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All Journal JURNAL SISTEM INFORMASI BISNIS Jupiter Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JRST (Jurnal Riset Sains dan Teknologi) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) JURIKOM (Jurnal Riset Komputer) JURTEKSI Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Jurnal Teknologi Dan Sistem Informasi Bisnis JATI (Jurnal Mahasiswa Teknik Informatika) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Teknik Informatika (JUTIF) Jurnal Pengabdian Masyarakat Indonesia Jurnal Ilmiah Wahana Pendidikan Jurnal PkM (Pengabdian kepada Masyarakat) Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan IJCOSIN : Indonesian Journal of Community Service and Innovation Jurnal Tren Bisnis Global Jurnal INFOTEL Jurnal Informatika: Jurnal Pengembangan IT JPMTT (Jurnal Pengabdian Masyarakat Teknologi Terbarukan) Edu Komputika Journal JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
M Yoka Fathoni
Intitut Teknologi Telkom Purwokerto
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Journal : Jurnal Teknik Informatika (JUTIF)

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TEXT CLASSIFICATION OF BULLYING REPORTS USING NLP AND RANDOM FOREST. Aldo, Dasril; Paramadini, Adanti Wido; Fathoni, M. Yoka
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.4032

Abstract

Bullying is a great concern that needs to be dealt with as early as possible, be it in the form of physical, verbal, social or cyber bullying. Using NLP algorithms, this paper intends to classify bullying report using Natural Language Processing in conjunction with Bag of Words. The study employs quantitative methodology. A total of 4671 reports of bullying are in essence categorized into physical, verbal, social, cyber and non-cyber bullying. We split the dataset into 80% training set (3737 reports) and 20% testing set (934 reports). The above model has achieved an accuracy of 94,76%, with good values of recall, precision and F1-score: 94,64%, 95,02% and 94,97% respectively. The dataset is then analyzed using Random Forest algorithm and Report of the Bullying Survey The model is to be effective in automatic Detection of Textual Bullying Reports Automated. While there has been no such effort in our institutions so far, automatic reporting of bullying will prove to be effective. This is because the system will allow a school or institution to have a precise constant monitoring of bullying reports. It will also allow an instantaneous action to be taken to protect the victim without letting the situation escalate.
Optimization Of Extreme Learning Machine Models Using Metaheuristic Approaches For Diabetes Classification Sulaeman, Gilang; Nur, Yohani Setiya Rafika Nur; Paramadini, Adanti Wido; Aldo, Dasril; Fathoni, M. Yoka
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4690

Abstract

Proper classification of diabetes is a significant challenge in contemporary healthcare, especially related to early detection and clinical decision support systems. This study aims to optimize the Extreme Learning Machine (ELM) model with a metaheuristic approach to improve performance in diabetes classification. The data used was an open dataset containing the patient's medical attributes, such as age, gender, smoking status, body mass index, blood glucose level, and HbA1c. The initial process includes data cleansing, one-hot coding for categorical features, MinMax normalization, and unbalanced data handling with SMOTE. The ELM model was tested with four activation functions (Sigmoid, ReLU, Tanh, and RBF) each combined with three metaheuristic optimization strategies, namely Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Bat Algorithm. The results of the evaluation showed that the combination of the Tanh activation function with GA optimization obtained the highest accuracy of 87.98% and an F1-score of 0.5489. Overall, GA optimization appears to be superior to all other measurement configurations in consistent classification performance. The main contribution of this study is to offer a systematic approach to select the best combination of activation functions and optimization algorithms in ELM, as well as to provide empirical evidence to support the application of metaheuristic strategies to improve the accuracy of disease classification based on health data. This research has direct implications for the development of a more precise and data-based medical diagnostic classification system for diabetes.
Co-Authors 12.5202.0161 Daniel Yeri Kristiyanto Adanti Wido Paramadini Ade Tiara Rosalinda Alfarisi, Gitasari Kurnia Ali, Nizar Alika, Shintia Dwi Anggraeni, Selly Apriliana Puspitaningrum Ardi Susanto Ardi Susanto Arif Riyandi Aruga Yudish Firmansyah Ayu Kusumaningtyas Ayu Nirwana, Zahra Fikri Azzahra, Anisa Cahyo Prihantoro Dairoh Dairoh Dandi Sunardi Dany Candra Febrianto Darmansah Darmansah, Darmansah Dasril Aldo Dea Caesy Rahmadani Dedy Agung Prabowo Dega Surono Wibowo Dega Surono Wibowo, Dega Surono Dwi Januarita Dwi Januarita Dwi Mustika Kusumawardani, Dwi Mustika Fadhilah, Syifa Nur Fajriyah, Nindi Ilmiyati Farkhan Hariyadi Berbudi Bowoleksono Fauzi, Faiq Firmansyah, Aruga Yudish Firmansyah, Muhammad Raafi'u Friliyani, Radiyana Aniq Garin Indra Prameswara Hutanti Setyodewi Ike Kurnia Putri Irwan Susanto Jihan Shinta Celina Kusuma Ningrum, Linda Ayu Lina Fatimah Lishobrina Logiandani Logiandani M Nishom M Nishom Mahazam Afrad Maulida, Elsa Miftahul Jannah Monsya Juansen Muhamad Albirra Arsyi Rizqi Muhamad Awiet Wiedanto Prasetyo Muhammad Fikri Hidayattullah Muhammad Imanullah Nabila Azahra Naufal Ibrahim Nevandra Putra Andyka Ni Wayan Wardani Nicolaus Euclides Wahyu Nugroho Nindi Ilmiyati Fajriyah Nishom, M Nunik Oktaviani Olivia Sari Purba, Yessi Pahrizal, Pahrizal Pero Roberto Kristovic Radiyana Aniq Friliyani Rahmadani, Dea Caesy Raihan Zidane Ramadhan Ramadhan, Firman Adi Ramadhani S, Bunga Rona Nisa Sofia Amriza Safhira Nanda Rahmadhani Salsabila Cahya Alifia Sandhy Fernandes Sandhy Fernandez Sayyidah Jasinda Amalia Selly Anggraeni Sena Wijayanto Setyodewi, RR Hutanti Sisilia Thya Safitri Siti Khomsah, Siti Sudianto Sukmadiningtyas Sulaeman, Gilang Syabrian, Izma Syifa Nur Fadhilah Tegar Wahyudi Adha Tomy Nanda Putra Toni Anwar Toni Anwar Toyib, Rozali Waliulu, Raditya Faisal Wiedanto, Muhammad Awiet Yedija Maarende, Tigris Yogo Dwi Prasetyo Yogo Dwi Prasetyo Yohani Setiya Rafika Nur Yulia Darmi Yustia Hapsari Zahra Fikri Ayu Nirwana