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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Simantec Jurnal Informatika dan Teknik Elektro Terapan Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Komtika (Komputasi dan Informatika) Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Nasional Komputasi dan Teknologi Informasi CSRID (Computer Science Research and Its Development Journal) Jurnal IMPACT: Implementation and Action Seminar Nasional Teknologi Informasi Komunikasi dan Administrasi [SEMINASTIKA] Jurnal Informasi dan Teknologi JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) JURNAL PENDIDIKAN, SAINS DAN TEKNOLOGI Tematik : Jurnal Teknologi Informasi Komunikasi Journal of Computer Networks, Architecture and High Performance Computing Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Walisongo Journal of Information Technology Jumat Informatika: Jurnal Pengabdian Masyarakat Majalah Ilmiah UPI YPTK Jurnal Media Informatika TIERS Information Technology Journal Jurnal Informatika Dan Tekonologi Komputer (JITEK) Digital Transformation Technology (Digitech) Adpebi Science Series INSTALL: Information System and Technology Journal INTECH (Informatika dan Teknologi) Jurnal Komtika (Komputasi dan Informatika) Jurnal Informatika Dan Tekonologi Komputer
Finki Dona Marleny
Universitas Muhammadiyah Banjarmasin
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Mathematics Neuroscience Social Sciences Transportation Other

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EXPLANATORY DATA ANALISIS UNTUK MENGEVALUASI PENELUSURAN KATA KUNCI VIDEO PEMBELAJARAN DI YOUTUBE DENGAN PENDEKATAN MACHINE LEARNING Mambang Mambang; Ahmad Hidayat; Finki Dona Marleny; Johan Wahyudi
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 2 No. 2 (2022): Juli : Jurnal Informatika dan Teknologi Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v2i2.287

Abstract

The purpose of this study was to find correlations related to the variable number of impressions, likes, subscribers, and comments on each learning video keyword search on YouTube. This research uses quantitative methods and experiments with secondary data sources. Exploratory Data Analysis in machine learning using several libraries in Python programming produces image visualizations that provide information related to the dataset that has been processed, such as boxplot graphs, histograms, line plots, and correlation graphs. Exploratory Data Analysis with machine learning that we have done finds results on boxplot graphs on five variables showing a whisker more elongated upwards which states positive data results. The difference in this histogram chart is in the duration variable. On the line plot graph, we find the keywords learning videos learning mathematics have the advantage of four variables and the keywords of accounting learning videos one variable. Exploratory Data Analysis using the correlation head map in the seaborn library shows that the like and comment variables strongly correlate with a value of 1. Duration variables have a low and negative correlation with other variables. The subscribers variable has a high correlation with the like variable 0.95. Thus, several indicators need to be considered in making learning videos, such as content or content of innovative and creative learning videos, so that the number of likes and comments becomes high. The length of time in learning videos does not affect the number of likes, subscribers, and comments.
Enhancing Water Quality Early Warning System Accuracy in Pangasius Aquaculture Using Machine Learning Hadyan, M Rais; finki dona marleny; ayu ahadi ningrum
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 1 (2026): Call for Paper for Machine Learning / Artificial Intelligence, Januari 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i1.7479

Abstract

Intensive catfish (Pangasius sp.) aquaculture faces significant economic risks driven by mass mortality events linked to unstable water quality, particularly toxic ammonia spikes and pH fluctuations. Although Internet of Things (IoT) technology enables real-time monitoring, the resulting time-series data presents complex challenges, including high sensor noise, asynchronous transmission, and severe class imbalance, which compromise standard reactive monitoring methods. This study aims to enhance diagnostic accuracy by comparing Support Vector Machine (SVM), Random Forest (RF), and XGBoost algorithms to construct a robust Early Warning System (EWS). A quantitative experimental methodology was applied to real-world sensor data, with temporal aggregation preprocessing to reduce noise. To ensure rigorous validation simulating real-world deployment, the dataset utilized a strict chronological split (80% training, 20% testing) and was further tested using 5-Fold Time-Series Cross-Validation. The results demonstrated the definitive superiority of ensemble-based models; Random Forest and XGBoost achieved 100.00% accuracy on the test set, successfully eliminating the critical false negatives exhibited by the SVM model (99.80%). Stability analysis further confirmed the robustness of Random Forest (98.35%) and XGBoost (98.32%) compared to SVM (97.02%). Additionally, feature importance analysis unequivocally identified ammonia as the dominant predictor of critical conditions. Crucially, the study detected a “concept drift” phenomenon in which “Safe” conditions disappeared during the final cultivation phase. These findings conclude that ensemble models provide the optimal architecture for EWS. However, the presence of concept drift necessitates adaptive retraining strategies to ensure long-term reliability in dynamic pond environments.
Implementasi Algoritma LSTM Pada Sistem Monitoring Iot Untuk Penanganan Resiko Kebakaran Ahmad Nawawi; Kamarudin; Finki Dona Marleny
INTECH Vol. 6 No. 2 (2025): INTECH (Informatika Dan Teknologi)
Publisher : Informatics Study Program, Faculty of Engineering and Computers, Baturaja University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54895/intech.v6i2.3317

Abstract

Penelitian ini mengimplementasikan algoritma Long Short-Term Memory (LSTM) untuk memprediksi pola suhu sebagai bagian dari sistem monitoring berbasis IoT dalam rangka mitigasi risiko kebakaran. Dataset diperoleh dari sensor berbasis ESP32 yang merekam data suhu dan waktu. Model LSTM dilatih menggunakan data suhu yang telah dinormalisasi dengan prediksi lima langkah ke depan. Pra-pemrosesan meliputi penggabungan data tanggal dan waktu menjadi indeks waktu, kemudian dilanjutkan dengan normalisasi dan pembentukan data dalam format pembelajaran terawasi. Arsitektur model terdiri dari satu lapisan LSTM dan satu lapisan keluaran dense. Hasil prediksi menunjukkan nilai Mean Squared Error (MSE) yang rendah, menandakan efektivitas model LSTM dalam mendeteksi potensi bahaya kebakaran secara dini. Penelitian ini berkontribusi pada upaya mitigasi risiko secara real-time melalui peningkatan akurasi prediksi pada lingkungan IoT.
Analysis of the Impact of Violent Content on Social Media on Adolescent Cyberpsychology Using Support Vector Machine and Random Forest Febriani, Wulandari; Mambang, Mambang; Prastya, Septyan Eka; Sabella, Billy; Marleny, Finki Dona
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11415

Abstract

Adolescent exposure to violent content on social media has emerged as a critical issue due to its potential impact on mental health and cyberpsychological well-being. This study aims to classify multiple cyberpsychological impacts experienced by adolescents as a result of exposure to violent content on social media using a multi-label machine learning approach. A quantitative method was employed using self-reported data collected from 550 Indonesian adolescents aged 12–18 years through an online questionnaire. Psychological impacts were measured using adapted instruments from the Depression Anxiety Stress Scales (DASS-21) and cyberpsychology scales, then transformed into multi-label targets. Support Vector Machine (SVM) and Random Forest algorithms were implemented using a One-vs-Rest strategy. Model performance was evaluated using Hamming Loss, precision, recall, and Macro F1-score. The results indicate that SVM outperformed Random Forest with a Hamming Loss of 23.16% and a Macro F1-score of 0.42, particularly in predicting dominant labels such as anxiety and decreased self-confidence. However, both models showed limited performance in predicting minority labels such as depression and academic decline due to data imbalance. These findings highlight the importance of handling imbalanced data in cyberpsychology-based machine learning research and demonstrate the potential of multi-label classification in representing the complexity of psychological impacts of digital violence on adolescents.
Co-Authors Ade Putri Maharani Adha, Muhammad Iqbal Ahadi Ningrum, Ayu Ahmad Faisal Hamidi Ahmad Hidayat Ahmad Hidayat Ahmad Nawawi Ahmad Riki Renaldy Akhmad Baddrudin Antonia Yenitia Aqli, Ahmad Aulia Fitri Aulia Fitri Aulia Fitri, Aulia Ayu Ahadi Ningrum ayu ahadi ningrum Bambang Lareno, Bambang Bayu Nugraha Bima Wicaksono Damayanti, Alfisah Dixky Dixky Elisa Fitriana Fatahulrahman, Maman Febriani, Wulandari Fitriansyah, Muhammad Gazali, Mukhaimy Hadyan, M Rais Hamdani Hamdani Haniffah Sri Rinjani Hudatul Aulia Ihdalhubbi Maulida Ihsanudin Indah Wulandari Jaya Hari Santoso Johan Wahyudi Johan Wahyudi, Johan Kamaruddin Kamarudin Kamarudin Kamarudin Kartika Kartika Liliana Swastina Lufila, Lufila M Samsul Hasbi M Samsul Hasmi Maman Fatahulrahman Mambang Mambang Fitriansyah Maria Ulfah Maulida, Ihdalhubbi Meila Izzana, Meila Melda Melda Miranda Miranda Muhammad Khairul Akbar Muhammad Noval Muhammad Riduan Syafi’i Muhammad Satrio Ayuba Muhammad Tantowi Jauhari Muhammad Zaini Bakri Muhammad Ziki Elfirman Muhammad Ziki Elfirman Muhammad Ziki Elfirman, Muhammad Ziki Muhammad Zulfadhilah Mukhaimy Gazali Mutmainah Mutmainah Nahdi Saubari Nalo Valentino Ningrum, Ayu Ahadi Nor Azizah Novita Sari Novriansyah, Irvan Nur Hafiz Ansari Nur Meilianti Maulida Nurhaeni Nurhaeni Prastya, Septyan Eka Putri Putri Putri Putri, Putri Rahmini Rahmini Reni Emiliya Ricardus A P, Ricardus A Risma Maulida Risma Risma Rismawati Rismawati Rizkian Muhammad Fikri Ropikah Ropikah Rudy Ansari Rudy Ansari Rudy Ansari, Rudy Sabella, Billy Samita, Mambang Sandro Nesta Pembriano Sa’adah Sa’adah Septian Eka Prastya Septyan Eka Prastya Septyan Eka Prastya Subhan Panji Cipta Susanti, NurAina Tasya Salsabila Theresia Kurniati Seran Tiara, Astia Rahma Tumanggor, Agustina Hotma Uli Winda Astria Nuansa Saputri Winda Astria Nuansa Saputri Windarsyah Windarsyah Wulandari Febriani Yulisa Suryana Yuslena Sari, Yuslena