Garuda - Garba Rujukan Digital

Ade Putera Kemala

Bina Nusantara University

Author-ID : 3682939

Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

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Pengembangan Smart Air Condition Control Menggunakan Platform Blynk Berbasis Mikrokontroler ESP8266 dan Sensor DHT11 Ade Putera Kemala; Muhammad Edo Syahputra; Henry Lucky; Said Achmad
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 4 No. 1 (2022): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v4i1.8072

Air Conditioners (AC) are increasingly used to get the room temperature as desired, starting from home use to keep the room cool and enjoyable, to specific room like server rooms or ATM which are focused on keeping the room cool in order to keep the equipment cool. The role of AC is quite important to maintain room temperature, and its increasing use has led to the growing need for users to control the AC. The Internet of Things allows users to remotely control air conditioners using the gadgets used and get real time room temperature information. The AC control system based on Internet of Things (IoT) utilizes an internet connection to monitor room temperature and control AC remotely. The devices used are DHT11 as a temperature sensor to get room temperature, Infrared Receiver to read the code sent by the remote AC, Infrared Transmitter to send commands to the AC in the room, and ESP8266 as a microcontroller and a link to the internet. The IoT platform used is Blynk which has the ability to access the microcontroller from the user's gadget. The tests are running on room air conditioners such as the Panasonic CS-PC18PKP series, the Panasonic CS-YN18TKP series and the Samsung AR09TGHQASINSE series. The test results showed that the room air conditioner was successfully controlled, and the room temperature was read in real time via an android smartphone.

Analysis of Indonesian Language Dataset for Tax Court Cases: Multiclass Classification of Court Verdicts Ade Putera Kemala; Hafizh Ash Shiddiqi
Jurnal Riset Informatika Vol. 5 No. 3 (2023): June 2023
Publisher : Kresnamedia Publisher

Tax is an obligation that arises due to the existence of laws, creating a duty for citizens to contribute a certain portion of their income to the state. The Tax Court serves as a judicial authority for taxpayers seeking justice in tax disputes, handling various types of taxes on a daily basis. This paper presents an analysis of an Indonesian language dataset of tax court cases, aiming to perform multiclass classification to predict court verdicts. The dataset undergoes preprocessing steps, while data augmentation using oversampling and label weighting techniques address class imbalance. Two models, bi-LSTM and IndoBERT, are utilized for classification. The research produced a final result of model with 75.83% using IndoBERT model. The results demonstrate the efficacy of both models in predicting court verdicts. This research has implications for predicting court conclusions with limited case details, providing valuable insights for legal decision-making processes. The findings contribute to the field of legal data analysis, showcasing the potential of NLP techniques in understanding and predicting court outcomes, thus enhancing the efficiency of legal proceedings.

Clickbait Detection in Indonesia Headline News Using Indobert and Roberta Muhammad Edo Syahputra; Ade Putera Kemala; Dimas Ramdhan
Jurnal Riset Informatika Vol. 5 No. 3 (2023): June 2023
Publisher : Kresnamedia Publisher

This paper explores clickbait detection using Transformer models, specifically IndoBERT and RoBERTa. The objective is to leverage the models specifically for clickbait detection accuracy by employing balancing and augmentation techniques on the dataset. The research demonstrates the benefit of balancing techniques in improving model performance. Additionally, data augmentation techniques also improved the performance of RoBERTa. However, it resulted differently for IndoBERT with slightly decreased performance. These findings underline the importance of considering model selection and dataset characteristics when applying augmentation. Based on the result, IndoBERT, with a balanced distribution, outperformed the previous study and the other models used in this research. This study used three dataset distribution settings: unbalanced, balanced, and augmented with 8513, 6632, and 15503 total data counts, respectively. Furthermore, by incorporating balancing and augmentation techniques, the research surpasses previous studies, contributing to the advancement of clickbait detection accuracy, contributing to the advancement of clickbait detection accuracy with 95% accuracy in f1-score with unbalanced distribution. However, the augmentation method in this study only improved the RoBERTa model. Moreover, performance might be boosted by gathering more varied datasets. This work highlights the value of leveraging pre-trained Transformer models and specific dataset-handling techniques. The implications include the necessity of dataset balancing for accurate detection and the varying impact of augmentation on different models. These insights aid researchers and practitioners in making informed decisions for clickbait detection tasks, benefiting content moderation, online user experience, and information reliability. The study emphasizes the significance of utilizing state-of-the-art models and tailored approaches to improve clickbait detection performance.

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