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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi Elkom: Jurnal Elektronika dan Komputer Bulletin of Electrical Engineering and Informatics Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Journal of Telematics and Informatics INFOKAM Sisforma: Journal of Information Systems CESS (Journal of Computer Engineering, System and Science) Proceeding SENDI_U Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Rekam Medis dan Informasi Kesehatan Media Ilmu Kesehatan Jurnal Teknik Informatika UNIKA Santo Thomas J-SAKTI (Jurnal Sains Komputer dan Informatika) Jesya (Jurnal Ekonomi dan Ekonomi Syariah) JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Riset Informatika Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming SOSCIED Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Ilmiah Intech : Information Technology Journal of UMUS Tematik : Jurnal Teknologi Informasi Komunikasi Journal of Computer Networks, Architecture and High Performance Computing Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Journal of Business and Technology J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Pengabdian Masyarakat Intimas (Jurnal INTIMAS): Inovasi Teknologi Informasi Dan Komputer Untuk Masyarakat Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Seminar Nasional Ilmu Terapan Jurnal Kabar Masyarakat Journal of Computing Theories and Applications Jurnal Informatika: Jurnal Pengembangan IT Journal of Future Artificial Intelligence and Technologies Proceeding of The International Conference on Mathematical Sciences, Natural Sciences, and Computing
Dian Kristiawan Nugroho
Universitas Stikubank
Automotive Engineering 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 Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Usability of Brain Tumor Detection Using the DNN (Deep Neural Network) Method Based on Medical Image on DICOM Niken Puspitasari; Kristiawan Nugroho; Kristhoporus Hadiono
CESS (Journal of Computer Engineering, System and Science) Vol 8, No 2 (2023): July 2023
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v8i2.48727

Abstract

Deteksi tumor otak merupakan bidang penelitian yang menarik untuk diteliti. Perkembangan teknologi informasi menghasilkan berbagai metode yang dipergunakan antara lain menggunakan CT (Computed Tomography) scan atau dikenal dengan teknologi CT scan. CT Scan mempunyai berbagai macam keunggulan dalam mendeteksi tumor otak antara lain pada sisi kecepatan, kemampuan memvisualisasikan citra 3 dimensi dan kemampuan membedakan antar jaringan yang berbeda. Keunggulan CT Scan tersebut membuat para peneliti tertarik untuk mengembangkan berbagai jenis metode yang dipergunakan untuk menganalisis dan memprediksikan hasil CT scan tersebut. Salah satu metode yang dipergunakan adalah menggunakan pendekatan Machine Learning (ML). ML dapat digunakan untuk deteksi tumor otak dengan CT scan. Prosesnya melibatkan penggunaan algoritma ML untuk mengidentifikasi pola-pola yang terdapat pada gambar CT scan pasien dengan tumor otak. Dalam hal ini, CT scan pasien dengan tumor otak digunakan sebagai dataset pelatihan untuk membangun model ML. Namun penggunaan Machine Learning juga memiliki keterbatasan dalam hal kurang handal nya Model dan kesulitan hasil deteksi yang diinterpretasikan dokter. Metode ML akan mengalami ketidakakuratan prediksi dengan model training data yang semakin besar sehingga membutuhkan metode lain yang bisa menghasilkan tingkat akurasi yang tinggi. Deep Learning (DL) merupakan fenomena baru pada dunia teknologi informasi dan telah berhasil diimplementasikan pada berbagai macam bidang penelitian. DL memberikan tingkat akurasi yang semakin tinggi jika didukung data yang semakin besar. Penelitian ini mengaplikasikan salah satu metode DL yaitu Deep Neural Network (DNN) untuk memprediksi tumor otak dari hasil CT Scan yang akan disimpan pada cloud server sehingga bisa diakses kapanpun dan dimanapun juga sepanjang tersedia teknologi Internet. Hasil penelitian ini akan bermanfaat bagi para tenaga medis dalam memprediksi tumor otak dengan lebih akurat berdasarkan gambar citra dari CT scan.Detection of brain tumors is an interesting field of research to study. The development of information technology has resulted in various methods being used, including using a CT (Computed Tomography) scan or known as CT Scan technology. CT Scan has various advantages in detecting brain tumors, including in terms of speed, the ability to visualize 3-dimensional images and the ability to distinguish between different tissues. The superiority of the CT Scan makes researchers interested in developing various types of methods used to analyze and predict the results of the CT Scan. One of the methods used is the Machine Learning (ML) approach. ML can be used to detect brain tumors with CT scans. The process involves using ML algorithms to identify patterns present in the CT scan images of patients with brain tumors. In this case, CT scans of patients with brain tumors are used as a training dataset to construct the ML model. However, the use of Machine Learning also has limitations in terms of the lack of reliability of the model and the difficulty of interpreting the results of detection by doctors. The ML method will experience prediction inaccuracies with the larger training data model, requiring other methods that can produce a high level of accuracy. Deep Learning (DL) is a new phenomenon in the world of information technology and has been successfully implemented in various research fields. DL provides a higher level of accuracy if it is supported by larger data. This study applies one of the DL methods, namely Deep Neural Network (DNN) to predict brain tumors from CT Scan results which will be stored on a cloud server so that they can be accessed anytime and anywhere as long as Internet technology is available. The results of this study will be useful for medical personnel in predicting brain tumors more accurately based on images from CT scans.
Improving Indonesian multietnics speaker recognition using pitch shifting data augmentation Kristiawan Nugroho; Isworo Nugroho; De Rosal Igniatus Moses Setiadi; Omar Farooq
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1901-1908

Abstract

Speaker recognition to recognize multiethnic speakers is an interesting research topic. Various studies involving many ethnicities require the right approach to achieve optimal model performance. The deep learning approach has been used in speaker recognition research involving many classes to achieve high accuracy results with promising results. However, multi-class and imbalanced datasets are still obstacles encountered in various studies using the deep learning method which cause overfitting and decreased accuracy. Data augmentation is an approach model used in overcoming the problem of small amounts of data and multiclass problems. This approach can improve the quality of research data according to the method applied. This study proposes a data augmentation method using pitch shifting with a deep neural network called pitch shifting data augmentation deep neural network (PSDA-DNN) to identify multiethnic Indonesian speakers. The results of the research that has been done prove that the PSDA-DNN approach is the best method in multi-ethnic speaker recognition where the accuracy reaches 99.27% and the precision, recall, F1 score is 97.60%.
INDONESIAN LANGUAGE CLASSIFICATION OF CYBERBULLYING WORDS ON TWITTER USING ADABOOST AND NEURAL NETWORK METHODS Kristiawan Nugroho
Jurnal Riset Informatika Vol. 3 No. 2 (2021): March 2021 Edition
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v3i2.54

Abstract

Cyberbullying is a very interesting research topic because of the development of communication technology, especially social media, which causes negative consequences where people can bully each other, causing victims and even suicide. The phenomenon of Cyberbullying detection has been widely researched using various approaches. In this study, the AdaBoost and Neural Network methods were used, which are machine learning methods in classifying Cyberbullying words from various comments taken from Twitter. Testing the classification results with these two methods produces an accuracy rate of 99.5% with Adaboost and 99.8% using the Neural Network method. Meanwhile, when compared to other methods, the results obtained an accuracy of 99.8% with SVM and Decision Tree, 99.5% with Random Forest. Based on the research results of the Neural Network method, SVM and Decision Tree are tested methods in detecting the word cyberbullying proven by achieving the highest level of accuracy in this study.
PENENTUAN PEMILIHAN VARITAS UNGGUL PADA TANAMAN PADI MENGGUNAKAN LOGIKA FUZZY TSUKAMOTO BERBASIS WEB Yoga Ryan Fatony; Kristiawan Nugroho
Elkom : Jurnal Elektronika dan Komputer Vol 16 No 2 (2023): Desember : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v16i2.1389

Abstract

Rice plants (Oryza sativa L.) are rice-producing plants which are a source of carbohydrates for most of the world's population. Almost 95% of Indonesia's population consumes rice as a staple food, so every year the demand for rice increases as the population increases. Therefore, farmers must choose quality seeds. In this era of fast and efficient technological progress, this is a very good thing for all progress in various fields. more and more fields of knowledge are developing, one of which is the existence of a decision-making system. a set of model-based procedures for processing and valuing data to help managers make decisions. This decision-making system uses several variables as input consisting of: type of variety, seed shape, seed color, root, seed age. The method used by the author is Fuzzy Tsukamoto. In the Tsukamoto method, it is explained that each consequence in IF-Then must be explained with a fuzzy set that has a membership function that does not change or is monotonous and for programming it uses PHP. The results obtained from the research that the authors conducted were in the form of a decision-making system to get the best seed yields.
PENDAMPINGAN SISWA SMK ISLAMIC CENTRE BAITURRAHMAN SEMARANG DALAM PENINGKATAN MOTIVASI DAN STRATEGI PEMILIHAN PROGRAM STUDI PADA PERGURUAN TINGGI Nugroho, Kristiawan; Setiawan, Mulyo Budi; Mulyani, Sri; Nugroho, Isworo
Intimas Vol 3 No 2 (2023)
Publisher : Fakultas Teknologi Informasi dan Industri Unisbank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/intimas.v3i2.9557

Abstract

This community service activity aims to provide assistance to provide motivation and understanding of further studies at tertiary institutions at the Baiturrahman Islamic Center Vocational School, Semarang. This was done in order to support students' design creativity. In the initial discussions that were conducted, it was seen that knowledge regarding further studies in tertiary institutions was still lacking. Activities carried out in the form of interactive presentations and questions and answers with students regarding their desire to continue their education to the tertiary level. This activity is expected to provide education about the importance of determining the right strategy in choosing majors and tertiary institutions so that students can choose the right majors according to their wishes to continue their education to the tertiary level as a means of achieving their goals in the future.
Pendampingan Digital Marketing Produk UMKM Desa Manggihan Kabupaten Semarang Budi Hartono; Veronica Lusiana; Kristiawan Nugroho; Mohammad Riza Radyanto
Jurnal Kabar Masyarakat Vol. 2 No. 1 (2024): Februari : JURNAL KABAR MASYARAKAT
Publisher : Institut Teknologi dan Bisnis Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jkb.v2i1.1634

Abstract

Located in Getasan subdistrict, Semarang district, Manggihan village has 506 families. Most of the population works as farmers, cattle breeders and entrepreneurs. The problem faced is that micro, small and medium enterprises (MSMEs) cannot market their products widely. The superior products are mushroom chips, onion crackers and processed catfish. Marketing of these products experienced a decline in turnover during the Covid 19 Pandemic. The solution to this problem is to provide MSME partners who are affected by the pandemic through digital marketing assistance. Empowerment through this assistance involves various elements: academics (Lecturers, Unisbank Semarang Students), government (Semarang Village and Regency Government), business (farmers, MSMEs, digital marketing providers). The method used to solve this problem uses business assistance through digital marketing training with social media, creating market place accounts, logo branding and etiquette. The output target that partner MSMEs want to achieve is helping increase turnover through post-pandemic digital marketing and strengthening product branding and expanding markets
Sales Conversion Optimization Analysis Using the Random Forest Method Nugroho, Kristiawan; Wismarini , Th. Dwiati; Murti, Hari
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12943

Abstract

Sales conversion is a challenging field of work in sales and business. Companies are competing to be winners by improving their services and hoping that their product sales can increase in various ways, including by using optimization theory. However, the lack of data analysis is a problem that is often encountered in optimizing sales conversions. Various machine learning-based methods have also been used to help analyze sales conversion optimization. This research uses the Random Forest method which is one of the more robust machine learning methods compared to other methods, namely Adaptive Booster (AdaBoost) and K-Nearest Neighbor (KNN) in analyzing sales conversion optimization. The results showed that the Random Forest method had the best performance in classifying data, by using the 10 cross validation technique the results were obtained with a Mean Squared Error (MSE) value of 0.928 and a Root Mean Square Error (RMSE) of 0.963, better than the Adaptive Booster method. and K-Nearest Neighbor which has lower performance. Sales conversion optimization processing using Random Forest is proven to have the best performance as evidenced by the small Mean Squared Error and Root Mean Square Error which means it has an accurate level of performance compared to other methods.
Rainfall Monitoring Using Aloptama Automatic Rain Gauge And The Network Development Life Cycle Method Nugroho, Kristiawan; Afandi , Afandi; Rokhayadi, Wakhid; Budiarto, Indri; Hermawan, Taufan
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.13908

Abstract

Examining the role of rainfall data management in monitoring and reducing natural disasters. Between the observation post and the coordinating office of the Central Java Meteorology, Climatology and Geophysics Agency, there are problems in managing rainfall data. To increase the accuracy and efficiency of rainfall monitoring, the Central Java BMKG Coordinator has used various platforms that are considered very good, such as Grafana, Node-RED, Xampp, and MQTT. Previous research has shown that the use of the Automatic Rain Gauge (ARG) and the Network Development Life Cycle (NDLC) method is very effective in creating an accurate and reliable rainfall monitoring system. This research uses the NDLC model, which consists of analysis, design, prototype simulation, implementation, monitoring and management stages. It is hoped that the research results will help improve visual monitoring of rainfall in local areas and increase understanding of rainfall patterns, flood prediction, water resource management and mitigation measures. This will serve as a reference for governments and institutions working together to make decisions to avoid catastrophic climate change.
Application of the Arima Method to Prediction Maximum Rainfall at Central Java Climatological Station Ruslana, Zauyik Nana; Prihatin, Rudi Setyo; Sulistiyowati, Sulistiyowati; Nugroho, Kristiawan
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.13984

Abstract

The existence of extreme weather that is difficult to predict results in frequent hydrometeorological disasters. ARIMA is a prediction method that can capture trend patterns, seasonal cycles, and random fluctuations that are often found in patterned data. Although many samples of rain data collection points are needed to produce denser data, one point can be considered to represent an area that is not too large, such as Semarang City. This method is quite accurate for short-term forecasts, with the results of monthly maximum rainfall forecasts in 2023 showing varying MAPE values. For the 12-month forecast, prediction results range from fair to very accurate. The 7-month forecast also shows decent to very accurate results. However, the 5-month forecast shows less accurate results. This shows that ARIMA can be a useful method in forecasting monthly maximum rainfall, especially during the dry season. The application of ARIMA in Semarang City can help in planning hydrometeorological disaster mitigation, considering that the Semarang City area often experiences extreme weather that is difficult to predict. Thus, the use of ARIMA can provide significant benefits in preparing for and reducing the impact of hydrometeorological disasters in the region. In addition, with more accurate forecasts, the government and society can take preventative steps earlier, such as better water management, creating an adequate drainage system, and increasing public awareness of the threat of disasters. Therefore, this research emphasizes the importance of using reliable prediction methods such as ARIMA to improve preparedness in dealing with hydrometeorological disasters.
Enhanced multi-ethnic speech recognition using pitch shifting generative adversarial networks Nugroho, Kristiawan; Hadiono, Kristophorus; Sutanto, Felix; Marutho, Dhendra; Farooq, Omar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2904-2911

Abstract

Research in the field of speech recognition is a challenging research area. Various approaches have been applied to build robust models. A problem faced in speech recognition research is overfitting, especially if there is insufficient data to train the model. A large enough amount of data can train the model well, resulting in high accuracy. Data augmentation is an approach often used to increase the quantity of dataset. This research uses a data augmentation approach, namely pitch shifting, to increase the quantity of speech dataset, which is then processed into spectrogram data and then classified using a generative adversarial network (GAN). Using the pitch shifting-generative adversarial network (PS-GAN) model, this research produces high accuracy performance in multi-ethnic speech recognition, namely 98.43%, better than several similar studies.
Co-Authors Achmad Nuruddin Safriandono Afandi , Afandi Afif, Randi Ahmad Fathoni Ajib Susanto Ajie, Ach. Ridlo Bayu Alex Chandra Iswanto Alfiqhyanto, Damas Aminudin, Agus Anjis Sapto Nugroho Anton Sujarwo Anton Sujarwo Aprico, Fikky Apriyanti, Dewi Aquinia, Ajeng Araaf, Mamet Adil Arsyad , Muhammad Rafi Haidar budi hartono Budiarto, Indri Cahaya, Agus Indra De Rosal Ignatius Moses Setiadi Dhendra Marutho Dwi Agus Diartono Dwi Budi Santoso Edy Winarno Eka Ardhianto Eko Ariyanto Eko Prasetyo Eko Prasetyo Eksawati, Rini Endang Tjahjaningsih Eri Zuliarso Ermillian, Ade Faizi, Aditya Wahyu Nur fakhri Farooq, Omar Fitrianto, Lindu Hari Murti Hermawan, Taufan Hidayat, Suluh Irawan, Sandy Islam, Hussain Md Mehedul Isworo Nugroho Kasmari . Kirana, Heni Candra Kristhoporus Hadiono Kristianto, Taufik Fredy Kristiyono, Budi Kristophorus Hadiono Lie Liana Lie Liana . Minantri Haika, Shara Muh Kholid Rizky Sapawi Muhamad Riski Atarik Mulyani , Wahyu Sri Mulyo Budi Setiawan Munna, Aliyatul Muslikh, Ahmad Rofiqul Niken Puspitasari Nurmakhlufi, Alfin Ojugo, Arnold Adimabua Omar Farooq Palupi, Dian Perdana, Willy Yudha Prabowo, Ardian Adi Prihatin, Rudi Setyo Rachman, Rahadian Kristiyanto Raden Mohamad Herdian Bhakti Radyanto, Mohammad Riza Rahadiyanto, Cahyono Raharjo, Fajar Retnowati Rokhayadi, Wakhid Ruslana, Zauyik Nana Saputra, Roni Halim Saputro, Risky Wisnu Sariyun Naja Anwar Sarwo Edi, Sarwo Setyaningtyas, Elvanita Sri Mulyani Sugeng Murdowo Suhana Suhana Sulastri Sulastri Sulistiyowati Sulistiyowati Sunardi Sunardi Suprapto, Yossy SUTANTO, FELIX Syahroni Wahyu Iriananda, Syahroni Wahyu Teguh Khristianto Veronica Lusiana Vici Tiara Anjarsari Warto - Wijayanto, Wendhie Tri Wiratno, Amat Wismarini , Th. Dwiati Wiwien Hadi Kurniawati Yayi Suryo Prabandari Yoga Ryan Fatony Yoga Ryan Fatony