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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) JSiI (Jurnal Sistem Informasi) 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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Abstract

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.
Prediksi Perubahan Luas Perkebunan Aren di Jawa Barat Berbasis Geospasial dengan Algoritma ARIMA dan Machine Learning Dadan Zaliluddin; Asep Dian Heryadiana; Dimar Pateman
Jurnal Sistem Informasi Vol. 13 No. 1 (2026)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/djx30932

Abstract

Aren palm (Arenga pinnata) plays a significant role as an economic commodity and a renewable energy source in West Java, Indonesia. However, fluctuations in plantation areas caused by land use change, climate variability, and socio-economic factors have created challenges for sustainable management. Accurate prediction of aren plantation area dynamics is required to support decision-making and policy design for renewable energy development and environmental sustainability.This study aims to predict changes in aren plantation areas in West Java using a combination of Autoregressive Integrated Moving Average (ARIMA) for time-series forecasting and Machine Learning algorithms for enhanced prediction accuracy. Historical data of aren plantation areas from 2013 to 2023 were collected from official government databases. ARIMA was applied to model temporal trends, while Machine Learning approaches such as Random Forest and Long Short-Term Memory (LSTM) were employed to capture non-linear relationships and integrate external factors such as rainfall, soil characteristics, and urbanization patterns. In addition, a geospatial approach using Geographic Information System (GIS) was adopted to visualize spatial changes in plantation areas.Preliminary results indicate that ARIMA successfully models short-term trends with relatively low forecasting errors (RMSE < 15%). Machine Learning models demonstrate the potential to improve robustness and predictive accuracy by incorporating multidimensional variables. The integration of spatial visualization enables stakeholders to identify high-risk regions for land conversion and areas with strong potential for sustainable aren cultivation. The findings of this research provide a foundation for developing a decision support system to enhance sustainable plantation management and bioethanol policy planning in West Java. The proposed predictive framework contributes not only to the field of computational forecasting but also to the strategic alignment of renewable energy development with local socio-economic priorities. Keywords: ARIMA, Machine Learning, Geospatial, Aren Plantation, Forecasting  
Co-Authors Asep Dian Heryadiana Dadan Zaliluddin Dhea Maulida Rahma Harun Sujadi Neng Cahya Ningsih Rizky Adin Adriansah Tri Ferga Prasetyo