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Sentiment Analysis of Twitter Data on the 2024 Indonesian Presidential Election Using BERT Roihan, Ahmad; Atmojo, Tito Tri; Wardoyo, Rizky A; Saputra, Muhamad Stabil Tanwin
CCIT (Creative Communication and Innovative Technology) Journal Vol 18 No 1 (2025): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v18i1.3210

Abstract

Social media platforms, particularly Twitter, are frequently employed by individuals to articulate their opinions on various subjects in textual form. The proliferation of viewpoints from diverse sources can influence public perceptions on these topics. The greater the popularity of a topic, the more abundant the opinions generated. Currently, the most widely discussed topic is the 2024 Indonesian presidential election. Sentiment analysis, or opinion mining, is an academic discipline that examines sentiments towards a given entity, while text mining involves the extraction of information through processing, classifying, and analyzing extensive datasets. This study will utilize data crawling techniques to gather data from Twitter which will subsequently undergo preprocessing and cleaning. Following this, the cleaned data will be classified by sentiment (positive, negative, or neutral) using a pre-trained language model (BERT) and Natural Language Toolkit (NLTK). The classified data will then be visualized with tools such as Matplotlib and Wordcloud to elucidate the data distribution.
Digital Innovation in Smart Waste Sorting Using Renewable Energy for Sustainable Startups Rahardja, Untung; Santoso, Nuke Puji Lestari; Oganda, Fitra Putri; Madani, Muchlishina; Saputra, Muhamad Stabil Tanwin
Startupreneur Business Digital (SABDA Journal) Vol. 5 No. 1 (2026): Startupreneur Business Digital (SABDA)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/sabda.v5i1.1063

Abstract

The accumulation of inorganic waste in urban environments requires inno- vative technological solutions that support the Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities) and SDG 12 (Responsible Consumption and Production). Smart waste management systems integrating Artificial Intelligence (AI) and the Internet of Things (IoT) have emerged as promising digital innovations to improve waste sorting efficiency. This study presents the development of a smart waste sorting system called Orange Box, designed to support sustainable startup initiatives in environmental technology. A major challenge in deploying IoT-based devices in outdoor public areas is the limited availability of conventional electrical infrastructure, where reliance on extension cables is inefficient and potentially unsafe. Therefore, this research aims to design and evaluate an independent off-grid electrical system based on renewable energy to ensure continuous operation of the device. The proposed system utilizes solar panels as the primary energy source, with energy conversion and distribution managed through a 500W inverter and a 20A Power Supply Unit (PSU) that supplies power to a Raspberry Pi 5–based control system. Experimental measurements indicate that the system operates with an average power consumption of approximately 10–12W and reaches a peak load of 17.17W during active waste sorting operations. The estimated daily energy consumption ranges from 288Wh to 338Wh when considering inverter efficiency. These findings demonstrate that integrating renewable energy infrastructure with IoT-based smart waste sorting technology represents a viable digital innovation to support sustainable startups while contributing to SDG 7 (Affordable and Clean Energy).