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All Journal Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Jurnal Teknika : Jurnal Teoritis dan Terapan Bidang Keteknikan JOINTECOMS (Journal of Information Technology and Computer Science) AnoaTIK: Jurnal Teknologi Informasi dan Komputer Diteksi : Jurnal Pengabdian Kepada Masyarakat International Journal of Artificial Intelligence in Medical Issues
Ade Chandra Saputra
Universitas Palangka Raya
Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Medicine & Pharmacology Public Health

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PERBANDINGAN NILAI AKURASI DISTILBERT DAN BERT PADA DATASET ANALISIS SENTIMEN LEMBAGA KURSUS Saputra, Ade Chandra; Saragih, Agus Sehatman; Ronaldo, Deddy
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 18 No. 2 (2024): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v18i2.13278

Abstract

Penelitian ini bertujuan untuk menerapkan Analisis Sentimen dalam Ulasan Kursus dengan menggunakan pendekatan Transfer Learning menggunakan model bahasa DistilBERT dalam konteks pengembangan sistem pendidikan. Dengan pertumbuhan yang pesat dalam domain e-learning dan layanan kursus online, pemahaman pengguna terhadap berbagai kursus menjadi semakin penting bagi institusi pendidikan. Metode transfer learning, yang mengandalkan model-model NLP yang sudah terlatih seperti DistilBERT, telah terbukti efektif dalam tugas analisis sentimen dengan kinerja yang baik dan efisiensi yang tinggi. Dengan peningkatan minat pada pembelajaran online, penelitian ini menginvestigasi bagaimana pendekatan analisis sentimen dapat memberikan wawasan yang lebih dalam terhadap ulasan kursus. Dengan penerapan teknik DistilBERT, diharapkan sistem mampu efektif dalam mengekstrak sentimen yang terkandung dalam ulasan tersebut, memberikan pemahaman menyeluruh terkait pendapat dan perasaan pengguna terhadap kursus yang mereka ikuti. Melalui penelitian ini, diharapkan dapat memberikan kontribusi penting bagi penyelenggara kursus dalam meningkatkan kualitas layanan pendidikan yang mereka tawarkan, memberikan umpan balik yang lebih terperinci dan tepat waktu kepada pengguna. Diharapkan diseminasi hasil penelitian ini memberikan pandangan yang lebih luas mengenai penerapan transfer learning dalam analisis sentimen, terutama dalam konteks ulasan kursus
KLASIFIKASI TINGKAT KEMATANGAN BUAH NAGA KRISTAL BERDASARKAN WARNA KULIT MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBORS Erick Hernando; Ade Chandra Saputra; Jadiaman Parhusip
AnoaTIK: Jurnal Teknologi Informasi dan Komputer Vol 2 No 2 (2024): Desember 2024
Publisher : Program Studi Ilmu Komputer FMIPA-UHO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33772/anoatik.v2i2.77

Abstract

This research aims to develop an effective method for determining the maturity level of dragon fruit in the harvestable, ripe, raw classes automatically by utilizing the K-Nearest Neighbors (K-NN) algorithm through the Knowledge Discovery in Databases (KDD) process. The KDD process, which involves a series of steps starting from data selection, data preprocessing, data transformation, to applying algorithms to produce useful knowledge, is used in this research to process and analyze dragon fruit image data. In this research the classification is processed through the KDD stages, including a preprocessing process to clean and prepare the data, the use of Min-Max Normalization to standardize the data so that all features are on the same scale, very important for the performance of the K-NN model, transformation to extract class data, and application of the K-NN algorithm for fruit maturity classification. The selection of the K-NN algorithm in the KDD stage is based on its simplicity and ability to classify data with a high level of accuracy. The research results show that the KDD method applied with the K-NN algorithm is able to classify the ripeness of dragon fruit with the best accuracy obtained at a value of K = 3 with an accuracy percentage of 91% without requiring physical cutting of the fruit. Thus, this research not only contributes to the field of precision agriculture but also shows how the KDD method can be applied effectively to solve real problems in the field.
Explainable Machine Learning for Predicting the Mental Health Impact of AI and Digital Platform Usage among Students Agus Halid; Dwi Amalia Purnamasari; Ade Chandra Saputra; Nicodemus Mardanus Setiohardjo
International Journal of Artificial Intelligence in Medical Issues Vol. 4 No. 1 (2026): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/pxn6qg39

Abstract

The increasing use of artificial intelligence and digital platforms among students has created new opportunities for learning support, academic assistance, and digital interaction. However, intensive platform usage may also be associated with mental health concerns, sleep disruption, and negative effects on students’ daily life. This study aims to develop and evaluate machine learning models for predicting the overall impact of AI and digital platform usage among students by integrating demographic, behavioral, sleep-related, and mental health-related variables. The dataset consisted of 1,705 student records with features including age, gender, academiclevel, country, average daily usage hours, most-used platform, sleep hours per night, and mental health score. The target variable was Overall_Impact, categorized into Negative, Neutral, and Positive classes. Six supervised machine learning algorithms were evaluated: Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, and Gradient Boosting. Model performance was assessed using accuracy, precision, recall, F1-score, Cohen’s Kappa, MAE, RMSE, ROC-AUC, and confusion matrix. The results showed that Random Forest achieved the best performance, with an accuracy of 99.71%, F1-macro of 99.52%, Cohen’s Kappa of 0.9950, and ROC-AUC of 0.9994 on the testing set. Feature importance analysis revealed that Mental_Health_Score, Sleep_Hours_Per_Night, and Avg_Daily_Usage_Hours were the most influential predictors. The findings indicate that machine learning can effectively predict the impact of digital platform usage and provide useful insights for AI-driven health informatics and student well-being monitoring. However, further validation using longitudinal and clinically grounded datasets is recommended.
Co-Authors Agus Halid Agus Sehatman Saragih Ahmadi Ahmadi Akbar, Nur Charisma Anak Agung Istri Sri Wiadnyani Ariesta Lestari Efrans Christian Enny Dwi Oktaviyani Erick Hernando Felicia Sylviana Fieradova, Deanyse Oxana Jadiaman Parhusip, Jadiaman Karolita, Devi Licantik, Licantik Nicodemus Mardanus Setiohardjo Purnamasari, Dwi Amalia Ronaldo, Deddy Rosadi, Reza Ghaniyy Siregar, Rayanti Veronica