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All Journal TIN: TERAPAN INFORMATIKA NUSANTARA Bulletin of Computer Science Research Journal of Informatics Management and Information Technology
Kusnandar Kusnandar
STMIK Widya Cipta Dharma, Samarinda
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Penerapan Internet of Things (IoT) Untuk Monitoring Kondisi Tanah Gembur Pada Tanaman Hias Pucuk Merah Secara Real-Time Anggit Wahyu Edwinata; Kusnandar Kusnandar; Presa Taruna Oliver
Journal of Informatics Management and Information Technology Vol. 6 No. 2 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jimat.v6i2.1099

Abstract

This research aims to develop a real-time Internet of Things (IoT) based loose soil pH monitoring system for application to red shoots ornamental plants. The system designed in this research combines a soil pH sensor to measure soil acidity levels, a NodeMCU ESP32 microcontroller as a data processor, a 16x2 I2C LCD to display local information, and provides real-time data to users through the Blynk application. The purpose of developing this system is that users can easily monitor soil conditions in real-time and take necessary actions to increase plant productivity, especially red shoots ornamental plants that require special attention to their soil conditions. This research is expected to be a continuation of previous research which is still in the design stage of a soil pH monitoring system. This development is focused on a different research object, namely loose soil pH in red shoots ornamental plants, so it is expected to be able to produce a more practical, applicable loose soil pH monitoring system, and provide a real contribution to the management of red shoots ornamental plants effectively and efficiently.
Analisis Komentar Youtube Terhadap Polemik Ijazah Presiden Ke 7 Indonesia Menggunakan Support Vector Machine Ignasius Aditya Anggoro Putra; Salmon Salmon; Kusnandar Kusnandar
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.883

Abstract

This study aims to analyze public sentiment toward the controversy surrounding President Joko Widodo’s academic credentials by examining user comments on YouTube. A total of 20,294 comments were collected and processed through text cleaning, normalization, tokenization, stopword removal, and stemming. Sentiment labels were assigned using a lexicon-based approach, producing positive, negative, and neutral categories. The experimental results indicate that the combination of SVM, TF-IDF, and SMOTE achieved strong classification performance, with an accuracy of 86.87%. The model demonstrated better performance in identifying negative and neutral sentiments, while some positive sentiments tended to be misclassified as neutral. Overall, this study shows that sentiment analysis based on YouTube comments can serve as an effective approach for mapping public opinion on socio-political issues in an automated and large-scale manner. Feature extraction utilized Term Frequency–Inverse Document Frequency (TF-IDF), and sentiment classification was performed using a Support Vector Machine (SVM). The model achieved an accuracy of 86.87% and a macro F1-score of 0.87, indicating that the integration of TF-IDF, SMOTE, and SVM is effective for large-scale sentiment classification of YouTube comments related to socio-political issues.
Analisis Komentar Youtube Terhadap Kebijakan Bebas Impor Oleh Pemerintah Pusat Menggunakan Support Vector Machine Ignasius Aditya Anggoro Putra; Salmon Salmon; Kusnandar Kusnandar
Bulletin of Computer Science Research Vol. 6 No. 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i3.995

Abstract

YouTube has become an important platform for expressing public opinion on government policies, including the free import policy. This study aims to analyze the sentiment of YouTube user comments regarding the free import policy using the Support Vector Machine (SVM) algorithm. The data were collected through web scraping using the YouTube Data API v3 from a Kompas.com video, resulting in 3,267 raw comments. The research stages include text preprocessing, feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF), lexicon-based sentiment labeling, and sentiment classification using SVM. To address data imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied. Model performance was evaluated using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results show that the SVM model achieved an accuracy of 77.00% without tuning and 75.15% after hyperparameter optimization, with improved balance across sentiment classes. These findings indicate that SVM is effective for sentiment classification of YouTube comments.
Sistem Pemantauan dan Pengawasan Keamanan Siswa Berbasis Wearable IoT di Lingkungan Sekolah Madrasah Ibtidaiyah Aqmal Fahat Syafiq Putra; Eka Arriyanti; Kusnandar Kusnandar
TIN: Terapan Informatika Nusantara Vol 7 No 3 (2026): August 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i3.10348

Abstract

Student safety within school environments remains a critical concern, as manual supervision is constrained in its capacity to monitor students’ whereabouts and conditions rapidly and continuously. This study contributes by providing a technology-based medium that integrates IoT-enabled wearable devices, real-time data communication, and a centralized monitoring dashboard to strengthen school surveillance systems, while simultaneously addressing a gap in the literature concerning the implementation of wearable IoT in the context of Indonesian madrasah ibtidaiyah (elementary Islamic schools). The study aims to design and develop a wearable IoT-based student safety monitoring and supervision system capable of tracking students’ locations and basic physiological conditions in real time and transmitting emergency notifications to school authorities. The research was conducted at MI Al Azhar, Samarinda, East Kalimantan, from 20 July to 25 August 2026, involving six respondents: one principal, one teacher, one student affairs coordinator, one school IT technician, and two sixth-grade students. A Research and Development (R&D) approach employing a prototype model was utilized, encompassing stages of needs analysis, design, implementation, testing, and evaluation. Findings indicate that the system is capable of monitoring students’ conditions and whereabouts in real time, detecting potential risks via wearable devices, displaying movement histories and student status on a dashboard, and delivering emergency notifications more rapidly. The system also demonstrably enhances the effectiveness of student safety supervision in schools, as information can be monitored centrally and responses to incidents become more targeted. Based on testing outcomes and respondent feedback, the system is deemed feasible for use as an innovative solution to support student safety within school environments. The study’s contributions include the provision of an adaptable wearable IoT implementation framework for other schools, the strengthening of the empirical basis regarding the effectiveness of student monitoring systems at the elementary level, and the formulation of technical design recommendations and operational policies to support scalability and secure data governance.
Co-Authors Anggit Wahyu Edwinata Aqmal Fahat Syafiq Putra Eka Arriyanti Ignasius Aditya Anggoro Putra Presa Taruna Oliver Salmon Salmon