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All Journal Transformasi Jurnal Kabar Masyarakat Scientific Journal of Informatics
Nur Latifah Dwi Mutiara Sari
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences

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Microsoft Word Skills Development Training for Final Semester Students: Pelatihan Pengembangan Keahlian Microsoft Word Bagi Mahasiswa Semester Akhir Wardhani, Anindya Khrisna; Nur Latifah Dwi Mutiara Sari; Astrid Novita Putri
Jurnal Kabar Masyarakat Vol. 2 No. 1 (2024): Februari : JURNAL KABAR MASYARAKAT
Publisher : Institut Teknologi dan Bisnis Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jkb.v2i1.1609

Abstract

The use of Microsoft Word skills is important in the academic world, especially for final semester students who are completing their final assignments. This community service aims to provide Microsoft Word skill development training for final semester students with a focus on increasing the efficiency and quality of preparing thesis reports. Through a series of intensive training sessions, students will be equipped with an in-depth understanding of Microsoft Word's advanced features that can support the research process and preparation of scientific documents. This training was attended by 129 participants from various universities via zoom meetings. The activity lasted for 4 hours consisting of material presentation, case studies, questions and answers and ended with closing. After participating in this activity, participants receive significant benefits in improving students' abilities so that they can make maximum use of word processing software, and can produce quality final assignment reports that meet academic standards. Apart from that, community service this time can also motivate students to develop information technology skills which will be an added value in their future careers.
PREDIKSI PENYEBARAN TUBERKULOSIS DI INDONESIA MENGGUNAKAN SINGLE MOVING AVERAGE Wardhani, Anindya Khrisna; Putri, Astrid Novita; Sari, Nur Latifah Dwi Mutiara
TRANSFORMASI Vol 19, No 2 (2023): TRANSFORMASI
Publisher : STMIK BINA PATRIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56357/jt.v19i2.386

Abstract

Penelitian ini bertujuan untuk meramalkan penyebaran Tuberkulosis (TB) di Indonesia menggunakan metode Single Moving Average (SMA) dengan tiga variasi ordo: 10, 5, dan 3, berdasarkan data jumlah penderita TB dari tahun 2000 hingga 2022. Hasil prediksi menunjukkan bahwa SMA berordo 3 memberikan kinerja terbaik dengan Mean Squared Error (MSE) sebesar 45,315, Root Mean Squared Error (RMSE) sebesar 952, dan Mean Absolute Deviation (MAD) sebesar 287,542. Model ini lebih sensitif terhadap perubahan dalam data, memberikan prediksi yang lebih akurat dibandingkan dengan ordo 5 dan 10. Kesimpulan menyoroti bahwa penggunaan SMA berordo 3 dapat memberikan informasi yang lebih akurat tentang tren penyebaran TB di Indonesia. Rekomendasi penelitian lebih lanjut mencakup eksplorasi metode analisis prediktif lainnya dan mempertimbangkan faktor-faktor tambahan yang dapat memengaruhi penyebaran TB, seperti faktor lingkungan dan kebijakan kesehatan.
Mental Health Chatbot Application on Artificial Intelligence (AI) for Student Stress Detection Using Mobile-Based Naïve Bayes Algorithm Mariyana, Ekanata Desi Sagita; Novita, Mega; Nur Latifah Dwi Mutiara Sari
Scientific Journal of Informatics Vol. 12 No. 2: May 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i2.24307

Abstract

Purpose: This study aims to design and evaluate a chatbot-based artificial intelligence system to identify stress levels in students using the Naïve Bayes classification method. With increasing mental health concerns among students, early stress detection is considered crucial for timely intervention Methods: This study proposes an AI-based chatbot system to detect student stress levels using a comparative approach between Naïve Bayes and Support Vector Machine (SVM) algorithms. A Kaggle dataset with 15 psychological and academic indicators was preprocessed and balanced using SMOTE. Naïve Bayes showed higher accuracy (90%) than SVM (89%). The trained model was deployed via Flask with Ngrok tunneling and integrated into a Flutter mobile app connected to the Gemini AI API for real-time stress screening. This research offers a practical and scalable solution for early mental health detection in students through intelligent chatbot interaction. Result: The findings show that the Naïve Bayes model achieves a classification accuracy of 90%, slightly surpassing the SVM model, which records an accuracy of 89%. Evaluation through ROC and AUC metrics supports the reliability of Naïve Bayes in detecting stress levels. The integrated chatbot offers a responsive and engaging platform for preliminary mental health assessments. Novelty: This research presents a unique contribution by combining AI-driven stress detection with a real-time chatbot interface, offering an accessible and scalable approach to student mental health support. The integration of machine learning models with conversational AI provides an innovative solution for early intervention. Future developments may involve deep learning and more diverse psychological inputs to further improve accuracy and effectiveness.
PREDIKSI PENYEBARAN TUBERKULOSIS DI INDONESIA MENGGUNAKAN SINGLE MOVING AVERAGE Wardhani, Anindya Khrisna; Putri, Astrid Novita; Sari, Nur Latifah Dwi Mutiara
TRANSFORMASI Vol 19, No 2 (2023): TRANSFORMASI
Publisher : STMIK BINA PATRIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56357/jt.v19i2.386

Abstract

Penelitian ini bertujuan untuk meramalkan penyebaran Tuberkulosis (TB) di Indonesia menggunakan metode Single Moving Average (SMA) dengan tiga variasi ordo: 10, 5, dan 3, berdasarkan data jumlah penderita TB dari tahun 2000 hingga 2022. Hasil prediksi menunjukkan bahwa SMA berordo 3 memberikan kinerja terbaik dengan Mean Squared Error (MSE) sebesar 45,315, Root Mean Squared Error (RMSE) sebesar 952, dan Mean Absolute Deviation (MAD) sebesar 287,542. Model ini lebih sensitif terhadap perubahan dalam data, memberikan prediksi yang lebih akurat dibandingkan dengan ordo 5 dan 10. Kesimpulan menyoroti bahwa penggunaan SMA berordo 3 dapat memberikan informasi yang lebih akurat tentang tren penyebaran TB di Indonesia. Rekomendasi penelitian lebih lanjut mencakup eksplorasi metode analisis prediktif lainnya dan mempertimbangkan faktor-faktor tambahan yang dapat memengaruhi penyebaran TB, seperti faktor lingkungan dan kebijakan kesehatan.
Co-Authors Astrid Novita Putri, Astrid Astrid Novita Putri, Astrid Novita Mariyana, Ekanata Desi Sagita Mega Novita Wardhani, Anindya Khrisna