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All Journal Jurnal Ilmu Komputer, Teknologi Dan Informasi Pascal: Journal of Computer Science and Informatics
Rangkuti, Fiqri Hidayat
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Library & Information Science Social Sciences Other

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Classification of Types of Crimes Against Human Physique Using the K-Means Clustering Method Fadillah, Rizkah; Rangkuti, Fiqri Hidayat; Siregar, Anggi Jaya Maulana; Ananda, Rizky; Syahrizal, Muhammad
Jurnal Ilmu Komputer, Teknologi Dan Informasi Vol 3 No 1 (2025): Januari 2025
Publisher : CV. Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/jurikti.v3i1.192

Abstract

Human physical crimes are unlawful acts and prohibited by the rule of law, which can harm or damage the body of others. This study aims to examine the number of groupings of types of crimes against human physique in 2019 to 2020 in all areas of East Nusa Tenggara province. To do this, we use the K-Means Clustering method to group the types of physical crimes against humans. The data used came from the Central Statistics Agency of East Nusa Tenggara province. The K-Means method is one of the non-hierarchical data clustering methods that seeks to partition data into the form of one or more clusters/groups. After the application of the K-Means algorithm in the grouping of types of crimes against human bodies in 2019 to 2020 in the East Nusa Tenggara region, there are 3 centroids, C1 for areas with low crimes, C2 for areas with moderate crimes and C3 for areas with high crimes. The initial centroid value is determined randomly and then for the next centroid is adjusted to the result of the calculation of the closest distance (minimum). The final results obtained are areas with low crime totaling 13 regions, namely East Sumba, Lembata, Sikka, Ende, Ngada, Manggarai, Rote Ndao, West Manggarai, Central Sumba, Southwest Sumba, Nagekeo, East Manggarai, and Sabu Raijua. There are 7 areas with moderate crimes, namely West Sumba, Kupang, South Central Timor, North Central Timor, Belu, Alor, and East Flores. As for the area with high crime, there is 1 area, namely Kupang City.
Application of Natural Language Processing Based on Machine Learning and IoT Data Pratiwi, Adellia; Lubis, Erliani Syahputri; Rangkuti, Fiqri Hidayat; Suyudi, M. Karim; Jefry, Togap Aland
Pascal: Journal of Computer Science and Informatics Vol. 3 No. 01 (2025): Pascal: Journal of Computer Science and Informatics
Publisher : Devitara Innovations

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Abstract

The development of the Internet of Things (IoT) and Natural Language Processing (NLP) has opened new opportunities to build intelligent monitoring systems capable of processing multiformat data simultaneously. This study aims to apply machine learning–based NLP methods to analyze IoT data in order to improve the accuracy of real-time environmental condition detection. The dataset used consists of temperature and humidity parameters collected from IoT sensors, as well as textual data in the form of environmental condition reports. The textual data are processed through tokenization, lowercasing, stopword removal, stemming, and lemmatization, followed by feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF). The Naive Bayes algorithm is employed to classify conditions into Normal, Warning, and Critical based on a combination of sensor data and textual features. The experimental results show that integrating NLP with IoT data increases classification accuracy from 82% (using sensor data alone) to 91% and enables automatic, real-time condition detection. This study demonstrates that multiformat data integration through NLP and machine learning can enhance the effectiveness of intelligent monitoring systems and can be implemented in environmental, industrial, healthcare, and security domains, thereby making a significant contribution to data-driven decision-making.
Co-Authors Ananda, Rizky Fadillah, Rizkah Jefry, Togap Aland Lubis, Erliani Syahputri Muhammad Syahrizal Pratiwi, Adellia Siregar, Anggi Jaya Maulana Suyudi, M. Karim