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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Proceedings of KNASTIK TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Informatika Jurnal Sarjana Teknik Informatika Rekursif: Jurnal Informatika Jurnas Nasional Teknologi dan Sistem Informasi Komuniti : Jurnal Komunikasi dan Teknologi Informasi Jurnal Pengabdian UntukMu NegeRI EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Mobile and Forensics Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) ABDIMASTEK
Dewi Soyusiawaty
Universitas Ahmad Dahlan
Religion Aerospace Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Neuroscience Public Health Social Sciences

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Pengembangan Korpus Bahasa Minang pada Spell Error Corpus for Minang Language (SPEML) Soyusiawaty, Dewi; Fadlil, Abdul; Sunardi, Sunardi
Jurnal Nasional Teknologi dan Sistem Informasi Vol 11 No 1 (2025): April 2025
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v11i01.2025.17-26

Abstract

Bahasa Minang merupakan bahasa daerah kelima dengan jumlah penutur terbanyak di Indonesia, namun minim sumber daya linguistik dan teknologi pemrosesan bahasa alami yang mendukung. Keterbatasan ini menyulitkan pengembangan aplikasi seperti mesin penerjemah dan pemeriksa ejaan otomatis. Saat ini hanya tersedia korpus kesalahan ejaan dalam Bahasa Indonesia dengan kesalahan hanya satu karakter pada setiap token. Korpus belum mencakup kesalahan penulisan kata serapan. Selain itu belum ada korpus khusus yang dikembangkan untuk kesalahan ejaan dalam bahasa daerah di Indonesia, termasuk Bahasa Minang. Penelitian ini bertujuan mengembangkan korpus kesalahan ejaan Bahasa Minang, yang dinamakan Spell Error Corpus for Minang Language (SPEML). SPEML mencakup kesalahan ejaan sampai dengan tiga karakter dan kesalahan penulisan kata serapan. Pengembangan SPEML melibatkan proses pengumpulan data korpus Bahasa Minang, data kata serapan yang sering digunakan, serta pembentukan korpus kesalahan ejaan. Kesalahan ejaan dibentuk dengan mengacak token secara sistematis pada satu karakter, dua karakter, hingga tiga karakter, disesuaikan dengan panjang token. Hasil penelitian ini berupa SPEML yang mampu mengklasifikasikan tujuh jenis kesalahan ejaan, yaitu: penyisipan karakter, penghapusan karakter, pindah posisi karakter, penggantian karakter, kesalahan tanda baca, kesalahan kata nyata, dan kesalahan penulisan kata serapan. Pengembangan SPEML menjadi langkah awal dalam mendukung pengembangan teknologi pemrosesan bahasa alami untuk bahasa daerah, khususnya Bahasa Minang.
SMOTE-SVM for Handling Imbalanced Data in Obesity Classification Biddinika, Muhammad Kunta; Yuliansyah, Herman; Soyusiawaty, Dewi; Razak, Farhan Radhiansyah
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 2 (2025): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.103994

Abstract

 Obesity is a significant health issue associated with various chronic diseases, making its early classification critical for effective interventions. This study investigates the performance of Support Vector Machine (SVM) models with Radial Basis Function (RBF) and Linear kernels on imbalanced obesity datasets. To address data imbalance, Synthetic Minority Over-sampling Technique (SMOTE) and Random Undersampling (RUS) were applied. The results reveal that balancing techniques significantly enhance classification performance, with the Linear model achieving the highest accuracy of 96.54% when balanced using SMOTE. However, limitations include reduced recall for minority classes and potential overfitting risks. These findings underscore the importance of balancing techniques in health data classification and offer insights for further optimizing model performance. The study highlights the need for advanced data balancing strategies to improve predictive accuracy and equity across all classes.
Klasifikasi Jenis Kejahatan berdasarkan Teks Amar Putusan Pengadilan Hukum Pidana KUHP menggunakan IndoBERT Perdana, Tirtanusa Kurnia Adhi; Soyusiawaty, Dewi
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 2 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i2.30326

Abstract

The increasing number of the court’s rulings each year presents a challenge for the judiciary. One strategic solution is the application of Artificial Intelligence (AI). Indonesian-based models such as IndoBERT is potential to ease workloads by automatically classifying legal cases. This study aims to explore the capability of IndoBERT to automatically classifying the verdict of section of Indonesian KUHP rulings to accelerate crime type identification. This is an experimental study using supervised text classification. The dataset consists of 12000 verdicts collected from the Indonesian Supreme Court website, classified using IndoBERT fine-tuned with various hyperparameter configuration. Our findings show that the model with a batch size of 8 and learning rate 5e-5 achieved accuracy of 92.59%, precison of 92.93%, recall of 92.59%, and F1-Score of 92.59% on unseen test data. The high accuracy is supported by the explicit mention of crime types within verdict texts. To date, no study has specifically utilized IndoBERT or other models for automatic classification of KUHP articles. This finding has the potential to be integrated into the Supreme Court’s Directory of Decision as a support tool for automatic classification and legal document archiving.
Analisis Sentimen Program Makan Siang Gratis di Twitter/X menggunakan Metode BI-LSTM Attaulah, Dimas Thaqif; Soyusiawaty, Dewi
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29725

Abstract

The free lunch program became a widely discussed topic on social media, reflecting public opinion towards the policy. This research aims to analyze public sentiment towards free lunch program to evaluate the policy's effectiveness and understand public perception. Data was collected through web crawling techniques on the Twitter/X platform, resulting in 7,441 data. Processing stages include preprocessing, sentiment labeling using VADER, keyword visualization with wordcloud, and application of word embedding using Word2Vec. The oversampling technique is used to overcome data imbalance. Sentiment classification was developed using Bi-LSTM and evaluated with accuracy, precision, recall, and F1-score. The developed Bi-LSTM model achieved 88.75% accuracy, with 88.9% precision, 88.8% recall, and 88.8% F1-score. Analysis results show that the majority of public responses are positive or neutral, although there were negative sentiments that highlighted potential problems such as corruption and increasing national debt. These results provide insight into public opinion on the free lunch policy and demonstrate the effectiveness of the Bi-LSTM model in social media sentiment classification.
Empowering Teachers in Muhammadiyah Boarding School Yogyakarta toward Safer Digital Behavior through Smartphone Security Education Rakhmadi, Aris; Wintolo, Hero; Putri Silmina, Esi; Soyusiawaty, Dewi; Sunardi; Fadlil, Abdul
JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Vol. 6 No. 4 (2025)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/jurpikat.v6i4.2843

Abstract

Abstract: This community-service program was implemented through the Program Pemberdayaan Umat (PRODAMAT) of Universitas Ahmad Dahlan with the aim of enhancing digital literacy and cybersecurity awareness among teachers at Muhammadiyah Boarding School (MBS) Yogyakarta. The activity focused on smartphone account security education through practical steps such as password management, two-factor authentication (2FA), and phishing awareness. A participatory approach was applied through training involving 15 teachers and staff, combining interactive discussions, demonstrations, and pretest–posttest evaluation. The results showed an increase in the average knowledge score from 4.63 to 4.90, digital awareness from 4.05 to 4.45, and intention and safe digital behavior from 4.35 to 4.73. These improvements reflect positive changes in participants’ understanding, awareness, and behavior toward digital security. The program highlights the importance of integrating technological skills with ethical and religious values to promote sustainable digital empowerment in Islamic educational environments.
Pelatihan Kreasi Konten Digital dengan Komunikasi melalui Tools Kecerdasan Artifisial Winiarti, Sri; Soyusiawaty, Dewi; Umar, Rusydi; Yuliansyah, Herman
Jurnal Pengabdian UntukMu NegeRI Vol. 9 No. 3 (2025): Pengabdian Untuk Mu negeRI
Publisher : LPPM UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jpumri.v9i3.10417

Abstract

Seiring dengan adanya kebijakan Kementrian Pendidikan Dasar dan Menengah Republik Indonesia terkait penerapan Kecerdasan Artifisial (KA) dalam pembelajaran jenjang Sekolah Dasar (SD) hingga Sekolah Menengah Atas (SMA), maka semua sekolah memerlukan adanya pemahaman terhadap pelaksanaan pembelajaran KA. Perkembangan KA telah memberikan peluang baru dalam mendukung kreativitas dan komunikasi digital dalam pelaksanaan pembelajaran. Namun, banyak guru masih kesulitan berinteraksi secara efektif dengan perangkat KA untuk menghasilkan konten pembelajaran yang meraik, khususnya untuk pembelajaran dengan model unplugged. Kegiatan pengabdian kepada masyarakat ini bertujuan meningkatkan kompetensi guru dalam berkomunikasi dengan perangkat KA melalui pelatihan bertema “Kreasi Konten Digital dengan Komunikasi melalui Tools Kecerdasan Artifisial.” Pelatihan dilaksanakan pada 9 Juli 2025 di Kabupaten Sleman dengan peserta sebanyak 24 guru Sekolah Menengah Pertama (SMP). Metode yang digunakan meliputi tranfer pengetahuan, praktik langsung Aplikasi KA, praktek mengajar dengan Aplikasi KA dengan pendekatan PjBL dan evaluasi pelaksanaan. Materi pelatihan berupa konsep KA dalam pembelajaran, cara berkomunikasi dengan perangkat KA yang efektif dengan menggunakan prompt yang efektif. Kegiatan pelatihan ini meningkatkan keterampilan komunikasi guru sebesar 17,4% (dari 69,4% menjadi 86,8%), menunjukkan efektivitas pendekatan PjBL dalam memperkuat literasi digital dan kreativitas guru.
Co-Authors Abdul Fadlil Aldini Fujiarti Aning Setiya Rini Ardian M.N. Aris Rakhmadi Attaulah, Dimas Thaqif Buhari Muslim Catur Prasongko Devi Ayunani Wandasari Endri Rahmawanto Enggar Tyasto Wingwirawan Esty Istiqamah Failasuf Abid Dainuri Fathurrahman Fathurrahman Freddy Alpino Furqon, Ihtiar Nur Hapsari, Intan Herman Yuliansyah, Herman Irfan Nur Aditya Jones, Anna Hendri Soleliza Kholid Rohman Saputro Linda Mariana Lisna Zahrotun Muhammad Kunta Biddinika Nizar Anggit Pramana Nuri Piyanto Perdana, Tirtanusa Kurnia Adhi Putri Silmina, Esi Ramadhan, Imam Razak, Farhan Radhiansyah Rosary Indah Dewi Rusydi Umar Sri Winiarti Sunardi Sunardi Sunardi Wintolo, Hero