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All Journal ComEngApp : Computer Engineering and Applications Journal Jurnas Nasional Teknologi dan Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Abdimasku : Jurnal Pengabdian Masyarakat Advance Sustainable Science, Engineering and Technology (ASSET) Community: Jurnal Pengabdian Pada Masyarakat Jurnal Informatika: Jurnal Pengembangan IT
Erwin Yudi Hidayat
Faculty of Computer Science, Universitas Dian Nuswantoro
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Social Sciences

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Pengenalan Ekspresi Wajah Menggunakan Transfer Learning MobileNetV2 dan EfficientNet-B0 dalam Memprediksi Perkelahian Handayani, Ni Made Kirei Kharisma; Hidayat, Erwin Yudi; Naufal, Muhammad; Putra, Permana Langgeng Wicaksono Ellwid
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7048

Abstract

Expressions play an important role in recognizing someone's emotions. Recognizing emotions can help understand someone's condition and be a sign of their possible actions. Fighting is one of the violences that occur due to someone's negative emotions that need to be prevented and treated immediately. In this study, expression recognition is used to predict the possibility of a fight based on the expression shown by a person. The dataset used is FER-2013 which has been modified into two labels, namely "Yes" and "No". The data undergoes a preprocessing step which includes resizing and normalization. Model experiments using transfer learning from the MobileNetV2 and EfficientNet-B0 architectures have been modified by performing hyperparameter and fine tuning which includes freezing the layer by 25% in the first layers of each model and adding several layers such as flatten and dense. In the training process, some parameters used are 30 epochs, batch size 32, and Adam optimization with a learning rate of 0.0001. Model performance evaluation is measured using Confusion Matrix, then the results are compared and obtained the model that produces the best accuracy value is EfficientNet-B0 which is 82%. Meanwhile, based on the training time and model weight, MobileNetV2 is 1 hour 1 minute 43 seconds faster and 21.57 MB smaller than EfficientNet-B0.
Implementation of Adam Optimizer using Recurrent Neural Network (RNN) Architecture for Diabetes Classification Nugroho, Nur Cahyo Tio; Hidayat, Erwin Yudi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7254

Abstract

Non-communicable diseases (NCDs) present a considerable worldwide health dilemma, resulting in considerable expenses for treatment and heightened rates of mortality. Conditions like diabetes mellitus, cardiovascular diseases, cancer, and chronic respiratory diseases are primary causes of global mortality, making up 71% of total global deaths in 2016, as reported by the World Health Organization (WHO). Diabetes Mellitus (DM), marked by prolonged elevated blood glucose levels, stands out as a significant metabolic disorder. This research delves into the implementation of Recurrent Neural Networks (RNNs) utilizing the Adaptive Moment Estimation (Adam) optimizer for classifying Diabetes Mellitus (DM). RNNs, a subset of artificial neural networks tailored for sequential data processing, are employed to make predictions by incorporating recurrent connections. Situated within the dynamic landscape of Artificial Intelligence and Machine Learning, the research exhibits promising outcomes via k-fold cross-validation, confusion matrix analysis, loss graph examination, and classification report. The RNN-Adam model showcases commendable overall performance, achieving an average accuracy of 80.20% through k-fold cross-validation and 81.60% accuracy as revealed by the confusion matrix. This research offers valuable insights into the effectiveness of the RNN-Adam model for diabetes classification.
Perbandingan Metode Naïve Bayes dan Support Vector Machine Pada Klasifikasi 22 Bahasa Daerah Rakajati, Bima; Hidayat, Erwin Yudi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7236

Abstract

Indonesia boasts a rich cultural diversity, encompassing over 1300 ethnic groups and 2500 regional languages. The challenge arises due to the multitude of regional languages in Indonesia, making language identification in textual form difficult. This research compares Machine Learning methods for classifying 22 regional languages in Indonesia, aiming to provide a deep understanding of the relative performance of each method. The study successfully addresses the primary difficulty, which is the identification of regional languages in Indonesia. The main constraint of this research lies in the complexity of regional languages in Indonesia, with various characteristics, variations in grammar, and differing sentence structures, resulting in accuracy not yet reaching perfection. This factor opens opportunities for future research through parameter optimization or exploration of alternative methods. Evaluation results indicate that the Support Vector Machine achieves the highest accuracy, reaching 89.41%, making it the preferred choice for model implementation. Although Naïve Bayes yields good results with an accuracy of 82.08%, Support Vector Machine remains the preferred option. The application of the model using Streamlit demonstrates the effectiveness of the Support Vector Machine in accurately predicting Javanese song lyrics. This research has the potential to assist users in identifying regional languages based on text and contributes significantly to understanding Machine Learning methods for classifying regional language texts. Despite its limitations, this study can be extended to other regional languages, enhancing model accuracy through parameter improvements.
Pendampingan bagi Siswa SMP Negeri 7 Semarang dalam Penggunaan Software Aplikasi Hidup Bersih dan Sehat Astuti, Yani Parti; Luthfiarta, Ardytha; Hidayat, Erwin Yudi; Nugraha, Adhitya; Subhiyakto, Egia Rosi; Octaviani, Dhita Aulia
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 8, No 1 (2025): JANUARI 2025
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v8i1.2698

Abstract

Sekolah adalah tempat menuntut ilmu dan juga tempat sosialisasi dan interaksi antar sesamA. Selain itu sekolah juga harus beradaptasi dengan lingkungan sekitar. Seperti halnya di Sekolah Menengah Pertama (SMP) Negeri 7 Semarang yang berada di Tengah kota yang dikenal oleh banyak orang khususnya warga Semarang. Di sekolah tersebut terdapat banyak fasilitas yang sesuai dengan standart di setiap sekolah. Salah satunya adalah adanya kantin sekolah yang berada dalam lingkungan sekolah. Namun demikian banyak juga yang berjualan di luar lingkungan sekolah yang setiap hari baik sebelum jam dimulai dan jam sekolah berakhir, jajanan di luar sekolah itu banyak dikunjungi siswa. Dengan kondisi seperti itu, maka perlu diwaspadai tentang Kesehatan siswa yang guru tidak mungkin mengawasi secara terus menerus. Untuk itu perlu adanya penyuluhan dan pengarahan bagi siswa agar tidak jajan sembarangan. Jajanan yang harus dibeli harus memperhatikan dari sisi gizi yang dikandungnya. Sekarang banyak jajanan yang super pedas, mengandung pengawet dan masih banyak lagi jajanan yang hanya mengejar murah dan rasa menendang. Dalam pengarahan ini, selain menghimbau untuk memperhatikan nilai gizinya, juga diperlihatkan akibat dari jajanan yang kurang sehat. Hal ini akan ditunjukkan dengan software aplikasi digital yang memberikan pengetahuan tentang akibat dari usus yang tidak sehat. Dengan begitu, siswa akan memperhatikan jajanan setiap hari. Selain jajanan, yang perlu diperhatikan lagi adalah tentang lingkungan sekitar yaitu bagaimana siswa membuang sampah, cuci tangan sebelum makan dan lain sebagainya. Karena selain Kesehatan usus, banyak juga penyakit yang disebabkan oleh kurang bersihnya lingkungan sekitar. Sehingga dengan adanya penyuluhan ini, siswa akan terdorong melakukan pola hidup bersih dan sehat yang merupakan slogan dari pemerintah khususnya pada bidang Kesehatan
Emotion Classification in Indonesian Text Using IndoBERT Rizky, Aditya Saiful; Hidayat, Erwin Yudi
Computer Engineering and Applications Journal Vol 14 No 1 (2025)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v14i1.494

Abstract

Mental health issues have become a challenge that affects many individuals around the world. A 2018 WHO report noted an increase in deaths by suicide, with a frequency of one case every 40 seconds. The Ipsos Global 2023 survey showed that 44% of respondents in 31 countries are concerned about mental health, while 30% identified stress as a major issue. In Indonesia, the mental health situation is also a serious concern. The 2022 I-NAMHS survey found that 34.9% of adolescents face mental health problems, but only 2.6% of them utilize counseling services. Emotion detection in text is challenging due to the absence of facial expressions or voice modulation. This study aims to classify emotions in Indonesian text using the IndoBERT model. The dataset used consists of 5079 tweets with five emotion labels: Angry, Fear, Joy, Love, and Sad. Parameter variations include the composition of training, validation, and test data split (80:10:10, 75:15:15, and 60:20:20), as well as the combination of learning rate (1e-2 to 1e-7) and batch size (8, 16, and 32). The model was trained for 25 epochs with the application of early stop and patience for 5 epochs. The experimental results showed that the composition of data split 80:10:10, learning rate 1e-6, and batch size 8 resulted in optimal classification. Although some experiments showed indications of overfitting, this research has important implications in the early detection of emotions and can help in mental health treatment efforts.
Pendampingan ibu - ibu PKK tentang Deteksi Kanker Serviks Melalui Software Aplikasi Hidayat, Erwin Yudi; Astuti, Yani Parti; Salam, Abu; Nugraha, Adhitya; Paramita, Cinantya; Octaviani, Dhita Aulia
Community : Jurnal Pengabdian Pada Masyarakat Vol. 4 No. 1 (2024): Maret : Jurnal Pengabdian Pada Masyarakat
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/community.v4i1.495

Abstract

One feared cancer among mothers is cervical cancer. Cervical cancer is a type of cancer that occurs in the cervix of women. Due to its hidden location, women may not be able to detect early on whether they have cervical cancer or not. Meanwhile, this disease ranks among the top three causes of death in Indonesia. To determine early on whether a woman is affected by cervical cancer, various methods are employed. Some search for symptoms on social media, some take preventive measures with various herbal remedies to avoid cervical cancer, and many other actions are taken by women. With the touch of artificial intelligence technology, these issues can be addressed. Therefore, the dedicated team is trying to create an application that can detect cervical cancer. With this application, women can find out early on whether they have or are approaching cervical cancer. Although the accuracy of this application is not 100%, at least women can be aware of the detection of this disease and can promptly seek treatment or prevention. With the existence of this application, it is hoped that it can be beneficial for the mothers of PKK Perum Kandri Persona Asri RT 04 RW 04, who are the subjects of this dedication.
Pendampingan Pemanfaatan Google Site Sebagai Media Pembelajaran Berbasis Web di SMPN 7 Semarang Rakasiwi, Sindhu; Kurniawan, Defri; Hidayat, Erwin Yudi; Zeniarja, Junta; Dzaky, Azmi Abiyyu; Haresta, Alif Agsakli
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 8, No 2 (2025): MEI 2025
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v8i2.2970

Abstract

A website is the heart of an institution, school or company profile. With a web appearance that is always active and always has useful content, it will add to the image of the owner of the website. Because of this, the community service team wants to provide assistance to teachers so that they can also contribute to filling the website. So not only IT teachers can contribute to the website, but all teachers can contribute so that the website can be more active and interactive for students, parents of students and even for the general public who want to know information about SMPN 07 Semarang. And through this assistance, it also utilizes the Google site for more interactive learning and students are also more active in creating learning for the future.
Emotion Classification in Indonesian Text Using IndoBERT Rizky, Aditya Saiful; Hidayat, Erwin Yudi
Computer Engineering and Applications Journal (ComEngApp) Vol. 14 No. 1 (2025)
Publisher : Universitas Sriwijaya

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

Abstract

Mental health issues have become a challenge that affects many individuals around the world. A 2018 WHO report noted an increase in deaths by suicide, with a frequency of one case every 40 seconds. The Ipsos Global 2023 survey showed that 44% of respondents in 31 countries are concerned about mental health, while 30% identified stress as a major issue. In Indonesia, the mental health situation is also a serious concern. The 2022 I-NAMHS survey found that 34.9% of adolescents face mental health problems, but only 2.6% of them utilize counseling services. Emotion detection in text is challenging due to the absence of facial expressions or voice modulation. This study aims to classify emotions in Indonesian text using the IndoBERT model. The dataset used consists of 5079 tweets with five emotion labels: Angry, Fear, Joy, Love, and Sad. Parameter variations include the composition of training, validation, and test data split (80:10:10, 75:15:15, and 60:20:20), as well as the combination of learning rate (1e-2 to 1e-7) and batch size (8, 16, and 32). The model was trained for 25 epochs with the application of early stop and patience for 5 epochs. The experimental results showed that the composition of data split 80:10:10, learning rate 1e-6, and batch size 8 resulted in optimal classification. Although some experiments showed indications of overfitting, this research has important implications in the early detection of emotions and can help in mental health treatment efforts.
Pengenalan Computational Thinking Sebagai Metode Problem Solving Kepada Guru dan Siswa Sekolah di Kota Semarang Sukamto, Titien S.; Pertiwi, Ayu; Affandy, Affandy; Syukur, Abdul; Hafidhoh, Nisa'ul; Hidayat, Erwin Yudi
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 2, No 2 (2019): Juli 2019
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1534.132 KB) | DOI: 10.33633/ja.v2i2.51

Abstract

Problem solving merupakan salah satu kemampuan yang sangat dibutuhkan untuk menghadapi persaingan global. Maka dari itu perlu untuk dilatih sedari dini. Melihat pada perkembangan teknologi dan imlu komputer, lahirlah sebuah pendekatan problem solving skill yang dikenal dengan nama Computational Thinking (CT). CT dikembangkan dari konsep dasar ilmu komputer, dengan cara mengabstraksi permasalahan kemudian mengilustrasikan dan menyusun solusi. Mulai tahun 2016, Indonesia secara aktif berpartisipasi dalam Komunitas Bebras dan mengkampanyekan Computational Thinking dengan mengadakan Bebras Challenge bagi siswa sekolah di seluruh Indonesia. Fakultas Ilmu Komputer UDINUS menjadi salah satu Bebras Biro yang ikut sebagai penyelenggara Bebras Challenge di Kota Semarang. Penyuluhan Bebras kepada Guru dimaksudkan untuk mengenalkan skill Computational Thinking ini, sehingga ke depannya setiap guru dapat menyampaikan dan melatih siswanya dalam pengembangan skill problem solving. Penyuluhan diikuti oleh guru perwakilan dari 27 sekolah dasar di Kota Semarang. Sebagai rangkaian kampanye, Bebras Challenge diikuti oleh total 169 siswa dari SD dan SMP di Kota Semarang. Hasil Bebras Challenge, terdapat 1 peserta asal Bebras Biro UDINUS yang berhasil masuk peringkat 3 besar nasional.
Analisis Sentimen Twitter untuk Menilai Opini Terhadap Perusahaan Publik Menggunakan Algoritma Deep Neural Network Hidayat, Erwin Yudi; Hardiansyah, Raindy Wicaksana; Affandy, Affandy
Jurnal Nasional Teknologi dan Sistem Informasi Vol 7 No 2 (2021): Agustus 2021
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v7i2.2021.108-118

Abstract

Dalam menaikkan kinerja serta mengevaluasi kualitas, perusahaan publik membutuhkan feedback dari masyarakat / konsumen yang bisa didapat melalui media sosial. Sebagai pengguna media sosial Twitter terbesar ketiga di dunia, tweet yang beredar di Indonesia memiliki potensi meningkatkan reputasi dan citra perusahaan. Dengan memanfaatkan algoritma Deep Neural Network (DNN), neural network yang tersusun dari layer yang jumlahnya lebih dari satu, didapati hasil analisa sentimen pada Twitter berbahasa Indonesia menjadi lebih baik dibanding dengan metode lainnya. Penelitian ini menganalisa sentimen melalui tweet dari masyarakat Indonesia terhadap sejumlah perusahaan publik dengan menggunakan DNN. Data Tweet sebanyak 5504 record didapat dengan melakukan crawling melalui Application Programming Interface (API) Twitter yang selanjutnya dilakukan preprocessing (cleansing, case folding, formalisasi, stemming, dan tokenisasi). Proses labeling dilakukan untuk 3902 record dengan memanfaatkan aplikasi Sentiment Strength Detection. Tahap pelatihan model dilakukan menggunakan algoritma DNN dengan variasi jumlah hidden layer, susunan node, dan nilai learning rate. Eksperimen dengan proporsi data training dan testing sebesar 90:10 memberikan hasil performa terbaik. Model tersusun dengan 3 hidden layer dengan susunan node tiap layer pada model tersebut yaitu 128, 256, 128 node dan menggunakan learning rate sebesar 0.005, model mampu menghasilkan nilai akurasi mencapai 88.72%. 
Co-Authors Abdul Syukur Abu Salam Adhitya Nugraha Affandy Affandy Ardytha Luthfiarta Astuti, Yani Parti Ayu Pertiwi Darmawan, Raihan Aris Dzaky, Azmi Abiyyu Egia Rosi Subhiyakto, Egia Rosi Hafidhoh, Nisa'ul Handayani, Devioletta Handayani, Ni Made Kirei Kharisma Hardiansyah, Raindy Wicaksana Haresta, Alif Agsakli Junta Zeniarja Kurniawan, Defri Muhammad Naufal, Muhammad Nugroho, Nur Cahyo Tio Octaviani, Dhita Aulia Paramita, Cinantya Putra, Permana Langgeng Wicaksono Ellwid Rakajati, Bima Rizky, Aditya Saiful Sindhu Rakasiwi Sukamto, Titien S.