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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) JURNAL TEKNIK
Dimar Pateman
Universitas Majalengka
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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IMPLEMENTATION OF AUTOMATIC GARBAGE BIN USING LINE FOLLOWER ROBOT BASED ON ARDUINO UNO MICROCONTROLLER METHOD Dimar Pateman; Neng Cahya Ningsih; Rizky Adin Adriansah; Dhea Maulida Rahma
Jurnal Teknik Vol 12, No 2 (2023): Juli - Desember 2023
Publisher : Universitas Muhammadiyah Tangerang

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A line follower robot-based automatic garbage collection system with Arduino Uno has been developed to improve efficiency and convenience in garbage collection. This system uses a robot controlled by Arduino Uno and equipped with sensors to identify and collect garbage automatically. A line follower robot is a robot that can follow a predetermined path. In this system, the predetermined path is a path that has been equipped with black lines as a guide for the robot. The robot is equipped with an infrared sensor to detect the black lines and follow the path precisely. The system is also equipped with a garbage sensing sensor attached to the robot. These sensors use technologies such as ultrasonic sensors or infrared sensors to detect the presence of garbage around the robot. When trash is detected, the robot will stop its movement and use a mechanical hand or suction system to pick up the trash. Arduino Uno acts as the main brain of this system. The Arduino Uno microcontroller controls the robot's movement based on inputs from the sensors installed. In addition, the Arduino Uno also manages the interaction with the garbage sensing sensors, processes the sensor data, and makes the necessary decisions for efficient garbage collection. The implementation of this system uses the Arduino platform and electronic components that are easy to find and affordable. Thus, this system can be implemented at an affordable cost and easy to develop. With this line follower robot-based automatic garbage can, it is expected to increase efficiency and convenience in garbage collection. This system can reduce human intervention in the waste collection process, reduce the time and effort required, and promote better and more efficient waste management.
SENTIMENT ANALYSIS OF GOVERNMENT ON TIKTOK AND X PLATFORMS WITH SVM AND SMOTE APPROACH Dimar Pateman; Tri Ferga Prasetyo; Harun Sujadi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6645

Abstract

This study aims to analyze public sentiment toward the government on TikTok and X (formerly Twitter) using the Support Vector Machine (SVM) algorithm optimized with the Synthetic Minority Over-sampling Technique (SMOTE). Data were collected through keyword-based scraping of posts containing the word “pemerintah” (government) and processed using standard NLP pre-processing techniques. Results show that SVM combined with SMOTE significantly improves classification accuracy from 61% to 76% on TikTok, and from 74% to 86% on X. Word cloud analysis confirms these findings: TikTok content tends to reflect neutral and positive sentiments, while X contains predominantly negative expressions. These differences highlight platform-specific public discourse characteristics. The findings suggest that public communication strategies should be tailored accordingly: TikTok for positive narrative and outreach, X for monitoring feedback and criticism. This approach demonstrates the effectiveness of machine learning-based sentiment analysis in supporting data-driven public policy communication.
Co-Authors Dhea Maulida Rahma Harun Sujadi Neng Cahya Ningsih Rizky Adin Adriansah Tri Ferga Prasetyo