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All Journal Journal of Telematics and Informatics JUITA : Jurnal Informatika Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Jurnal Teknik Komputer AMIK BSI Cakrawala : Jurnal Humaniora Bina Sarana Informatika Paradigma Jurnal Sains dan Informatika Jurnal Teknik Informatika STMIK Antar Bangsa Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Jurnal Infortech Akrab Juara : Jurnal Ilmu-ilmu Sosial Jurnal Sistem Informasi IAIC Transactions on Sustainable Digital Innovation (ITSDI) Computer Science (CO-SCIENCE) INSANtek – Jurnal Inovasi dan Sains Teknik Elektro Conten : Computer and Network Technology Indonesian Journal of Networking and Security - IJNS Jurnal Insan Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi Media Teknologi dan Informatika Komputasi : Jurnal Ilmiah Ilmu Komputer dan Matematika Jurnal Teknik Informatika dan Teknologi Informasi Jurnal Infortech
Rachmat Adi Purnama
Universitas Bina Sarana Informatika
Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Social Sciences Other

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Energy Efficiency Optimization in Smart Buildings Using NodeMCU and Cloud Monitoring Sandro Arnesto; Muhammad Rizqi Saputra; Sujiliani Heristian; Jordy Lasmana Putra; Musriatun Napiah; Rachmat Adi Purnama
Jurnal Infortech Vol. 8 No. 1 (2026): June 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/infortech.v8i1.12819

Abstract

Inefficient energy management in modern building infrastructure is often caused by a lack of real-time visibility of power consumption and reliance on manual controls that are unresponsive to environmental dynamics. This study proposes the design of an integrated Internet of Things (IoT)-based Smart Building system for energy efficiency optimization. This system was developed using the NodeMCU ESP8266 microcontroller architecture, which orchestrates DHT11 and ACS712 sensors for the acquisition of precise data related to environmental parameters and electrical loads. The main contribution of this research lies in the implementation of a non-blocking programming algorithm to ensure stable sensor readings without interruption, as well as the application of a hysteresis control method in air conditioning (AC) units with thresholds of 30°C (ON) and 28°C (OFF). This hysteresis approach is designed to mitigate compressor short-cycling, which wastes energy. System testing shows that the integration of the RemoteXY mobile interface and OLED display is capable of presenting data telemetry with a response latency of less than 2 seconds. This system has proven effective in providing stable hybrid (automatic and manual) control, offering a low-cost but significant solution for reducing the operational energy consumption of buildings.
Analyzing Public Sentiment Toward Makanan Bergizi Gratis Program Using Machine Learning Musriatun Napiah; Sujiliani Heristian; Mugi Raharjo; Rachmat Adi Purnama
Computer Science (CO-SCIENCE) Vol. 6 No. 1 (2026): January 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/co-science.v6i1.10445

Abstract

Makanan Bergizi Gratis (MBG) program is a strategic initiative of the Indonesian government to improve the nutritional quality of schoolchildren. This research seeks to examine public sentiment regarding the MBG program by leveraging 10,000 tweets obtained from Kaggle. The method used combines Natural Language Processing (NLP) and Machine Learning approaches, several algorithms such as Logistic Regression, Support Vector Machine (SVM), Random Forest, Naive Bayes, XGBoost, and LightGBM were tested to compare classification performance. The dataset contains a collection of public reviews categorized into three sentiment classes: positive, negative, and neutral. The analysis process includes text cleaning, tokenization, stopword removal, and stemming to obtain a cleaner text representation. Text features were then extracted using the Term Frequency–Inverse Document Frequency (TF-IDF) method. The results showed that the Logistic Regression 97% with an F1-score of 0.9552 models showed the most optimal performance. Sentiment analysis revealed 65% positive responses, 25% neutral, and 10% negative, with the dominant keywords being “nutrisi,” “sehat,” “anak sekolah,” and “gratis.” The results visualization, in the form of a Word Cloud and a bar chart, indicate that public opinion tends to be positive towards the implementation of the MBG program, particularly regarding improving the nutrition of schoolchildren. This research is expected to provide input for policymakers in evaluating public perceptions of the implementation of food-based social programs.
Komparasi SVM dan Random Forest Berbasis Histogram Warna untuk Deteksi Penyakit Anggur Muhammad Faqihuddin; Rachmat Adi Purnama
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 8 No 1 (2026): Januari 2026
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i1.2340

Abstract

The decline in grape (Vitis vinifera) productivity is often caused by leaf diseases such as Black Rot, which are challenging to detect accurately through manual visual inspection The key point of this research is to compare the performance of two Machine Learning classification algorithms, namely Support Vector Machine (SVM) and Random Forest, to identify the most optimal model for disease detection. The methodology employs digital image processing with Histogram Color (HSV) feature extraction, which is chosen for its efficiency in representing color changes caused by infection. The grape leaf disease image dataset was classified and evaluated. The comparative results demonstrate that Random Forest achieved the highest accuracy of 95.32%, slightly surpassing SVM which reached 94.48%. These findings prove that both algorithms perform excellently, but Random Forest is more recommended for this dataset due to its superior robustness in accurately predicting disease classes.
Co-Authors Ade Christian Ade Setiawan Adi Tri Laksono Putra Aditya, Tri Putra Agus Tri Purnomo Agustinus Aditya Bintara Ahmad Setiadi Andy Rafsandi Anton Atrinawati, Lovinta Happy Ayi Rohmayati - STMIK Nusa Mandiri Jakarta Baginda Oloan Lubis Bagus Adi Wibowo Dedi Triyanto Dennis Gunawan, Dennis DENY KURNIAWAN Dyas, Pramudya Widyastama Ehon Abdulmanan Eka Kusuma Pratama Eka Ria Nugraha El Faraboy Endri Maulana Fadhil Naufal Shodiq Faqihuddin, Muhammad Firmansyah Firmansyah Firmansyah Firmansyah Firmansyah Firmansyah Firmansyah Firmansyah Firmansyah Firmansyah Gregorius Bayu Listyoputro Hanggoro Aji Al Kautsar Jordy Lasmana Putra Kaisar Ages Querio Kartika Handayani Krisnavaro Raihananta Mahatma Mahesa Mochamad Rizky Ainur Ridho Mochamad Wahyudi Mugi Raharjo Mugi Raharjo Muhammad Faqihuddin Muhammad Rifki Zhaki Muhammad Rizqi Saputra Musriatun Napiah Mustofa Mustofa Pedro Lucio Parera Prima Cop Rachmawati Darma Astuti Raharjo, Mugi Rame Santoso Rian Septian Anwar Samuel Orief Rosario Sandro Arnesto Sari Dewi Sari Dewi Sujiliani Heristian Sumanto Sumanto Teuku Vaickal Rizki irdian Tri Putra Aditya Veti Apriana, Veti Waeisul Bismi Wahyutama Fitri Hidayat wati erawati Yesni Malau