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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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Analisis Sentimen Media Sosial Pada Pengguna Twitter Terhadap Pemilu 2024 Menggunakan Metode LSTM Setyaningtyas, Elvanita; Nugroho, Kristiawan
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 9, No 2 (2024): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v9i2.799

Abstract

Elections are a crucial democratic procedure as they offer citizens the privilege of exercising their rights. Consequently, there is a wide range of contrasting responses on social media, particularly on Twitter. Twitter is a platform that facilitates sentiment analysis, a valuable tool for comprehending the public's perception of leaders and the topics addressed in a campaign. This analysis encompasses both positive and negative opinions, which are of particular importance for study. Sentiment analysis can be used to determine the propensity of Twitter users to publish material. This study commenced with gathering data from Twitter, followed by data modeling utilizing the LSTM technique and real-time implementation, resulting in the classification of individuals into supporters, non-supporters, and neutrals. LSTM, short for Long Short-Term Memory, is a sophisticated deep learning technique that serves as the foundational framework for the research project titled "Sentiment Analysis of Twitter Users towards the 2024 Election using the LSTM Method". LSTM has the benefit of being capable of retaining and manipulating long-term knowledge, as well as accessing and modifying past information. The objective of this study is to ascertain the sentiment analysis of Twitter users on the 2024 election, categorizing it as favorable, negative, or neutral. The study yielded an accuracy rate of 78% utilizing the LSTM approach.
Enhanced multi-lingual Twitter sentiment analysis using hyperparameter tuning k-nearest neighbors Nugroho, Kristiawan; Winarno, Edy; Setiadi, De Rosal Ignatius Moses; Farooq, Omar
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7265

Abstract

Social media is a medium that is often used by someone to express themselves. These various problems on social media have encouraged research in sentiment analysis to become one of the most popular research fields. Various methods are used in sentiment analysis research, ranging from classic machine learning (ML) to deep learning. Researchers nowadays often use deep learning methods in sentiment analysis research because they have advantages in processing large amounts of data and providing high accuracy. However, deep learning also has limitations on the longer computational side due to the complexity of its network architecture. K-nearest neighbor (KNN) is a robust ML method but does not yet provide high-accuracy results in multi-lingual sentiment analysis research, so a hyperparameter tuning KNN approach is proposed. The results showed that using the proposed method, the accuracy level improved to 98.37%, and the classification error (CE) improved to 1.63%. The model performed better than other ML and even deep learning methods. The results of this study indicate that KNN using hyperparameter tuning is a method that contributes to the sentiment analysis classification model using the Twitter dataset.
Comprehensive Analysis and Classification of Skin Diseases based on Image Texture Features using K-Nearest Neighbors Algorithm Araaf, Mamet Adil; Nugroho, Kristiawan; Setiadi, De Rosal Ignatius Moses
Journal of Computing Theories and Applications Vol. 1 No. 1 (2023): JCTA 1(1) 2023
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jcta.v1i1.9185

Abstract

Skin is the largest organ in humans, it functions as the outermost protector of the organs inside. Therefore, the skin is often attacked by various diseases, especially cancer. Skin cancer is divided into two, namely benign and malignant. Malignant has the potential to spread and increase the risk of death. Skin cancer detection traditionally involves time-consuming laboratory tests to determine malignancy or benignity. Therefore, there is a demand for computer-assisted diagnosis through image analysis to expedite disease identification and classification. This study proposes to use the K-nearest neighbor (KNN) classifier and Gray Level Co-occurrence Matrix (GLCM) to classify these two types of skin cancer. Apart from that, the average filter is also used for preprocessing. The analysis was carried out comprehensively by carrying out 480 experiments on the ISIC dataset. Dataset variations were also carried out using random sampling techniques to test on smaller datasets, where experiments were carried out on 3297, 1649, 825, and 210 images. Several KNN parameters, namely the number of neighbors (k)=1 and distance (d)=1 to 3 were tested at angles 0, 45, 90, and 135. Maximum accuracy results were 79.24%, 79.39%, 83.63%, and 100% for respectively 3297, 1649, 825, and 210. These findings show that the KNN method is more effective in working on smaller datasets, besides that the use of the average filter also has a significant contribution in increasing the accuracy.
Enhanced Vision Transformer and Transfer Learning Approach to Improve Rice Disease Recognition Rachman, Rahadian Kristiyanto; Setiadi, De Rosal Ignatius Moses; Susanto, Ajib; Nugroho, Kristiawan; Islam, Hussain Md Mehedul
Journal of Computing Theories and Applications Vol. 1 No. 4 (2024): JCTA 1(4) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.10459

Abstract

In the evolving landscape of agricultural technology, recognizing rice diseases through computational models is a critical challenge, predominantly addressed through Convolutional Neural Networks (CNN). However, the localized feature extraction of CNNs often falls short in complex scenarios, necessitating a shift towards models capable of global contextual understanding. Enter the Vision Transformer (ViT), a paradigm-shifting deep learning model that leverages a self-attention mechanism to transcend the limitations of CNNs by capturing image features in a comprehensive global context. This research embarks on an ambitious journey to refine and adapt the ViT Base(B) transfer learning model for the nuanced task of rice disease recognition. Through meticulous reconfiguration, layer augmentation, and hyperparameter tuning, the study tests the model's prowess across both balanced and imbalanced datasets, revealing its remarkable ability to outperform traditional CNN models, including VGG, MobileNet, and EfficientNet. The proposed ViT model not only achieved superior recall (0.9792), precision (0.9815), specificity (0.9938), f1-score (0.9791), and accuracy (0.9792) on challenging datasets but also established a new benchmark in rice disease recognition, underscoring its potential as a transformative tool in the agricultural domain. This work not only showcases the ViT model's superior performance and stability across diverse tasks and datasets but also illuminates its potential to revolutionize rice disease recognition, setting the stage for future explorations in agricultural AI applications.
PERANCANGAN APLIKASI MOBILE BIMBINGAN DAN MONOTORING TA BERBASIS WEB ENGINEERING DENGAN UNIFIED MODELING LANGUAGE (UML) Nugroho, Kristiawan
Seminar Nasional Ilmu Terapan Vol 1 No 1 (2017): Seminar Nasional Ilmu Terapan (SNITER) 2017
Publisher : Universitas Widya Kartika Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (438.682 KB)

Abstract

Keberhasilan pembelajaran di tingkat perguruan tinggi membutuhkan partisipasi dari segenap elemen baik dari dosen maupun mahasiswa. Mahasiswa berkewajiban dalam menyelesaikan semua matakuliah yang harus ditempuh termasuk matakuliah Tugas Akhir (TA) dalam menyelesaikan proses perkuliahannya, Saat ini masih banyak mahasiswa diperguruan tinggi yang melakukan bimbingan tugas akhir secara konvensional dimana mahasiswa harus datang ke dosen secara langsung untuk melakukan kegiatan bimbingan TA. Permasalahan yang terjadi adalah kesulitan dalam mengatur waktu bimbingan antara dosen dengan mahasiswa, terutama bagi mahasiswa yang sudah bekerja yang hanya memiliki waktu malam hari untuk melakukan bimbingan. Penelitian ini bertujuan untuk membuat model aplikasi berbasis mobile berbasis sms gateway dengan UML yang bisa diakses oleh setiap mahasiswa dengan menggunakan media smartphone dan website,Teknik perancangan sistem yang digunakan adalah menggunakan UML(Unified Modelling Language) yang merupakan software yang akan membantu mendesign arsitektur sistem yang berbasis object. Dengan UML akan membantu menghasilkan design sistem yang akan dibangun secara lebih terstruktur. Metode yang digunakan dalam membangun aplikasi ini adalah dengan Web Engineering yang bermanfaat dalam merancang aplikasi berbasis web secara lebih terstruktur, Dengan aplikasi ini diharapkan mempermudah komunikasi antara dosen dan mahasiswa dalam proses bimbingan TA, sehingga akan lebih meningkatkan mutu pembelajaran terutama bimbingan TA pada perguruan tinggi .
Optimasi Klasifikasi Data Stunting Melalui Ensemble Learning pada Label Multiclass dengan Imbalance Data Prasetyo, Eko; Nugroho, Kristiawan
Techno.Com Vol. 23 No. 1 (2024): Februari 2024
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v23i1.9779

Abstract

Salah satu permasalahan kesehatan yang sering ditemui di banyak negara termasuk Indonesia adalah stunting. Stunting telah mendapat banyak perhatian di Indonesia, terlihat dari alokasi APBN masing-masing sebesar Rp48,3 triliun dan Rp49,4 triliun pada tahun 2022 dan 2023 untuk bidang ini. Pada tahun 2022, Kementerian Kesehatan merilis temuan dari Survei Status Gizi Indonesia (SSGI) yang menyatakan bahwa angka stunting di Indonesia mencapai 21,6% pada saat Rapat Kerja Nasional BKKBN pada 25 Januari 2023.Hal ini menunjukkan pentingnya untuk mengerti pemahaman mendalam tentang faktor-faktor yang mengidentifikasi anak-anak berisiko tinggi terkena stunting. Banyak penelitian sebelumnya yang membahas faktor resiko stunting, namun masih sedikit penerapannya dalam metode machine learning, dalam data yang kompleks dan tidak seimbang.Penelitian ini mengevaluasi kinerja dari berbagai metode machine learning yang bertujuan dapat memberikan kontribusi penting dalam bidang kesehatan anak dan analisis data. Diantara metode machine learning yang dipilih metode Bagging Decision Tree mendapatkan nilai accuracy yang terbaik sebesar 78,93%, precision 78% dan recall sebesar 77,99%. Dalam penelitian ini menunjukkan bahwa metode ensemble learning mampu bekerja dengan baik dalam atribut multiclass dan data yang tidak seimbang pada dataset pertumbuhan balita.
Sentiment Analysis on BNI Mobile Application Review Using K- Nearest Neighbors Algorithm Nurmakhlufi, Alfin; Arsyad , Muhammad Rafi Haidar; Mulyani , Wahyu Sri; 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.v8i4.14136

Abstract

Advances in Science and Technology continue to evolve in response to the demands of modern times, particularly in various fields such as banking. The development of information technology has transformed the way transactions are conducted from traditional to digital, accessible flexibly through Mobile Banking. BNI has created the BNI Mobile Banking application to facilitate customers in their transactions. The objective of this study is to investigate how the use of BNI Mobile can influence the ease of customers in conducting transactions. The data collection method used in this study is the K-Nearest Neighbors method, focusing on user experience with the BNI Mobile Banking application
Comparison of RNN and LSTM Algorithms Based on Fasttext Embeddings in Sentiment Analysis on the Merdeka Mengajar Platform Nugroho, Anjis Sapto; Nugroho, Kristiawan
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14296

Abstract

As of 2024, the Merdeka Mengajar Platform has been used by more than 3.5 million teachers across Indonesia. This number represents an increase of more than 3.85% compared to the previous academic year, which was 3.37 million. However, the utilization of this application has not yet reached the expected target number of users, so an analysis is needed to identify the factors causing this. This research uses Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) to perform sentiment analysis on reviews of the Merdeka Mengajar platform. RNN and LSTM are chosen for their advantages in handling sequential data, particularly in text processing for sentiment analysis. This research aims to address the challenges in understanding the positive or negative sentiments of users on the platform. The research methodology includes important stages such as data cleaning, preprocessing, and transforming text into numerical vectors using FastText embedding. Next, RNN and LSTM models are applied to predict sentiment based on patterns in the text data. The research results show that the LSTM model is capable of capturing long-term relationships in sequential data with an expected accuracy of 93.58%. Meanwhile, the RNN model yields a lower accuracy of 91.70%. The LSTM model is more effective in classifying sentiment with high accuracy, especially in text data with complex temporal contexts. This research contributes to understanding user perceptions and feedback regarding the Merdeka Mengajar platform, which is expected to provide insights for platform developers to enhance service quality.
Design of Intelligent Model for Text-Based Fake News Detection Using K-Nearest Neighbor Method Murti, Hari; Sulastri, Sulastri; Santoso, Dwi Budi; Diartono, Dwi Agus; Nugroho, Kristiawan
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14306

Abstract

Text-based fake news detection is a crucial issue considering its negative impacts on society and individuals. One of the main impacts that has a significant and detrimental impact on society is disinformation, where false or misleading information can cause confusion and uncertainty in society. This can lead to misunderstandings and develop into riots in society which can lead to legal problems that are detrimental to society. In order to overcome this problem, a method is needed to detect fake news. This study aims to build a fake news detection method using machine learning, which is a technology widely used by researchers to detect and analyze past data. Various methods have been produced using machine learning, including the K-Nearest Neighbor (K-NN) method which is proposed as an effective solution to detect fake news. K-NN is a machine learning algorithm that works by classifying text based on its proximity to known data in feature space. This method is proposed because of its ability to handle non-linear data and its low complexity. The application of K-NN can increase the accuracy in detecting fake news by utilizing the characteristics of relevant text, thus helping in efforts to filter information and maintain the integrity of news circulating in the community. In a study conducted using the FakeNewsDetection dataset, the model evaluation results showed that KNN produced a Mean Absolute Error (MAE) of 0.011 and a Root Mean Squared Error (RMSE) of 0.077, better than the performance of other methods such as SVM and Neural Network.
Sistem Rekomendasi Wisata di Pekalongan melalui Chatbot dengan Framework Rasa Fakhri; Nugroho, Kristiawan
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 1 (2025): JANUARI-MARET 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i1.3000

Abstract

Pekalongan, a city renowned for its batik and rich in cultural and natural attractions, has great potential in the tourism sector. However, limited access to integrated and easily accessible information poses challenges for tourists planning their trips. The Rasa-based Telegram chatbot addresses these challenges as an innovative solution. Through interactive engagement, tourists can receive recommendations for destinations, culinary spots, and other relevant information tailored to their preferences. This system leverages Rasa Natural Language Understanding (NLU) to interpret user queries and provide appropriate responses. Comprehensive tourism data of Pekalongan is embedded into the system to ensure accurate and relevant recommendations. The chatbot's evaluation includes direct user testing to measure the accuracy of recommendations, user satisfaction, and ease of use. Results indicate that the Rasa-based Telegram chatbot can deliver personalized and accurate recommendations, enhancing the travel planning experience for tourists visiting Pekalongan.
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