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All Journal Jurnal Ilmiah KOMPUTASI Journal of Information System, Applied, Management, Accounting and Research REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Jurnal Restikom : Riset Teknik Informatika dan Komputer Bulletin of Information Technology (BIT) Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB IT For Society : Journal of Information Technology SAINTEK
Muhammad Makmun Effendi
Universitas Pelita Bangsa
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Industrial & Manufacturing Engineering

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Journal : Bulletin of Information Technology (BIT)

 1 
Analisis Sentimen Masyarakat Indonesia Dalam Konflik Rusia-Ukraina Di Twitter Muhammad Makmun Effendi; Zaenal Mustofa; Ahmad Turmudi
Bulletin of Information Technology (BIT) Vol 3 No 4: Desember 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v3i4.418

Abstract

Russia is a big superpower that has power and plays an important role in international politics, while Ukraine, a former Soviet Union country, became independent on December 1, 1991. In 2014 there was also a conflict between Russia and Ukraine which was a meeting of superpowers. In February 2022, Russia resumed armed conflict with Ukraine. The Russia-Ukraine conflict has garnered many responses in the form of tweets from various circles of society, resulting in many traces of tweets containing public opinion on the Russia- Ukraine conflict on Twitter social media. This study aims to determine the results of the positive or negative impact of the conflict between Russia and Ukraine on the economy in Indonesia and to determine the results of accuracy, precision, recall resulting from the use of the Naïve Bayes method and feature selection Particle Swarm Optimization in RapidMiner Studio software. Particle Swarm Optimization is an optimization method inspired by the behavior of fish and poultry flocks in search of food sources. The preprocessing stage in this research includes cleansing, removing duplicates, data selection, normalization, case folding, tokenizing, filtering, stopwords, stemming, and labeling. The classification results obtained by 55.11% of twitter users commented negatively and 44.89% of twitter users commented positively about the conflict between Russia and Ukraine. By looking at the results of the sentiment analysis data above, where the number of Twitter users who commented negatively is higher, it can be concluded that the Indonesian people are worried about the surge in prices of basic daily necessities, as indicated by one of the tweets commenting on rising oil and gas prices BBM
Prediksi Penyakit Jantung Dengan Algoritma Regresi Linier Agung Wijayadhi; Muhammad Makmun Effendi; Sugeng Budi Rahardjo
Bulletin of Information Technology (BIT) Vol 4 No 1: Maret 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v4i1.463

Abstract

In this study, we evaluated the ability of a linear regression algorithm to predict heart disease risk in individuals. We use data from trusted sources and perform the necessary preprocessing to clean and provide the data for the model. The results of the analysis show that the linear regression algorithm can be used well to predict the risk of heart disease in individuals with a fairly high degree of accuracy. We also evaluated several factors that influence heart disease risk and demonstrated that they could be identified and integrated into our model to improve its performance. In addition, we also evaluated the validation methods used to evaluate our models and demonstrated that they can be used to objectively determine model performance. The results from this study provide a solid foundation for developing a better heart disease prediction system in the future. And the results of this study are quite accurate enough to give good results with a Root Mean Squared Error: 0.379 +/- 0.000 and Squared Error: 0.144 +/- 0.229
Prediksi Persediaan Barang Tepat Waktu dengan Menerapkan Algoritma Apriori Muhammad Makmun Effendi; Farish Al Khairi; Arif Siswandi
Bulletin of Information Technology (BIT) Vol 4 No 2: Juni 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v4i2.622

Abstract

he Apriori algorithm is a data mining technique for determining associative rules for a combination of components, this study aims to find the pattern frequency of each component that has been ordered by the customer so that the order is in accordance with the number and timely delivery, therefore to support the problems faced by PT. . SMF for the combination of inventory to purchase of goods, PT SMF applies the Apriori Algorithm to predict stock of goods so that when there is an order, the goods are not lacking and the delivery is also on time. The results of the research carried out obtained the most data on goods produced every 1 month, including the Pipe Bracket and Solenoid Bracket pattern of linkages in terms of predicting the number of goods at PT. If SMF produces Pipe Brackets, it must also produce Selenoid Brackets where the resulting confidence is 60%, Nc : 4, and for the lift ratio test it is 2.307. Whereas if you produce the Selenoid Valve Bracket, you must also produce the air pipe bracket for the resulting confidence of 75%, Nc: 5, and the lift ratio test of 2.272.
Analisis Gempa Bumi Di Indonesia Dengan Metode Clustering Arji Prasetio; M. Makmun Effendi; M. Najamuddin Dwi M
Bulletin of Information Technology (BIT) Vol 4 No 3: September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v4i3.820

Abstract

Indonesia is known as an archipelagic country because it consists of thousands of islands stretching from Sabang in the west to Merauke in the east. Testing earthquake data using the K-Means algorithm, where the results also show a new insight, namely the grouping of earthquake-prone areas in Indonesia based on 3 clusters. Cluster 1 is a category of areas with a relatively low level of earthquake-prone areas in Indonesia, namely 209 out of 1113 categories of the number of cases based on the area tested, then cluster 2 is a category of areas with a moderate level of earthquake-prone areas in Indonesia, namely 863 out of 1113 the category of the number of cases based on the area tested, and finally cluster 3 is the category of area with a high level of earthquake-prone areas in Indonesia, namely 41 out of 1113 categories of the number of cases based on the area tested. Tests using the earthquake clustering method with the K-Means algorithm can produce clusters that have cluster group members according to manual calculations such as Cluster_0 in Rapid Miner has 209 cluster members representing the Low cluster, Cluster_1 has 863 cluster group members representing the Medium cluster, and Cluster_2 has 41 cluster members corresponding to the cluster representation High.
Co-Authors A Yudi Permana A. Yudi Permana Agung Wijayadhi Ahmad Turmudi Ahmad Turmudi Zy Aida Ratnasari2 Arie Setiawan2 Arif Siswandi Arji Prasetio Diah Rahmawati Ermanto Ermanto Farid Wajdillah Farish Al Khairi Hengki Anthony Juliyanto M. Najamuddin Dwi M Mochammad Rahmat Faisal Moh Khamim Muhammad Adipa Nanang Tedi Kurniadi Puguh Ariyadi Sugeng Budi Rahardjo Sugeng Budi Raharjo Suherman Suherman Tri Hartono Zaenal Mustofa