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All Journal TEKNIK INFORMATIKA JUITA : Jurnal Informatika Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JSAI (Journal Scientific and Applied Informatics) Building of Informatics, Technology and Science Jurnal Teknik Elektro dan Komputasi (ELKOM) JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Jurnal Teknik Informatika (JUTIF) Journal of Dinda : Data Science, Information Technology, and Data Analytics
Sudianto
Institut Teknologi Telkom Purwokerto
Humanities Computer Science & IT Education Electrical & Electronics Engineering Engineering Mechanical Engineering Public Health Other

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COMPARISON OF RANDOM FOREST AND SUPPORT VECTOR MACHINE METHODS ON TWITTER SENTIMENT ANALYSIS (CASE STUDY: INTERNET SELEBGRAM RACHEL VENNYA ESCAPE FROM QUARANTINE) Sudianto; Puspa Wahyuningtias; Hapsari Warih Utami; Uli Ahda Raihan; Hasna Nur Hanifah; Yehezkiel Nicholas Adanson
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 1 (2022): JUTIF Volume 3, Number 1, February 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.1.168

Abstract

Coronavirus (Covid-19) is an infectious disease spreading widely throughout the world. Covid-19 has been declared a pandemic. Transmission of Covid-19 spreads through the Droplet. The Indonesian government has made efforts to prevent the spread of Covid-19, one of which is the implementation of quarantine regulated through Circular Letter Number 8 of 2021 concerning International Travel Health Protocols. The case of Selebgram Rachel Vennya's escape from quarantine had become a trending topic on Twitter. Many Twitter users in Indonesia gave their opinions and comments on this case. Therefore, it is necessary to research public sentiment on the case of the escape of Selebgram Rachel Vennya from quarantine. The data used is taken from netizen comments from social media, namely Twitter, in the form of positive and negative comments; the algorithms used are Random Forest (RF) and Support Vector Machine (SVM). This study aims to compare the classification method to public sentiment regarding the case of the escape of Selebgram Rachel Vennya from quarantine using the Random Forest and SVM methods. The classification results show that the Random Forest algorithm has an accuracy value of 94%. In comparison, the SVM algorithm classification results get an accuracy value of 93%. So it can be concluded, Twitter sentiment analysis in the case study of Rachel Venya's escape from quarantine that the Random Forest algorithm got the best results.
Penerapan Algoritma Support Vector Machine dan Multi-Layer Perceptron pada Klasifikasi Topik Berita Sudianto Sudianto; Asti Dwi Sripamuji; Imada Riski Ramadhanti; Risa Riski Amalia; Julian Saputra; Bagas Prihatnowo
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 11 No. 2 (2022)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

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

Abstract

Salah satu aktivitas masyarakat Indonesia di internet adalah mengakses berita online dengan persentase 15,5%. Dalam mencerna suatu informasi berita online, masyarakat Indonesia masih memiliki kebiasaan yang buruk ketika membaca lebih dari satu sumber media online pada kasus yang sama. Sehingga perlu adanya klasifikasi berita secara otomatis. Penelitian sebelumnya menggunakan Mutual Information and Bayesian Network untuk mengkategorikan suatu berita, dalam format data tekstual dengan akurasi 75.34%. Penelitian ini memberikan suatu inovasi baru berupa pengimplementasian algoritma Support Vector Machine dan Multi-Layer Perceptron untuk klasifikasi menggunakan pembelajaran supervised atau yang disebut backpropagation. Tujuan dari pengklasifikasian topik berita menggunakan Algoritma Support Vector Machine memudahkan pembaca untuk menemukan informasi yang pembaca butuhkan, dengan secara otomatis mengelompokkan berita ke dalam kategori tertentu sesuai informasi yang terkandung. Metode yang digunakan yaitu metode Term Frequency – Inverse Document Frequency untuk feature selection data pelatihan dan algoritma Support Vector Machines dan Multi-Layer Perceptron untuk klasifikasi. Hasil yang diperoleh menunjukan skor akurasi sebesar 74% pada SVM dan 78% pada MLP. Sementara itu, nilai precision dan recall yaitu 76% dan 74% pada SVM serta 79% dan 78% pada MLP.
Prediksi Harga Komoditas Pangan Menggunakan Algoritma Long Short-Term Memory (LSTM) Rizki Mugi Setya Adi; Sudianto Sudianto
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.2229

Abstract

Food is a basic need for human survival. The existence of food is influenced by production and selling prices. The problem that exists is that food producers lose out with the dynamics of selling prices. In addition, the low selling price is not commensurate with the production costs that have been spent, especially for food producers in agricultural commodities, namely local farmers. Local farmers lose money because they do not know the price of commodities when selling their agricultural products. In addition, the game of intermediaries causes local farmers to sell their crops at low prices. So from the existing problems, it is necessary to predict commodity prices to help farmers determine the commodity prices before selling their agricultural products to the market. This study aims to predict the price of food commodities, especially in Banyumas, so that local farmers can find the price of commodities before they are sold to the market. The Deep Learning method used is Long Short-Term Memory (LSTM), which can remember a collection of information that has been stored for a long time with time series data. The results obtained, the model can predict food commodity prices. Meanwhile, the prediction model with epoch 50 shows the lowest Root Mean Squared Error (RMSE) with a value of 79.19%
Analisis Kinerja Algoritma Machine Learning Untuk Klasifikasi Emosi Sudianto Sudianto
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.2261

Abstract

Social media is a place to express or share daily activities. Various new events are often discussed on social media, such as on Twitter. Frequently, the conversations conducted by Twitter users when giving a review or opinion have various emotions, such as anger, sadness, fear, or joy. Emotions are difficult to describe the challenges that occur, sometimes leading to multiple interpretations and misunderstandings leading to debates and reporting to the authorities. So this shows that emotions in reviews and opinions are essential for classification because emotions that come from texts are difficult to understand. In addition, the classification of emotions needs to be done to speed up the identification of emotions. The purpose of this study is to find out which algorithm has optimal performance in the classification of emotions. Machine Learning methods are the Naïve Bayes algorithm, Random Forest, and Support Vector Machines; this is done to determine the dominant algorithm in classifying emotions. The results of the modeling and classification using the Random Forest algorithm obtained a dominant accuracy with an accuracy value of 81.3%, followed by the SVM algorithm with an accuracy value of 76.6% and an accuracy value of 79.1% Naïve Bayes algorithm. In addition, from the speed of time in completing the classification, the Random Forest algorithm has the fastest time of 1.27 seconds
Identifikasi Sebaran Nitrogen pada Tanaman Padi berbasis Pengetahuan Fenologi dan Remote Sensing Sudianto Sudianto; Reni Dyah Wahyuningrum
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 11 No. 3 (2022)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v11i3.50051

Abstract

Nitrogen sangat penting untuk keberlanjutan tanaman padi. Kebutuhan nitrogen pada tanaman padi berguna untuk produktivitas dan peningkatan hasil panen. Masalah yang sering terjadi, kekurangan dan berlebihan nitrogen, juga tidak cocok untuk tanaman, terutama pada puncak fenologi tanaman padi. Oleh karena itu, penelitian ini bertujuan untuk identifikasi sebaran nitrogen pada tanaman padi pada puncak fenologi tanaman. Identifikasi nitrogen dilakukan dengan data citra satelit dan indikator indeks spectral seperti NDVI, NDRE, NDMI, dan data curah hujan. Selain itu, pemodelan tren musiman dibangun dengan regresi linier dan model harmonik sebagai basis pengetahuan untuk pola penanaman padi dari fenologi tanaman. Hasil yang diperoleh menunjukkan puncak penanaman padi dalam satu tahun masa tanam, terdapat dua siklus puncak; (1) dari bulan Januari hingga Maret; (2) dari bulan Juli hingga Agustus. Kemudian pada siklus puncak penanaman kedua, konservasi terhadap identifikasi sebaran nitrogen dalam kondisi normal. Sementara itu, pada siklus penanaman puncak pertama, tanaman padi pada bulan Januari ketersediaan sebaran nitrogen belum merata dan belum tercukupi.
Comparison of Support Vector Machines and K-Nearest Neighbor Algorithm Analysis of Spam Comments on Youtube Covid Omicron Sudianto Sudianto; Juan Arton Arton Masheli; Nursatio Nugroho; Rafi Wika Ananda Rumpoko; Zarkasih Akhmad
JURNAL TEKNIK INFORMATIKA Vol 15, No 2 (2022): JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v15i2.24996

Abstract

Every time a new variant of Coronavirus (Covid-19) appears, themedia or news platforms review it to find out whether the new variantis more dangerous or contagious than before. One of the media orplatforms that is fast in presenting news in videos is YouTube.YouTube is a social media that can upload videos, watch videos, andcomment on the video. The comment field on YouTube videos cannotbe separated from spam comments that annoy other users who want tofollow or participate in the comment column. Indication of spamcomments is still done by observing one by one; this is very inefficientand time-consuming. This study aims to create a model that canclassify spam on YouTube comments. The classification method uses the SVM (Support Vector Machines) algorithm and the KNN (K-Nearest Neighbor) algorithm to identify spam comments or not with comment data taken from Omicron's Covid-19 news video on national news channels. The classification results show that the SVM method is superior inaccuracy with the Linear SVC algorithm of 75.12%, SVC of 76.11%, and Nu-SVC of 77.11%. While the KNN algorithm with k=2 is 65.67%, k=4 is 64.51%, k=6 is 62.35%.
Prediction of Covid-19 Cases in Central Java using the Autoregressive (AR) Method Tangguh Widodo; Siti Maghfiroh; Surya Haganta Brema Ginting; Alif Aryaputra; Sudianto Sudianto
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 3 No 1 (2023): February
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v3i1.740

Abstract

Since the beginning of the Covid-19 case in Indonesia in March 2020, more than 6 million confirmed cases had been confirmed. The rapid development of this case can be accessed through the covid19.go.id page. In Central Java province, confirmed cases as of July 6, 2022, reached 628,393 people, with the number of recovered patients reaching 594,783 people and the number of patients dying as many as 33,215 people. With this data, a prediction is needed to help the government anticipate an increase in Covid-19 cases in Central Java Province. This study aims to create a forecasting model using the Autoregressive (AR) method by optimizing the function parameters. Then Mean Squared Error (MSE) to analyze the results of forecasting data errors. The results are the best parameter functions on AR (30) with the smallest MSE. Furthermore, predictions are made from July 1 to August 30, 2022, showing an increase in cases
Diabetes Diagnostic Expert System using Website-Based Forward Chaining Method Tiara Khumaira Putri; Mahda Laina Arnumukti; Khusnul Khatimah; Egidya Zalsabila; Sudianto Sudianto
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 3 No 1 (2023): February
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v3i1.752

Abstract

Diabetes is a chronic disease. The World Health Organization predicts that Indonesia's number of diabetic patients will continue to increase significantly to 16.7 million in 2045. As early prevention, early diagnosis is needed to anticipate more severe diabetes. This study aims to build an expert system for detecting diabetes using a web-based forward chaining method. The expert system is built by collecting indications from experts by collecting facts using the forward chaining method. Furthermore, judging by the unhealthy lifestyle of many people who consult with hospitals or health workers. From the results obtained, the system can work well based on knowledge from experts
An Expert System for Diagnosing the Impact of Traffic Accidents using the Forward Chaining Method Akbar Maulana Yusuf; Jonathan Indra Chelidivano; Tavany Amalia Rizky; Yanuar Sabikhi; Sudianto Sudianto
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 3 No 1 (2023): February
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v3i1.767

Abstract

Unexpected events that we often hear about are traffic accidents caused by many factors. Accidents also cause impacts in terms of health. This study aims to provide information regarding the effects of traffic accidents in terms of health based on some visible symptoms that emerged from the victim's body at the scene using an expert system. The Expert System is designed on a website-based application. The forward chaining method is used to get a conclusion based on the facts. The results of this research users gain knowledge about the impact of traffic accidents and the diagnosis on the victim's body that is close to the knowledge of experts with accuracy 87.5%. The website is designed to be used as a guide for users to be able to provide appropriate first aid to accident victims.
Analisis Sentimen Sepak Bola Indonesia pada Twitter menggunakan K-Nearest Neighbors dan Random Forest Dedy Agung Prabowo; Sudianto
JSAI (Journal Scientific and Applied Informatics) Vol 6 No 2 (2023): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v6i2.5337

Abstract

Twitter is one of the most widely used social media today. Twitter allows users to provide the latest news and comments about ongoing events in the World. In Indonesia, the final match of the AFF Suzuki Cup 2020 became a hot topic because, for the sixth time, Indonesia was runner-up after 2000, 2002, 2004, 2010, and 2016 appearances for the Indonesian national team. With so many opinions and criticisms circulating, distinguishing between positive and negative opinions takes a long time. Therefore, a sentiment analysis model is needed that can classify positive and negative opinions as evaluation material for the Indonesian National Team in the future. This study uses the K-Nearest Neighbors and Random Forest algorithm methods in sentiment analysis classification. The data comes from a reply to Joko Widodo's Twitter account regarding congratulations to the Indonesian national team after competing against Thailand at the AFF Suzuki Cup 2020. Based on the test results, the accuracy of the K-Nearest Neighbors algorithm is 75% better than the Random Forest algorithm, with an accuracy of 71%..
Co-Authors Adzan Bari Naufal Akbar Maulana Yusuf Alif Aryaputra Alvina Tahta Indal Karim Andini Mulia Kusumaningrum Asti Dwi Sripamuji Bagas Prihatnowo Chandra Ayunda Apta Soemedhy Dedy Agung Prabowo Dwi Puspa Martiyaningsih Egidya Zalsabila Hapsari Warih Utami Hasna Nur Hanifah Hikari Adriansyah Imada Riski Ramadhanti Jonathan Indra Chelidivano Juan Arton Arton Masheli Julian Saputra Khusnul Khatimah Mahda Laina Arnumukti Martin Parhusip Moh Aminullah Al Fachri Muhammad Hilmi Kafabi Muhammad Rahaji Jhaerol Nawang Anggita Winanti Nora Trivetisia Nursatio Nugroho Puspa Wahyuningtias Rafi Wika Ananda Rumpoko Reni Dyah Wahyuningrum Reni Dyah Wahyuningrum Risa Riski Amalia Rizki Mugi Setya Adi Satria Nur Saputro Siti Maghfiroh Surya Haganta Brema Ginting Tangguh Widodo Tavany Amalia Rizky Tiara Khumaira Putri Tri Ginanjar Laksana Tri Wulandari Utami Uli Ahda Raihan Widi Afandi Yanuar Sabikhi Yehezkiel Nicholas Adanson Zarkasih Akhmad