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All Journal TEKNIK INFORMATIKA JUITA : Jurnal Informatika JOIV : International Journal on Informatics Visualization Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JSAI (Journal Scientific and Applied Informatics) Building of Informatics, Technology and Science JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Teknik Elektro dan Komputasi (ELKOM) JATI (Jurnal Mahasiswa Teknik Informatika) JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Jurnal Teknik Informatika (JUTIF) Journal of Dinda : Data Science, Information Technology, and Data Analytics JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Informatika: Jurnal Pengembangan IT JPMTT (Jurnal Pengabdian Masyarakat Teknologi Terbarukan)
Sudianto
Institut Teknologi Telkom Purwokerto
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Library & Information Science Mechanical Engineering Public Health Other

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Dominant Requirements for Student Graduation in the Faculty of Informatics using the C4.5 Algorithm Alvina Tahta Indal Karim; Sudianto Sudianto
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 3 No 2 (2023): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v3i2.1040

Abstract

Graduating on time is one of the indicators in the achievement and ranking of educational institutions. The achievement of graduating on time in educational institutions is essential to balance incoming and graduating students. The problem that occurs, the attributes for graduating on time have varying weightings, so the determinants of the attributes for passing on time need to be known so that the anticipation of achieving graduation on time can be met. The purpose of this study is to find out the dominant attributes in the prediction of graduating on time for students. The attributes used are credit scores (Semester Credit Units), GPA scores (Grade Point Average), and English scores (TOEFL). The method used is the C4.5 Algorithm which is one of the classification methods in data mining. The data used was 262 data, split randomly with a composition of training and testing data of 80:20. Data is processed using the data mining process by creating decision trees. The decision tree results using the C4.5 Algorithm show that the GPA value is the most influential attribute in predicting a student's graduation time. In addition, predictions based on the decision tree of the C4.5 Algorithm with criterion = 'gini' and max_depth = 5 showed an accuracy result of 77%.
Implementation of Chatbot for Merdeka Belajar Kampus Merdeka Program using Long Short-Term Memory Muhammad Rahaji Jhaerol; Sudianto Sudianto
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 2 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i2.58794

Abstract

Good service can help the organization improve efficiency and effectiveness in operations. Optimal service can also improve the customer experience and provide added value to an organization that provides services. One of the services that can be optimized is the Merdeka Belajar Kampus Merdeka (MBKM) program which is a learning program organized by the Ministry of Education, Culture, Research, and Technology (Kemendikbudristek), especially MBKM services at the Institut Teknologi Telkom Purwokerto (ITTP). The problem is that the MBKM service at ITTP is not optimal due to inaccessibility to anyone and so many programs available. Thus, resulting in not optimal services provided. Therefore, this study aims to implement a Chatbot service in the MBKM program at ITTP. The method used in building a Chatbot service is the Deep Learning Long Short-Term Memory (LSTM) algorithm. LSTM is a type of artificial neural network architecture that matches text data. The results show an accuracy score of 100% and a loss of 0.121%. Meanwhile, the results of the further evaluation are in the form of average weights consisting of precision, recall, and F1-score, respectively of 100%, 100%, and 100%.
Implementation of Internet of Things Appropriate Technology as River Mitigation in Tubing Tourism Sudianto Sudianto; Reni Dyah Wahyuningrum
JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Vol 7 No 3 (2023): Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Publisher : Dewan Pimpinan Daerah (DPD) Perkumpulan Dosen Indonesia Semesta (DIS) Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36339/je.v7i3.786

Abstract

The purpose of this activity is the application of Internet of Things (IoT)-based Appropriate Technology (TTG) for the establishment and mitigation of sustainable river tubing tourism on the Pelus Karanggitung river, Banyumas. This activity is carried out as mitigation and warning for the river system at tourist locations. The method used in this activity is the installation of appropriate technology tools for the Internet of Things and assistance. The output target of this activity is to install appropriate technology such as a warning and mitigation system at tourist attractions. In addition, it is hoped that the continuation of this activity will make the Pelus Tubing Tour run and have a warning system for river mitigation. Based on the results of the activity, it can be concluded that this activity is in accordance with the needs of partners, because it can prevent unexpected events from river water at tubing tourism sites. Apart from that, this activity can increase partners' awareness of the river environment.
Analisis Komparasi Algoritma Machine Learning untuk Sentiment Analysis (Studi Kasus: Komentar YouTube “Kekerasan Seksual”) Chandra Ayunda Apta Soemedhy; Nora Trivetisia; Nawang Anggita Winanti; Dwi Puspa Martiyaningsih; Tri Wulandari Utami; Sudianto Sudianto
Jurnal Informatika: Jurnal Pengembangan IT Vol 7, No 2 (2022)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v7i2.3547

Abstract

Cases of sexual violence in the last decade have been rampant in Indonesia. Cases of sexual violence are increasingly exposed, along with the increasing use of social media. One of them is violence against women. Cases of sexual violence often cause various kinds of stigma in the community, so this study aims to determine the public's response to cases of sexual harassment using sentiment analysis. The data used is sourced from YouTube comments with the title "Kasus Bunuh Diri NW: Bripda Randy Tersangka, Penanganan Polisi Dikritik | Narasi Newsroom." The method used is Machine Learning algorithms such as the SVM algorithm, Naive Bayes, and Random Forest. The results of comparing the three Machine Learning algorithms, Random Forest, obtained the best accuracy rate of 78% compared to the other two algorithms in conducting sentiment analysis on YouTube comments about sexual harassment discussions.
Klasifikasi Judul Berita Clickbait menggunakan RNN-LSTM Widi Afandi; Satria Nur Saputro; Andini Mulia Kusumaningrum; Hikari Adriansyah; Muhammad Hilmi Kafabi; Sudianto Sudianto
Jurnal Informatika: Jurnal Pengembangan IT Vol 7, No 2 (2022)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v7i2.3401

Abstract

Amid technological developments, online news of various life topics is shared across various platforms. Many media often take advantage of this opportunity by uploading their news on several online platforms to increase the traffic and rankings they upload to make much profit. However, many online media attract readers' attention by exaggerating the headlines or news headlines they upload. That way, the news title is often not by the content of the news. This phenomenon is commonly known as "clickbait" among the public. The media usually do this to increase traffic, rankings, and finances. Therefore, this study classified the news with clickbait and non-clickbait titles using the RNN-LSTM architecture. In this study, the classification of clickbait news titles uses the RNN-LSTM architecture. The classification results obtained calculation accuracy of 79% on training data and 77% accuracy on test data.
Sentiment Analysis of the Public Towards the Kanjuruhan Tragedy with the Support Vector Machine Method Martin Parhusip; Sudianto Sudianto; Tri Ginanjar Laksana
JUITA: Jurnal Informatika JUITA Vol. 11 No. 2, November 2023
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v11i2.17405

Abstract

A tragedy occurred in the Indonesian football world during the Arema vs. Persebaya match on October 1, 2022, resulting in the loss of approximately 714 lives, including 131 fatalities and 583 injuries. The tragedy is believed to have been caused by tear gas in the spectator stands and the closure of exits at the Kanjuruhan stadium. This event sparked a diverse range of public responses on social media, which can be analyzed through sentiment analysis. In this study, we employed the Support Vector Machine (SVM) algorithm, known for its speed and accuracy in text classification, to process and analyze tweets from October 1 to 31, 2022, as well as YouTube comments related to the Kanjuruhan tragedy from October 1 to November 20, 2022. Among the different SVM kernels, the RBF kernel exhibited the highest accuracy, precision, recall, and F1 scores, reaching 76.40%, 75.74%, 76.40%, and 75.18% respectively, when predicting data with three labels. Furthermore, the RBF kernel showed the best performance for data with two labels, achieving the highest accuracy, precision, recall, and F1-Score, which increased to 81.54%, 81.56%, 81.54%, and 81.56%, respectively.
Pelatihan Pembuatan Media Pembelajaran yang Informatif dan Kreatif Menggunakan PowerPoint Bagi Guru SDN Wiradadi Dedy Agung Prabowo; M. Yoka Fathoni; Sudianto Sudianto; Sandhy Fernandes; Cahyo Prihantoro; Nicolaus Euclides Wahyu Nugroho
JPMTT (Jurnal Pengabdian Masyarakat Teknologi Terbarukan) Vol. 2 No. 2 (2022): Oktober
Publisher : Lembaga Penelitian Pengabdian Masyarakat Penerbitan dan Percetakan Indonesian Scholar Khiar Wafi (LPPMPP IKHAFI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54650/jpmtt.v2i2.479

Abstract

Membuat media pembelajaran yang interaktif  dan informatif dengan program Powerpoint bagi guru-guru SDN Wiradadi Kabupaten Banyumas ini bertujuan untuk meningkatkan kualitas pembelajaran, agar guru-guru mendapat tambahan wawasan ilmu pengetahuan tentang pembuatan media pembelajaran multimedia yang interaktif dan atraktif menggunakan powerpoint . Metode yang diterapkan dalam kegiatan ini adalah metode presentasi mengenai pengantar materi tentang multimedia  animasi dengan powerpoint sebagai media pembelajaran. Kegiatan dilanjutkan dengan praktik pembuatan media pembelajaran dengan animasi program powerpoint. Kegiatan dilanjutkan dengan evaluasi. Pelaksanaan PKM diawali dengan penyampaian materi tentang powerpoint dan pemanfaatan fasilitas pada program tersebut untuk membuat animasi. Kegiatan dilanjutkan dengan praktik pembuatan media pembelajaran dengan penerapan animasi menggunakan program powerpoint. Hasil kegiatan PKM ini berupa media powerpoint yang mengaplikasikan animasi
Classification of Sugarcane Area Using Landsat 8 and Random Forest based on Phenology Knowledge Sudianto Sudianto; Yeni Herdiyeni; Lilik Budi Prasetyo
JOIV : International Journal on Informatics Visualization Vol 7, No 3-2 (2023): Empowering the Future: The Role of Information Technology in Building Resilien
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3-2.1401

Abstract

Indonesia is one of the largest countries globally with an area for planting sugarcane. The current problem is that determining the planting area of sugarcane is still done conventionally; this is very limited and wastes time. Thus, knowing the sugarcane planting area becomes essential for policymaking through Remote Sensing technology. However, the challenge of Remote Sensing is the limited data due to weather and the spectral variability of other plants. So, it is necessary to classify based on phenological knowledge. The study aims to classify sugarcane areas based on phenological knowledge using Remote Sensing and Machine Learning. This application finished on the cloud platform Google Earth Engine (GEE) through Landsat 8 satellite imagery data. The knowledge of sugarcane phenology was built based on the Normalized Difference Vegetation Index (NDVI) spectral value and built with the harmonic model. In addition, classification is accomplished by object-oriented (OO) methods for segmentation classification. Object-oriented is solved by the Simple Non-Iterative Clustering (SNIC) algorithm for spatial cluster identification, the Gray-Level Co-occurrence Matrix (GLCM) for texture extraction, and the Random Forest algorithm for Land Use-Land Cover (LULC) classification. The results of the accuracy analysis using the confusion matrix and the classification of sugar cane areas based on phenological knowledge obtained the best results with an overall accuracy of 95.9% with a Kappa coefficient of 0.92. It can be concluded that a classification approach with knowledge of plant phenology can help better classify the availability of land for plantations in the future.
English Indonesia-Chan: OPUS-MT Powered Chatbot Lasama, Jerry; Sudianto, Sudianto; Ramadhani, Rafian; Hilmawan, Muhammad David; Aldean, Muhammad Yusril; Satria, Muhammad Adhan Hady
Jurnal Teknik Elektro dan Komputasi (ELKOM) Vol 6, No 1 (2024): ELKOM
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/elkom.v6i1.18613

Abstract

The COVID-19 pandemic has shown an increasing trend of digital platform users on social media such as Whatsapp, Facebook, Instagram, and Discord. The social media that is widely used to communicate massively is Discord. Discord already has 250 million registered active users from various countries worldwide. However, users from various countries create language differences when communicating. So we need a method for translating foreign languages, especially English to Indonesian, easily and quickly to make communication more understandable. This study aims to create a Discord chatbot that translates English sentences into Indonesian. The method built in the chatbot is designed using the MarianNMT model for language translation and the English corpus dataset from Open Parallel corPUS (OPUS). The model was trained using 15 epochs and obtained evaluation results with a loss of 0.0047.
Implementation of Chatbot System on Tourism Objects in Banyumas Regency with AIML and Chatterbot Naufal, Adzan Bari; Sudianto, Sudianto; Al Fachri, Moh Aminullah
Jurnal Teknik Elektro dan Komputasi (ELKOM) Vol 5, No 2 (2023): ELKOM
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/elkom.v5i2.18615

Abstract

Information technology can be applied to all fields, including tourism. Tourism object information media can be implemented into the chatbot system to make the information search process more flexible. Currently, searching for tourist spot information is often done manually; this makes tourist information services limited in time, while the need for tourism information must always be available. This research aims to build a chatbot system using Artificial Intelligence Markup Language (AIML) and ChatterBot methods. Both methods are accessed from libraries in Python using input in the form of natural language that has been processed into certain patterns. The pattern determination process is carried out by classifying a collection of questions on the chatbot using the Support Vector Machines (SVM) method. Then the classification is divided into five attributes, namely address, ticket price, facilities, description, and access. The SVM model built obtained an accuracy rate of 88%. Based on the testing results on both models that have been tested, the approach with AIML results in an accuracy rate in answering questions correctly of 90%, while ChatterBot is 40%.
Co-Authors Abdul Aziz Akbar Maulana Yusuf Al Fachri, Moh Aminullah Aldean, Muhammad Yusril Alif Aryaputra Alvina Tahta Indal Karim Andini Mulia Kusumaningrum Asti Dwi Sripamuji Ayuningtyas, Puji Bagas Prihatnowo Cahyo Prihantoro Chandra Ayunda Apta Soemedhy Dedy Agung Prabowo Dwi Puspa Martiyaningsih Egidya Zalsabila Hapsari Warih Utami Hasna Nur Hanifah Hermanto, Agyl Restu Hikari Adriansyah Hilmawan, Muhammad David Imada Riski Ramadhanti Jonathan Indra Chelidivano Juan Arton Arton Masheli Julian Saputra Khusnul Khatimah Lasama, Jerry LILIK BUDIPRASETYO M Yoka Fathoni Mahda Laina Arnumukti Martin Parhusip Muhammad Hilmi Kafabi Muhammad Rahaji Jhaerol Naden, Yoga Naufal, Adzan Bari Nawang Anggita Winanti Nicolaus Euclides Wahyu Nugroho Nora Trivetisia Nursatio Nugroho Puspa Wahyuningtias Rafi Wika Ananda Rumpoko Ramadhani, Rafian Reni Dyah Wahyuningrum Reni Dyah Wahyuningrum Risa Riski Amalia Rizki Mugi Setya Adi Sandhy Fernandes Satria Nur Saputro Satria, Muhammad Adhan Hady Siti Khomsah, Siti Siti Maghfiroh Surya Haganta Brema Ginting Tangguh Widodo Tavany Amalia Rizky Tiara Khumaira Putri Tri Ginanjar Laksana Tri Wulandari Utami Uli Ahda Raihan Ummi Athiyah Widi Afandi Yanuar Sabikhi Yehezkiel Nicholas Adanson Yeni Herdiyeni Zarkasih Akhmad