada suatu medium dengan nilai tertentu (intensitas) yang memiliki koordinat x dan y. Citra Rontgent merupakan salah satu jenis citra medis yang dapat digunakan untuk mendeteksi dan mempelajari suatu penyakit. Namun citra rontgen terkadang terlihat kabur sehingga sedikit sulit untuk mengintepretasi citra. Terlebih lagi adanya redaman sinar-X yang berbeda antara kelenjar pada jaringan yang normal dengan yang terpapar penyakit. Dengan mengimplementasikan deep learning dengan metode klasifikasi citra dapat memilah gambar berdasarkan ekstrasi fitur dan bobot pada jaringan syaraf tiruan. Ketika GPU yang dimiliki adalah AMD, salah satu cara agar dapat menjalankan Deep Learning menggunakan AMD adalah mnggunakan PlaidML.Tahapan yang dilakukan pada pelatihan dan pengujian adalah melakukan pre-procesessing, ektraksi fitur menggunakan lapisan JST VGG16 tanpa lapisan pengklasifikasi (konvolusi dan pooling) yang menghasilkan bottleneck.npy, kemudian membuat lapisan pengklasifikasi sendiri untuk melatih klasifikasi kelas covid dan normal menggunakan data bottleneck.npy. Tingkat akurasi yang diperoleh pada tahap pelatihan beserta validasi pada pelatihan, dan pengujian berturut-turut adalah 99%, 97%, dan 94%. Selanjutnya ketika dievaluasi dengan F1 Score mendapatkan hasil 0,939.
BIG DATA CONCEPT ANALYSIS FOR AGRICULTURAL SUITABLE LAND GEOGRAPHIC INFORMATION SYSTEM APPROACH Agus Qomaruddin Munir; Farida Nur Aini; Evrita Lusiana Utari; Naufal Naja Hafidhah
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 4 (2023): JUTIF Volume 4, Number 4, August 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.4.1328

Abstract

Big data analysis for agriculture provides farmers with a comprehensive view of the concept of increasing agricultural productivity using the effectiveness of irrigation canals, predicting rainfall to determine outcrop patterns, and identifying the adequacy of agricultural land. It also allows farmers to optimize irrigation, increasing yields while reducing costs and environmental impact. It also will enable farmers to optimize irrigation; Rainfall predictions are used to determine cropping patterns and identify suitability for permits. It can also be used to deal with weather patterns and climate change, allowing farmers to adapt their practices to reduce the impact of climate change, ultimately protecting their crops and currency. This research aims to develop plant productivity through several stages of research and the use of methods. The methods used in this study are 1)Prediction of water discharge using the linear regression method; 2)Prediction of Rainfall for Planting Pattern Training using the SARIMA method, and 3)Suitability of Agricultural Land using the Cluster Area Analysis Approach. The results of this study are that in the Sleman region, the adequacy of water for agricultural areas is in the excellent category (fulfilled), cropping pattern spending is divided into 2, namely dry and wet months. In the wet months (high rainfall), rice is suitable for planting from January to May; for the dry months between June and October, tobacco, soybeans, corn, peanuts, green beans, cassava, and sweet potatoes. As for land suitability, it consisted of 46025.36 Ha (81%) suitable and 10811.48 Ha not suitable for use.
Convolutional Long Short-Term Memory (C-LSTM) For Multi Product Prediction Putu Sugiartawan; Yusril Eka Saputra; Agus Qomaruddin Munir
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 4 (2023): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.90149

Abstract

The retail company PT Terang Abadi Raya has a solid commitment to supporting distributors of LED lights and electrical equipment who have joined them, helping to spread their products widely in various regions. To face increasingly intense market competition, it is essential to produce high-quality products to win the competition and meet consumer demands. To achieve this, efficient production planning is necessary. The Convolutional Long Short-Term Memory (C-LSTM) method is used in this study to forecast product sales at PT Terang Abadi Raya. The research results show that C-LSTM has the potential to predict sales effectively. Evaluation is conducted using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The calculations reveal that the smallest values are obtained at epoch 10, with an MAE of 0.1051 and a MAPE of 22% in the testing data. For the cable data, the smallest values are found at epoch 100, with an MAE of 0.0602 and a MAPE of 44% in the testing data. The Long Short-Term Memory (LSTM) method with ten neurons produces the most minor errors during training.
INVENTARISASI DATA IRIGASI MENGGUNAKAN SISTEM INFORMASI GEOGRAFI UNTUK MENDUKUNG PEMBAGIAN DEBIT AIR Munir, Agus Qomaruddin
Jurnal Sistem Informasi Vol. 8 No. 2 (2021)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsii.v8i2.3299

Abstract

Abstrak - Kabupaten Sleman merupakan salah satu wilayah propinsi Daerah Istimewa Yogyakarta yang dijadikan sebagai daerah lumbung padi. Hal ini menjadi salah satu potensi di bidang pertanian untuk mencukupi kebutuhan pangan bagi penduduk di wilayah sekitarnya. Hasil panen di wilayah kabupaten Sleman yang terbesar salah satunya adalah padi. Pengembangan dan pemeliharaan padi di area persawahan di Kabupaten Sleman tergantung dari ketersediaan air pada jaringan irigasi sehingga dibutuhkan data tentang kondisi jaringan irigasi dan informasi terkait yang diperlukan. Informasi dapat dihasilkan dari perancangan sistem informasi geografis yang bertujuan untuk mendapatkan detail data jaringan irigasi dan lokasi mana saja yang dapat dijadikan sebagai sumber air. Sistem informasi geografis dibuat agar masyarakat dapat mengetahui wilayah-wilayah yang terdapat pembangunan jaringan pemanfaatan air yang tersebar di wilayah tertentu khususnya di daerah kabupaten Sleman. Perancangan dan implementasi sistem dibuat guna memberikan solusi tentang manfaat sistem informasi geografi sebagai alat bantu dalam melakukan pemetaan wilayah irigasi dan memberikan perkiraan pembagian air untuk wilayah pertanian dan perikanan di Kabupaten Sleman Propinsi Daerah Istimewa Yogyakarta dalam bentuk visualisasi pemetaan wilayah berbasis sistem informasi geografi. Hasil dari penelitian ini adalah berupa Sistem Informasi Geografis berbasis web untuk mengelola data irigasi guna pembagian debit air di wilayah Kabupaten Sleman. Kata kunci :Debit Air, Inventarisasi Irigasi, Sistem Informasi Geografi.
Optimalisasi Tata Laksana Administrasi Desa Berbasis Teknologi Informasi bagi Perangkat Desa Katekan Gantiwarno Klaten Munir, Agus Qomaruddin; Listiawan, Indra; Wijaya, Nurhadi; Utari, Evrita Lusiana
Wikrama Parahita : Jurnal Pengabdian Masyarakat Vol. 8 No. 1 (2024): Mei 2024
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jpmwp.v8i1.6634

Abstract

Perangkat desa di Desa Katekan, Gantiwarno, Klaten, Jawa Tengah, menghadapi banyak tantangan, pada era digital saat ini terutama dalam hal mendapatkan informasi dan pelayanan administrasi berbasis TIK yang dapat dimanfaatkan secara publik untuk kepentingan masyarakat. Perangkat desa seringkali tidak maksimal dalam memberikan layanan yang optimal, terutama jika mereka tidak mampu memanfaatkan teknologi informasi untuk mendukung proses pelayanan yang lebih baik. Untuk membantu perangkat desa menggunakan tata kelola administrasi berbasis TI, workshop aplikasi kantor diberikan dengan tiga materi pokok pada microsof office yaitu miscrosoft word, microsoft excel dan microsoft power point. Kegiatan yang dilaksanakan diawali tahap persiapan, pembekalan bagi mahasiswa serta pelaksanaan kegiatan berupa pemberian workshop secara periodik. Hasil kegiatan menunjukkan peningkatan pemahaman perangkat desa tetang tatakelola administrasi berbasis teknologi informasi serta pengetahuan perangkat desa untuk mengakses informasi dan pengetahuan dari internet untuk mendukung pelayanan kepada masyarakat.
Co-Authors Agus Prayitno Andri Syafrianto Anindita Septiarini, Anindita Anny Kartika Sari Bagus Subekti Nuswantoro Bayu Indra Wahyudi Bekti Wulandari Budi Rahmani Daud Andang Pasalli, Daud Andang Desty Ervira Puspaningtyas Dewanto, Satriyo Agung Dharmawan, Dhimas Arief Didit Suprihanto, Didit Dina Fitri Septarini, Dina Fitri Evrita Lusiana Utari Evrita Lusiana Utari Evrita Lusiana Utari Evrita Lusiana Utari Evrita Lusiana Utari, Evrita Lusiana Farida Nur Aini Hamdani Hamdani HERU Heru Ismanto Ikhwan Mustiadi Indra Listiawan Irawadi Buyung Irawadi Buyung, Irawadi Jayanti, Rafika Latifah Listyalina Listiawan, Indra Listyalina, Latifah Muh. Ridho Wahid Solihin Naufal Naja Hafidhah Nurkhamid , Nurkhamid Nurkhamid, Nurkhamid Prastowo Kristiyanto Putra Wanda Putu Sugiartawan Rahman, Dzul Fadli Ratna Wardani Ratnaningsih, Wahyu Sabdullah, Mursid Setiyawan, Ramadhana TAUFIQ HIDAYAT Wijaya, Nurhadi Yusriadi Yusriadi Yusril Eka Saputra Zaenal Mustofa Zaidir