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All Journal Jurnal Masyarakat Informatika Jurnal Sains dan Teknologi Jurnal Simetris Jurnal Buana Informatika CommIT (Communication & Information Technology) Scientific Journal of Informatics Proceeding SENDI_U Jurnal Terapan Abdimas Sistemasi: Jurnal Sistem Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Infotekmesin Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Buletin Poltanesa Advance Sustainable Science, Engineering and Technology (ASSET) Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Jurnal Algoritma Community: Jurnal Pengabdian Pada Masyarakat Jurnal Informatika: Jurnal Pengembangan IT LogicLink: Journal of Artificial Intelligence and Multimedia in Informatics
Egia Rosi Subhiyakto, Egia Rosi
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

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PENERAPAN HASIL PENELITIAN APLIKASI GAME LISTENING BAHASA INGGRIS BAGI SISWA SMA MARDISISWA SEMARANG Astuti, Yani Parti; Subhiyakto, Egia Rosi; Umaroh, Liya
Jurnal Terapan Abdimas Vol 7, No 2 (2022)
Publisher : UNIVERSITAS PGRI MADIUN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25273/jta.v7i2.10945

Abstract

Abstract. The national education system has set lessons that must be followed by all students. Like English lessons. Indonesian is a compulsory subject from Junior High School (SMP) to Higher Education. Listening is a new skill in high school because in junior high school listening skills are not yet known. Because listening is a new thing at the high school level, students find it difficult to learn listening. This is because the voice actors who listen can come from within or outside the country who have different pronunciations. For this reason, this service will apply the results of the research in the form of English listening games. This application is very flexible to use because it can be accessed on Android smartphones. The use of foreign android smartphones for students today. So they can easily use it and understand it and can be used at any time. This listening game application will be applied to high school students who are the goal of this service. Students who will become partners are students of SMA Mardisiswa Banyumanik Semarang. This Mardisiswa High School has a problem that its students are not familiar with listening material. This is because there has been a pandemic, while listening material is material in the high school curriculum. For this reason, in order to be faster in delivering, in this service an application about listening is made. With this application, this application has filled in several forms of listening questions. However, for students' abilities, 29 listening questions will be given. Each question is given a weight of 3. And the results obtained are still unsatisfactory because the highest student points are 54. This can open up students' abilities to a maximum of only 62%. Abstrak. Dalam sistem pendidikan nasional telah menetapkan pelajaran – pelajaran yang wajib diikuti oleh semua peserta didik. Seperti halnya pelajaran Bahasa Inggris. Bahasa Inggris merupakan mata pelajaran wajib dari Sekolah Menengah Pertama (SMP) sampai dengan Pendidikan Tinggi. Listening merupakan ketrampilan yang baru di SMA karena saat SMP belum dikenalkan ketrampilan listening. Karena listening adalah hal yang baru pada jenjang SMA, maka siswa – siswa merasa kesulitan untuk belajar listening. Hal ini dikarenakan pengisi suara listening bisa berasal dari dalam maupun luar negeri yang mempunyai lafal yang berbeda – beda. Untuk itu pengabdian ini akan menerapkan hasil dari penelitian berupa aplikasi game listening Bahasa Inggris. Aplikasi ini sangat fleksibel digunakan karena bisa diakses di smartphone android. Penggunaan smartphone android tidaklah asing bagi peserta didik saat ini. Sehingga mereka bisa dengan mudah menggunakannya dan memahaminya serta bisa digunakan sewaktu waktu. Aplikasi game listening ini akan diterapkan pada siswa SMA yang menjadi tujuan dari pengabdian ini. Siswa yang akan menjadi mitra adalah siswa SMA Mardisiswa Banyumanik Semarang. SMA Mardisiswa ini mempunyai kendala bahwa siswa – siswanya belum pernah mengenal materi listening Hal ini disebabkan karena adanya pandemic, sementara materi listening merupakan materi yang ada di kurikulum SMA. Untuk itu, agar lebih cepat dalam pengyampain, maka dalam pengabdian ini dibuat aplikasi tentang listening. Dengan aplikasi ini, Pada aplikasi ini telah diisikan beberapa bentuk soal listening. Namun untuk mengevaluasi kemampuan siswa akan diberikan 29 soal listening. Masing – masing soal diberi bobot nilai 3. Dan hasil yang didapat adalah masih kurang memuaskan karena poin tertinggi siswa adalah 54. Hal ini dapat disimpulkan kemampuan siswa maksimal hanya 62%.
Penerapan Algoritma Fuzzy Simple Additive Weighting untuk Pemeringkatan Kinerja Pegawai Astuti, Yani Parti; Miyanthi, Sheryn Aulia; Subhiyakto, Egia Rosi
Jurnal Masyarakat Informatika Vol 13, No 2 (2022): JURNAL MASYARAKAT INFORMATIKA
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.13.2.49804

Abstract

PDAM Tirta Giri Nata merupakan salah satu cabang perusahaan daerah air minum yang berada di Kota Cirebon. Pengelolaan data kenaikan gaji berkala di PDAM ini dilakukan berdasarkan dari data absensi. Namun proses pengolahan dan pemilahan data menjadi sulit dikarenakan jumlah pegawai yang banyak dan masih menggunakan metode manual. Penelitian ini bertujuan untuk mendapatkan nilai akurasi sistem yang dibangun untuk memudahkan proses pengolahan dan pemilahan data pegawai yang telah memenuhi syarat. Pengelolaan data pegawai dengan menormalisasi data kemudian memasukkan nilai masing-masing kriteria menggunakan metode Fuzzy Simple Additive Weighting, sehingga muncul nilai preferensi yang dapat digunakan untuk pendataan peringkat pegawai yang telah memenuhi syarat. Terdapat beberapa kiriteria yang digunakan yakni presentase absen, kinerja pegawai, golongan, pangkat, dan status. Pegawai dengan kode A1 merupakan pegawai yang mendapatkan nilai preferensi tertinggi, yaitu berada pada angka 1. Sistem telah dibangun dengan menerapkan metode F-SAW dengan tingkat keakuratan sistem yang dibangun sebesar 93,33%.
Optimization Chatbot Services Based on DNN-Bert for Mental Health of University Students Dzaky, Azmi Abiyyu; Zeniarja, Junta; Supriyanto, Catur; Shidik, Guruh Fajar; Paramita, Cinantya; Subhiyakto, Egia Rosi; Rakasiwi, Sindhu
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.7403

Abstract

Attention to mental health is increasing in Indonesia, especially with the recent increase in the number of cases of stress and suicide among students. Therefore, this research aims to provide a solution to overcome mental health problems by introducing a chatbot system based on Deep Neural Networks (DNN) and BiDirectional Encoder Representation Transformers (BERT). The primary objective is to enhance accessibility and offer a more effective solution concerning the mental health of students. This chatbot utilizes Natural Language Processing (NLP) and Deep Learning to provide appropriate responses to mild mental health issues. The dataset, comprising objectives, tags, patterns, and responses, underwent processing using Indonesian language rules within NLP. Subsequently, the system was trained and tested using the DNN model for classification, integrated with the TokenSimilarity model to identify word similarities. Experimental results indicate that the DNN model yielded the best outcomes, with a training accuracy of 98.97%, validation accuracy of 71.74%, and testing accuracy of 71.73%. Integration with the TokenSimilarity model enhanced the responses provided by the chatbot. TokenSimilarity searches for input similarities from users within the knowledge generated from the training data. If the similarity is high, the input is then processed by the DNN model to provide the chatbot response. This integration of both models has proven to enhance the responsiveness of the chatbot in providing various responses even when the user inputs remain the same. The chatbot also demonstrates the capability to recognize various inputs more effectively with similar intentions or purposes. Additionally, the chatbot exhibits the ability to comprehend user inputs although there are many writing errors.
Pendampingan Pembuatan Konten Youtube Bagi Siswa SMA At Thohiriyyah Semarang astuti, yani parti; Subhiyakto, Egia Rosi; Dolphina, Erlin; Sutojo, Totok; Rafrastara, Fauzi Adi; Kartikadarma, Etika
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 7, No 2 (2024): MEI 2024
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v7i2.2261

Abstract

The extraordinary use of YouTube at this time indicates that developments in the world of technology are very rapid. But it must always be aware that technological developments also affect the psychological development of children. As is the case at the age of children - teenagers who sometimes cannot control. The development of the use of technology, information and communication in the digital world has had various impacts on our lives. As happened at SMA At Thohiriyyah Semarang, which has a middle to lower economic background and is located on the outskirts of East Semarang. At that high school, the students still don't understand the correct use of YouTube. They just watch content that is sometimes not useful. With these problems, they must be given assistance on how to use YouTube properly. For this reason, they must be made active in using YouTube by having an account and being able to create useful content for other people. Apart from that, they are expected to be able to entertain other people through the content they create
Klasifikasi Kanker Kulit menggunakan Convolutional Neural Network dengan Optimasi Arsitektur Sinaga, Jesica Trivena; Faudyta, Haniifa Aliila; Subhiyakto, Egia Rosi
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6141

Abstract

Skin cancer is a severe condition characterized by the abnormal growth of skin cells, often triggered by ultraviolet exposure and genetic factors. Early detection of skin cancer is essential for improving patient recovery rates, given the high incidence and significant impact of the disease. This study aims to develop a skin cancer classification system using the Convolutional Neural Network (CNN) method with the VGG-16 architecture, known for its effectiveness in medical image analysis. The CNN method was chosen because it can extract complex features from images. At the same time, the VGG-16 architecture was selected for its depth and ability to capture fine details in images—critical for distinguishing between types of skin cancer. The dataset was sourced from the ISIC platform and optimized through data augmentation techniques to address data imbalance issues. The research results indicate that while a basic CNN can provide good accuracy, implementing the VGG-16 architecture significantly increases accuracy. The basic CNN model achieved a training accuracy of 95.68% and a validation accuracy of 89.83%, whereas the CNN with VGG-16 reached a training accuracy of 96.21% and a validation accuracy of 90.89%. These findings suggest that combining CNN with VGG-16 effectively detects skin cancer, with VGG-16 providing a slight accuracy improvement, highlighting this architecture's potential as a more accurate tool to support skin cancer diagnosis.
Implementasi Transfer Learning Menggunakan Convolutional Neural Network untuk Deteksi Jenis Kulit Wajah Septiani, Karlina Dwi; Subhiyakto, Egia Rosi
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6154

Abstract

In Indonesia, extreme tropical climate conditions with high humidity and sun exposure increase the risk of facial skin problems for the community. Facial skin that is not properly cared for is often prone to disorders, ranging from dry skin, oily skin, to acne. However, Indonesian people's awareness of the importance of maintaining healthy skin is still relatively low, which is exacerbated by limited time and access to consult a dermatologist. Most people may not know their skin type, even though each skin type requires different care to stay healthy and avoid more serious skin problems. To answer this problem, this study aims to develop an iOS-based application that is able to automatically detect facial skin types using transfer learning with a Convolutional Neural Network (CNN) architecture. The model was developed by training a dataset of facial images to classify skin types such as dry, oily, normal, and acne-prone, and integrated into an iOS application for real-time analysis through user facial images. The evaluation results showed a model accuracy of 87% and an application accuracy of 83.3% in identifying facial skin types. It is hoped that this application will help Indonesian people better understand their skin conditions and obtain appropriate treatment recommendations to maintain healthy skin in a tropical climate.
Integrating ELECTRA and BERT models in transformer-based mental healthcare chatbot Zeniarja, Junta; Paramita, Cinantya; Subhiyakto, Egia Rosi; Rakasiwi, Sindhu; Shidik, Guruh Fajar; Andono, Pulung Nurtantio; Savicevic, Anamarija Jurcev
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp315-324

Abstract

Over the last decade, the surge in mental health disorders has necessitated innovative support methods, notably artificial intelligent (AI) chatbots. These chatbots provide prompt, tailored conversations, becoming crucial in mental health support. This article delves into the use of sophisticated models like convolutional neural network (CNN), long-short term memory (LSTM), efficiently learning an encoder that classifies token replacements accurately (ELECTRA), and bidirectional encoder representation of transformers (BERT) in developing effective mental health chatbots. Despite their importance for emotional assistance, these chatbots struggle with precise and relevant responses to complex mental health issues. BERT, while strong in contextual understanding, lacks in response generation. Conversely, ELECTRA shows promise in text creation but is not fully exploited in mental health contexts. The article investigates merging ELECTRA and BERT to improve chatbot efficiency in mental health situations. By leveraging an extensive mental health dialogue dataset, this integration substantially enhanced chatbot precision, surpassing 99% accuracy in mental health responses. This development is a significant stride in advancing AI chatbot interactions and their contribution to mental health support.
Deteksi Dini Cacar Monyet menggunakan Convolutional Neural Network (CNN) dalam Aplikasi Mobile Triginandri, Rifqi; Subhiyakto, Egia Rosi
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.27625

Abstract

Monkeypox is a skin infection that has become a serious concern in Indonesia since the increase in cases in 2022. Diagnosis of monkeypox requires special expertise, laboratory tests, and clinical observations. Diagnosis generally uses PCR tests which are often not available in remote areas. This study aims to develop a deep learning-based mobile application for early detection of monkeypox through image classification of skin lesions. The CRISP-DM methodology is applied in developing this application, starting with collecting datasets from the Kaggle site consisting of 8,910 images and divided into 80% training groups, 10% validation, and 10% testing with augmentation techniques to improve model accuracy. The developed CNN model was implemented using Create ML on the iOS platform. The model evaluation uses several metrics such as accuracy, precision, recall, and F1 score, with the threshold being the highest probability of the model predicting model evaluation results show an accuracy of 81%, precision of 80.2%, recall of 76%, and F1 score of 0.78 for the test data. The resulting application allows rapid detection of monkeypox and is accessible to the wider community, thereby helping to reduce delays in diagnosis, especially in hard-to-reach areas. This study shows significant potential in supporting the health system in Indonesia through the application of artificial intelligence technology for infectious diseases.
Centing: Aplikasi Cegah Stunting Anak berbasis Android menggunakan TensorFlow Lite Abiyyi, Ryandhika Bintang; Subhiyakto, Egia Rosi; Sabilillah, Ferris Tita
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.27875

Abstract

Stunting is a serious health problem that affects children's growth and development, especially in areas with limited access to early detection. This research aims to develop a TensorFlow Lite-based “CENTING” Android application to detect stunting risk quickly and accurately. The prototyping method is used with the stages of identifying user needs, making initial prototypes, testing, and refinement based on the feedback of health workers and parents, until the application is ready to be implemented optimally. The dataset contains 121,000 child growth data from public sources, with variables such as age, gender, height, and nutritional status to detect stunting traits early. The data was processed and split 80:20 for training and testing, resulting in a detection accuracy of 98%. The selection of TensorFlow Lite is based on its advantage in response speed on mobile devices. The results showed that the CENTING application functioned optimally with a user acceptance score of 89.5%. The app supports self-detection, prevention education, and offline access, relevant for network-limited areas. These findings accelerate stunting intervention efforts and support government programs in reducing stunting prevalence.
Analisis Value Proposition dan Persepsi Pengguna Terhadap Sistem Informasi Laboratorium (LIS) di Rumah Sakit Cahyati, Ade Puput; Subhiyakto, Egia Rosi
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.27997

Abstract

Patient data management information in the health sector is not only a technological consideration but also includes an evaluation to facilitate medical personnel to store patient data. Thus, the hospital, which is a forum for medical personnel, needs to consider the development of a new recording system. This study aims to analyze the value proportion and user perceptions of Laboratory Information System (LIS) for application development so that developers can offer features and designs that users need. Data collection techniques in this study used a questionnaire with a sample of 52 people. The data analysis technique used uses the UX Honeycomb method and the System Usability Scale to be able to analyze the LIS application. The results of this study indicate that the value proposition variable has a significant influence on the LIS application based on the UX Honeycomb indicator. The dominant indicators are useful and usable, while the indicators that need to be improved are findable and valuable. User perception has a significant influence on the LIS application; the average value of 85.14 means that it has a very usable influence and is easy to use. So that hospitals can switch to digital data and reduce physical documents.
Co-Authors Abiyyi, Ryandhika Bintang Abu Salam Adhitya Nugraha Andreas Wilson Setiawan Ardytha Luthfiarta Astuti, Yani Parti Astuti, Yani Parti Astuti, Yani Parti Bahres, Salim Bin Sulaiman Cahyati, Ade Puput Catur Supriyanto Dzaky, Azmi Abiyyu Erlin Dolphina Erwin Yudi Hidayat Etika Kartikadarma Fahmi Amiq Faradeya, Muhammad Az-Zauqy Faudyta, Haniifa Aliila Fauzi Adi Rafrastara Guruh Fajar Shidik Hafidhoh, Nisa'ul Hapsari, Swita Amalia Hulaifah Al Abrori, Zahra Zul Ika Novita Dewi Iskandar, Marcelino Junta Zeniarja Kurniawan, Defri Kusumasari, Dinita Liya Umaroh, Liya Manurung, Ayub Michaelangelo Miyanthi, Sheryn Aulia Mutia Rahmi Pratiwi Ningrum, Novita Kurnia Ningrum, Novita Kurnia Octaviani, Dhita Aulia Pamungkas, Azriel Sebastian Paramita, Cinantya Prajanto Wahyu Adi, Prajanto Wahyu Prihatmoko, Radig Gedhe Pulung Nurtantio Andono Puspitaningrum, Hapsari Retno Rahadian, Arief Rohman, Muhammad Syaifur Sabilillah, Ferris Tita Safina, Nabila Safina, Nabila Santoso, Ilham Savicevic, Anamarija Jurcev Septiani, Karlina Dwi Simbolon, Calvin Samuel Sinaga, Jesica Trivena Sindhu Rakasiwi Sudibyo, Usman Swanny Trikajanti Widyaatmadja T. Sutojo Triginandri, Rifqi Triono, Justin Matthew Utomo, Danang Wahyu Wardatunizza, Indah Wibowo, Alrico Rizki Widi Utomo, Emanuel Pinesthi Winarsih, Nurul Anisa Sri Yunianto, Amar Haris