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Made Adi Paramartha Putra
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Smart Techno (Smart Technology, Informatic and Technopreneurship)
Published by Universitas Primakara
ISSN : -     EISSN : 25410679     DOI : 10.59356
Core Subject : Science,
Computer Science & IT
Jurnal Smart-Techno merupakan jurnal ilmiah dan bersifat terbuka untuk menampung hasil penelitian ilmiah. Jurnal ini bersifat elektronik dengan harapan memungkinkan penyebaran informasi ilmiah tanpa batas ke seluruh wilayan Indonesia. Secara garis besar, Jurnal Smart-Techno menampung hasil karya ilmiah yang berasal dari penelitian di bidang Smart Technology, Informatics and Technopreneurship. Jurnal online ini terbit 2 (dua) kali dalam setahun yaitu pada bulan Februari dan September secara berkala. Adapun topik-topik yang dapat diterbitkan melalui karya ilmiah ini meliputi bidang-bidang (namun tidak terbatas pada): Technopreneurship Digital Start-up Technology Innovation Virtual Reality Data Mining Data Warehousing Matematika Diskrit Teori Graph Artificial Intelligence Natural Language Processing Robotic Image Processing Microcontroller User Experience (UX) Mobile Computing Distributed/Parallel Computing Communication System Network Security Wireless Communication Internet of Things Smart Home Smart City Smart Village Smart System E government E learning
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 104 Documents
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Comparative Analysis of Naïve Bayes and SMOTE-Based Long Short-Term Memory (LSTM) for Electric Vehicle Sentiment Analysis on YouTube Gimnastiar, Rayhan; Lestari, Fania Indah; Prasetyo, Nadhif Daniswara; Prasetyo, Eko; Setyatama, Fardanto
Smart Techno (Smart Technology, Informatics and Technopreneurship) Vol. 8 No. 2 (2026)
Publisher : Primakara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59356/smart-techno.v8i2.230

Abstract

The transition to electric vehicles in Indonesia has generated diverse public opinions on social media. Most previous sentiment analysis studies have tended to employ a single classification method without in-depth comparison and have overlooked the issue of extreme data imbalance, which can introduce bias into classification models. Furthermore, a research gap remains regarding the effectiveness of complex deep learning models compared with simpler statistical models when applied to medium-dimensional Indonesian-language opinion texts. This study aims to address this gap by conducting a comparative evaluation of model performance. The dataset consists of 1,512 unstructured public opinion comments collected from YouTube, distributed across three sentiment classes: 868 negative, 503 positive, and 141 neutral comments. To address the class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. The study then compares the statistical Naïve Bayes method using TF-IDF weighting with the Deep Learning Long Short-Term Memory (LSTM) method using Word Embedding. Evaluation using a Confusion Matrix demonstrates that Naïve Bayes outperformed LSTM and exhibited greater stability in sentiment classification for this dataset, achieving an accuracy of 63.04%, compared with 57.43% for the LSTM model.
Sentiment Analysis Using Machine Learning for Evaluating Public Perception to Support Accountability in Vocational Higher Education Saputra, Putu Satya; Sudiatmika, I Putu Gede Abdi; Dewi, Ni Ketut Utami
Smart Techno (Smart Technology, Informatics and Technopreneurship) Vol. 8 No. 2 (2026)
Publisher : Primakara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59356/smart-techno.v8i2.240

Abstract

This study examines public sentiment toward vocational higher education in Indonesia using 2,014 YouTube comments from 79 videos and evaluates Naïve Bayes, Support Vector Machine (SVM), and Random Forest classifiers against human-validated labels. A vocational higher-education lecturer and an Indonesian-language lecturer annotated all comments independently. They initially agreed on 1,958 comments (97.22%; Cohen’s κ = 0.945), and resolved 56 disagreements by consensus. The final labels comprised 1,227 neutral (60.92%), 702 positive (34.86%), and 85 negative comments (4.22%). A stratified 80/20 split was created before any balancing; the 403-comment test set retained the natural distribution. On the training partition, unigram versus unigram, bigram features and no balancing, class weighting, or training-only undersampling were compared using repeated stratified five-fold cross-validation (two repeats). SVM with unigram TF-IDF and balanced class weights achieved the best cross-validated macro-F1 among the three selected model configurations (0.650 ± 0.054). On the untouched test set, SVM obtained 76.67% accuracy and 0.655 macro-F1, and recovered 6 of 17 negative comments (35.29% recall). Random Forest produced slightly higher accuracy (77.17%) but only 5.88% negative recall, while Naïve Bayes detected no negative cases. The results provide a reproducible screening pipeline that may complement institutional accountability mechanisms, but the limited recovery of negative feedback precludes its use as an autonomous early-warning system.
Toward a Distributed Cardiac Auscultation Simulator: Feasibility of MQTT-Synchronized Multi-Node Audio Playback Raharja, I Kadek Agus Wahyu; Adi Pratama Putra P.
Smart Techno (Smart Technology, Informatics and Technopreneurship) Vol. 8 No. 2 (2026)
Publisher : Primakara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59356/smart-techno.v8i2.241

Abstract

Cardiac auscultation training traditionally relies on passive manikins or direct patient contact, both of which limit exposure to varied heart sound patterns at multiple anatomical points and remain costly to acquire and maintain. This study investigated the feasibility of the Message Queuing Telemetry Transport (MQTT) protocol, combined with Network Time Protocol (NTP) based timestamp synchronization, for coordinating audio playback across distributed low-cost microcontroller nodes as a first step toward a distributed cardiac auscultation simulator. A prototype consisting of two ESP32 microcontrollers, each driving two independent I2S audio channels through MAX98357A amplifiers, was connected to a Mosquitto MQTT broker running on a laptop acting as a temporary central controller. Three experimental scenarios were conducted: baseline synchronized playback on a single channel per node, synchronized playback on a second channel per node, and simultaneous two-channel playback within a single node. Across 25 to 30 valid trials per scenario, median command latency ranged from 44.5 to 124 milliseconds, and median inter-node synchronization error ranged from 243.5 to 369 milliseconds. Simultaneous two-channel playback within one node revealed a median 9.7-second delay between channels, attributed to blocking audio playback in the current firmware. These results demonstrate that MQTT-based synchronization is technically feasible on low-cost hardware, though the achieved accuracy falls short of a ±50 millisecond target, indicating specific firmware optimizations needed to improve synchronization precision.
Smart Aquarium Maintenance System with Solar-Powered Energy Supply Yoga Divayana; Linawati; Agung, I. G. A. P. Raka; Janardana, I. G. N.; Khrisne, D. C.; Saputro, D. S.; Halim, S.; Kurniawan, R. B.
Smart Techno (Smart Technology, Informatics and Technopreneurship) Vol. 8 No. 2 (2026)
Publisher : Primakara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59356/smart-techno.v8i2.246

Abstract

Aquarium maintenance needs constant monitoring of water quality and all its equipment to keep fish and other aquatic life healthy. Manual maintenance is inefficient, since it requires routine checks and control of several parameters at the same time. This study designed a smart aquarium maintenance system to monitor water quality, controls equipment automatically, and draws its power from a renewable energy source. The system used an ESP32 S3 microcontroller as its main controller, integrated with pH, water temperature, turbidity, and ultrasonic sensors. A water pump, a relay module, a servo motor, a Peltier-based cooling module, and a heater worked together to keep the aquarium environment within the desired range. Then a solar power plant charged the battery through an MPPT controller. The Android application, named AquaKeep, was chosen to connect to the hardware through Firebase Realtime Database and lets the user monitor and control the system remotely. Testing showed that the temperature, ultrasonic, and pH sensors reach average errors of 1.13%, 1.98%, and 1.70%, all within the 5% threshold set for this study. The automatic feeding system reached a 100% success rate across eight test scenarios. The cooling system lowered the water temperature by an average of 2.9°C every 10 minutes, and the solar power supply kept the system running for 17 hours without sunlight, exceeding the 12-hour target. These results showed that the developed system works accurately, automatically, and sustainably in keeping the aquarium environment stable.

Page 11 of 11 | Total Record : 104

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