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UJI KETERKAITAN FENOMENA KEDATANGAN KOMET 1P/HALLEY TAHUN 760 MASEHI DENGAN ISI PRASASTI DINOYO MELALUI PENDEKATAN ARKEOASTRONOMI Imandiharja, Ide Nada; Mochamad Ikbal Arifyanto
AMERTA Vol. 41 No. 1 (2023)
Publisher : Penerbit BRIN (BRIN Publishing)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/amt.2023.878

Abstract

Abstract. Testing The Connection between The Appearance of Comet 1P/Halley in 760 CE and The Contents of Dinoyo Inscription through The Archaeoastronomical Approach. Astronomical knowledge has been attached to the ancestors of the Indonesian people since ancient times. The assumption raised in this study is that the Dinoyo Inscription is a response from the Kanjuruhan community to the phenomenon of the arrival of comet 1P/Halley which was identified in the same year as the year the inscription was issued. This research was conducted from the point of view of archeoastronomy. In this study, three types of tests were carried out, namely tests in chronological, geographical, and cultural aspects. Tests on chronological and geographical aspects use inscription data reviewed with the Stellarium astronomy application. Meanwhile, testing on cultural aspects is carried out by interpreting the symbols contained in the contents of the inscriptions. The results of these tests are used to strengthen the argument about the existence of a relationship between the contents of the Dinoyo Inscription and the arrival of comet 1P/Halley. The lack of data hampers the validity of the arguments that have been developed. However, based on the results of the tests that have been carried out, the behavior of the Kanjuruhan people on the Dinoyo Inscription is a response to the phenomenon of the return of comet 1P/Halley in 760 CE. Keywords: Dinoyo Inscription, Comet 1P/Halley, Archaeoastronomy   Abstrak. Pengetahuan astronomi telah melekat pada nenek moyang bangsa Indonesia sejak zaman dahulu kala. Dugaan yang dimunculkan dalam penelitian ini adalah bahwa Prasasti Dinoyo merupakan respon masyarakat Kanjuruhan terhadap fenomena kedatangan komet 1P/Halley yang teridentifikasi pada tahun yang sama dengan tahun dikeluarkannya prasasti. Penelitian ini dilakukan dalam pendekatan arkeoastronomi. Dalam penelitian ini, tiga jenis pengujian dilakukan, yaitu pengujian dalam aspek kronologis, geografis, dan budaya. Pengujian pada aspek kronologis dan geografis menggunakan data prasasti yang ditinjau dengan aplikasi astronomi Stellarium. Sementara itu, pengujian pada aspek budaya dilakukan dengan menafsirkan simbol-simbol yang terdapat pada isi prasasti. Hasil dari pengujian tersebut digunakan untuk memperkuat argumen tentang adanya hubungan antara isi Prasasti Dinoyo dengan peristiwa kedatangan komet 1P/Halley. Kurangnya data menghambat validitas argumen yang telah disusun. Namun demikian, berdasarkan hasil pengujian yang telah dilakukan, perilaku Masyarakat Kanjuruhan pada Prasasti Dinoyo merupakan respon atas fenomena kedatangan kembali komet 1P/Halley pada tahun 760 Masehi. Kata kunci: Prasasti Dinoyo, Komet 1P/Halley, Arkeoastronomi.
Prediction of Electricity Bill Payment Delays for Customers Using a Machine Learning Approach Dyah Puspita Sari Nilam Utami; Mochamad Ikbal Arifyanto
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/tc81dq58

Abstract

Electricity is a vital necessity in modern life, and the management of electricity bill payments is crucial for the continuity of services and the financial stability of electricity providers like PLN. Identifying potential delays in payments by customers is a strategic step to enable effective preventive actions. This study aims to develop a prediction model for payment delays using two machine learning methods, namely Random Forest Regressor and Bidirectional Long Short-Term Memory, based on historical customer data from the period of 2018–2023. The research process includes data preprocessing to ensure consistency and accuracy, dividing the data into training and testing sets, and training the models using both algorithms. The results show that the Random Forest model performed the best in recognizing long-term statistical patterns with the lowest Mean Absolute Error value of 0.00387 on the 12-month Moving Average feature, as well as optimal efficiency with a number of trees between 100–200. On the other hand, the Bidirectional LSTM model demonstrated competitive ability in capturing temporal patterns of sequential data, with the best configuration yielding a validation error value of 0.243 and the highest validation accuracy of 56.2%. Both models are effective in predicting customers who are likely to delay their electricity bill payments. This research provides significant contributions to PLN in supporting data-driven decision-making and facilitating mitigation strategies such as early notifications or rescheduling payment plans to reduce the risk of overdue payments.