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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 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) 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.
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
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%.
Pelabelan Sentimen Berbasis Semi-Supervised Learning menggunakan Algoritma LSTM dan GRU Ayuningtyas, Puji; Khomsah, Siti; Sudianto, Sudianto
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 9 No. 3 (2024): September 2024
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2024.9.3.217-229

Abstract

In the sentiment analysis research process, there are problems when still using manual labeling methods by humans (expert annotation), which are related to subjectivity, long time, and expensive costs. Another way is to use computer assistance (machine annotator). However, the use of machine annotators also has the research problem of not being able to detect sarcastic sentences. Thus, the researcher proposed a sentiment labeling method using Semi-Supervised Learning. Semi-supervised learning is a labeling method that combines human labeling techniques (expert annotation) and machine labeling (machine annotation). This research uses machine annotators in the form of Deep Learning algorithms, namely the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms. The word weighting method used in this research is Word2Vec Continuous Bag of Word (CBoW). The results showed that the GRU algorithm tends to have a better accuracy rate than the LSTM algorithm. The average accuracy of the training results of the LSTM and GRU algorithm models is 0.904 and 0.913. In contrast, the average accuracy of labeling by LSTM and GRU is 0.569 and 0.592, respectively.
PENEMPATAN PRODUK PENJUALAN PADA E-COMMERCE BERBASIS PERILAKU KONSUMEN MENGGUNAKAN ALGORITMA ECLAT Naden, Yoga; Sudianto, Sudianto; Athiyah, Ummi
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 4 (2024): JATI Vol. 8 No. 4
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i4.10129

Abstract

Perdagangan elektornik berkembang sangat pesat pada era modern seperti sekarang ini, yang menjadi faktor utama berkembangnya perdagangan elektronik adalah masyarakat modern yang lebih suka belanja secara daring karena dianggap lebih efisien dibandingkan dengan pasar tradisional. Padatnya persaingan perdagangan elektronik menjadi tantangan tersendiri bagi pelaku bisnis, maka dibutuhkan strategi untuk meningkatkan penjualan, dengan memanfaatkan tata letak produk yang baik dapat meningkatkan penjualan dan mempermudah konsumen menemukan barang dan layanan atau jasa yang mereka cari. Salah satu strategi yang dapat digunakan adalah dengan menganalisa perilaku konsumen, dengan menganalisa perilaku konsumen kita dapat menentukan strategi penjualan berdasarkan data transaksi penjualan untuk mencari barang apa saja yang dibeli pada waktu yang sama pada satu keranjang belanja. Penelitian ini bertujuan untuk mencari hubungan antar barang dengan menggunakan metode Market Basket Analysis dan algoritma asosiasi untuk menemukan hubungan pada tiap item yang dibeli secara bersamaan menggunakan data berdasarkan perilaku konsumen dengan mencari nilai support, confidence, dan lift. Dengan menggunakan data transaksi berdasarkan perilaku konsumen ditemukan hubungan yang kuat antara Travel Voucher, Travel Case Game dan Casing & silicon game console ditemukan nilai confidence sebesar 100% dan nilai support sebesar 2%. Hasil analisa yang ditemukan kemudian dapat digunakan sebagai panduan untuk membuat desain tata letak produk sebagai strategi untuk meningkatkan penjualan pada e-commerce Tokopedia.
Perbandingan Arsitektur MobileNetV2 dan RestNet50 untuk Klasifikasi Jenis Buah Kurma Hermanto, Agyl Restu; Aziz, Abdul; Sudianto, Sudianto
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 12, No 4 (2024)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v12i4.80358

Abstract

Kurma adalah buah yang populer di Indonesia, terutama saat bulan Ramadhan karena mayoritas penduduknya beragama Islam. Buah ini berwarna coklat, berbentuk lonjong, dan tumbuh di pohon palem, serta kaya akan zat besi, kalsium, kalium, dan vitamin C. Kurma memiliki berbagai jenis dengan bentuk dan warna yang mirip, sehingga sulit diidentifikasi. Penelitian ini bertujuan untuk mengklasifikasikan jenis-jenis kurma menggunakan perbandingan arsitektur transfer learning. Metode yang digunakan adalah model CNN (Convolutional Neural Network) dengan arsitektur MobileNetV2 dan RestNet50, yang dilatih kembali menggunakan dataset citra untuk membedakan tiga jenis kurma: Ajwa, Alwassim, dan Khenaizi. Kedua model dilatih dengan parameter epoch 20, 40, dan 60. Hasil penelitian menunjukkan bahwa MobileNetV2 unggul dibandingkan RestNet50 dalam semua metrik evaluasi (accuracy, precision, recall, f1-score), dengan akurasi tertinggi 95% pada MobileNetV2. Hal ini mengindikasikan bahwa MobileNetV2 lebih efisien dalam memanfaatkan proses transfer learning dan lebih efektif dalam mengidentifikasi tiga jenis kurma pada dataset.
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 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 Zarkasih Akhmad