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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Pendidikan Vokasi Jurnal Jurusan Pendidikan Teknik Elektro Jurnal Pendidikan Teknologi dan Kejuruan Jurnal Sains dan Teknologi JUTI: Jurnal Ilmiah Teknologi Informasi JOIV : International Journal on Informatics Visualization JUTIK : Jurnal Teknologi Informasi dan Komputer COUNS-EDU: The International Journal of Counseling and Education Journal of Educational Research and Evaluation WIDYA LAKSANA Journal of Education Technology Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal EDUTECH Undiksha Indonesian Journal of Data and Science Jurnal Pengabdian kepada Masyarakat Nusantara Unri Conference Series: Community Engagement JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia) MASALIQ: Jurnal Pendidikan dan Sains Al-DYAS : Jurnal Inovasi dan Pengabdian kepada Masyarakat Asian Journal of Science, Technology, Engineering, and Art International Journal of Artificial Intelligence in Medical Issues ALSYSTECH Journal of Education Technology Mikailalsys Journal of Advanced Engineering International International Journal of Education, Management, and Technology
Agus Aan Jiwa Permana
Universitas Pendidikan Ganesha
Religion Arts Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Trends, Technology, and Implementation of Digital Counseling in a Human Mental Health Agus Aan Jiwa Permana; Rukmi Sari Hartati; Made Sudarma; I Made Sukarsa
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 1 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i1.60163

Abstract

This research discusses various trends, methods, and implementation of the art of digital-based counseling services. With increasing public awareness of mental health, since the womb, parents have thought about how to form good character and mental health for their children. Purpose of this study is to utilize digital counseling services to help early detection of depression and the resilience of children and adolescents in dealing with life's problems. This research collects articles according to the topic, then looks at cases handled, types of data, and methods used in both synchronous and asynchronous-based digital counseling. The next stage is research grouping based on research objects, data, methods, results, and deficiencies in research. The focus of the research is on cases of depression, and resilience, with machine learning methods. Digital counseling has been widely used for early detection in cases of depression, drug abuse, youth suicide, and alcohol addiction among adolescents
IDENTIFIKASI DAN NORMALISASI TEKS SLANG DENGAN FASTTEXT PADA TWITTER DALAM BAHASA INDONESIA pande sindu; Agus Aan Jiwa Permana; I Nyoman Saputra Wahyu Wijaya
Jurnal Pendidikan Teknologi dan Kejuruan Vol. 21 No. 1 (2024): Edisi Januari 2024
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jptkundiksha.v21i1.66381

Abstract

Salah satu dampak yang signifikan dari popularitas media sosial adalah munculnya istilah slang yang semakin banyak. Istilah slang adalah bahasa yang digunakan oleh kelompok-kelompok tertentu untuk berkomunikasi secara informal. Slang juga dapat muncul melalui singkatan, penggunaan kata-kata yang berbeda dari arti aslinya, atau penggabungan kata-kata yang tidak konvensional. Dalam pengolahan bahasa alami (Natural Language Processing) Slang sering kali memiliki makna yang tidak jelas atau ambigu, dan kata-kata slang dapat memiliki konotasi yang berbeda tergantung pada konteks dan subkultur tertentu. Ini dapat menyebabkan kesalahan dalam pemrosesan bahasa alami dan menghasilkan hasil yang tidak akurat atau salah dalam tugas seperti klasifikasi teks atau analisis sentimen. Dari permasalahan tersebut dalam penelitian ini dikembangkan suatu metode untuk mengidentifikasi dan melakukan normalisasi slang pada kalimat yang akan diproses oleh NLP. Proses normalisasi slang ke bahasa yang lebih standar dilakukan dengan memanfaatkan pretrain model dari fasttext untuk mencari kata – kata yang memiliki kedekatan dengan slang. Data yang digunakan pada penelitian ini didapatkan dari sosial media twitter. Sebelum dinormalisasi data melewati beberapa proses seperti preprocessing data yang meliputi proses cleaning, case folding, dan stopword removal kemudian dilanjutkan dengan proses identifikasi slang pada kalimat dan terakhir dilakukan proses normalisasi slang yang didapatkan. Penelitian ini menemukan bahwa metode fasttext masih belum cukup baik melakukan normalisasi slang dikarenakan masih ada sekitar 1329 data dari 3239 data yang tidak berhasil dinormalisasi dengan baik yaitu sekitar 41%. Penelitian ini memberikan kontribusi dalam membantu proses pengolahan kata yang lebih baik untuk NLP.
PENGUMPULAN DATA TWEET BERDASARKAN KATA KUNCI DEPRESI DAN KISAH HIDUP DI KALANGAN MAHASISWA BERBASIS PHQ-9 I Gusti Agung Putu Bagus Satria Wicaksana; Agus Aan Jiwa Permana; Ni Putu Novita Puspa Dewi
Jurnal Pendidikan Teknologi dan Kejuruan Vol. 21 No. 1 (2024): Edisi Januari 2024
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jptkundiksha.v21i1.66460

Abstract

Dalam konteks pandemi COVID-19 yang telah berlalu, mahasiswa menghadapi tantangan baru dalam pembelajaran dan kesejahteraan mental mereka. Dampak pandemi dapat menyebabkan peningkatan stres dan tekanan, yang berkontribusi pada gejala depresi. Oleh karena itu, penting untuk memahami bagaimana mahasiswa mengungkapkan emosi terkait depresi dalam lingkungan media sosial. Penelitian ini menggunakan metode data mining dengan menggunakan Snscrape sebagai alat untuk mengambil data dari platform media sosial, khususnya Twitter. Data yang diambil mencakup periode dari tahun 2019 hingga 2023, memungkinkan identifikasi perubahan tren dan pola ungkapan emosi terkait depresi pada mahasiswa dari waktu ke waktu. Proses pemilihan data melibatkan penentuan kriteria pencarian berdasarkan kata kunci dari kuisioner PHQ-9 dan batas waktu periode yang relevan. Data yang diungkapkan oleh mahasiswa yang mencerminkan pengalaman pribadi dan kisah mereka dalam menghadapi depresi menjadi fokus dalam proses pengambilan data. Selanjutnya, data yang telah berhasil diambil disimpan dalam format file CSV, yang memungkinkan pengolahan data yang mudah dan kompatibilitas yang luas dengan perangkat lunak analisis data. Dalam penelitian ini, didapatkan data sebanyak 2581 data dimana 924 dikategorikan sebagai depresi ringan, 397 depresi sedang, dan 1260 depresi berat yang merupakan hasil scraping menggunakan tools Snscrape. Hasil penelitian ini diharapkan dapat menjadi landasan bagi upaya untuk meningkatkan kesejahteraan dan dukungan bagi mahasiswa yang mengalami depresi.
Pelatihan Kepemimpinan dan Pendidikan Karakter untuk Guru dan Siswa di Sekolah Binaan Berbasis Game Edukasi Permana, Agus Aan Jiwa; Kertiasih, Ni Ketut; Sindu, I Gede Partha; Wijaya, I Gede Saputra Wahyu; Setemen, Komang; Pracasitaram, I Gede Made Surya Bumi; Pageh, I Made
Al-DYAS Vol 3 No 3 (2024): OKTOBER
Publisher : Lembaga Yasin AlSys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/aldyas.v3i3.3848

Abstract

Leadership training and character education for teachers in assisted schools is carried out to improve the quality of education through increasing leadership abilities and instilling character values. As educators and classroom leaders, teachers have a major impact on students' intellectual and moral development. Therefore, this training is designed to provide the skills and knowledge needed so that teachers can become leaders and shape students' strong character and integrity. This program includes leadership theory, effective communication techniques, conflict management, getting to know one's identity, as well as strategies for developing student character according to the independent curriculum. The approach used combines active learning methods such as group discussions and case studies, so that teachers can apply the concepts learned in real contexts. The main focus is on developing student character through values ​​such as honesty, responsibility, empathy and cooperation. This training can improve the leadership competence of school principals and classroom teachers, create a positive school environment, and produce the next generation with strong character and harmony. Students are expected to be able to have better character in the future in knowing themselves, culture, the environment and nationalist spirit.
Performance Metrics of AdaBoost and Random Forest in Multi-Class Eye Disease Identification: An Imbalanced Dataset Approach Tarigan, Thomas Edyson; Susanti, Erma; Siami, M. Ikbal; Arfiani, Ika; Jiwa Permana, Agus Aan; Sunia Raharja, I Made
International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 2 (2023): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v1i2.98

Abstract

This study presents a comprehensive evaluation of AdaBoost and Random Forest Classifier algorithms in the classification of eye diseases, focusing on a challenging scenario involving an imbalanced dataset. Eye diseases, particularly Cataract, Diabetic Retinopathy, Glaucoma, and Normal eye conditions, pose significant diagnostic challenges, and the advent of machine learning offers promising avenues for enhancing diagnostic accuracy. Our research utilizes a dataset preprocessed with Canny edge detection for image segmentation and Hu Moments for feature extraction, providing a robust foundation for the comparative analysis. The performance of the algorithms is assessed using a 5-fold cross-validation approach, with accuracy, precision, recall, and F1-score as the key metrics. The results indicate that the Random Forest Classifier outperforms AdaBoost across these metrics, albeit with moderate overall performance. This finding underscores the potential and limitations of using advanced machine learning techniques for medical image analysis, particularly in the context of imbalanced datasets. The study contributes to the field by providing insights into the effectiveness of different machine learning algorithms in handling the complexities of medical image classification. For future research, it recommends exploring a diverse range of image processing techniques, delving into other sophisticated machine learning models, and extending the study to encompass a wider array of eye diseases. These findings have practical implications in guiding the selection of machine learning tools for medical diagnostics and highlight the need for continuous improvement in automated systems for enhanced patient care.
Analysis of Erythema Migrans Rashes for Improved Lyme Disease Diagnosis Using Ensemble Machine Learning Techniques Jiwa Permana, Agus Aan
International Journal of Artificial Intelligence in Medical Issues Vol. 2 No. 1 (2024): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v2i1.151

Abstract

This study addresses the challenge of diagnosing Lyme disease through automated classification of Erythema Migrans (EM) rashes, a primary symptom. Employing a Voting Classifier within a k-fold (k=5) cross-validation framework, we developed and validated a model based on a curated dataset of EM rash images and similar dermatological conditions. Image pre-processing involved segmentation and feature extraction using Hu Moments, preparing the data for effective machine learning application. The classifier demonstrated an average accuracy of 81.37%, with variations in precision, recall, and F1-scores across folds, indicative of the model’s robustness and areas for improvement. The results suggest that while the Voting Classifier is a promising tool for Lyme disease diagnosis, further enhancements are required to optimize its diagnostic performance fully. Significant research contributions include the development of a publicly accessible EM rash dataset and the application of ensemble learning techniques to medical image classification, offering a foundation for future advancements in automated disease diagnosis. Recommendations for ongoing research include expanding the dataset diversity and integrating multi-modal clinical data to enhance model accuracy and applicability.
Implementasi Sistem Pakar untuk Klasifikasi Tanaman Padi (Oryza sativa L. ) Berdasarkan Ciri-Ciri Morfologi Gede Wahyu Purnama; Agus Aan Jiwa Permana; I Kadek Nicko Ananda; Ni Luh Ita Purnami; Gede Nanda Ageng Nugraha; Ida Bagus Sebali Mahesa Yogi
Jurnal Pendidikan Teknik Elektro Undiksha Vol. 13 No. 2 (2024): JPTE Periode Agustus 2024
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jjpte.v13i2.72554

Abstract

Penelitian ini dilakukan untuk mengembangkan sebuah sistem yang merupakan salah satu cabang kecerdasan buatan (AI) yaitu sistem pakar. Sistem pakar kami akan digunakan untuk mengklasifikasi varietas tanaman padi (Oryza sativa L.). Langkah-langkah yang kami lakukan untuk membangun sistem pakar ini dimulai dengan proses pengumpulan data ciri-ciri morfologi tanaman padi melalui sumber-sumber terpercaya (Feri Hendriawan Nasrez Akhir, 2019; Ibadin, 2021; KEW, 2023; WIS, 2021; Wopereis, 2009)), lalu ciri-ciri tersebut akan digunakan untuk membuat aturan (rule) yang akan diimplementasikan dalam PROLOG. Setelah itu kami membuat mesin inferensi dengan pendekatan botton-up inference. Kami berhasil membuat sistem pakar menggunakan salah satu dialek dari bahasa PROLOG yaitu SWI-PROLOG (Lucas & Van Der Gaag, 1991). Namun sistem kami hanya dapat mengklasifikasikan 5 varietas tanaman padi yaitu padi indica , japanica, ketan putih, ketan hitam dan ketan merah (Feri Hendriawan Nasrez Akhir, 2019; Wopereis, 2009).
SISTEM PAKAR: PENERAPAN METODE FORWARD CHAINING UNTUK IDENTIFIKASI TANAMAN HIAS BERDASARKAN CIRI STRUKTUR TAKSONOMI DIATMIKA, KETUT TUTUR; Suardana, Putu; Winata, I Gede Arya; Octavia, I Gusti Ayu Adiani; Marta Dinata, Kadek Prima Giant; Permana, Agus Aan Jiwa
Jurnal Pendidikan Teknik Elektro Undiksha Vol. 13 No. 2 (2024): JPTE Periode Agustus 2024
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jjpte.v13i2.72636

Abstract

An expert system is a subset of artificial intelligence that widely applies specialized knowledge to address human-level problems requiring specific expertise. Forward Chaining is one of the techniques used in the inference engine, which performs forward tracking based on known facts to draw conclusions. In this study, forward chaining is employed for the identification of ornamental plants based on their taxonomic structure characteristics by tracing facts present in the knowledge base. The dataset used to construct the knowledge base in this research is derived from literature sources such as journals and published scientific articles. This research utilizes the Prolog programming language to build the expert system with the goal of identifying ornamental plants based on their taxonomic structure. The expert system is designed to facilitate the identification process effectively and efficiently. With the forward chaining method and the use of Prolog, this expert system is capable of identifying ornamental plants based on the characteristics of their taxonomic structure.
Sistem Pakar Klasifikasi Tanaman Pinus Berbasis Forward Chaining pada Sistem Pakar Pande, Satria Imawan Adi Putra Pande; Putu Ony Andewi; Gede Arya Ardivan Pratama Saputra; I Gd Ny Werdyana Guna Mertha; Agus Aan Jiwa Permana
Jurnal Pendidikan Teknik Elektro Undiksha Vol. 13 No. 2 (2024): JPTE Periode Agustus 2024
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jjpte.v13i2.72690

Abstract

Penelitian ini menggali penerapan teknologi Kecerdasan Buatan (AI) dalam klasifikasi taksonomi tanaman pinus. Sistem pakar berbasis forward chaining dengan menggunakan bahasa pemrograman Prolog diimplementasikan untuk menyederhanakan identifikasi tanaman pinus yang kompleks. Pineaceae, keluarga tanaman pinus, memiliki peran ekologis dan ekonomis signifikan, tetapi distribusi spesies yang luas menimbulkan tantangan dalam manajemen hutan dan keberlanjutan. Metode penelitian melibatkan analisis masalah, pengumpulan data, desain rule dalam CNF, implementasi Prolog, dan evaluasi. Hasilnya menunjukkan bahwa sistem dapat mengklasifikasikan tanaman pinus dengan tingkat akurasi yang tinggi. Simpulan penelitian ini menegaskan keberhasilan pengembangan sistem pakar berbasis forward chaining dengan Prolog dalam klasifikasi taksonomi tanaman pinus.
Development of a Decision Tree Classifier for Breast Cancer Diagnosis Using Fine Needle Aspirate Data Halid, Agus; Wikranta Arsa, I Gusti Ngurah; Azdy, Rezania Agramanisti; Jiwa Permana, Agus Aan
Indonesian Journal of Data and Science Vol. 5 No. 3 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i3.202

Abstract

Breast cancer is one of the leading causes of mortality among women globally, necessitating early and accurate detection to improve survival rates. This study leverages machine learning to develop a decision tree classifier for distinguishing between benign and malignant breast masses using the Kaggle Breast Cancer FNA dataset. The dataset underwent rigorous pre-processing, including the removal of irrelevant columns, data cleaning, label encoding, and feature scaling. The model was evaluated using 5-fold cross-validation, achieving an average accuracy of 84.0%, with a test set accuracy of 83.72%. Performance metrics such as precision, recall, and F1-score further validated the model's robustness, with an overall accuracy of 90.24% on the test set. The decision tree classifier demonstrated high interpretability, making it a practical tool for aiding clinical decision-making. While the results are promising, the study highlights opportunities for improvement, including the use of ensemble methods and larger datasets to enhance generalizability. This research contributes to the growing body of evidence supporting machine learning applications in medical diagnostics, particularly in breast cancer detection.
Co-Authors A. A. Gede Yudhi Paramartha Agus Halid, Agus Agus Seputra I Ketut Alkautsar, Yoga Rizky Arditya, I Putu Dion Artha, I Kadek Bayu Danu Artha, I Komang Windra Baskara Nugraha, I Gusti Bagus Darmayasa, Ngakan Nyoman DIATMIKA, KETUT TUTUR Elly Herliyani Erma Susanti Gede Aditra Pradnyana Gede Arya Ardivan Pratama Saputra Gede Nanda Ageng Nugraha Gede Saindra Santyadiputra Gede Wahyu Purnama Gunawan, I Gede Made Deny Surya I Gd Ny Werdyana Guna Mertha I Gusti Agung Putu Bagus Satria Wicaksana I Gusti Ayu Purnamawati I Gusti Ngurah Wikranta Arsa, I Gusti Ngurah I Kadek Nicko Ananda I Kadek Suranata I Ketut Gading I Ketut Purnamawan I Made Ardwi Pradnyana I Made Pageh I Made Putrama I Made Sukarsa I Made Sukarsa I Nyoman Laba Jayanta I Nyoman Saputra Wahyu Wijaya I Nyoman Saputra Wahyu Wijaya Ida Bagus Sebali Mahesa Yogi Ifdil Ifdil Ika Arfiani Kadek Wirahyuni Komang Setemen Kusuma, I Komang Arya Adi Kusumadewi, Ni Putu Ari Made Sudarma Made Sudarma Mahagangga, Komang Adi Satya Marta Dinata, Kadek Prima Giant Naitboho, Okthen Orlanda Ni Ketut Kertiasih Ni Luh Ita Purnami Ni Putu Dwi Sucita Dartini Ni Putu Novita Puspa Dewi Ni Wayan Marti Octavia, I Gusti Ayu Adiani Paholo Iman Prakoso pande sindu Pande, Satria Imawan Adi Putra Pande Pracasitaram, Gede Made Surya Bumi Pracasitaram, I Gede Made Surya Bumi Pramudya, Dewa Gede Bhaskara Pranadi Sudhana, I G P Fajar Puridiasta, I Gede Deindra Dwija Putrama, Made Putu Ony Andewi PUTU SUGIARTAWAN Rezania Agramanisti Azdy, Rezania Agramanisti Rukmi Sari Hartati Rukmi Sari Hartati Saputra Wahyu Wijaya Siami, M. Ikbal Sindu, I Gede Partha Sunia Raharja, I Made Swari, Gusti Putu Ayu Mas Meita Pradnya Tarigan, Thomas Edyson Widodo Prijodiprodjo Wijaya, I Gede Saputra Wahyu Winata, I Gede Arya Wirayani, Made Padmi Witjaksana, Putu Gede Dimas Yoga Rizky Alkautsar Yoga Sucipta, Gede Yudhantara, Kadek Prasta